BACKGROUND OF THE INVENTION One of the major duties of coroners in handling deaths falling under their jurisdiction is to determine the time of death, thus giving, in some cases, vital evidence to prove or disprove an individual's guilt or innocence. Determination of the time of death is vital in both criminal and civil cases. In murder cases the coroner sets the time of death, thereby eliminating or suggesting suspects, confirming or disproving alibis. In civil cases, the time of death may determine who inherits property or whether an insurance policy was in force.

One study contacted 10% of the American forensic lab market and indicated that such labs were reviewing 86,000 coroner cases in the United States, of which more than 8,000 required a determination of the time of death. See, e. g., Frost, R., supra, Analysis of Time of Death Technology Survey, McNeill & Associates, pp. 1- 9, March-April 1996. Thus, there appear to be more than 80,000 cases that would most likely require the determination of the time of death.

Unfortunately, methods currently used to determine the time of death are somewhat inconclusive. Such methods often give vague or dubious answers. Factors that have been used to determine time of death include: (1) liver mortis; (2) body temperature; (3) rigor mortis; (4) degree of composition; (5) chemical changes; (6) stomach contents; (7) insect activity; and (8) scene markers. See, e. g., Frost, R., supra, Analysis of Time of Death Tech. Survey, McNeill & Associates, pp. 1-9, March- April 1996; Henssge, C., et al., Death Time Estimation in Case Work. II. Integration of

Different Methods, Forensic Science Int'l., Vol. 39, pp. 77-87,1968; Madea B., References for Determining the Time of Death by Potassium in Vitreous Humor, Forensic Science International. 40, pp. 231-243,1989; Henssge, C., Death Time Estimation in Case Work. I. The Rectal Temperature Time of Death Nomogram, Forensic Science International: 38, pp. 209-236,1988. The determination is time consuming and approximate.

Thus, there has gone unmet a need for accurate determination of the time of death of a subject. The present invention provides these and other related advantages.

SUMMARY OF THE INVENTION The present invention provides methods and apparatus that permit the retroactive determination of the time of death by examining measurable changes in cellular features that occur during cellular degradation or the post-death degradation of a cell. The cellular features are preferably identified and at least partially analyzed using a high resolution imaging system such as high resolution image cytometry. See, e. g., Doudkine, A., et al., Nuclear Texture Measurements In Image Cvtometry, Pathologica. 87, pp. 286-299,1995; Poulin, N., et al., Quantitative Precision of an <BR> <BR> <BR> <BR> Automated Image Cvtrometric Svstem for the Measurement of DNA Content and Distribution in Cells Labeled With Fluorescent Nucleic Stains, Cvtometrv : 16, pp. 227- 235,1994; Grohs, H., Automated Cervical Cancer Screening, Cvtology Automation Update: XV, pp. 16-9,1994; Palcic, B. and MacAulay, C., Algorithms in Preparation for Decision-Making, In: Automated Cervical Cancer Screening, 52-61 (Chapter 5) Grohs, H. K., and Husain, O. A. N., eds. (Igaku-Shoin Med., 1994); Jaggi, B., and Palcic, B., Image Acquisition in Cvtometry : Optics, Sensor, and Signal Processing, In: Automated Cervical Cancer Screening, 52-61 (Chapter 9) Grohs, H. K., and Husain, O. A. N., eds. (Igaku-Shoin Med., 1994); Palcic, B., et al., Fluorescence Image Cytometry: A Comparison And Correlation Of Nuclear Feature Measurements With Absorption Image Cytometry, Bioimaging: 1, pp. 166-175,1993. Tracking observable changes in such cellular features, one is able to quantify the changes in the cellular

features over time; such quantification can provide improvements over prior methods of determining the time of death.

Currently, high resolution imaging of cellular features has been used for early detection of cancers as well as detection of pre-cancerous lesions in the breast, cervix, lung and possibly other tissues. See, e. g., Susnik, B. et al., Differences In Quantitative Nuclear Features Between Ductal Carcinoma In Situ (DCIS) With And Without Accompanying Invasive Carcinoma In The Surrounding Breast, Analvt. Cell.

Pats.. 8, pp. 39-52,1995 and Palcic, B., Nuclear Texture. Can It Be Used as a Surrogate Endpoint Biomarker?, J. Cell. Biochem., Supplement 19, pp. 40-46,1994.

Cellular features that can be identified using high resolution imaging include DNA distribution patterns in cell nuclei. Such cellular features can be reliably measured, for example, in tissue sections, single cell preparations such as smears from fine needle aspirates ("FNAs") or cell suspension preparations.

Desirable cellular features can be identified from diagnostic cells (e. g., myopathic cells which can be readily recognized by pathologists), as well as from clinically diagnosed normal myocytes, or other cells which show no sign of pathological change after intensive examination. Suitable selected tissues from which cells can be sampled include, for example, the heart, kidney, liver, brain, stomach, skin, gastrointestinal tract including the stomach and intestines, breast, salivary glands, pancreas, lymph nodes, spleen, cervix, prostate, bone, skeletal muscles and other muscles. The subject from which the sample is obtained is preferably a multi-celled organism, further preferably a mammal, particularly a human being. Small animal studies and human studies have been conducted; the standard deviation in time of death (actual versus estimate by cytometry) has been found to be less than 2 hours in some cases better standard deviations can be obtained. The standard deviation can be lowered after taking account of the information (i. e., the standards) learned in previous studies.

In one approach for analyzing a selected tissue sample, 100-20,000 non- overlapping cell nuclei of atypical cells or normal cells are selected. The cell nuclei are placed in exact focus and segmented semi-automatically. Digitized images are captured in a computer memory for further processing. If desired, nuclear masks can be

examined and, if desired, corrected manually to define the nuclear boundary pixels.

From each nuclear image, over 120 cellular features can be extracted. They range from bulk features (size, shape, DNA amount) to more sophisticated texture features (both discrete and continuous) related to the chromatin distribution in nuclei. Examples of such features are described in Example 1. Lymphocytes can be used as internal diploid DNA controls to normalize each slide to correct for staining variations. Statistical analyses, preferably including discriminant function analysis, are then performed on the data.

Thus, in one aspect the present invention provides methods of making a correlative standard that indicates the amount of time that has passed between the time of death of an organism and fixation of a sample of cells from a selected tissue from the organism. The methods can comprise the steps of: analyzing the fixed sample of cells from the selected tissue under conditions suitable and for a time sufficient to perform high resolution image cytometry of the cells to provide a high resolution image of the cells; examining the high resolution image of the cells to determine at least one cellular feature of the cells that changes over time in correlation with the cellular degradation of the cells from the time of death of the organism until the fixation of the cells, thereby providing the standard from which the amount of time that has passed between the death of the organism and the fixation of the sample can be assessed.

In preferred embodiments, the step of examining comprises a multivariate analysis and determines at least one or two (typically four or more) cellular features of the cells that change over time in correlation with the cellular degradation of the cells from the time of death of the organism until the fixation of the cells. In other preferred embodiments, the selected tissue is selected from at least one of the heart, lung, liver, kidney, brain and skeletal muscle, and/or the method comprises analyzing samples from at least two different selected tissues to provide at least two different high resolution images of the cells, and then examining each of the at least two high resolution images to determine at least one cellular feature in each of the images that is correlated with cellular degradation of the cells from the time of death of the organism until the fixation of the cells, and then combining the analyses to provide one or more

standards from which the amount of time that has passed between the death of the organism and the fixation of the samples can be assessed. Preferably, at least two of the samples from different selected tissues, which can be kidney and liver.

In further preferred embodiments, the step of examining comprises use of multivariate analysis comprising use of linear regression analysis leading to a linear weighted sum of sample feature means and feature standard deviations that correlates the linear weighted sum with the amount of time that has passed between the time of death until the time that the sample was fixed. In other preferred embodiments, the step of examining comprises use of multivariate analysis comprising use of a neural network that correlates the features with the amount of time that has passed between the time of death until the time that the sample was fixed.

In another aspect, the present invention provides correlative standards or set of correlative standards that indicate the amount of time that has passed between a time of death of an organism and fixation of a sample of cells from a selected tissue from the organism. Preferably, the correlative standards comprise a plurality of data sets that track the status over time of a plurality of cellular features that progressively change over time in a repeatable sequence that occurs after the time of death and that are optically distinguishable using high resolution cytometry. In preferred embodiments, the correlative standards comprise at least two different groups of data sets that track the status over time of a plurality of cellular features from at least two different selected tissues from the organism. In other preferred embodiments, the at least two different groups of data sets are combined together. Also preferably, the correlative standards are produced according to the methods described herein.

The correlative standards can be maintained as a chart or graph, or in a series of written descriptions, or in any other suitable format that conveys the information in the correlative standards. Also preferably, the correlative standards are stored in a computer memory.

In a further aspect, the present invention provides methods of estimating the amount of time that has passed between the time of death of an organism and fixation of a sample of cells from a selected tissue from the organism. Such methods

comprise the steps of : analyzing the fixed sample of cells under conditions suitable and for a time sufficient to provide a high resolution image of the cells (for example, via high resolution image cytometry) that comprises at least one cellular feature that is correlated with cellular degradation of the cells from the time of death of the organism until the fixation of the cells; examining the high resolution image of the cells to determine the status of the at least one cellular feature; and, comparing the status of the at least one cellular feature with a standard correlative that indicates the amount of time that has passed between the time of death of the organism and fixation of the sample of cells from the selected tissue based upon the status of the at least one cellular feature, and therefrom estimating the amount of time that has passed between the time of death of an organism and fixation of a sample of cells. Preferably, the correlative standard is produced according the methods described herein.

In preferred embodiments, the step of examining comprises determining the status of at least two different cellular features, each of which is correlated with the amount of time that has passed between the time of death of the organism and fixation of the sample of cells, and the step of comparing comprises comparing the status of each of the at least two cellular features with at least one correlative standard that indicates the amount of time that has passed between the time of death of the organism and fixation of the sample of cells based upon the status of the respective cellular feature. Preferably, the method comprises analyzing, examining and comparing fixed cells from at least two different selected tissues from the organism. Also preferably, the step of comparing comprises combining the comparison of the cellular features from the at least two different selected tissues to provide a narrower estimate of the amount of time that has passed between the time of death of the organism and fixation of the samples of cells than the estimate obtained from any one of the selected tissues alone.

In still another aspect, the present invention provides further methods of estimating the amount of time between death of an organism and fixation of a sample of cells from the organism. Such methods comprise the steps of : analyzing the fixed sample of cells under conditions suitable and for a time sufficient to effect high resolution image cytometry of the cells; analyzing the cells to determine a mean and/or

variance at least one cellular feature from at least two of the groups of morphology, photometric, discrete texture, markovian, non-markovian and length; measuring the mean and/or variance for each of the cellular features and therefrom determining the time of death of the organism.

In preferred embodiments, at least two cellular features are analyzed from at least one of the groups, and/or at least one cellular feature is analyzed from each of the groups. Also preferably, the cellular features include at least one cellular feature from the group fractals. In further preferred embodiments, most of the selected cellular features are selected from the group consisting of Mell_ori, Mreg_hig, MarmonO3, Mar inte, MD maxim, MedDNAar. MedDNAam, Medden, Montrast, Menht, Mendrk, Mentero, Mizetxt, Vrea, Var_rad, Vpherici, Vlongati, Varmon03, Varmon04, Vean_int, VDmaxim, VDkurto, Vedavd, VowVShig, Vowden, vlshade, Vangeex, Vractal, Vize txt, VhortO r, Vhort45, VongO_ru, Vun45_le, Vun90_le, which features are described further below, particularly in Example 1.

In still yet another aspect, the present invention provides methods for estimating the amount of time between the death of an organism and fixation of a sample of cells from the organism comprising high resolution image cytometry of selected cellular features, wherein the sample of cells is fixed about 4 hours after the time of death and the method comprises performing high resolution image cytometry on the sample of cells to identify cellular features of the cells and then performing multivariate analysis on the features to provide a predicted the time that of death within about 20 minutes of the actual time of death. In other embodiments, such methods comprise fixing the sample about 12 hours after the time of death and predicting the time of death within about + 1 hour of the actual time of death; fixing the sample about 24 hours after the time of death and predicting the time of death within about 2 hours of the actual time of death; fixing the sample about 250 hours after the time of death and predicting the time of death within about + 12 hours of the actual time of death.

In other embodiments, the sample of cells is fixed from about 4 to about 250 hours after the time of death and the predicted time window for the time of death

comprises a window of about 30 minutes at about 4 hours after the time of actual death to about _ 12 hours at about 250 hours after the actual time of death.

In still yet a further aspect, the present invention provides methods of assessing time of death of an organism by looking at cellular features corresponding to cellular degradation occurring between the time of death of the organism to the time a sample of cells from the organism was fixed, the method comprising the steps of : providing a microscope comprising a high sampling density light transducer and an imaging board able to process images from the light transducer; acquiring precise and reproducible focus of the nucleus of at least one of the cells in the sample in an image from the microscope by maximizing contrast of the nuclear material as a function of focal position; thresholding the image to produce a threshold mask such that the threshold mask comprises at least all edges of the nucleus to obtain a first approximation of a real edge of the nucleus; determining the real edge of the nucleus by step-by-step removal of pixels with the least gradient so as not to break a continuous contour around the nucleus to provide an image comprising a cell nucleus comprising a DNA distribution having cellular features; analyzing the cellular features of the DNA distribution within the cell nucleus; and performing multivariate analysis on the cellular features of the image of the nucleus comprising linear regression analysis leading to a weighted sum of sample feature means and feature standard deviations that assesses the amount of time that has passed between the time of death until the time that the sample was fixed.

In yet another further aspect, the present invention provides methods of assessing time of death of an organism by looking at cellular features corresponding to cellular degradation occurring between the time of death of the organism to the time a sample of cells from the organism was fixed, the method comprising the steps of : providing a microscope comprising a high sampling density light transducer and an imaging board able to process images from the light transducer; acquiring precise and reproducible focus of the nucleus of at least one of the cells in the sample in an image from the microscope by maximizing contrast of the nuclear material as a function of focal position; thresholding the image to produce a threshold mask such that the

threshold mask comprises at least all edges of the nucleus to obtain a first approximation of a real edge of the nucleus; determining the real edge of the nucleus by step-by-step removal of pixels with the least gradient so as not to break a continuous contour around the nucleus to provide an image comprising a cell nucleus comprising a DNA distribution having cellular features; analyzing the cellular features of the DNA distribution within the cell nucleus; and performing multivariate analysis comprising use of a neural network on the cellular features of the image of the nucleus such that the analysis assesses the amount of time that has passed between the time of death until the time that the sample was fixed.

In preferred embodiments, the methods herein are computer implemented, at least in part. Preferably, such computer comprises a program that selectively determines and/or measures the mean and/or variance of the features utilized for determining the time of death and that excludes non-selected features from the determination of the time of death.

These and other aspects of the present invention will become evident upon reference to the following Detailed Description, drawings and other information included in this application. In addition, various references are set forth herein that describe in more detail certain procedures or compositions; all such references are incorporated by reference in their entirety.

BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a graph of the results of a time since death analysis on mouse liver wherein samples were taken from 10-20 hours after the time of death, and 9 cellular features were used for the analysis.

Figure 2 is a graph of the results of a time since death analysis on mouse liver wherein samples were taken from 22-36 hours after the time of death, and 12 cellular features were used for the analysis.

Figure 3 is a graph of the results of a time since death analysis on mouse liver samples were taken from 10-36 hours after the time of death, and 23 cellular features were used for the analysis.

Figure 4 is a box and whisker plot of the predicted time since death for mouse kidney cells from 10-36 hours post-mortem.

Figure 5 is a table of the mean and standard deviation reflected in the box and whisker plot in Figure 4.

Figure 6 is a box and whisker plot of the results set forth in Figure 4, showing the variation of the prediction results relative to the actual time of death.

Figure 7 is a box and whisker plot of the predicted time since death for mouse kidney cells from 20-36 hours post-mortem.

Figure 8 is a table of the mean and standard deviation reflected in the box and whisker plot in Figure 7.

Figure 9 is a box and whisker plot of the results set forth in Figure 7, showing the variation of the prediction results relative to the actual time of death.

Figure 10 is a box and whisker plot of the predicted time since death for mouse kidney and liver combined cells from 10-36 hours post-mortem.

Figure 11 is a table of the mean and standard deviation reflected in the box and whisker plot in Figure 10.

Figure 12 is a box and whisker plot of the results set forth in Figure 10, showing the variation of the prediction results relative to the actual time of death.

Figure 13 is a graph of the results of a time since death analysis on human heart wherein samples were taken from 0-250 hours after the time of death, and 31 cellular features were used for the analysis.

Figure 14 is a graph of the results of a time since death analysis on mouse heart wherein samples were taken from 12-20 hours after the time of death, and 35 cellular features were used for the analysis.

DETAILED DESCRIPTION OF THE INVENTION The present invention provides apparatus and methods that permit the determination of the length of time that has passed since an organism has died. The present invention is based, in part, upon a sequence of degradative events that can be observed in one or more optically distinguishable cellular characteristics, which are referred to herein as cellular features. Thus, the progression of such degradative events, or cellular degradation, is correlated with the appearance of and/or changes over time in the cellular features. Such cellular features, and thus such repeatable progression, can be identified using a high resolution imaging system, such as a high resolution image cytometer. In a preferred embodiment, these degradative events are measured as a feature of DNA distribution patterns in cellular nuclei, which cellular features can be reliably measured, for example, in tissue sections, single cell preparations or cellular suspension preparations.

In preferred embodiments, the present invention provides methods and apparatus that are capable of providing a predicted time of death that is within about 1-3 hours of the actual time of death at about 12 hours since actual time of death, to about a predicted time of death that is within about 1-12 hours of the actual time of death after about 36 to 48 or 60 hours since the actual time of death. In addition, the methods and apparatus of the present invention can be used at extended periods since the actual time of death, such as 5 days, 10 days, and even up to 30 days or more, since the actual time of death.

Time of death, or time since death, as used herein is defined to be the amount of time that has passed between the occurrence of death in an organism to the time of fixing a sample of cells from the organism, for example by immersion of the sample in formaldehyde or other fixative. Such fixing stops (or arrests) the degradative processes occurring in the cells of the organism.

High resolution imaging systems are capable of identifying a large number of cellular features (120 or more). Therefore, the present invention also provides systems of determining an appropriate set of features from a selected tissue for the determination of the time since death.

In order to select an appropriate set of features to determine a predicted time since death, a set of sample cells is preferably subjected to staining or other techniques for enhancing the visual characteristics of the cell, sorted by automatic and/or manual means to select suitable cell nuclei (e. g., cell nuclei that are not folded over on themselves, overlapping due to multiple thicknesses of cells being present, or otherwise containing inhibitory artifacts) and then subjected to high resolution imaging.

Such procedures provide a very high resolution image of a target substrate, generally to a resolution of less than about 2 jj. m, typically less than about 1 pm, preferably less than about 0.5 m, and further preferably less than about 0.3 pm. When such a system is used to image cells, it is referred to as high resolution image cytometry. See generally, Doudkine, A., et al., Nuclear Texture Measurements In Image Cvtometry, Pathologica : 87, pp. 286-299,1995; Poulin, N., et al., Quantitative Precision of an Automated Image Cvtrometric System for the Measurement of DNA Content and Distribution in Cells Labeled With Fluorescent Nucleic Stains, Cytometry. 16, pp. 227-235,1994; Grohs, H., Automated Cervical Cancer Screening, Cytology Automation Update : XV, pp. 16-9, 1994; Palcic, B. and MacAulay, C., Algorithms in Preparation for Decision-Making, In: Automated Cervical Cancer Screening, 52-61 (Chapter 5) Grohs, H. K., and Husain, O. A. N., eds. (Igaku-Shoin Med., 1994); Jaggi, B., and Palcic, B., Image Acquisition in Cvtometry. Optics, Senso7 and Signal Processing, In: Automated Cervical Cancer Screening, 52-61 (Chapter 9) Grohs, H. K., and Husain, O. A. N., eds. (Igaku-Shoin Med., 1994); Palcic, B., et al., Fluorescence Image Cytometry. A Comparison And Correlation Of Nuclear Feature Measurements With Absorption Image Cytometry, Bioimaging. 1, pp. 166-175,1993.

Thus, the high resolution image cytometer is able to capture images, preferably digitized, of cell nuclei (or other desired cellular substrates), preferably at the approximate diffraction limit of spatial resolution for 600 pm light ( 0.0.3 pm). One example of such a high resolution image cytometry system is a high resolution image cytometer (Cyto-savant) developed by the British Columbia Cancer Agency (BCCA) in collaboration with Oncometrics Imaging, Vancouver, British Columbia, Canada.

Such a system employs a light transducer in the form of a charge-coupled device (CCD)

with properties suited for precision work in quantitative microscopy, such as small sensors (6.8 pm x 6.8 pm) that sense over their entire surface (100% fill factor). The transducer can be positioned in the primary image plane of an aberration-free objective (planApo 20x/0.75), which results in an effective pixel size of 0.34 pm x 0.34 llm (0.1 pm2) at 20x magnification with a signal-to-noise ratio of better than 200.1.

A sample of cells provides a very large data set of cellular features. Such data set (also called a feature set) is analyzed to determine a particular set of cellular features (which can also be termed optical features) that most highly correlates with a measured time since death of the samples. Then, the cells are analyzed to determine a set of cellular features that provides the next best correlation with the measured time since death, and so to provide a series of cellular features that correlate with both a predicted time of death and a measured time of death. This grouping of such cellular features provides a correlative standard against which a future sample from a source having an objectively unknown time of death can be compared, and the time of death can then be retroactively estimated. The correlative standard (s) can be maintained in graphical or other form, and are preferably stored in a computer-readable memory, such as a CD-ROM, DAT tape, or computer hard drive. If desired, the standard (s) can be updated and revised as further verifiable information becomes available. In some embodiments, the present invention provides kits comprising the standards described herein, preferably in a computer-readable format, with written instructions and/or with labeling describing the use of the standards. Such methodology and apparatus are discussed further herein.

The following provides a more detailed explanation of several methods for determining appropriate feature set (s) as a part of creating standards and/or determining the time since death.

As noted above, cellular features are optically distinguishable cellular characteristics. In a preferred embodiment, the cellular features are defined by the nucleic acids, particularly DNA in the cell, although other cellular components can also provide cellular features. A data set that comprises such cellular features (e. g., feature means and/or feature standard deviations) can have a large dimensionality because of

the large number of features that can be observed. Thus, a first step can be to reduce the data to its significantfeatures, for example using principal component analysis (PCA).

A limited set of those features is then used to predict the time since death. significant features (as defined by the PCA) that have a linear correlation with the measured time since death of the samples may be used in the creation of the correlative standards discussed herein.

More fully, the selection of significant features (and concomitant elimination of undesirable features) can comprise the use of PCA, which takes all the features and then finds a linear combination of such features that describes the largest amount of variability in the data. This is known as the first principal component (or first eigenvector) and it has an associated weighting value known as its eigenvalue.

Next, the same process is applied to find the second principal component (or second eigenvector), which is the linear combination of features that describes the majority of the variability left in the data after the variability described by the first component has been removed. This second principal component has associated with it a second eigenvalue, which will be less than the eigenvalue of the first principal component.

This process is continued for as long as desired by a user, preferably until substantially all the variability in the data has been described. The components with the larger eigenvalues are the ones which describe more of the data and are the ones which are used in the following steps.

The eigenvectors having eigenvalues larger than a preselected level (e. g., the top 1 or 2 to 30 or 40) and having correlation values with the dependent variable (which is the measured time since death) above a preselected level are selected. Such eigenvectors are then used to perform a step-wise linear regression analysis to provide a predicted time since death wherein the eigenvectors are the independent variables in the linear regression analysis and the dependent variable is the measured time since death.

The linear regression equation so generated is a linear weighted sum of a selected number of eigenvectors that themselves are a linear weighted sum of all the features measured. Thus, a few selected, calculated features (i. e., eigenvectors) can be used to define a predicted time since death equation that does not overtrain the linear

prediction model and hence will be more reproducible when applied to subsequent samples.

In an alternate embodiment, the selection of cellular features can be performed, in whole or in part, by visually inspecting a given cellular feature with respect to its behavior over a desired post-mortem period (e. g., 10 to 36 hours or 36 hours to 72 hours) and then selecting a subset of features from all the features reviewed.

Such subset has a correlation to the time since death as with the features selected using PCA and linear regression analysis. A linear regression equation can then be made using only a subset of these visually selected features, if desired. A person of ordinary skill in the art, in view of the present specification, will be able to identify and select desirable cellular features using still other methods, for example a neural network.

The identification and selection of cellular features outlined herein provides a set of cellular features correlating to the progressive changes that occur during cellular degradation that happens after an organism has died. This set of features, and the quantification of progressive changes that occur in the features over time (including the presence, absence or transformation of such features), provide a standard, i. e., a set or pattern of comparison criteria, by which the amount of time that has passed since the death of the organism can be estimated or predicted. The progressive change in appearance of a cellular feature subsequent to the time of death of an organism gives the cellular feature a predictable status at a given time after the time of death. In other words, the status of the cellular feature means the appearance of the cellular feature at a given time after the time of death of the organism.

Preferably, during the making of the standard, the predicted time of death that is determined is compared to known samples having an objectively ascertained time of death, for example because a person was present to record the time of death at its occurrence. The estimated time of death obtained from the preferred standard is then compared to the actual time of death to verify the correlation of the two; the standard can be updated and/or revised over time as further information becomes available. Preferably, however, a standard from a selected tissue from a given species is used to compare the same tissue from the same species (e. g. a standard

created from myocytes from human heart tissue is compared with observations of cellular features from myocytes from human heart tissue).

The standards can be maintained in a graphical format or otherwise in written format on a suitable substrate such as paper. Preferably, the standards are maintained in a computer memory, such as a hard drive for a personal computer, a CD- ROM, a zip drive, or any other computer-readable memory format. Of course, a given standard can be maintained in more than one format. Thus, the present invention also provides computer programs or software comprising at least one standard produced according to the methods described herein.

In a preferred embodiment, the determination of the time since death of an organism according to the present invention is applied to cells that have been substantially simultaneously affected by anoxia (for example, when an organism is gun shot, cells that have not been obliterated by the bullet are affected by cessation of the circulation which was initiated, more or less at the same time, because the systems of the organism have shut down and the supply of oxygen and nutrients to the cells has been inhibited).

In a preferred embodiment, comparing the cellular sample from an sample of unknown age to a standard comprises comparing cellular features from at least two different selected tissues from the organism from which the sample (s) were taken. As discussed further below, it has been found that combining the comparisons of cellular features in a given selected tissue with the comparison of features from another selected tissue enhances the accuracy of the estimate of the time since death (i. e., such combining reduces the standard deviation associated with such estimate). This can be done, for example, using either a single regression or two regressions during the analysis.

In some experiments, analysis of results over a time period from 10 to 36 hours since death indicates that there may be a different process occurring in the 10 to 20 hour time period versus the 20 to 36 hour time period. Thus, the two time periods can be fitted independently. (See Figures 1-3.)

Turning to apparatus comprising high resolution imaging systems suitable for use in accordance with the present invention, a preferred high resolution image cytometer system can comprises a computer controlled motorized precision microscope stage in association with a slide loading unit. The precision stage is capable of being displaced under control of a computer in the x, y and z axes. The computer can be a 486 type of personal computer or other computer as known in the art.

An object lens connected to the microscope is provided. A digital high resolution camera is positioned in the primary or the secondary image plane of the microscope. One example of such a digital high resolution camera is a MicroImager from Xillix Technologies Corp., Vancouver, British Columbia, Canada. Suitable cameras preferably comprise a light transducer consisting of a scientific grade charge coupled device with a 100% fill factor and more than 500 gray levels of photometric <BR> <BR> <BR> <BR> resolution with a pixel size of 6.8 llm x 6.8 um. The transducer is mounted in the primary image plane, or in a camera port, if this camera port of the microscope provides negligible image distortion, and an imaging board is provided for capturing and processing the images of cell nuclei.

The camera is connected to a digital image processing unit, which is in turn connected to an image monitor for viewing digitized images of the field under observation. A digital processing unit invention comprise Matrox 1280 high performance image processing boards. The computer is connected to a computer monitor, a keyboard and a mouse and other peripherals such as printers, etc. The computer is also connected for example via by computer bus, to the digital image processing unit and to the motorized precision stage. The computer provides control to the motorized precision stage and controls the software output functions of the invention other than those which are handled by the digital image processing unit.

Typically, a 40x objective would be used to view cells of myocardium cells. However, the present inventors have found that a 40x objective can result in difficulties in focusing and therefore in segmenting images of the nuclei due to the distortion effect in the lenses. In addition, the 40x objective is bulky and may hit the slide cover slip and have a shallow depth of focus. Accordingly, a preferred

embodiment of the invention uses a 20x/. 75 objective together with a high sampling density sensor mounted on a microscope. A suitable objective is supplied by Nikon and known as the CF Plan Apochromat.

In an embodiment where the present invention comprises analysis of cellular features associated with nucleic acids, particularly DNA in certain embodiments, the samples to be examined are fixed, preferably using buffered formalin, and then treated by stoichiometric staining of the nuclear (DNA) material. One preferred stain comprises a modified Feulgen stain produced using thionine chloride, although other known stains such as stoichiometric absorbence stains (e. g., gallocyanin, azure-A, etc.) or stoichiometric fluorescence stains (e. g., DAPI, propidium iodide, etc.).

In order to identify regressing myocytes, the device is preferably capable of capturing images in precise and reproducible focus. The following describes how this is done in the commercially available Cyto-Savant system. A person of ordinary skill in the art, in view of the present specification, will recognize that other high resolution imaging systems, such as other automated cytometers, can also perform the following tasks, or otherwise provide the cellular feature identification and analysis discussed herein.

Turning to the steps performed using a cytometer as described above, the focus is achieved by maximizing contrast of the nuclear material as a function of focal position. Given the pixel size and spatial resolution, the image of a typical nucleus is about 3-20 J. m in diameter and comprises of several hundred individual pixels. The ability to focus at resolution is achieved in part by the use of a 20x objective with a transducer placed in the primary image plane of the objective.

Turning to the identification of the cellular features in a sample, the first step is to achieve a relatively coarse focus level determination for the global field of view on the slide. The field of view typically includes a number of individual cells. A first focus is achieved by controlling the precision stage so as to cause a displacement along the z-axis. The image in the field of view is digitized by the digital camera. The computer then causes the digital image processing unit to effect a full field calculation to determine whether there are any objects present in the field. There are many possible

approaches to performing this evaluation. A preferred embodiment relies on summing the product of the difference in gray level of a pixel as compared to the background level and a coefficient which is a function of the difference to enhance contrast.

If no object is found in the field, the routine iterates by adjusting the z-axis displacement of the precision stage in a given direction and conducting a similar evaluation at that level. If necessary, the routine will also adjust the x and y axes of the precision stage to identify other fields which may contain objects. When an object is located, the routine zeroes in on a precise focus as described more particularly below.

Such coarse focus acquisition is programmed as software embodied in the computer, and can be readily implemented by those skilled in the art in view of the present specification.

Having achieved a coarse focus, more precise focus can then be achieved as follows. The coarse focus contrast is initiated as value"y". The precision stage is adjusted to focus on the slide. Identification of the location of the object is done by locating the gray level maxima. The contrast calculation is performed in the image and the result is compared to y to determine whether there has been an improvement in focus. If so, the precision stage is adjusted incrementally in the direction of the improved contrast. A new image is acquired and digitized and the contrast calculation is performed once again. These steps are iterated until the precision stage has moved beyond the optimal focus resulting in an indication that the contrast has deteriorated rather than improved. The precision stage is then displaced in the opposite direction by a smaller increment than the increment initially used. Contrast is again evaluated. The process continues until the optimal focus displacement of the precision stage has been achieved. A final image acquisition is performed and the image is digitized.

The detection of regressing myocytes preferably includes very precise and reproducible focus. As can be seen, a relatively large variation in contrast (14%) occurs in less than one micron of relative focus position. The combination of the digital high resolution CCD camera mounted in the primary image plane, the motorized precision stage and the coarse and fine focus acquisition techniques discussed herein is one embodiment to attain such focus. Other methods of attaining such focus, when it is

desired, will be apparent to a person of ordinary skill in the art in view of the present specification.

In a preferred embodiment, all images are segmented exactly such that all pixels covering the nucleus belong to a mask. A simple thresholding of an image is obtained from a calibrated image (corrected for lens aberrations, dark current of camera, and other imperfections) and is corrected by the absorbence of material around the nucleus, (i. e., cytoplasm) which is assumed to be present in about equal amounts over and around the nucleus. The threshold mask edge of the nucleus represents the first approximation of the real edge of the nucleus. The latter is obtained by an edge relocation algorithm which operates by dilating the approximate edge and then step removing the pixels with the least gradient so as not to break a continuous contour around the nucleus.

Secondary segmentation is applied by first dilating the approximate edge of the nucleus, both inwardly and outwardly resulting in an approximation of the edge of the nucleus. The digital image of the nucleus is scanned and evaluated to determine the presence of dark and light areas. Light areas may represent an indentation in the edge of the nucleus. Accordingly, light areas are added to the dilated boundary. Dark areas are removed from the image so as to yield a dilated boundary corrected for light and dark areas. The edge is then eroded by step-by-step removal of pixels with the least gradient, provided the removal of the pixel does not interrupt a continuous contour around the edge of the nucleus.

The result of this process is a very precisely defined edge of the nucleus, in a very precise focus,. See e. g., Doudkine, A., et al., Nuclear Texture Measurements In Image Cvtometry, Pathologica : 87, pp. 286-299,1995; Poulin, N., et al., Quantitative Precision of an Automated Image Cytrometric System for the Measurement of DNA Content and Distribution in Cells Labeled With Fluorescent Nucleic Stains, Cytometry : 16, pp. 227-235,1994; Grohs, H., Automated Cervical Cancer Screening, Cytology Automation Update : XV, pp. 16-9,1994; Palcic, B. and MacAulay, C., Algorithms in Preparation for Decision-Making, In: Automated Cervical Cancer Screening, 52-61 (Chapter 5) Grohs, H. K., and Husain, O. A. N., eds. (Igaku-Shoin Med., 1994); Jaggi, B.,

and Palcic, B., Image Acquisition in Cytometry : Optics, Sensor, and Signal Processing, In: Automated Cervical Cancer Screening, 52-61 (Chapter 9) Grohs, H. K., and Husain, O. A. N., eds. (Igaku-Shoin Med., 1994); Palcic, B., et al., Fluorescence Image <BR> <BR> <BR> <BR> Cytometry: A Comparison And Correlation Of Nuclear Feature Measurements With Absorption Image Cytometry, Bioimaging : 1, pp. 166-175,1993.

Algorithms selecting only images of the nuclei of normal cells (as opposed to cells that are folded over or otherwise not useful for consistent analysis) are then used to reduce the number (typically 1-2%) of artifacts that are present in the population. This can be achieved by discriminate function analysis and a decision tree process, but can also be achieved by other statistical or neural network procedures, as will be appreciated by those skilled in the art in view of the present specification. The discriminate function analysis and decision process are discussed further both above and below.

The images so collected are then preferably reviewed by a human observer (although a computer can also perform the analysis if desired) to ensure that, preferably, correctly focused and imaged cells are used in the subsequent analysis. For each sample the mean feature values and standard deviation of feature values of the cells collected can be established. These mean values can then be entered into a linear regression model to correlate the mean and standard deviation of features from a sample with the time since death. Such analysis generates a weighted linear summation of sample features (means and standard) that predicts the time since death.

Various features of the segmented digital images are then analyzed by the computer. Preferably, the DNA distribution and the degradation of the DNA of the cell are analyzed. Typically, over 100 nuclear features are analyzed, of which about 20- 40 are typically used in the multivariable analysis.

Turning to the analysis of the cellular features identified using high resolution cytometry such as that discussed above, or other high resolution analysis as would be apparent to a person of ordinary skill in view of the present specification, such analysis comprises review of features such as those set forth in Example 1. Such features comprise seven different feature groups: morphology, photometric, discrete

texture, markovian, densitometric (or non-markovian), fractal and run length. The analysis to determine time of death can comprise analysis of at least one feature (and further preferably two, three or more features from one or more groups) from each of morphology, photometric, discrete texture, markovian, non-markovian, and run length, although more or less features can be analyzed if desired. In one preferred embodiment, the analysis comprises features selected from the a majority of, or almost all or all, recited groups.

EXAMPLES SUMMARY OF EXAMPLES Example 1 describes a number of cellular features that are detectable using a high resolution image cytometer.

Example 2 provides an example of the staining of cells in a sample where the staining is intended to act on nucleic acid material.

Example 3 discusses the harvesting of samples of selected tissues from mice, and binary decision trees to separate desired sample cells from non-desired sample cells from a selected tissue.

Example 4 discusses the analysis of and estimation of time since death using sample cells from mouse liver tissue.

Example 5 discusses the analysis of and estimation of time since death using sample cells from mouse kidney tissue.

Example 6 discusses an analysis of sample cells from mouse kidney similar to that in Example 5, except that two regressions were used.

Example 7 discusses an estimation of time since death using a combination of results from sample cells from mouse kidney and mouse liver, using a single regression.

Example 8 discusses an analysis that is similar to that in Example 7, except that two regressions were used.

Example 9 discusses an estimation of the time since death using sample cells from a human heart.

Example 10 discusses a protocol for the obtaining of further samples from human tissues.

EXAMPLE 1 1 PREAMBLE NOTES 1.1 COORDINATE SYSTEMS. JARGON AND NOTATION Each image is a rectangular array of square pixels that contains within it the image of an (irregularly shaped) object. surrounded by background. Each pixel, Pi, is an integer representing the photometric value (gray scale) of a corresponding small segment of the image, and can range from 0 (completely opaque) to 255 (completely transparent). The image rectangle is larger than the smallest rectangle that can completely contain the object by at least two rows, top and bottom, and two columns left and right, ensuring that background exists all around the object. The rectangular image is a matrix of pixels, P ;., spanning/== 1, L columns and j = 1, M rows and with the upper left hand pixel as the coordinate system origin, i =j= 1.

The region of the image that is the object is denoted by its characteristic function, Q; this is also sometimes called the"object mask"or, simply, the"mask."For some features, it makes sense to dilate the object mask by one pixel, all around the object; this mask is denoted Q+. Similarly, an eroded mask is denoted Q-. The object mask is a binary function: Q = (#1,1#1,2...#i,j...#L,M) (1) where, l if (i, j) o object Qi, if (i, j) # object and, where "(i,j) # object"means pixels at coordinates: (i, j) are part of the object, and " (i, j) E object"means pixels at coordinates: (i, j) are not part of the object.

2 MORPHOLOGICAL FEATURES Morphological features estimate the image size, shape, and boundary variations 7,8,9,10,11; the citations in this Example are listed at the end of this Example).

2.1 AREA Area, A, is defined as the total number of pixels belonging to the object, as defined by the mask Q: area = where i, j and Q are defined in Section 1.2 above. <BR> <BR> <P> 2.2 XCENTROID. YCENTROID<BR> <BR> xcentroid and y_centroid are the coordinates of the geometrical center of the object, defined with respect to the image origin (upper left hand corner): where ij and Q are defined in Section 1.2 above. x centroid y_ centroid where i and j are the image pixel coordinates and Q is the object mask, as defined in Section 1.2 above, and A is the object area.

2.3 MEANRADIUS.MAXRADIUS mean-adius and max-adius are the mean and maximum values of the length of the object's radial vectors from the object centroid to its 8 connected edge pixels: means radius =

max_ radius = max (rk) (6) where rk is the kth radial vector, and N is the number of 8 connected pixels on the object edge [1].

2.4 VAR-RADIUS var radius is the variance of length of the object's radius vectors, as defined in Section 2.3. var radius = where 7-k. is the kth radius vector, r is the mean radius, and N is the number of 8 connected edge pixels.

2.5 SPHERICITY sphericity is a shape measure, calculated as a ratio of the radii of two circles centered at the object centroid (defined in Section 2.2 above) [2]. One circle is the largest circle that is fully inscribed inside the object perimeter, corresponding to the absolute minimum length of the object's radial vectors. The other circle is the minimum circle that completely circumscribes the object's perimeter, corresponding to the absolute maximum length of the object's radial vectors. The maximal sphericity value: 1 is given for a circular object: <BR> <BR> <BR> <BR> <BR> <BR> min_radiusmin(rk)<BR> sphericity= = (8)<BR> max_radius max(rk) where rk is the kth radius vector.

2.6 ECCENTRICITY eccentricity is a shape function calculated as the square root of the ratio of maximal and minimal eigenvalues of the second central moment matrix of the object's characteristic function, Q: eccentricity where A1 and 2 are the maximal and minimal eigenvalues, respectively, and the characteristic function, Q, is given by Equation 1. The second central moment matrix is calculated as:

Xmoment2 Xycrossmoment2 (10) XYcrossmoment2Ymoment2 eccentricity may be interpreted as the ratio of the major axis to minor axis of the"best fit"ellipse which describes the object, and gives the minimal value 1 for circles [2].

2.7 INERTIASHAPE inertiashape is a measure of the"roundness"of an object calculated as the moment of inertia of the object mask, normalized by the area squared, to give the minimal value 1 for circles [3]: inertia_ shape = where Ri is the distance of the pixel, Pj, to the object centroid (defined in Section 2.2), and A is the object area, and Q is the mask defined by Equation 1.

2.8 COMPACTNESS compactness is another measure of the object's"roundness." [4] It is calculated as the perimeter squared divided by the object area, giving the minimal value 1 for circles: <BR> <BR> p2<BR> <BR> compactness=(12)<BR> 4#A

where P is the object perimeter and A is the object area. Perimeter is calculated from boundary pixels (which are themselves 8 connected) by considering their four connected neighborhood [5]: where N, is the number of pixels on the edge with 1 non-object neighbor, N2 is the number of pixels on the edge with 2 non-object neighbors, and N3 is the number of pixels on the edge with 3 non-object neighbors.

2.9 CELLORIENT cellorient represents the object orientation measured as a deflection of the main axis of the object from the y direction [4]: cell_ onent = + arctan (14) xJ'cross rnoment2 where Ymoment2 and XYcrOss_mOment2 are the second central moments of the object's <BR> <BR> characteristic function Q defined by Equation 1 above, and Rl is the maximal eigenvalue of the second central moment matrix of that function (see Section 2.6 above). The main axis of the object is defined by the eigenvector corresponding to the maximal eigenvalue. A geometrical interpretation of the cellorient is that it is the angle (measured in a clockwise sense) between the y axis and the"best fit"ellipse major axis.

For slides of cell suspensions, this feature should be meaningless, as there should not be any a priori preferred cellular orientation. For histological sections, and, possibly, smears, this feature may have value. In smears, for example, debris may be preferentially elongated along the slide long axis.

2.10 ELONGATION Features in Sections 2.10 to 2.13 are calculated by sweeping the radius vector (from the object centroid, as defined in Section 2.2, to object perimeter) through <BR> <BR> 128 discrete equal steps (i. e., an angle of 27r/128 per step), starting at the top left most

object edge pixel, and sweeping in a clockwise direction. The function is interpolated from an average of the object edge pixel locations at each of the 128 angles. elongation is another measure of the extent of the object along the principal direction (corresponding to the major axis) versus the direction normal to it.

These lengths are estimated using Fourier Transform coefficients of the radial function of the object: elongation = where a,, b, are Fourier Transform coefficients of the radial function of the object, r (S), defined by [6,7]: 2.11 FREOLOWFFT freq_low_fft gives an estimate of coarse boundary variation, measured as the energy of the lower harmonics of the Fourier spectrum of the object's radial function (from 3rd to 11th harmonics): freqlowfft = where a, bu are Fourier Transform coefficients of the radial function, defined in Equation 16.

2.12 FREOHIGHFFT fisq_high-fft gives an estimate of the fine boundary variation, measured as the energy of the high frequency Fourier Spectrum (from 12th to 32nd harmonics) of the object's radial function: &eq_ high_ fft = where aQ, bÇ are Fourier Transform coefficients of the nth harmonic, as defined by Equation 16.

2.13 HARMONOLFFT..... HARMON32FFT harmon0l-fft,..., harmon32_fft are estimates of boundary variation, calculated as the magnitude of the Fourier Transform coefficients of the object radial function for each harmonic, 1-32: harmon n fft = where a"bt} are Fourier Transform coefficients of the nth harmonic, as defined by Equation 16.

3 PHOTOMETRIC FEATURES Photometric features give estimations of absolute intensity and optical density levels of the object, as well as their distribution characteristics 13, 14].

3.1 DNA_AMotrNT DNAAmount is the"raw" (unnormalized) measure of the integrated optical density of the object, defined by a once dilated mask, Q- DNA_Amount= where the once dilated mask, # is defined in Section 1.2 and OD is the optical density, calculated according to [12]: log10IB-log10Ii,j(21)ODi,j= where IB is the intensity of the local background, and Iij is the intensity of the ijth pixel.

3.2 DIA INDEX DNA_Index is the normalized measure of the integrated optical density of the object: DNA Amount (22)DNA_Index= <BR> <BR> iodnorm where iodnorm is the mean value of the DNA amount for a particular object population from the slide (e. g., leukocytes).

3.3VARJNTENSITY. MEANJNTENSITY varintensity and mean_intensity are the variance and mean of the intensity function of the object, I, defined by the mask, Q: var-intensity = where, A is the object area, Q is the object mask defined in Equation 1, and I is given by: I is the"raw" (unnormalized) mean intensity. mean-intensity is normalized against iodnorm defined in Section 3.2: iodnorm mean_intmean_intensity = I (25)# 100 3.4OU-MAXIMUM<BR> <BR> Ou maximum is the largest value of the optical density of the object, normalized to iodnorm, as defined in Section 3.2 above: 100 max(ODi,j)##(26)OD_maximum= iodnorm 3.5 ODVARIANCE OD variance is the normalized variance (second moment) of optical density function of the object [13,14]: OD_variance= where Q is the object mask as defined in Section 1.2, OD is the mean value of the optical density of the object:

and A is the object area (total number of pixels). The variance is divided by the square of the mean optical density in order to make the measurement independent of the staining intensity of the cell.

3.6 ODSKEWNESS ODskewness is the normalized third moment of the optical density function of the object [9,13]: OD skewness = where Q is the object mask, as defined in Section 1.2, OD is the mean value of the optical density of the object and A is the object area (total number of pixels).

3.7 ODKURTOSIS ODkurtosis is the normalized fourth moment of the optical density function of the object: OD kurtosis = where Q is the object mask, as defined in Section 1.2, OD is the mean value of the optical density of the object and A is the object area.

4 DISCRETE TEXTURE FEATURES The discrete texture features are based on segmentation of the object into regions of low, medium and high optical density [8,10,11,14,15,16,17]. This segmentation of the object into low, medium and high density regions is based on two thresholds: optical density high threshold and optical density medium threshold. These thresholds are scaled to the sample's iodnOrm value, based on the DNA amount of a particular subset of objects (e. g., lymphocytes), as described in Section 3.2 above.

By default, these thresholds have been selected such that the condensed chromatin in leukocytes is high optical density material. The second threshold is located half way between the high threshold and zero. These thresholds are normally stored in the image header by the ACQUIRE program, although they may be reset by the user with various utilities.

The default settings from which these thresholds are calculated are stored in. PRM parameter files as: sysparm [CHROMATINHIGHTHRES]=36 sys_parm [CHROMATIN-MEDIUM-THRES] = 18 The actual thresholds used are these parameters, divided by 100, and multiplied by the factor iodnorm 100 In the following discussion, Ql°w, Qmed and Qhi are the masks for low, medium, and high density optical density regions of the object, respectively, defined in analogy to Equation 1.

4.1 LowDNAAREA ! MEDDNAAREA HIDNAAREA These discrete texture features represent the ratio of the area of low, medium and high optical density regions of the object to the total object area. lowDNAarea =

medDNAarea = hiDNAarea = where Q is the object mask as defined in Equation 1, and A is the object area.

4.2 LOWDNAMNT. MEDDNAAMNT. HIDNAAMNT These discrete texture features represent the total extinction ratio for low, medium and high optical density regions of the object, calculated as the value of the integrated optical density of the low, medium and high density regions, respectively, divided by the total integrated optical density. lowDNAamnt = medDNAamnt = hiDNAamnt = where Q is the object mask as defined in Equation 1, and OD is the optical density as defined by Equation 21.

4.3 HIDDNACOMP,MHDNACOMPMEDDNACOMP, These discrete texture features are characteristic of the compactness of the low, medium, high, and combined medium and high density regions respectively, treated as single (possibly disconnected) objects. They are calculated as the perimeter squared of each region, divided by 47r (area) of the region. (Plow)2 (36)lowDNAcomp= ##Alow (Pmed)2 medDNAcomp4 Amed (Phi)2 hiDNAcomp = (38) 4#Ahi (Pmed+Phi)2 mhDNAcomp=(39) 4#(Amed+Ahi) where P is the perimeter of each of the optical density regions, defined in analogy to Equation 13 and A is the region area, defined in analogy to Equation 2.

4.4 LOWAVDST.MEDAVDST.HIAVDST.MHAVDST These discrete texture features represent the average separation between the low, medium, high and combined medium and high density pixels from the center of the object, normalized by the object mean-radius. Z. M L M E E Ri jQi°jW low t i=lj=l --Al°W. radius L M med Lr i, j i, j med av dst-i=lj=l (41) A means radius

L M E E Ri iQlhi I,j i, j hi av dst = t=1=1 (42) A 1 means_ radius L M L M v v med v v nhi I, j I, j ! L i, j i, j Z Z RijQij +z Ri jS i=lj=l i=lj=lmh av dst= (43)--{Amed + AhiA means radius where Rjj is defined in Section 2.7 as the distance from pixel P,,, to the object centroid (defined in Section 2.2), and the object mean-radius is defined by Equation 5.

4.5 LOWVSMED_DNA LOWVSHIGH_DNA 1OWVSMH_DNA These discrete texture features represent the average extinction ratios of the low density regions, normalized by the medium, high, and combined medium and high average extinction values, respectively. They are calculated as the mean optical density of medium, high, and combined medium and high density clusters divided by the mean optical density of the low density clusters. L M L M med' pDlow i, j i, j lowVSmed_DNA = t-1 j=lAmed, t=1 j=lAlow (44) L M L M LMZ. M lowVShi-DNA Ahi Alow (45) iowVSMDNA= ------- (45)

lowVSmhDNA= where OD is the region optical density defined in analogy to Equation 21, Q is the region mask, defined in analogy to Equation 1, and A is the region area, defined in analogy to Equation 2.

4.6 LOWDENOBJ.MEDDENOBJ.HIGHDENOBJ These discrete texture features are the numbers of discrete 8-connected subcomponents of the objects consisting of more than one pixel of low, medium, and high density.

4.7 LOWCNTRMASS.MEDCNTRMASS.HIGHCNTRMASS These discrete texture features represent the separation between the geometric center of the low, medium and high optical density clusters (treated as if they were single objects) and the geometric center of the whole object, normalized by its mean radius. L nul _ L M 2 I low_cntr-mass =AloW-x-centroid slow L M 2,/2 E L j Q10w + Alow y_centroid. (means_radius) (47)

L M 2 EE Qi, X l7=l med_ cntr_ mass = y med-x_ centroid L M 2'2 I, j EM' EE7- A med (means radius) (48) J L ED- 17=1 hi-cntr-mass= 1 lJAhi-x_centroid phi 1 M 2 2 E E j Qhi A hi (means-radius) 4 where mean radius of the object is defined by Equation 5, the object's centroid is defined in Section 2.2, Q is the region mask defined in analogy to Equation 1, and A is the region area defined in analogy to Equation 2.

5 MARKOVIAN TEXTURE FEATURES Markovian Texture features are defined from the co-occurrence matrix, A of object pixels. Each element of that matrix stands for the conditional probability of the pixel of gray level X occurring next (via 8-connectedness) to a pixel of gray level, where, are row and column indices of the matrix, respectively [18]. However, the computational algorithms used here for the calculation of Markovian texture features uses so-called sum and difference histograms: H3, and Hd, where H3, is the probability of neighboring pixels having gray levels which sum to 1, and Hdm is the probability of

neighboring pixels having gray level differences of m, where an 8-connected neighborhood is assumed [19]. Values of gray levels, l, m used in the sum and difference histograms are obtained by quantization of the dynamic range of each individual object into 40 levels.

For completeness, the formulae that follow for Markovian texture features include both the conventional formulae and the computational formulae actually used.

5.1 ENTROPY entropy represents a measure of"disorder"in object gray level organization: large values correspond to very disorganized distributions, such as a"salt and pepper"random field: entropy=-, log10A<, (conventional) entropy=-HZ log10 Hl-E Hm log1O Hm (computational) (50) 1 m 5.2 ENERGY energy gives large values for an object with a spatially organized gray scale distribution. It is the opposite of entropy, giving large values to an object with large regions of constant gray level: energy = A (conventional) ksi energy= E (Hls) + 5£ (Hm) (computational) (51) 1 m 5.3 CONTRAST contrast gives large values for an object with frequent large gray scale variations: contrast= (conventional) contrast= E m 2H d (52) m

5.4 CORRELATION A large value for correlation indicates an object with large connected subcomponents of constant gray level and with large Grey level differences between adjacent components: correlation = X E- j- J ) I m correlation = /-27] - (53) where I q is the mean intensity of the object calculated for the gray scale quantized to 40 levels.

5.5 HOMOGENEITY homogeneity is large for objects with slight and spatially smooth gray level variations: 1 homogenity = E E 2 u (conventional) A d (54) homogenity= I H, (computational) (1+) 5.6 CL SHADE cl_shade gives large absolute values for objects with a few distinct clumps of uniform intensity having large contrast with the rest of the object. Negative values correspond to dark clumps against a light background while positive values indicate light clumps against a dark background: /3 cl_shade = E + p-2I q) Ax p (conventional) 3 E-)' cl_ shade = 1 3 (computational) (55) 2 E (I-2Iq) Hl3) /

5.7CLPROMINENCE cl_prominence measures the darkness of the clusters. - cl_ pro min ence = E + p-2I q) A,, (conventional) k p E (1 2Iq) Hl3 cl_prominence = 1 2 (computational) (56) Y, (1-2 q 3 / 6 NON-MARKOVIAN TEXTURE FEATURES These features describe texture in terms of global estimation of gray level differences of the object [11,20].

6.1 SPOT,DENDRKSPOTLIT These are the numbers of local maxima and local minima, respectively, of the object intensity function based on the image averaged by a 3x3 window, and divided by the object area. L M L 6 maux L_r i'j' den-lit-spot = l A (57) and, L M 6 mit LI ij, E EÕ mlnden-drk_spot = (58) where, mal if there exists a local maximum of 7,'','with value maxi',j' = 0 otherwise and, 1 if there exists a local minimum withvaluemini',j'Ii'',j' #i',j'min=# 0otherwise

and, where and Iis the object intensity, Q is the object mask, and A is the object area.

6.2 RANGEEXTREME This is the intensity difference between the largest local maximum and the smallest local minimum of the object intensity function, normalized against the slide DNA amount, iodnorm, defined in Section 3.2. The local maxima, max,,'', and minima, min, y, are those in Section 6.1 above. 100 (59)range_extreme=(max(maxi',j')-min(mini',j'))## iodnorm 6.3 RANGEAVERAGE This is the intensity difference between the average intensity of the local maxima and the average intensity of the local minima, normalized against the slide <BR> <BR> DNA amount value, iodno,, defined in Section 3.2 above. The local maxima, max ;',<BR> <BR> and minima, min ; y, values used are those from Section 6.1 above. L M L M maxi minj. j 100 100 range average ="='i, =l i'=l j'=l-100 (60) Sm ; n, iod"o", i 6.4 CENTER_OF_GRAVITY center of gravity represents the distance from the geometrical center of the object to the"center of mass"of the optical density function, normalized by the mean radius of the object: centerofgravity = I/2 G M G M EED,, Q,, EE-oD,, '-''-1-x centroid + -'--y centroid L M-L M . Lr ODI, %, J G. OD, J S , J i=I j=I i=I j=1 meanradius This gives a measure of the nonuniformity of the OD distribution.

7 FRACTAL TEXTURE FEATURES The fractal texture features are based on the area of the three dimensional surface of the object's optical density represented essentially as a three dimensional bar graph, with the vertical axis representing optical density, and the horizontal axes representing the z and y spatial coordinates. Thus, each pixel is assigned a unit area in the x-v plane plus the area of the sides of the three dimensional structure proportional to the change in the pixel optical density with respect to its neighbors. The largest values of fractal areas correspond to large objects containing small subcomponents with high optical density variations between them.

The difference between fractall_area and fractal2 area is that these features are calculated on different scales: the second one is based on an image in which four pixels are averaged into a single pixel, thereby representing a change of scale of <BR> <BR> <BR> <BR> fractal larea. This calculation needs the additional mask transformation: Qi2, j2<BR> <BR> <BR> <BR> <BR> expanded by 4 so that each pixel in Q, 4 pixels in Q,, j.

7.1 FRACTAL1AREA fractall_area where OD ;, is the optical density function of the image scaled by a factor common to all images such that the possible optical density values span 256 levels.

7.2 FRACTAL2AREA This is another fractal dimension, but based on an image in which four pixel squares are averaged into single pixels, thereby representing a change of scale of fractall area in Section 7.1 above.

&actal2area where, with L2,M2 as integers, and ais scaled optical density function of the image, with 4 pixels averaged into one.

7.3 FRACTALDIMEN The fractaldimen (sion) is calculated as the difference between logarithms of fractal larea and fractal2 area, divided by log2. This varies from 2 to 3

and gives a measure of the"fractal behavior"of the image, associated with a rate at which measured surface area increases at finer and finer scales. <BR> <BR> <BR> <BR> <BR> log10(fractall_area)-log10(fractal2_area)<BR> fractal_dimen=(64)<BR> <BR> log102 8 RUN LENGTH TEXTURE FEATURES Run length features describe texture in terms of gray level runs, representing sets of consecutive, collinear pixels having the same gray level value. The length of the run is the number of pixels in the run. These features are calculated over the image with intensity function values transformed into 8 levels.

The run length texture features are defined using gray level length <BR> <BR> <BR> <BR> matrices, 93e,,, for each of the four principal directions: 0 = 0°, 45°, 90°, 135°, where the directions are defined clockwise with respect to the positive x-axis [21]. Note: As defined here, the run length texture features are not rotationally invariant, and therefore cannot, in general, be used separately since for most samples there will be no a priori preferred direction for texture. For example, for one cell, a run length feature may be oriented at 45°, but at 90° in the next; in general, these are completely equivalent. Each <BR> <BR> <BR> <BR> element of matrix 93o,, q specifies the number of times that the object contains a run length of q, in a given direction, O, consisting of pixels lying in gray level range, p (out of 8 gray levels). Let Ng = 8 be the number of gray levels, and N'be the number of different run lengths that occur in the object; then the run length features are described as follows: 8.1 SHORT90_RUNS,SHORT135_RUNSSHORT45_RUNS, These give large values for objects in which short runs, oriented at 0°, 45 °, 90°, or 135° dominate. shorte runs

8.2 LONGORUNS.LONG45RUNS.LONG90RUNS.LONGl35RUNS These give large values for objects in which short runs, oriented at 0°, 45 °, 90°, or 135° dominate. long6 runs = 8.3 GRAY90_LEVEL,GRAY135_LEVELGRAY45_LEVEL, These features estimate gray level nonuniformity, taking on their lowest values when runs are equally distributed throughout the gray levels. grey0_1evel = 8.4 RUNOLENGTH.RUN45LENGTH.RUN90LENGTH.RUNl35LENGTH These features estimate the nonuniformity of the run lengths, taking on their lowest values when the runs are equally distributed throughout the lengths. run6_percent = 8.5 RUNOPERCENT.RUN45PERCENT.RUN90PERCENT.RUNl35PERCENT These features are calculated as the ratio of the total number of possible runs to the object's area, having its lowest value for pictures with the most linear structure. run6percent = where A is the object's area.

8.6 TEXTURE ORIENT This feature estimates the dominant orientation of the object's linear texture. 180 # (#1' - ypseudo-monment2) +arctan###(70)texture_orient=# xypseudo-cross_moment2#2 where', is the maximal eigenvalue of the run_length pseudo-second moment matrix (calculated in analogy to Section 2.9). The run length pseudo-moments are calculated as follows: Xpseudo-moment2= <BR> <BR> Ypseudo-moment2 XYpseudo-crossmoment2 Orientation is defined as it is for cellorient Section 2.9 as the angle (measured in a clockwise sense) between the y axis and the dominant orientation of the image's linear structure.

8.7 SIZETXTORIENT This feature amplifies the texture orientation for long runs. size text orient =- (74) --<BR> <BR> <BR> <BR> <BR> where B'"2 are the maximal and minimal eigenvalues of the run_length pseudo-second moment matrix, defined in Section 8.6.

References [1] Horn B. K. P., Robot Vision. The MIT press, Cambridge Mas., 1986.

[2] Young I. T., Vanderlaan M. Kramhout L., Jensen R., Grover A., King E.

Morphologic changes in rat urothelial cells during carcinogenesis: II Image Cytometry. Cytometry, Vol. 5, pp. 454-462,1984.

[3] Danielson P. E., A new shape factor. Computer Graphics, Image Processing, Vol. 7, pp. 292-299,1978.

[4] Ballard D. H., Brown C. M. Computer Vision. Prentice-Hall Inc., Toronto, 1982.

[5] Vossepoel A. M., Smeulders A. W. M. Vector code probability and metrication error in the representation of straight lines of finite length. Computer Graphics, Image Processing, Vol. 20 pp. 347-364,1982.

[6] Holmquist J., Bengtsson E., Eriksson O., Nordin B., Stenkvist B. Computer analysis of cervical cells: Automatic feature extraction and classification.

Journal of Histochemistry and Cytochemistry, Vo. 26, pp. 1000-1017,1978.

[7] Stronjny P., Traczyk Z., Rozycka M., Bern W., Bem W., Sawicki W., Fourier analysis of nuclear and cytoplasmic shape of blood lymphoid cells from healthy donors and chronic lymphocytic leukemia patients. Analvtical and Quantitative Cytology and Histology, Vol. 9, pp. 475-479,1987.

[8] Peet F. G., Sahota T. S., A computer-assisted cell identified system. Analytical and Quantitative Cvtology and Histology Vol. 6, N1, pp. 59-70,1984.

[9] Katzko M. W., Pahlplatz M. M. M., Oud P. S., Vooijs G. P. Carcinoma in situ specimen classification based on intermediate cell measurements. Cytometry, Vol. 8, pp. 8-13,1987.

[10] Bibbo M., Bartels P. H., Sychra J. J., Wied G. L. Chromatin appearance in intermediate cells from patients with uterine cancer. Acta Cytologica, Vol. 25, pp. 23-28,1981.

[11] MacAulay C., Development, implementation and evaluation of segmentation algorithms for the automatic classification of cervical cells. Ph. D. Thesis, The University of British Columbia, 1989.

[12] Smeulders A. W. M., Dorst L., Measurements issues in morphometry. Analytical and Quantitative Cytology and Histology, Vol. 7, pp. 242-249,1985.

[13] Brugal G., Quirion C., Vassilakos P., Detection of bladder cancers using a SAMBA 200 Cell Image Processor. Analvtical and Quantitative Cytology and Histology, Vol. 8, pp. 187-194,1986.

[14] Lockart R. Z., Pezzella K. M., Kelley M. M., Toy S. T., Features independent of stain intensity for evaluating feulgen-stained cells. Analytical and Quantative Cytology and Histology, Vol. 6, pp. 105-111,1984.

[15] Vidal D. C. B., Schluter G., Moore G. W., Cell nucleus pattern recognition: Influence of staining. Acta Cytologica, Vol. 17 pp. 510-515,1973.

[16] Panno J. P., Computer analysis of age related chromatin condensation in the somayic cells of the housefly Musca domestica. Msc. Thesis, Simon Fraser Unviersity, 1984.

[17] Komitowski D., Zinser G., Quantitative description of chromatine structure during neoplasia by the method of image processing. Analytical and Quantitative Cytology and Histology, Vol. 7 pp. 178=182,1985.

[18] Presman, N. J. Markovian analysis of cervical cell images. J. Histochemistrv and Cvtochemistry, Vol. 24, pp. 138-144,1976.

[19] Unser M., Sum and difference histograms for texture classification. IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, pp.

118-125,1986.

[20] MacAulay C., Palcic B., Fractal texture features based on optical density surface area. Analytical and Quantitative Cytology and Histology, Vol. 12, pp. 118-125, 1986.

[21] Galloway M. M., Texture analysis using gray level run lengths. Computer Graphics, Image Processing, Vol. 4: 172-179,1975.

EXAMPLE 2 STAINING OF CELLS IN A SAMPLE In order to determine a suitable hydrolysis time for a Feulgen stain process, and in order to determine whether there is a difference in a Feulgen-staining procedure between mice that have been dead for 0 hours versus 24 hours, the following

experiment was performed. Seven CD1 mice were euthanized using CO2. Samples of heart, liver, kidney and skeletal muscle were harvested from 4 of the mice. The remaining 3 mice were retained at room temperature for 24 hours, and then samples from the same 4 tissue types were harvested. The tissue samples were fixed in buffered formalin. The tissue samples were then embedded in paraffin wax (Parablast (R) standard paraffin wax modification), 4 u. m sections were cut using a Zeiss microtome.

Selected tissue samples were hydrolyzed in 5N hydrochloric acid at room temperature. Slides were prepared from each of the tissue samples and hydrolysis times of 10,20,30,35,45,50 and 60 minutes. 3 slides were prepared for each time point. For heart and skeletal muscle, myocytes were the cell of choice, hepatocytes were the cells of choice in liver, and tubular epithelial were the source of cells of choice in kidney. A minimum of 100 undamaged examples of the desired nuclei type were collected per slide. The mean raw DNA amount, which is a photometric feature measuring the integrated optical density, was calculated per slide and plotted. It was found that 45 minutes was a suitable time for each of the tissue types at both 0 and 24 hours, and that formalin fixed and paraffin embedded tissue samples were also suitable for use. This approach for fixing, embedding and staining was found to be suitable to permit analysis of cells from selected tissue types to be selected for further analysis to determine the time between death and fixation of the cells based upon changes in cellular features.

EXAMPLE 3 SELECTION OF DESIRED SAMPLE CELLS FROM MOUSE FOR TIME SINCE DEATH ANALYSIS 1. Harvesting of Tissue.

210 CD1 mice were euthanized with CO, and then stored at room temperature (23°C). Each body was placed on its back so that the bodies of adjacent mice were not touching each other. Samples were taken from each of 15 mice, beginning at 10 hours from the time of sacrifice until 36 hours (i. e., 15 mice were sampled at each of 10,12,14,... 36 hours). Samples were taken from heart muscle, skeletal muscle, liver and kidney, and prepared as described above in Example 2.

Selected tissues from mice were harvested as follows. Tissue cassettes and slides were labeled to reflect the animal (i. e., mouse), organ (i. e., heart, skeletal muscle, liver or kidney), the time since death and the individual animal number (e. g., M-L-12-04 indicated that the sample was mouse liver taken at 12 hours since death from animal number 4). (Screw-cap centrifuge tubes were also labeled and tissue was placed therein, but such were not further used in the present Example.) A plastic tub was filled with 10% buffered formalin.

The mice to be harvested were stabilized by pinning the 4 limbs of the mouse prior to the time of harvest. The animal was sprayed with 70% ethanol and then the peritoneal and thoracic cavities were opened. The heart was removed with a cut just below the atria. The apex was then cut off and snap frozen. A small section of the heart was cut and placed in a TUNEL cassette and the remainder was placed in a tissue cassette and then in formalin. The liver was removed, a small piece was snap frozen, and then other small pieces were placed in a TUNEL cassette and a standard tissue cassette and then placed in formalin. The skin was dissected off of the rear legs to expose the thigh muscles, then two longitudinally cut pieces were removed and each placed in a TUNEL cassette or a tissue cassette, and then formalin, and a third piece was snap frozen. One kidney was removed with its fat pad, the kidney was bisected, and then pieces of the kidney were placed in each of a TUNEL cassette and a kidney tissue cassette and then placed in formalin. Another piece of the kidney was snap frozen. The animal is then turned, the back of the head was washed with 70% ethanol and then the skin dissected off to expose the skull. The skull was opened from the posterior inferior along the sides, the brain was removed and then a small piece of the brain was placed in a centrifuge tube, the remainder into a tissue cassette. The animals and disposable materials were then disposed of appropriately. Tubes used for snap freezing were removed from the liquid nitrogen and transferred to a-80°C freezer. The tissue samples were then embedded in paraffin wax (Parablast standard paraffin wax modification), 4 pm sections were cut using a Zeiss microtome, and placed in standard tissue cassettes and then stained using a Feulgen-thionine stain.

The samples were then subjected to classification of nuclei in the samples using a high resolution imaging system (Cyto-SavantTM) from Oncometrics, Vancouver, British Columbia. Binary decision trees were created to separate the nuclei in a given sample into different groups.

The binary decision trees contained three primary components: thresholds (thr), linear discriminants (dsc) and resets (rst). A threshold is a simple dividing line equivalent to a line running parallel to the Y axis or X axis on a graph.

Linear discriminants are equivalent to a diagonal line running between the X and Y axes of a graph. Resets shuffle nuclei from group to group and lock and unlock groups so that thresholds and linear discriminants should act only on certain groups of nuclei.

2. Decision Tree for Heart Muscle.

The three different file types, thresholds (*. thr), reset functions (*. rst) and linear discriminants (*. dsc) are set forth below in the order they work in the tree.

The file name will be underlined, as will any feature and group names.

Area5. thr: This acts on groups 0 and 5. It uses area. Any object greater than 1200 pixels in size is sent to 5 while anything less than 1200 is sent to 0. It is intended to get right of large objects that are not cells so that they are not counted as desired nuclei.

Hdna. thr: This acts on groups 0 and 4. It uses DNA Index. Objects with a DNA Index value of less than 0.4 are sent to 4 while anything greater than 0.4 is sent to 0. This acts to eliminate objects that are too small to be useful nuclei.

8-14. rst: This moves any nuclei in 4 to 5 and it also locks 5, so that certain thresholds and discriminants do not act on the contents of 5.

First. thr: This acts on 1 and 4. It uses lowVSmhDNA (3.378021), Varjntensity (25927.897531), MedDNAAmount (0.253511) & HiDNAAmount (0.729459). This sends myocyte-like objects to 1 and non-myocytes (most likely lymphocytes) to 4.

8first. rst: This unlocks 1.

Second. thr: This acts on 1 and 2. It uses sphericity (0.6). Objects that are greater than 0.6 (i. e., round) go to 2 while not so round (oblong) objects go to 1.

8second. rst: This locks 1 and unlocks 4.

Slymph. thr : This acts on 3 and 4. It uses HiDNAcomp (2.0). Objects less than 2.0 go to 3 while anything greater than 2.0 goes to 4. Lymphocytes have very uniform and concentrated DNA (very dark nuclei).

8third. rst: This moves 4 into 5.

IDNAone. thr: This acts on 3 and 4. It uses DNA Index (1.2). Objects greater than 1.2 go to 4. Objects less than 1.2 go to 3. This is meant to help select suitable lymphocyte nuclei.

8fourth. rst: This moves 4 into 5.

IDNAtwo. thr: This affects 3 and 4. It uses DNA Index (0.7). Objects less than 0.7 go to 4 while objects greater than 0.7 go to 3.

8fourth. rst: As above.

8round. thr: This acts on 3 and 4. It uses sphericity (0.6). Objects that are greater than 0.6 stay in 3 while those less than 0.6 go to 4. Preferred lymphocytes are nice and round.

8fourth. rst: As above 8area. thr: This acts on 3 and 4. It uses area (200). Objects greater than 200 go to 4. Objects less than 200 go to 3. Preferred lymphocytes are nice and round and small.

Sfive. rst : Moves 4 to 5 and unlocks 1.

8area2. thr: This acts on 1 and 4. It uses area (250). Objects less than 250 go to 4. The others stay in 1. This divides oblong myocytes into a"big"group and "small"group.

9one. rst: Moves 4 to 9 and unlocks 2.

8area3. thr: This acts on 2 and 4. It uses area (200). Objects less than 200 go to 4. Those greater than 200 go to 2. This divides rounded myocytes into a "big"group and"small"group.

9two. rst: Moves 4 to 8 and unlocks 1.

Fib. dsc: This acts on 1 and 4. It uses eccentricity (-2.01803), compactness (-5.592425), HiDNAcomp (1.282856), MhDNAcomp (0.982584), correlation (-0.108658), and contrast (-0.091919). The first constant is 21.744701. This sends fibroblasts to 4 and myocytes to 1.

9three. rst: This moves 4 to 7 and unlocks 1. blur. dsc: This works on 1 and 4. It uses var radius (0.360061), compactness (-11.371441), Variance (-25.639956), ODskewness (6.761003), correlation (-0.045926), contrast (0.273661), homogenity (36.655392), and clshade (2.974775). The first constant is 9.384482. This sends blurry objects to 4.

9four. rst: Moves 4 to 9 and unlocks 2.

9Rblur. dsc: This acts on 2 and 4. It uses energy (-87.122), correlation (- 0.059434), fractall_area (0.002571), and fractal2 area (-0.009278). The first constant is 9.384482. Blurs go to 4 the rest stay in 2.

9five. rst: This moves 4 to 8.

3. Decision Tree for Skeletal Muscle.

The three different file types, thresholds (*. thr), reset functions (*. rst) and linear discriminants (*. dsc) are set forth below in the order they work in the tree.

The file name will be underlined, as will any feature and group names.

Area5. thr: This acts on groups 0 and 5. It uses area. Any object greater than 1200 pixels in size is sent to 5 while anything less than 1200 is sent to 0. It is intended to get right of large objects that are not cells so that they don't count as desired nuclei.

Hdna. thr : This acts on groups 0 and 4. It uses DNA Index. Objects with a DNA Index value of less than 0.4 are sent to 4 while anything greater than 0.4 is sent to 0. This acts to exclude objects that are too small to be useful nuclei.

8-14. rst: This moves any nuclei in 4 to 5 and it also locks 5, so that during the next step thresholds and discriminants do not act on the contents of 5 (in other words, the nuclei in 5 are locked so that they will not be included in the next step (s), then unlocked thereafter for further actions).

First. thr: This acts on 1 and 4. It uses lowVSmhDNA (3.378021), Varintensity (25927.897531), MedDNAAmount (0.253511) & HiDNAAmount (0.729459). This sends myocyte-like objects to 1 and non-myocytes (most likely lymphocytes) to 4.

Ql. rst: This unlocks 4.

8lymph. thr: This acts on 3 and 4. It uses HiDNAcomp (2.0). Objects less than 2.0 go to 3 while anything geater than 2.0 goes to 4. Lymphocytes have very uniform and concentrated DNA (very dark nuclei).

8third. rst: This moves 4 into 5.

IDNAone. thr: This acts on 3 and 4. It uses DNA Index (1.2). Objects greater than 1.2 go to 4. Objects less than 1.2 go to 3. This is meant to help select suitable lymphocyte nuclei.

8fourth. rst: This moves 4 into 5.

IDNAtwo. thr: This affects 3 and 4. It uses DNA Index (0.7). Objects less than 0.7 go to 4 while objects greater than 0.7 go to 3.

8fourth. rst: As above.

8round. thr: This acts on 3 and 4. It uses sphericity (0.6). Objects that are greater than 0.6 stay in 3 while those less than 0.6 go to 4. Preferred lymphocytes are nice and round.

8fourth. rst: As above 8area. thr: This acts on 3 and 4. It uses area (200). Objects greater than 200 go to 4. Objects less than 200 go to 3. Preferred lymphocytes are nice and round and small.

Q2. rst: Moves 4 to 5 and unlocks 1.

Fib. dsc: This acts on 1 and 4. It uses eccentricity (-2.01803), compactness (-5.592425), HiDNAcomp (1.282856), MhDNAcomp (0.982584), correlation (-0.108658), and contrast (-0.091919). The first constant is 21.744701. This sends fibroblasts to 4 and myocytes to 1.

Q3. rst: This moves 4 to 7 and unlocks 1.

blur. dsc: This works on 1 and 4. It uses var radius (0.360061), compactness (-11.371441), OD variance (-25.639956), ODskewness (6.761003), correlation (-0.045926), contrast (0.273661), homogenity (36.655392), and clshade (2.974775). The first constant is 9.384482. This sends blurry objects to 4 and non- blurry objects to 1.

Q4. rst: Moves 4 to 8.

The Groups are: Group 1 Myocytes Group 3 Lymphocytes Group 5 Junk Group 7 Fibroblasts Group 8 Blurs (Note: This Decision Tree concerning skeletal muscle is a modified heart tree.) 4. Decision Tree for Liver.

The three different file types, thresholds (*. thr), reset functions (*. rst) and linear discriminants (*. dsc) are set forth below in the order they work in the tree.

The file name will be underlined, as will any feature and group names.

H&L. thr: This uses medavdst (0.848860), mean intensity (326.425812), xcentroid (11.543215), y centroid (11.180144), and contrast (52.664497). Groups affected are 1 & 3. This tends to separate hepatocytes into 1 and lymphocytes into 3.

Liv21. rst: This locks group 3.

Hdouble. thr: This uses excentricity (1.361122), sphericity (0.599599), elongation (1.333066), and compactness (1.225167). The groups are 1 & 5. Doubled nuclei to 5 while single hepatocytes go to 1.

Lclasl. rst: This locks groups 3 & 5.

Lclasl. thr : This uses DNA Index (1.896220) and DNAAmount (171.349355). Output to 1 & 2. Tetraploid hepatocytes to 2 while diploid hepatocytes go to 1.

Lclas2. rst: Locks 1,2, & 5 and unlocks 3.

Lclas2. thr: This uses DNA Index (1.2). Output to 3 & 4. Lymphocytes with a DNA Index of greater than 1.2 go to 4, those less than 1.2 go to 3.

Lclas3. rst: This moves 4 into 5, locks 5 and unlocks 3.

Lclas3. thr: This uses DNA Index (0.7). Groups are 3 & 4.

Lymphocytes with a DNA Index of less than 0.7 go to 4 while those greater than 0.7 go to 3.

Lclas4. rst: This moves 4 into 5, locks 5 and unlocks 3. This is the same as Lclas3. rst).

Lclas4. thr: This uses sphericity (0.55). The groups are 3 & 4. Non- round objects go to 4, while round objects stay in 3.

Lclas6. rst: This moves 4 into 5, and locks 5.

Lclas5. thr: This uses area (200). The groups are 3 & 4. Big objects go to 4 while small (lymphocytes) ones go to 3.

Lclas7. rst: This moves 4 into 5 and locks all groups.

Group 1 diploid hepatocytes Group 2 tetraploid hepatocytes Group 3 lymphocytes Group 5 junk 5. Decision Tree for Kidney.

The three different file types, thresholds (*. thr), reset functions (*. rst) and linear discriminants (*. dsc) are set forth below in the order they work in the tree.

The file name will be underlined, as will any feature and group names.

IDna. thr: This uses DNAINDEX (0.25). The groups in question are 0 & 7. Objects with DNA Index less than 0.25 go to 7, while those over go to 0. This acts to screen out artifacts and non-nuclei.

Kdouble. thr: This uses Sphericity (0.55). The groups in question are 0 & 5. Objects less than 0.55 go to 5, object that are over 0.55 go to 0. This is intended to screen out doubled or overlapping nuclei. Tubular epithelial cells, which contain the nuclei type of interest, are circular.

Prat. thr : This uses YCentroid (10.493424) & Var Intensity (32832.871094). The relevant groups are 1 & 3. Tubule-like nuclei go to 1, Lymphocyte-like nuclei go to 3. Lymphocytes are really dark; tubules are more gray.

Kclasl l. rst : This locks 1 & 7 and unlocks 3.

Kclasl3. thr : This uses DNA Index (0.7). Groups in question are 3 & 4.

Objects less than 0.7 go to 4, objects greater than stay in 3.

Kclasl2. rst : Moves 4 to 5 and locks 1 and unlocks 3.

Kclasl4. thr : This uses DNA Index (1.2). Groups are 3 & 4. Objects greater than 1.2 go to 4, while less than 1.2 stay in 3.

Kclasl2. rst : As above.

Kclasl6. thr : This uses Gray45_Level (21.919233), Area (209.801010), Mean-Radius (7.782900). Groups are 3 & 4. Separates out Non-lymphocytes to 4, lymphocytes should stay in 3.

Kclasl3. rst: Locks 3 & 1, moves 4 to 6 and locks 6.

Tree in question is Ksuppl. dsf.

Ksupl. rst: Locks all groups except 1 (which is unlocked). trutube. thr : This uses mean intensity (347.978363) and lowVSmhDNA (3.442550). It works on groups 1 and 7. Objects that are somewhat questionable tubules go to 7, while nuclei that are definitely tubules stay in 1. This tree is a two step process.

First, objects that may not be tubules are pulled out of group 1 (which should only be tubules) and put in group 7.

Ksup2. rst: Locks 1 and unlocks 7.

HiDNAcomp. thr: This uses HiDNAcomp (2.0). It works on 7 and 8.

Dark objects (most likely fat nuclei) go to 8 while objects that are probably tubules stay in 7.

Ksup3. rst : Moves 7 into 1 and locks all groups.

After this tree the groups should be as follows: Group 1 Tubules Group 3 Lymphocytes Group 5 Junk Group 8 Probably Fat nuclei

EXAMPLE 4 TIME SINCE DEATH ANALYSIS OF SAMPLE CELLS FROM MOUSE LIVER A set of sample cells on mouse liver were obtained as described above.

These cells were analyzed using a high resolution image cytometry system as described herein to determine 270 cellular features (i. e., the 135 cellular features set forth in Example 1 above were doubled because both feature means and feature variances were assessed. A number of manually selected slide features from the tissue tree were also assessed.

Due to the large dimensionality of the data set of features, the data was reduced to its significant components using principal component analysis (PCA). The limited set of such significant components was then used to retroactively predict the time since death. Briefly, the PCA took all of the cellular features analyzed and found a linear combination of such features that described the largest amount of variability in the data. This was termed the first principal component, or first eigenvector. It had associated with it a weighting value known as its eigenvalue. A second principal component, or second eigenvector was the linear combination of features that described the majority of the variability left in the data set after the variability described by the first principal component had been removed. The second principal component had associated with it a second eigenvalue, which was less than the eigenvalue of the first principal component. This process was continued until all the variability in the data had been described. The components with the larger eigenvalues were the ones that described most of the data, and were the ones that were used in the followings steps. A computer program suitable to conduct the principal component analysis is BMDP4R- Regression On Principal Components And Ridge Regression by BMDP from Statistical Software, Inc., Los Angeles, CA 90025 USA and Cork, Ireland; another suitable computer program is Statistics for Windows from Statsoft Inc., Tulsa Oklahoma.

As indicated below (see Figures 1-3), the analysis of the results from the time period from 10 hours to 36 hours since death indicated that there may be a different process

occurring in the 10 hour to 20 hour period versus the 20 hour to 36 hour period. Thus, the time periods were also fitted independently.

These results in Appendix 1 are set forth graphically in Figure 1. The results indicated that there is a standard deviation of 1.75 hours when predicting the time since death from 10-20 hours post-mortem using 9 features and 89 cases, R=0.785.

The results of the analysis in the 20 hour to 36 hour time period are set forth in Appendix 2 and graphically in Figure 2. The results indicated that there is a standard deviation of 2.27 hours when predicting the time since death frorn 20-36 hours post-mortem using 12 features and 118 cases.

The results of the liver study from 10-36 hours were also run, as depicted in Figure 3. A total of 23 features were reviewed (207 cases). The standard deviation was 3.6 hours and R=0.827. The results indicated that two different processes could be occurring between the time period from 10-20 hours versus the time period from 20-36 hours.

EXAMPLE 5 TIME SINCE DEATH ANALYSIS OF SAMPLE CELLS FROM MOUSE KIDNEY Analyses similar to that set forth in the above examples was performed on sample cells from mouse kidney.

Mouse kidneys were obtained and prepared as described above. The samples from the kidneys were analyzed and the cells in the samples were pre-classified into tubular epithelial cells (group 1) and lymphocytes (group 3), among others. The cells in group 1 were then normalized against the cells in group 3. These groups were then subjected to cellular feature analysis as described above for the liver. The mean and standard deviation of each feature was calculated. In order to reduce the number of features ultimately used to perform a regression analysis, features showing a coefficient of correlation below 0.3 with the time of death were excluded. An analysis of variance was performed with the remaining features. Only features showing overall p values (t- test) smaller than 0.05 were entered into the regression analysis.

80 features were entered into the regression analysis (which was a forward stepwise analysis: F-enter = 4, F-remove = 3.99). Regression analysis was then performed on the entire data set using four features. The coefficient of correlation was R = 0.82. The results are set forth in Table 1 below.

TABLE 1 BETA ST. B ST. T-188-P-LEVEL ERRS. ERRS. Intercept 57.92197 4.916521 11.78109 2.38E-24 HOMO VARa-0.29627 2.040735-3.86717 0.000152 RUN Oo/Ob-0. 29153 0. 089601-0. 62967 0.193525-3.25367 0.001351 LOWDNAAREA 0.196134 0.044351 0.456724 0.103278 4.42229 1.65E-05 Var' RUN45% VAR"-0. 22766 0.082695-0. 55847 0. 20286-2. 75299 0.006484 a = variance of homogeneity b = mean of runO percent c = variance of the lowDNAarea d = variance of the run45 percent The predicted values of the time of death as a function of the expected value were then determined and are depicted in the graph in Figure 4. For each case in the graph, the predicted value was subtracted from the expected value (i. e., time of death). The mean and standard deviation (stdv) of this difference for each time group is given in Figures 5 and 6. As noted above, the regression analyses were performed with a F-enter = 4. Decreasing this value causes more features to be used in the analysis.

The selection of an appropriate number of features can be made by a person of ordinary skill in the art in view of the present specification and in view of the needs of such skilled person. Generally, the person will select the maximum number of features to provide the closest correlation between predicted time of death and actual time of death while avoiding"over training"the analysis to a specific training set, which means that such training sample set is very highly described by the selected features but that such features do not adequately describe a new sample (such as is typically obtained in the field).

EXAMPLE 6 TIME SINCE DEATH ANALYSIS OF SAMPLE CELLS FROM MOUSE KIDNEY USING TWO DIFFERENT REGRESSIONS Based upon the results in Example 5, the data set was split into two groups, from 10-18 hours and from 20-36 hours. Two independent regressions were then performed on these two different data sets. From 10-18 hours, two different features were used. The coefficient of correlation was R = 0.54. The results are set forth in the following Table 2: TABLE 2 BETA ST. ERR. B ST. ERR. T68 PLEVEL Intercept 18.66439 3.435421 5.432927 8.07E-07 RUN135 VAa-0.53827 0.106925-0. 45201 0.089788-5.03414 3.75E-06 MEDDNAAMOU 0.336462 0.106925 2.930009 0.931132 3.146717 0.002451 NTb a = variance of the runl35 percent b = mean of the medDNAamount Four features were used for the regression analysis from 20-36 hours.

The coefficient of correlation was R = 0.82. The results of this analysis are set forth in Table 3: TABLE 3 BETA ST. ERR. B ST. ERR. T-117-P-LEVEL Intercept-84.1133 28.4855-2.95285 0.003806 RUN L 0% a-0. 47171 0. 096972-0.77854 0.16005-4.86439 3.61E-06 SHORT RUNb 5.445233 2.9E-07 Cell 1.753623 4.507396 1.57E-05 Orient va HOMO vard-0. 22282 0.0857-4.50898 1.734228-2.59999 0.010524 a = mean of runO percent b = mean of shortO runs c = variance of cell orient d = variance of homogeneity

A box plot of the predicted values given by the corresponding regressions as function of the expected values is depicted in Figure 7.

For each case in the above regressions, the predicted value was subtracted from the expected value (time of death). The mean and standard deviation of such differences for each time group are provided in Figures 8 and 9.

EXAMPLE 7 TIME SINCE DEATH ANALYSIS FROM A COMBINATION OF SAMPLE CELLS FROM KIDNEY AND LIVER: SINGLE REGRESSION The predicted values obtained with one regression analysis for kidney as well as the predicted values obtained with one regression analysis for liver were combined into a single new score. The mean and standard deviation of such score and the differences between the predicted time of death and the actual time of death are provided in the following Table 4: TABLE 4 Mean Pred-Expect StDv G 1: 10 14. 57458 4.574575357 3.801525 G2: 12 15.40799 3.407987343 2.598112 G3: 14 16.8341 2.834099539 2.816525 G4: 16 18.19244 2.192439793 2.160434 G5: 18 18.8821 0.88209595 2.991843 G6: 20 21.08301 1.083008794 1.683277 G 7: 22 23.07093 1.07093448 2.45836 G8: 24 24.14248 0.142478501 2.638934 G 9: 26 24.6482-1.351797526 1.962116 G 10: 28 27.36171-0.638289736 2.126598 G 11: 30 26.69344-3.306557204 2.323558 G 12: 32 28.80999-3.190013175 2.375371 G 13: 34 30.27636-3.723641409 4.33311 G_14: 36 30.24337-5.756627152 3.979933 All Grps 22.46115 0.154272485 3. 798282 A plot of these results is set forth in Figure 10.

EXAMPLE 8 TIME SINCE DEATH ANALYSIS FROM A COMBINATION OF SAMPLE CELLS FROM KIDNEY AND LIVER: TWO REGRESSIONS The predicted values attained with one regression for a sample from kidney cells as well as the predicted values obtained for one regression analysis of liver cells were combined into a new score. The mean standard deviation of the scores are provided in Figures 11 and 12.

EXAMPLE 9 TIME SINCE DEATH ANALYSIS OF SAMPLE CELLS FROM HUMAN HEART A heart was taken from a human cadaver, and then a sample was cut from the left ventricle of the heart. This sample was immersed in a 10% buffered formalin for 24 hours in order to fix the sample (the sample can be immersed for anywhere from about 8-24 hours, depending upon the size of the sample; the sample can also be flash frozen with liquid nitrogen or dry ice, or otherwise fixed in accordance with methods known in the art). The sample was then imbedded in paraffin and then cut to provide a 4 llm slice; multiple slices can be obtained if desired. The slice was placed on a glass slide and then the slide and slice were heated at about 50°C for 40-60 minutes so that the sample stuck to the slide. The sample was then stained with Feulgen stain (other nuclear stains are also suitable). The slide was then bar coded and placed in an automated Cyto-Savant cytometer, Oncometrics, Vancouver, British Columbia, Canada.

The cytometer picked about 1,000-1,500 objects on the slide for analysis. The cytometer analyzed each object for the about 130 features described in Example 1 above. Review of this analysis (which was done in automated fashion by the cytometer (and its related computer) permitted selection of about 200 normal, non- diseased nuclei. Although lesser preferred, all material on the slide can be analyzed if desired. Next, the analysis was limited to the 34 features described below in Table 5.

TABLE 5 SAMPLE OF VARIABLES IN EOUATION STD. ERR F VARIABLE COEFF. OF COEFF TOL. REMOVE (L) (CONSTANT 72.7043) <BR> <BR> <BR> <BR> <BR> Mell ori-0. 0279 0. 0112 0. 4301 6.18 (1)<BR> <BR> <BR> <BR> <BR> <BR> <BR> Mreg_hig 2. 9846 1. 4423 0. 0799 4.28 (1)<BR> <BR> <BR> <BR> <BR> <BR> <BR> Marmon03-7.2639 1. 1795 0. 2419 37.93 (1) 0.33E-030.171635.90(1)Mar_inte0.198E-02 18.95440.030915.19(1)MD_maxim-73.8663 <BR> <BR> <BR> <BR> <BR> <BR> MedDNAar 108. 3785 29. 9852 0. 0100 13.06 (1) (1) 1.83790.04919.50(1)Med_den_-5.6657 Montrast 0. 6287 0. 2916 0. 0651 4.65 (1) 305.27190.02533.32(1)Men_lit_555.8749 Mendrk620. 8950242. 06420. 05056.58 (1) <BR> <BR> <BR> <BR> <BR> <BR> Menter_o-59. 8877 38. 7672 0. 2407 2.39 (1)<BR> <BR> <BR> <BR> <BR> <BR> <BR> Mize_txt-0. 1144 0. 0319 0. 0613 12.88 (1) (1) <BR> <BR> <BR> <BR> <BR> <BR> Var_radi 1. 6280 0. 4594 0. 0368 12.56 (1)<BR> <BR> <BR> <BR> <BR> <BR> <BR> <BR> Vpherici-100.1963 22. 6721 0. 3044 19.53 (1) Vlongati 10. 4257 6. 0577 0. 0250 2.96 (1) 1.41920.28597.33(1)Varmon033.8423 <BR> <BR> <BR> <BR> <BR> <BR> Varmon04-5.1870 1. 1030 0. 0392 22.11 (1) 0.03120.239211.03(1)Vean_int-0.1036 42.30890.10102.18(1)VD_maxim62.4873

STD. ERR F VARIABLE COEFF. OF COEFF TOL. REMOVE (L) VD kurto 3.2618 1.2072 0.0703 7.30 (1) Vedavd-64.7413 26.4753 0.1951 5.98 (1) VowVShig 3.7524 1.8835 0.0566 3.97 (1) Vow den_-2.2720 1.6174 0.1529 1.97 (1) vlshade-25.5225 9.6923 0.1299 6.93 (1) Vange_ex 0.0579 0.0411 0.1630 1.98 (1) Vractal--81.0319 31.4628 0.3362 6.63 (1) Vizetxt 0. 515E-01 0.92E-02 0.0615 31.32 (1) Vhort0r 304.8987 64.7767 0.0779 22.16 (1) Vhort45-114.7498 39.4763 0.1833 8.45 (1) VongO_ru 0.5883 0.5362 0.2492 1.20 (1) Vun45_1e-0.1274 0.0820 0.1647 2.42 (1) Vun90 le-0.2661 0.0513 0.2219 26.90 (1) m = mean v = variance The feature names above in Table 5 correspond to names in Example 1 except that the feature names have been: (1) truncated to 8 characters, and (2) the first character has been replaced by m (= mean) or v (= variance). For example, mellori = mean of cell orientation, vpherici = variance of sphericity, and var radi = variance of the variance of radius.

For each feature, mean + variance is calculated for all cells selected for each slide, and the means and variances are calculated for each feature, for example: ncells I fi k iN = fk N N = mean of each feature

n ee//s n cells i (f k-fi,) = variance of each feature The mean values of the slide (tissue sample) x (the coefficient for all features) + the constant (72.704, here) provides the predicted time of death.

After such analysis, the cytometer identified a predicted time of death.

In each of the samples that were analyzed by the cytometer, the cytometer reviewed each of the 34 indicated features in each of the 200 or so selected nuclei. Figure 13 depicts the results obtained from 74 samples from 74 human cadavers, with one circle on the graph for each cadaver. For example, a circle on the graph at the 100 hour measured time (y axis) and 100 hour predicted time (x axis) indicates that the predicted time of death for the person was 100 hours prior to the time of fixing the sample, plus or minus the given standard deviation. The standard deviation represented in Figure 13 indicates plus or minus 8.9 hours for a sample having a predicted time of death of 250 hours before fixation of the sample. Testing on mouse hearts has provided an estimate of time of death of plus or minus 57 minutes at 24 hours between time of death and fixation of the sample. Figure 14.

EXAMPLE 10 TISSUE COLLECTION FROM HUMANS Cases will be selected based upon proper consent for the collection of tissue samples, the time of death will be known or can be determined to within 20 minutes by methods other than the examination of cellular features as discussed in the present specification, and environmental conditions will be known or accurately extrapolated for the entire post-mortem period. A separate case history form and tissue collection form will be completed for each case. All dates will be recorded according to day/month/year, and times will be recorded using the 24 hour clock. Each case will receive its own ID number.

The time of death will be recorded as observed or estimated. If the time of death is estimated, then the possible range of time of which the actual time of death occurred will be recorded. Information regarding the environmental conditions that the body is exposed to from the time of death until the last specimen sample is collected will be recorded. There will preferably be a continuous account for the entire post- mortem period. The post-mortem period will be broken down into phases of discrete environmental conditions. Phase 1 will be the scene at which the subject died. Each subsequent phase will be assigned and noted when the body is moved to a new location or exposed to a different environment. Data that will be recorded will include the air temperature, the body temperature (if measured) and a brief description of the location or environment. The autopsy report will be obtained.

The exact date and time will be recorded for actual tissue collection. The specimen will be labeled with a bar code sticker and a companion identical bar code sticker will be placed on a tissue collection form. Needle biopsy specimens will be collected sequentially at various times following death. Samples will include liver and quadriceps muscle at the scene, or at the earliest possible time after death, then periodically for as long as the body is available to the investigator. Needle biopsy specimens will be placed in 7 ml vials with 10% formalin.

Tissue sections of desired organs, such as heart, lung, kidney, liver, gastrointestinal tract, brain and spleen can be obtained. Section size of the tissue sections should be about 1 cm x 1 cm x 0.3 cm. Specimens will be placed in 20 ml vials with 10% formalin. In addition, vitreous fluid will be collected from the right eye at the same time as the first needle biopsy. The sample of vitreous fluid will be placed in a 7 ml vial, and the time and date recorded and appropriately labeled. A second sample of vitreous fluid from the left eye will be collected at the time of autopsy and also appropriately labeled.

After obtaining such samples from the field, the samples will be subjected to identification and selection of suitable cells as discussed above. Such suitable cells will then be subjected to time since death analyses as also discussed above to provide an estimate of the time of death based upon the cellular regression occurring

in the cells between the time of death of the subject and the time of fixation of a given sample. The results of such analyses will also be compared against standards to predict the time of death, and will further be used to create standards against which future determinations of time of death can be compared.

From the foregoing it will be appreciated that, although specific embodiments of the invention have been described herein for purposes of illustration, various modifications may be made without deviating from the spirit and scope of the invention. Accordingly, the invention is not limited except as by the appended claims.

APPENDIX 1<BR> SELECTED RESULTS FROM FILE L4R8<BR> VARIABLE NAMES<BR> independent = area,<BR> r_mean ,r_max ,<BR> r_var ,spher ,eccentr ,<BR> cell_or ,inertia ,compact ,<BR> elong ,r_fft_1,r_fft_h,<BR> fft_h1 ,fft_h2 ,fft_h3 ,<BR> fft_h4 ,fft_h5 ,fft_h6 ,<BR> fft_h7 ,fft_h8 ,fft_h9 ,<BR> fft_h10 ,fft_h11 ,fft_h12,<BR> fft_h13 ,fft_h14 ,fft_h15,<BR> fft_h16 ,fft_h17 ,fft_h18,<BR> fft_h19 ,fft_h20 ,fft_h21 ,<BR> DNA_ind ,DNA_amnt ,<BR> OD_max ,OD_var ,OD_skew ,<BR> OD_kurt ,DNA_ar_1 ,DNA_ar_m ,<BR> DNA_ar_h ,DNA_am_1 ,DNA_am_m , DNA_am_h ,DNA_c_1 ,DNA_c_m ,<BR> DNA_c_h ,DNA_c_mh ,av_dst_1 ,<BR> av_ds_m ,av_ds_h ,av_ds_mh ,<BR> DAN_Ivm ,DNA_1vh,DNA_1vmh ,<BR> 1_den_o ,m_den_o ,h_den_o ,<BR> entropy ,energy ,corr ,<BR> contrast ,homogen ,cl_shd ,<BR> cl_prom ,den_1_s ,den_d_s ,<BR> range_ex ,range_av ,c_of_gr ,<BR> c_m_1 ,c_m_m ,cr_m_h ,<BR> fract_di ,fract_al ,fract_a2,<BR> text_or ,sz_t_or,sh_run_0 ,<BR> sh_r_45 ,sh_r_90 ,sh_r_135,<BR> 1_r_0 ,1_r_45 ,1_r_90 ,<BR> 1_r_135 ,gr_1_0 ,gr_1_45 ,<BR> gr_1_90 ,gr_1_135 ,r_le_0 ,<BR> r_le_45 ,r_le_90 ,r_le_135 ,<BR> r_pc_0 ,r_pc_45 ,r_pc_90,<BR> r_pc_135, Varea ,<BR> Vr_mean ,Vr_max ,<BR> Vr_var ,Vspher ,Veccentr ,<BR> Vcell_or ,Vinertia ,Vcompact ,<BR> Velong ,Vr_fft_1,Vr_fft_h,<BR> Vfft_h1 ,Vff_h2 ,Vfft_h3 ,<BR> Vfft_h4 ,Vfft_h5 ,Vfft_h6 ,<BR> Vfft_h7 ,Vfft_h8 ,Vfft_h9 ,<BR> Vfft_h10,Vfft_h11,Vfft_h12,<BR> Vfft_h13,Vfft_h14,Vfft_h15,<BR> Vfft_h16,Vfft_h17,Vfft_h18,<BR> Vfft_h19,Vfft_h20,Vfft_h21 ,<BR> VDNA_ind ,VDNA_amn ,<BR> VOD_max ,VOD_var ,VOD_skew ,<BR> VOD_kurt ,VDN_ar_1 ,VDN_ar_m ,<BR> VDN_ar_h ,VDN_am_1 ,VDN_am_m ,<BR> VDN_am_h ,VDN_c_1 ,VDN_c_m ,<BR> VDN_c_h ,VDN_c_mh ,Va_ds_1 ,<BR> Va_ds_m ,Va_ds_h ,Va_ds_mh , VDN_1vm,VDN_1vh,VDN_1vmh ,<BR> Vl_den_o ,Vm_den_o ,Vh_den_o ,<BR> Ventropy ,Venergy ,Vcorr ,<BR> Vcontrast ,Vhomogen ,Vcl_shd ,<BR> Vcl_prom ,Vden_1_s ,Vden_d_s ,<BR> Vrange_ex ,Vrange_av ,Vc_of_gr,<BR> Vc_m_1 ,Vc_m_m ,Vcr_m_h ,<BR> Vfract_di ,Vfrac_al ,Vfrac_a2 ,<BR> Vtext_or ,Vsz_t_or,Vsh_run_0 ,<BR> Vs_r_45 ,Vs_r_90 ,Vs_r_135 ,<BR> Vl_r_0 ,Vl_r_45 ,Vl_r_90 ,<BR> Vl_r_135 ,Vg_1_0 ,Vg_1_45 ,<BR> Vg_1_90 ,Vg_1_135 ,Vr_le_0 ,<BR> Vr_1_45 ,Vr_1_90 ,Vr_1_135 ,<BR> Vr_pc_0 ,Vr_pc_45 ,Vr_pc_90,<BR> Vr_p_135,<BR> small,<BR> large,<BR> leuk, j40,<BR> blurry,<BR> numcell, numjunk,<BR> smallthr, largethr,<BR> TSDnumS,TSDNUMSc,tsdsmen,ftsds,tsdnuml,tsdnumlc,tsdlmen,ftsd l,<BR> notnumS,notNUMSc,notsmen,fnots,notnuml,notnumlc,notlmen,fnot l,<BR> blrmS,blrNUMSc,blrsmen,fblrs,blrnuml,blrnumlc,blrlmen,fblrl, <BR> holnumS,holNUMSc,holsmen,fhols,holnuml,holnumlc,hollmen,fhol l,<BR> sptnorm,sptnrmc,sptnrmm,f44,sptabnm,sptabnmc,sptabnmm,f48.&l t;BR> <P>CORR.<BR> <P>Limit = 0.05,4.0.

EIGENVALUES<BR> (those results correlating with Eigenvectors 1, 5, 16, 17, 20, 42, 49, 69 and 85 are in bold)<BR> 62.76022 51.70994 31.48885 18.45518 13.82135 8.39100 6.97239 6.24915 4.72123 3.97238<BR> 3.29037 3.02987 2.61366 2.48905 2.23936 2.16677 1.68856 1.45054 1.34672 1.21308<BR> 1.13201 1.03198 0.95707 0.89035 0.83949 0.75294 0.67645 0.65551 0.63632 0.58771<BR> 0.55054 0.49637 0.49412 0.47478 0.44890 0.39396 0.36929 0.34111 0.33213 0.28573<BR> 0.28463 0.27214 0.25261 0.23872 0.22450 0.21655 0.19164 0.19062 0.18347 0.17793<BR> 0.16022 0.15484 0.15145 0.13864 0.12891 0.11838 0.11250 0.10843 0.09862 0.09222<BR> 0.08093 0.08053 0.07850 0.07340 0.06402 0.05852 0.05637 0.05060 0.04908 0.04809<BR> 0.04495 0.04187 0.04013 0.03636 0.03540 0.03224 0.03022 0.02931 0.02370 0.02283<BR> 0.01924 0.01911 0.01456 0.01337 0.01243 0.01124 0.01097 0.00867 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 CORRELATION BETWEEN PRINCIPAL COMPONENTS AND DEPENDENT VARIABLE<BR> (Those results correlating with Eigenvectors 1, 5, 16, 17, 20, 42, 49, 69 and 85 are in bold)<BR> -0.39415 -0.10460 0.08387 0.11858 0.36218 -0.05716 0.08322 0.04244 -0.12755 0.08613<BR> -0.08753 0.10194 -0.13843 0.07769 0.05422 0.28799 -0.16482 0.10473 0.06157 0.18463<BR> 0.09805 -0.00121 0.08767 -0.00614 -0.00976 -0.00811 -0.04454 -0.00413 -0.04234 -0.01971<BR> 0.13780 0.03052 0.02067 0.02693 -0.00700 0.05014 0.00074 -0.00840 0.09682 0.06129<BR> -0.01516 -0.32508 -0.06105 -0.02501 0.14193 -0.00201 0.08158 -0.02132 -0.14478 0.00339<BR> -0.05718 0.04875 -0.12036 -0.06412 -0.05256 0.04835 -0.03439 -0.00569 0.09808 -0.07109<BR> -1.10607 -0.02256 0.02991 -0.05324 -0.12775 0.07912 -0.01708 -0.03173 -0.18091 -0.01212<BR> -0.08658 0.00763 -0.04165 0.08495 0.11627 0.09558 0.02739 0.05369 0.08360 0.04044<BR> -0.07341 -0.07948 -0.08120 0.02975 -0.15981 -0.00023 -0.09990 -0.00159 0.00018 0.00098<BR> -0.00249 0.00006 -0.00976 -0.00742 -0.01080 0.00581 0.00551 -0.00434 -0.00462 0.00132<BR> -0.00879 0.00324 0.01395 -0.00257 -0.00644 0.00714 0.00204 0.01003 -0.01003 0.00089<BR> -0.00726 0.00465 -0.00738 0.00476 0.01395 0.00615 0.00263 -0.01205 -0.00092 -0.00433<BR> -0.00152 -0.00004 -0.00795 0.00180 -0.00916 0.00097 -0.00418 -0.00283 -0.01011 -0.00081<BR> -0.01188 0.02246 0.01972 -0.00462 -0.00424 -0.00133 -0.01456 -0.00161 0.03015 -0.00266<BR> 0.01121 -0.01634 -0.01205 -0.00142 0.01310 0.01945 -0.00455 0.01361 0.02293 0.01720<BR> 0.00660 -0.00280 -0.01475 -0.01869 -0.01922 0.03162 -0.00279 0.00419 -0.02930 -0.01640<BR> -0.00901 -0.02932 -0.04182 -0.02421 0.00603 0.03826 -0.02495 0.03788 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 REGRESSION COEFFICIENTS OF PRINCIPAL COMPONENTS CONSTANT COMPONENTS<BR> (MEAN OF Y; those results correlating with Eigenvectors 1, 5, 16, 17, 20, 42, 49, 69 and 85 are in bold)<BR> 15.01124 -0.17178 -0.05022 0.05160 0.09530 0.33635 -0.06813 0.10881 0.05861 -0.20267<BR> 0.14919 -0.16660 0.20219 -0.29562 0.17001 0.12510 0.67549 -0.43793 0.30023 0.18318<BR> 0.57875 0.31818 -0.00413 0.30942 -0.02245 -0.03677 -0.03226 -0.18695 -0.01760 -0.18328<BR> -0.08875 0.64123 0.14957 0.10152 0.13493 -0.03605 0.27579 0.00422 -0.04965 0.58006<BR> 0.39586 -0.09812 -2.15152 -0.41939 -0.17670 1.03420 -0.01491 0.64340 -0.16858 -1.16700<BR> 0.02771 -0.49322 0.42776 -1.06776 -0.59456 -0.50546 0.48522 -0.35395 -0.05967 1.07834<BR> -0.80822 -1.28731 -0.27447 0.36862 -0.67850 -1.74310 1.12925 -0.24837 -0.48703 -2.81929<BR> -0.19083 -1.40986 0.12870 -0.71783 1.53805 2.13351 1.83786 0.54405 1.08285 1.87505<BR> 0.92393 -1.82720 -1.98501 -2.32314 0.88852 -4.94962 -0.00737 -3.29223 -0.05893 0.64741<BR> 4.35638 -12.61063 0.36365 -60.91028 -46.60155 -6.14169 38.63266 37.21372 -29.51041 -32.36774<BR> 9.36550 -62.91483 23.20888 100.99135 -19.61887 -49.35561 55.05512 16.68999 85.00495 -85.13248<BR> 7.59581 -62.07832 40.96981 -65.78125 42.91962 127.69487 56.82417 24.65474 -113.09018 -8.86552<BR> -43.38264 -15.33249 -0.42336 -83.06951 19.20374 -100.34457 10.66268 -48.92810 -33.27813 -130.67499<BR> -10.49088 -155.98611 307.20544 271.08905 -63.52302 -58.76460 -18.73222 -209.96953 -23.51846 440.15939<BR> -39.37244 168.73015 -253.19016 -188.56601 -22.96661 216.60564 335.45129 -80.68970 244.11002 420.56631<BR> 319.19940 122.78136 -58.70250 -318.11673 -419.25360 -446.05341 781.83057 -77.13952 119.59890 -897.76147<BR> -532.95233 -293.93973 -1178.46448 -1739.20313 -1037.55603 305.55042 2068.77271 -1920.37500 4853.33740 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 COEFFICIENTS OF VARIABLES OBTAINED FROM PRINCIPAL COMPONENTS REGRESSION<BR> INDEX OF RESIDUAL F-VALUES COMPONENTS SUM OF REGRESSION<BR> COMPONENT ENTERING SQUARES MODEL TO ENTER R2 CONSTANT VARIABLES<BR> 13 area 18 r_mean 19 r_max 20 r_var 21 spher 22 eccentr<BR> 1 886.01971 16.00 16.00 0.1554 -6.1046 -0.0001 -0.0068 -0.0021 0.0757 -0.7021 0.3772<BR> 5 748.41956 17.27 15.81 0.2865 -24.4827 0.0000 -0.0006 0.0066 0.1287 -1.3427 0.4668<BR> 42 637.56232 18.28 14.78 0.3922 -4.0504 0.0003 0.0327 0.0315 -0.0654 1.0648 1.9107<BR> 16 550.55927 19.01 13.27 0.4752 -24.2614 -0.0001 -0.0010 0.0075 0.0881 0.3847 3.9450<BR> 20 514.80243 17.23 5.76 0.5092 11.2749 0.0000 0.0047 0.0102 -0.0001 0.5181 3.3093<BR> 69 480.47141 16.17 5.86 0.5420 5.5564 -0.0007 -0.0923 -0.0317 0.6614 -2.4006 7.3299<BR> 17 451.97357 15.28 5.11 0.5691 -11.1125 -0.0006 -0.0806 -0.0197 0.7089 -2.4867 7.4326<BR> 85 425.18304 14.67 5.04 0.5947 -87.4857 0.0029 0.1303 0.2626 -1.0253 -20.4938 15.6907<BR> 49* 403.19531 14.06 4.31 0.6156 -106.6303 0.0023 0.0898 0.2423 -0.9758 -21.6262 18.6770 23 cell_or 24 inertia 25 compact 26 elong 27 r_fft_1 28 r_fft_h 29 fft_h1 30 fft_h2 31 fft_h3 32 fft_h4<BR> 1 -0.00015 4.20950 1.85850 0.36657 0.00817 0.15205 0.54626 0.01295 0.05376 0.09606<BR> 5 -0.00033 7.40470 3.90065 0.46731 0.01960 0.25348 1.20704 0.01645 0.13488 0.24114<BR> 42 0.00272 9.97103 14.50057 1.56405 0.01556 0.01791 1.99722 0.06044 -0.39382 -0.49619<BR> 16 0.01356 16.62727 11.68502 3.44152 -0.00075 0.22390 1.61339 0.12893 -0.55642 -0.71698<BR> 20 0.00900 11.51764 9.53561 2.81747 0.01110 0.13702 2.51690 0.10971 -0.39855 -0.62342<BR> 69 0.01070 25.93026 -32.74646 6.96817 0.12622 -0.4242 0.87289 0.28341 0.33494 0.88560<BR> 17 0.01137 27.00596 -31.39712 7.03726 0.13183 -0.52163 1.26560 0.28660 0.36035 0.91058<BR> 85 0.01634 -40.92718 34.39434 15.80306 -0.01640 0.20985 -5.42087 0.68778 0.99899 3.86346<BR> 49* 0.01298 -21.07554 33.37931 18.24974 -0.01482 -0.39953 -8.38599 0.77428 0.83353 3.88501 33 fft_h5 34 fft_h6 35 fft_h7 36 fft_h8 37 fft_h9 38 fft_h10 39 fft_h11 40 fft_h12 41 fft_h13 42 fft_h14<BR> 1 0.15873 0.23453 0.31016 0.45738 0.53560 0.64981 0.85275 0.84457 1.02099 1.18819<BR> 5 0.34713 0.53339 0.59048 0.90719 0.98887 1.20974 1.58618 1.38779 1.65153 2.53171<BR> 42 -0.31721 0.13936 -0.28990 4.36729 -2.54589 18.06603 -15.35465 -10.87780 -7.91265 1.37632<BR> 16 -0.60906 0.10250 -0.38405 4.07709 -1.95036 17.23498 -14.58612 -10.03059 -8.05464 1.96579<BR> 20 -0.61786 -0.29280 -1.24516 3.55153 -2.53195 15.95150 -15.44526 -10.38266 -8.77104 3.54817<BR> 69 -1.17302 7.91647 3.57224 -2.92673 3.36787 9.52335 -12.60558 12.25266 3.40961 -24.57057<BR> 17 -1.20259 8.11512 3.83467 -2.77683 3.76339 9.84235 -12.89058 11.48897 3.83289 -23.48681<BR> 85 -107066 5.84208 -2.12975 -1.68188 -6.81864 -0.33551 -12.84314 50.84490 -5.14911 -77.50372<BR> 49* -1.24264 6.43434 -0.70966 2.8166 -5.67403 -1.06653 -10.52659 43.63401 -12.55246 -82.85142 43 fft_h15 44 fft_h16 45 fft_h17 46 fft_h18 47 fft_h19 48 fft_h20 49 fft_h21 63 DNA_ind 64 DNA_amnt<BR> 65 OD_max<BR> 1 1.25371 1.41467 1.50899 1.33514 1.40651 1.26586 1.25993 0.06587 -0.00064 0.19053<BR> 5 1.92660 2.10057 3.09921 2.36506 2.50654 2.12769 2.85763 -0.12644 -0.00267 0.01469<BR> 42 -15.17151 -33.17091 19.08676 -13.47741 19.04956 9.31158 26.61067 0.63119 0.00350 0.40441<BR> 16 -13.74006 -28.22813 22.88810 -8.05542 23.69366 11.51081 30.41133 0.82214 0.00322 0.55856<BR> 20 -13.91840 -31.36178 23.07771 -11.40029 26.59032 5.67616 28.39959 1.18554 0.00239 0.86840<BR> 69 -27.14797 -59.83744 27.73639 46.91480 -13.32833 12.03217 6.66643 1.78752 -0.01243 1.51829<BR> 17 -27.87197 -60.88340 25.11048 48.41440 -15.03875 9.93112 5.21031 2.02696 -0.01128 1.78557<BR> 85 -27.36458 -32.94904 1.09432 8.30147 -3.76083 -22.95860 9.25074 5.05172 0.02275 -2.07200<BR> 49* -33.77812 -30.18830 16.00562 3.91907 7.50340 -37.07515 -11.12886 4.22024 0.02172 -2.54956 66 OD_var 67 OD_skew 68 OD_kurt 69 DNA_ar_1 70 DNA_ar_m<BR> 71 DNA_ar_h 72 DNA_am_1 73 DNA_am_m 74 DNA_am_h 75 DNA_c_1<BR> 1 0.18431 0.05951 0.01637 -0.15239 0.23814 0.19463 -0.19036 -0.32444 0.15113 -0.02380<BR> 5 0.89527 0.10368 -0.00393 0.11042 -0.50950 -0.07830 0.12203 -0.29249 -0.03670 -0.09464<BR> 42 0.64135 -0.09049 -0.26130 0.00041 1.00996 -0.18869 -0.07357 0.80335 -0.05330 -0.03804<BR> 16 0.21364 -0.20141 -0.34857 0.18437 0.31867 -0.34846 0.09474 0.75356 -0.14644 -0.01697<BR> 20 0.22568 -0.25429 -0.36714 0.32817 -0.07089 -0.50142 0.19696 0.93744 -0.22876 0.13855<BR> 69 0.70518 -0.15281 -0.45231 -0.34867 4.92027 -0.36906 -0.54275 4.50542 -0.22207 0.12204<BR> 17 0.73860 -0.14515 -0.48077 -0.35774 5.18530 -0.40418 -0.53006 4.61520 -0.24274 0.08986<BR> 85 -19.93099 0.19828 -1.58926 -0.34832 6.52728 -0.66808 0.58846 3.77341 -0.79749 -0.39939<BR> 49* -17.94316 0.53538 -1.52398 -0.09528 4.57524 -0.70159 0.82454 2.54212 -0.78846 0.54082 76 DNA_c_m 77 DNA_c_h 78 DNA_c_mh 79 av_dst_1 80 av_ds_m 81 av_ds_h 82 av_ds_mh 83 DNA_1vm<BR> 84 DNA_1vh 85 DNA_1vmh<BR> 1 -0.00793 0.00987 -0.00914 0.10955 0.95273 0.40164 0.85343 0.05882 0.03034 0.04649<BR> 5 0.00909 -0.00650 0.00890 -0.44072 2.23843 0.05220 2.37076 0.16403 0.02014 0.03292<BR> 42 0.01783 -0.03429 0.01716 -1.06888 -16.01620 1.89652 -4.54958 -0.62882 0.18093 0.13718<BR> 16 0.03539 -0.03263 0.03390 -1.84894 -24.53588 2.32926 -6.49102 -1.31621 0.07191 0.02403<BR> 20 0.05094 0.01964 0.04519 -2.03815 -28.95567 1.23010 -10.89221 -1.70089 -0.01812 -0.13444<BR> 69 0.01490 -0.06970 0.00736 -1.76506 -29.98211 3.40420 -6.09454 -1.52627 0.11548 -0.31991<BR> 17 0.01468 -0.06790 0.00907 -2.19838 -34.89094 3.66934 -6.27469 -1.65578 0.11711 -0.34276<BR> 85 -0.17296 -0.26478 -0.14037 3.42307 1.02131 10.32153 28.12038 -2.99116 0.10700 -0.15585<BR> 49* -0.17255 -0.26917 -0.12964 4.64699 9.72549 9.25431 28.96334 -2.28036 0.11832 -0.09189 86 l_den_o 87 m_den_o 88 h_den_o 89 entropy 90 energy 91 corr 92 contrast<BR> 93 homogen 94 cl_shd 95 cl_prom<BR> 1 0.00833 0.01146 -0.00369 0.10314 -1.27911 0.00053 -0.00153 -0.22732 -0.10121 0.00631<BR> 5 -0.00675 0.00821 -0.05120 1.22548 -6.83372 0.00558 -0.00964 -0.13214 -0.18069 -0.15361<BR> 42 -0.03371 0.21864 -0.08789 5.34033 -2.37261 0.02467 0.00465 6.44148 -0.42144 -0.80585<BR> 16 -0.04649 0.26239 -0.17042 7.17309 -9.21300 0.02772 0.00791 6.07568 -0.34243 -0.93151<BR> 20 -0.05631 0.29368 -0.12284 2.80840 7.99514 0.02565 -0.01352 5.90365 -0.33171 -0.33271<BR> 69 -0.03550 0.04977 0.02273 1.64546 -2.35139 0.04007 -0.02532 -8.08012 -2.50299 -0.33161<BR> 17 -0.03930 0.07700 -0.03696 3.71035 -12.16302 -0.04015 -0.01291 -9.59992 -0.237676 0.42555<BR> 85 -0.18008 -0.03809 0.34274 -0.90209 -28.77934 0.02018 -0.14643 -12.41602 -7.82707 -0.22934<BR> 49* -0.14475 -0.05744 0.43826 -0.68300 -25.48301 0.02261 -0.15157 -9.88406 -8.35423 -0.06542 96 den_l_s 97 den_d_s 98 range_ex 99 range_av100 c_of_gr 101 c_m_l 102 c_m_m<BR> 103 cr_m_h 104 fract_di105 fract_al<BR> 1 3.55201 -3.54144 0.00015 0.00030 2.17884 0.66691 -0.61514 -0.41819 -0.05224 0.00000<BR> 5 4.04181 -13.23697 0.00035 0.00076 4.84864 0.29617 2.67201 0.41098 -1.25067 -0.00002<BR> 42 -80.86092 -73.78616 0.00065 0.00277 22.70634 3.31263 -6.58282 0.93529 -7.15383 0.00000<BR> 16 -125.50686 -61.83700 0.00092 0.00330 18.03170 2.09186 07.39367 1.34074 -9.20722 -0.00001<BR> 20 -108.91275 -71.05984 0.00132 0.00371 10.90575 0.51468 -10.41668 1.07036 -6.92371 -0.00002<BR> 69 -38.89188 -84.38297 0.00238 0.00510 6.00437 2.61090 -19.37951 -0.52906 -3.00855 -0.00008<BR> 17 -32.82928 -58.12810 0.00254 0.00539 9.64364 2,88790 -18.82712 -0.09937 -3.48961 0.00007<BR> 85 197.80345 50.03966 0.00180 0.00907 54.17500 14.59007 -36.91759 9.97221 -15.86866 -0.00012<BR> 49* 129.39978 -79.78645 0.00159 0.00842 33.93962 12.17531 -39.35530 8.51247 -16.88954 -0.00011 106 fract_a2107 text_or 108 sz_t_or109 sh_run_0110 sh_r_45 111 sh_r_90 112 sh_r_135113 l_r_0 114<BR> l_r_45 115 l_r_90<BR> 1 -0.00002 0.00061 0.02697 0.09655 0.27457 0.58103 0.72810 -0.00565 -0.00979 -0.00627<BR> 5 -0.00007 0.00342 -0.00658 0.30456 -0.10661 1.15621 1.85035 0.00159 0.1087 0.00299<BR> 42 0.00008 -0.03607 -0.74683 2.72534 -10.78580 -6.43058 -21.06256 0.24324 0.33270 0.23667<BR> 16 0.00006 0.00016 -2.28762 3.17094 -10.12701 -8.53858 -10.81947 0.23568 0.33309 0.27329<BR> 20 0.00001 -0.02232 -2.48317 2.04759 -11.80707 -8.78639 -15.27433 0.22398 0.32895 0.26000<BR> 69 -0.00035 0.03367 -2.79074 29.26018 -3.28503 18.76287 -20.09924 0.31969 0.30394 0.35407<BR> 17 -0.00029 0.03529 -1.00745 29.30436 -0.21108 19.37404 -15.75760 0.29759 0.21132 0.32636<BR> 85 -0.00053 0.17963 -0.37806 -27.76514 36.62079 117.92596 -6.54465 0.27879 -0.10371 -1.08714<BR> 49* -0.00045 0.19080 -0.06286 -32.60677 36.65602 126.04004 -14.58390 0.33232 -0.02687 -1.11998 116 l_r_135 117 gr_l_0 118 gr_l_45 119 gr_l_90 120 r_l_135121 r_le_0 122 r_le_45<BR> 123 r_le_90 124 r_le_135125 r_pc_0<BR> 1 -0.00809 -0.00073 -0.00066 -0.00068 -0.00066 -0.00038 -0.00032 -0.00024 -0.00023 0.13022<BR> 5 -0.00348 -0.00017 -0.00030 -0.00055 -0.00050 -0.00008 -0.00009 -0.00006 -0.00009 0.14286<BR> 42 0.40168 -0.00438 -0.00714 -0.00330 -0.00717 -0.00088 -0.00514 -0.00047 -0.00304 -1.29012<BR> 16 0.28709 -0.01006 -0.01251 -0.00867 -0.01099 -0.00085 -0.00672 -0.00224 -0.00328 -1.31598<BR> 20 0.32553 -0.00689 -0.00990 -0.00602 -0.00884 -0.00082 -0.00673 -0.00196 -0.00352 -1.11036<BR> 69 0.79613 -0.01847 -0.01810 -0.01499 -0.02097 0.00347 -0.01024 -0.00325 -0.01130 10.25653<BR> 17 0.73921 -0.01750 -0.01614 -0.01417 -0.01977 0.00424 -0.00812 -0.00263 -0.01028 10.46062<BR> 85 1.21771 -0.01979 -0.02407 -0.00140 -0.02475 -0.01899 0.00422 0.01426 -0.01053 -1.07172<BR> 49* 1.31591 -0.2519 -0.02956 -0.00268 -0.02941 -0.02279 0.00267 0.01320 -0.01230 -3.18212 126 r_pc_45 127 r_pc_90 128 r_pc_135138 Varea 143 Vr_mean 144 Vr_max 145<BR> Vr_var 146 Vspher 147 Veccentr148 Vcell_or<BR> 1 0.21354 0.18305 0.23101 0.00003 0.01026 0.01226 0.8922 3.13496 1.01485 -0.00006<BR> 5 -0.03063 0.13288 0.32388 0.00008 0.01542 0.01865 0.10512 4.47291 1.56207 0.00143<BR> 42 -5.41976 -2.76190 -7.91656 -0.00093 -0.24054 -0.13960 -1.03925 -17.81981 2.61462 -0.00488<BR> 16 -5.37490 -3,72006 -5.04184 0.00073 -0.02600 0.13278 -0.72880 -32.17915 1.44925 -0.00746<BR> 20 -5.36526 -3.33870 -5.99789 -0.00056 -0.19441 -0.10666 -0.89026 -0.32575 2.61307 -0.00728<BR> 69 -0.16610 3.46327 -9.64306 0.00345 0.35761 -0.32080 -2.77592 18.79661 -11.05569 -0.00582<BR> 17 1.60494 3.90926 -8.32619 0.00242 0.22878 -0.45468 -2.96315 14.78244 -13.77011 -0.01257<BR> 85 14.10638 34.35690 -10.82405 -0.00767 -0.32722 -1.64562 -12.55992 -32.26094 10.59986 -0.00408<BR> 49* 12.85068 35.22396 -13.64750 -0.00860 -0.23942 -1.49224 -13.25396 -37.18088 14.07224 0.00102 149 Vinertia150 Vcompact151 Velong152 Vr_fft_1153 Vr_fft_h154 Vfft_h1 155 Vfft_h2 156 Vfft_h3<BR> 157 Vfft_h4 158 Vfft_h5<BR> 1 7.15110 5.46118 1.00690 0.01192 0.31425 0.68960 0.03944 0.10465 0.18745 0.28466<BR> 5 10.34472 12.50623 1.57931 0.02554 0.61523 1.43975 0.06349 0.23069 0.40522 0.53102<BR> 42 26.46757 9.32586 1.18450 0.08896 3.14683 -0.10819 0.00798 -0.31338 0.08808 -1.34542<BR> 16 20.31008 5.77988 -0.35284 0.05802 3.18856 -1.77678 -0.07749 -0.64762 -0.37124 -1.60663<BR> 20 37.30119 8.79387 1.04962 0.10987 4.27386 1.29263 -0.00476 -0.11767 0.19997 -1.41638<BR> 69 52.36081 -28.89997 -7.70081 0.18540 10.20403 -17.34847 -0.41167 1.86120 0.11702 -16.86698<BR> 17 44.25918 -25.60409 -10.28402 0.19746 10.39604 -17.04951 -0.52010 1.96930 0.07066 -17.21207<BR> 85 48.92205 -138.13972 4.17724 0.26800 25.46346 -29.68444 0.15479 -1.88820 8.48383 -9.55001<BR> 49* 82.68023 -145.52098 4.98851 0.26445 26.49265 -37.12057 0.12937 -2.38708 8.66023 -10.62976 159 Vfft_h6 160 Vfft_h7 161 Vfft_h8 162 Vfft_h9 163 Vfft_h10164 Vfft_h11165<BR> Vfft_h12166 Vfft_h13167 Vfft_h14168 Vfft_h15<BR> 1 0.39183 0.50962 0.65957 0.75108 0.88395 1.15412 1.21603 1.48890 1.91159 1.64858<BR> 5 0.80465 0.71143 1.37553 1.30341 1.83044 2.55292 2.35792 2.96732 2.62242 3.60931<BR> 42 -0.20432 3.07787 7.46978 2.47076 30.63672 -30.73833 28.25094 -24.82642 -7.04045 49.35922<BR> 16 0.43225 3.22621 6.81764 4.01831 28.47408 -30.14062 26.58983 -23.82873 -1.95075 54.37716<BR> 20 0.64257 2.68930 7.12253 4.89127 26.98964 -29.95626 28.98262 -24.65961 5.62884 62.89505<BR> 69 -0.52360 1.79920 21.12121 10.23669 -0.58315 -43.25531 42.75736 -11.55524 52.01060 44.12914<BR> 17 -0.01488 1.80788 21.26226 10.74353 -1.13486 -44.57825 40.93747 -10.76618 54.16054 42.55167<BR> 85 4.93058 -1.07792 14.55115 -11.34037 36.06652 -32.51954 25.31959 -15.29262 98.21642 32.96530<BR> 49* 5.57119 1.63882 18.70206 -12.06477 30.98483 -32.72065 10.94818 -18.76875 87.35009 18.16531 169 Vfft_h16170 Vfft_h17171 Vfft_h18172 Vfft_h19173 Vfft_h20174 Vfft_h21188 VDNA_ind189<BR> VDNA_amn190 VOD_max 191 VOD_var<BR> 1 2.33517 2.25264 1.94873 2.11768 2.01228 2.52254 0.09159 -0.00072 0.69774 0.23872<BR> 5 3.80085 4.10153 1.72526 4.28828 2.70881 6.17777 -0.25385 -0.00512 -0.12639 1.55440<BR> 42 -1.22286 -0.97968 39.14980 17.84179 16.04718 53.01527 0.62263 0.00230 2.12892 -1.10370<BR> 16 9.00134 5.95047 47.28288 23.13890 12.07366 66.29782 1.30075 0.00711 2.18707 -1.87115<BR> 20 7.57201 10.83334 43.16238 27.61946 0.45781 68.29294 1.34271 0.00294 1.03191 -1.53492<BR> 69 -71.09368 -0.650000 60.37488 74.08608 -75.67752 96.57629 2.80774 -0.00827 1.35133 5.69376<BR> 17 -73.53021 -10.48988 60.32332 72.75538 -72.57214 87.45671 3.28984 -0.00432 3.38685 4.51866<BR> 85 -80.94328 -40.30736 125.56635 -5.60472 -49.24242 114.84267 1.38146 0.02551 -18.01728 -9.00176<BR> 49* -46.35724 -0.61389 124_36813 -4.54491 -6.33409 129.59862 0.78678 0.02735 -19.54655 -3.91363 192 VOD_skew193 VOD_kurt194 VDN_ar_1195 VDN_ar_m196 VDN_ar_b197VDN_am_1198<BR> VDN_am_m199 VDN_am_h200 VDN_c_1 201 VDN_c_m<BR> 1 -0.10981 0.01628 -0.41946 -0.57731 0.37402 -0.37063 0.46244 0.36727 0.01178 -0.02091<BR> 5 -0.67281 0.01333 -0.34800 -0.65113 -0.90254 -0.14240 -0.40103 -1.00843 -0.23182 -0.00243<BR> 42 -7.20974 -0.34457 4.62941 11.64684 -0.30846 1.74853 -5.38073 -0.32675 0.44674 0.38439<BR> 16 -8.77521 -0.41599 5.57439 5.82562 -1.19002 2.32229 -10.62034 -0.89500 1.08183 0.36254<BR> 20 -10.95002 -0.57184 6.48867 1.42234 -2.11566 2.88130 -16.53887 -1.84603 1.97489 0.38806<BR> 69 -3.37350 -0.65520 12.77181 16.04001 -1.85141 7.16998 -16.37789 -0.83833 1.42745 0.63091<BR> 17 -3.64423 -0.61190 13.24512 13.97523 -1.61059 7.36309 -15.02314 -0.39931 1.39521 0.65328<BR> 85 10.44360 1.00776 16.87081 -6.26660 -3.71198 13.63699 -45.07723 -5.85699 1.25296 0.13561<BR> 49* 6.61681 0.74577 18.33927 -7.72012 -2.08171 14.35370 -40.00671 -4.01187 2.37816 0.21300 202 VDN_c_h 203 VDN_c_mh204 Va_ds_1 205 Va_ds_m 206 Va_ds_h 207<BR> Va_ds_mh208 VDN_lvm 209 VDN_lvh 210 VDN_lvmh211 Vl_den_o<BR> 1 0.00712 -0.2580 0.51003 -1.33963 -0.45087 -1.12065 0.02866 0.00047 0.03738 0.00232<BR> 5 -0.05692 -0.01274 -0.09073 0.68351 0.40569 1.24767 0.12995 0.08930 0.02256 -0.05750<BR> 42 -0.18769 0.26098 6.56269 4.91286 -2.00674 4.15749 -0.80192 -0.22392 -0.04023 0.32232<BR> 16 -0.10911 0.20667 6.26181 0.08350 -1.75041 6.19310 -0.94589 -0.36992 -0.20061 0.30808<BR> 20 -0.06121 0.21692 6.75072 -2.60507 -1.32059 8.78292 -0.93618 -0.41438 -0.29979 0.19904<BR> 69 -0.80112 0.69606 4.51636 3.67709 -2.88064 9.71433 -0.59452 -0.70037 -0.60796 0.58320<BR> 17 -0.79577 0.72933 4.45263 1.17100 -2.72136 9.61300 -0.74442 -0.72935 -0.64954 0.49426<BR> 85 -1.21765 0.65608 -4.28674 -7.70470 -0.94457 15.34826 -2.47946 1.49831 0.42745 0.61861<BR> 49* -1.07646 0.80315 -7.29502 1.68865 0.41680 20.40778 -1.82944 1.50985 0.51027 0.59696 212 Vm_den_o213Vh_den_o214 Ventropy215 Venergy Vcorr 217 Vcontras218 Vhomogen219Vcl_shd 220<BR> Vel_prom221 Vden_1_s<BR> 1 0.01366 -0.05339 0.46089 1.96811 0.00082 -0.01563 -0.14568 -0.07453 0.04310 5.50447<BR> 5 -0.00756 -0.09031 2.35683 7.54720 0.00609 -0.05026 -3.72746 -0.19578 0.09782 -11.74672<BR> 42 0.17768 0.22442 7.37761 7.58744 0.00820 -0.06662 -84.94247 -2.06462 -0.37915 1751.90308<BR> 16 0.18441 -0.00291 12.46637 61.67782 0.00737 0.24086 -65.82594 -2.29000 -0.35286 1340.42908<BR> 20 0.21779 0.07725 12.13319 74.82976 0.00699 0.01584 -53.89784 -1.88511 -0.36055 1202.52307<BR> 69 0.01356 -1.19344 26.80301 -3.78902 0.00875 -0.34612 -102.21731 -2.34284 -0.76339 1294.89270<BR> 17 0.01557 -1.27547 29.32017 36.42632 0.00335 -0.28495 -77.53695 -2.18913 -0.70925 1181.38306<BR> 85 1.14466 -1.13843 -2.54151 -156.34818 -0.06814 -0.22290 -279.74011 -8.97711 -1.29746 1877.80042<BR> 49* 1.23192 -1.75832 2.64361 -103.72733 -0.08144 -0.28044 -254.09245 -9.41917 -1.30692 1629.05237 222 Vden_d_s223 Vrange_e224 Vrange_a225 Vc_of_gr226 Vc_m_l 227 Vc_m_m 228 Vcr_m_h<BR> 229 Vfract_d230 Vfrac_a1231 Vfrac_a2<BR> 1 -3.82217 0.00051 0.00040 4.18490 1.17203 -1.11297 -0.51981 1.33436 0.00000 -0.00001<BR> 5 -0.55270 0.00097 0.00117 8.28969 0.84694 1.92031 0.45563 3.75510 -0.00002 -0.00009<BR> 42 221 01582 0.00160 0.00306 21.95017 4.23748 -10.04192 -1.40372 -63.40602 -0.00004 -0.00003<BR> 16 535 90100 0.00155 0.00347 3.29222 1.86405 -13.53351 -1.01381 -71.34790 0.00001 0.00023<BR> 20 481.61334 0.00187 0.00458 -10.82568 -1.05532 -15.34879 -1.45702 -61.56142 -0.00004 -0.00003<BR> 69 252.26500 -0.01357 -0.00274 -62.25496 4.10479 -16.34437 -4.57992 -92.31786 0.00004 0.00037<BR> 17 270.76337 -0.01497 -0.00312 -53.71441 4.86154 -16.18818 -3.64917 -102.24742 0.00005 0.00040<BR> 85 1085.33708 -0.01222 -0.00730 -239.77997 -13.84157 -47.99881 0.59653 -109.45778 0.00036 0.00069<BR> 49* 893.48816 -0.01444 -0.01057 -235.20267 -16.03268 -51.97952 4.16708 -105.84118 0.00034 0.00052 232 Vtext_or233 Vsz_t_or234 Vsh_run_235 Vs_r_45 236 Vs_r_90 237 Vs_r_135238 Vl_r-0 239 Vl_r_45<BR> 240 Vl_r_90 241 Vl_r_135<BR> 1 0.00074 0.00053 2.95616 2.39980 4.73452 5.95392 0.00678 -0.00383 -0.00367 0.00105<BR> 5 -0.00236 -0.00135 11.77486 11.87209 11.34708 20.24270 0.00149 0.03083 0.00371 0.00385<BR> 42 -0.05872 -0.02546 105.40346 96.70708 203.70950 159.28661 -0.30027 -1.40271 -0.63839 -0.54096<BR> 16 -0.06537 -0.07913 123.99741 130.16809 249.37157 182.55699 -0.35999 -1.18182 -0.42346 -0.70592<BR> 20 -0.07169 -0.08399 120.62257 135.19022 257.41098 191.62811 -0.27861 -1.04308 -0.34105 -0.65092<BR> 69 -0.07138 -0.10600 185.37311 209.81195 -182.81644 87.70583 1.41233 -0.85614 1.71406 0.76229<BR> 17 -0.05426 -0.04166 200.53638 219.20094 -113.77488 161.90208 1.38512 -0.99088 1.71388 0.70387<BR> 85 -0.32406 -0.00787 416.95322 115.04217 -479.41400 7.25916 0.91979 -5.80295 -1.16116 2.33961<BR> 49* -0.36111 0.00014 464.73773 146.08202 -568.93970 50.22042 0.87307 -6.70317 -1.64866 2.04956 242 Vg_1_0 243 Vg_1_45 244 Vg_1_90 245 Vg_1_135246 Vr_le_0 247 Vr_1_45<BR> 248 Vr_1_90 249 Vr_1_135250 Vr_pc_0 251 Vr_pc_45<BR> 1 -0.00028 -0.00037 -0.00035 -0.00040 0.00024 0.00021 0.00012 0.00014 2.15271 1.29858<BR> 5 -0.00180 -0.00236 -0.00214 -0.00233 0.00055 0.00052 0.00006 0.00012 1.38847 1.49608<BR> 42 -0.01107 -0.03002 0.00765 -0.02839 -0.00811 -0.01998 -0.00152 -0.02347 -17.24211 -108.03914<BR> 16 0.00496 -0.01701 0.01873 -0.01434 0.00170 -0.01133 0.00329 -0.01607 -10.99763 -65.92896<BR> 20 -0.01309 -0.03313 0.00477 -0.02925 -0.00438 -0.01514 -0.00043 -0.02107 -12.26345 -61.69035<BR> 69 0.02500 0.05254 0.14540 0.02993 -0.03944 -0.02053 0.01299 -0.06813 -42.84032 -194.98090<BR> 17 0.00679 0.03564 0.13086 0.01704 -0.04289 -0.02289 0.01029 -0.07005 -15.34756 -164.77769<BR> 85 -0.00805 0.14148 0.06993 0.08602 -0.12857 0.04117 0.00229 -0.08011 102.12849 -235.67673<BR> 49* -0.01573 0.13949 0.05620 0.08766 -0.13447 0.04196 -0.00809 -0.07570 177.66940 -298.39191 252 Vr_pc_90253 Vr_p_135254 small 262 large 270 leuk 286 j40 294 blurry 302 numcell<BR> 306 numjunk 310 smallthr<BR> 1 0.34357 0.92489 -0.00001 -0.00019 0.00035 0.00014 0.00009 -0.00004 0.00025 -0.00006<BR> 5 -2.71626 0.05857 0.00040 0.00002 0.00023 0.00017 0.00090 0.00034 0.00018 0.00032<BR> 42 -22.03463 -73.39414 0.00123 -0.00431 0.00023 -0.00100 0.00612 0.00038 -0.00320 0.00038<BR> 16 -5.58927 -64.29036 0.00116 -0.00511 0.00263 -0.00192 0.00940 0.00027 -0.00346 0.00031<BR> 20 -3.91602 -64.59608 0.00115 -0.00440 0.00419 -0.00222 0.00472 0.00027 -0.00282 0.00022<BR> 69 2.08629 -146.08598 0.00162 -0.00634 0.00119 -0.00049 0.00498 0.00031 -0.00868 -0.00039<BR> 17 25.77645 -122.00317 0.00157 -0.00575 0.01206 -0.00020 0.00629 0.00040 -0.00799 -0.00049<BR> 85 428.53290 121.94247 0.00027 0.00111 0.01827 0.00743 -0.00055 0.00038 -0.00995 -0.00080<BR> 49* 416.25711 101.99287 0.00041 0.00084 0.02574 0.00895 -0.00165 0.00042 -0.00834 -0.00075 314 largethr326 TSDnumS 327 TSDNUMSc328 tsdsmen 329 ftsds 330 tsdnuml 331<BR> tsdnumlc332 tsdlmen 333 ftsdl 334 notnumS<BR> 1 0.00013 -0.00007 -0.00008 -0.04897 -0.12591 0.00008 0.00024 -0.09420 0.14025 0.00007<BR> 5 0.00030 -0.00001 -0.00004 -0.08485 -0.11366 0.00019 0.00064 -0.09642 0.29062 - 0.00075<BR> 42 0.00019 -0.00001 0.00059 0.07012 -0.14988 0.00022 0.00577 -0.66027 0.51886 -0.00118<BR> 16 -0.00008 -0.00012 0.00052 0.02758 -0.33053 0.00031 0.00579 -0.46709 0.74651 -0.00158<BR> 20 0.00043 -0.00019 0.00049 0.00379 -0.50337 0.00041 0.00555 -0.42823 0.93160 -0.00114<BR> 69 0.00419 0.00013 -0.00051 -0.24106 -0.46782 -0.00001 0.00222 -0.49395 0.85252 -0.00136<BR> 17 0.00536 0.00017 -0.00051 -0.25213 -0.48778 -0.00002 0.00207 -0.41738 0.85442 -0.00145<BR> 85 0.00699 -0.00059 0.00112 0.10674 -1.60987 0.00103 -0.00269 -2.73875 2.69274 -0.00576<BR> 49* 0.00692 -0.00040 0.00086 0.09331 -1.27249 0.00078 -0.00283 -3.37529 2.06250 -0.00389 335 notNUMSc336 notsmen 337 fnots 338 notnuml 339 notnumle340 notlmen 341 fnotl<BR> 342 blrmS 343 blrNUMSc344blrsmen<BR> 1 0.00022 0.04040 0.13051 -0.00005 -0.00005 0.01415 -0.22772 -0.00005 -0.00005 -0.00864<BR> 5 0.00088 0.03079 0.90221 0.00035 0.00037 0.03987 0.72223 0.00033 0.00027 -0.06294<BR> 42 0.00472 0.54340 -0.40462 0.00049 0.00051 0.03848 1.99968 0.00043 0.00051 0.01460<BR> 16 0.00358 0.48621 -0.76718 0.00038 0.00036 0.05415 1.90681 0.00040 0.00052 -0.02432<BR> 20 0.00302 0.19559 0.46280 0.00036 0.00027 0.08274 1.41646 0.00042 0.00050 -0.16355<BR> 69 0.00191 -0.11130 4.26579 0.00042 0.00087 0.20858 0.75168 0.00049 0.00163 -0.01015<BR> 17 0.00232 0.07429 3.90859 0.00053 0.00100 0.25047 0.63565 0.00063 0.00183 0.05234<BR> 85 0.01209 -0.18865 12.18821 0.00070 0.00584 -0.13862 1.41837 0.00094 0.00139 -0.34498<BR> 49* 0.01872 0.09664 14.49743 0.00066 0.00608 -0.38958 0.10522 0.00092 0.00155 -0.26450 345 folrs 346 blrnuml 347 blrnumlc348 blrlmen 349 fblrl 350 holnumS 351<BR> holNUMSc352 holsmen 353 fhols 354 holnuml<BR> 1 -0.19870 0.00000 -0.00013 0.00360 -0.01164 -0.00005 -0.00005 0.01150 -0.16774 0.00000<BR> 5 0.61434 0.00059 0.00065 0.02748 0.23568 0.00024 0.00024 -0.01440 0.14286 0.00041<BR> 42 1.15998 0.00139 0.01072 1.41494 7.42848 -0.00011 0.00002 0.11800 -0.45848 0.00187<BR> 16 1.45594 -0.00152 0.00110 1.45518 2.70189 -0.00017 -0.00007 0.12720 -0.34674 0.00193<BR> 20 1.22391 -0.00304 -0.00737 1.64534 0.22404 -0.00026 -0.00018 0.23433 -0.86861 0.00227<BR> 69 -0.01236 -0.00341 0.00136 1.77349 8.41814 -0.00049 -0.00046 0.21679 -3.36912 0.00321<BR> 17 0.02448 -0.00456 0.00019 1.82303 6.68267 -0.00033 -0.00035 0.12686 -3.31957 0.00321<BR> 85 -0.11299 -0.00992 -0.03957 1.90937 17.00705 -0.00071 -0.00093 0.88755 -7.24390 0.00541<BR> 49* -0.95555 -0.01096 -0.03976 1.35001 13.14159 -0.00055 -0.00076 0.83589 -7.23448 0.00488 355 holnumlc356 hollmen 357 fholl 358 sptnorm 359 spinrmc 360 sptnrmm 361 f44<BR> 362 sptabnm 363 sptabnmc364 sptabnmm<BR> 1 0.00002 -0.00548 0.00855 -0.00003 -0.00001 0.01163 -0.06616 -0.00010 -0.00015 0.01429<BR> 5 0.00044 -0.01174 0.63996 0.00033 0.00036 0.06848 0.38320 -0.00003 -0.00011 0.07376<BR> 42 0.00214 0.07079 2.49976 -0.00110 -0.00096 0.38800 -1.82834 0.00219 0.00507 0.27879<BR> 16 0.00214 0.09005 2.73195 -0.00096 -0.00066 0.53588 -1.22932 0.00172 0.00495 0.08466<BR> 20 0.00242 0.13476 3.32010 -0.00112 -0.00098 0.21114 -1.65435 0.00182 0.00503 0.17523<BR> 69 0.00392 0.38938 5.95893 -0.00126 -0.00094 0.20292-3.39087 0.00146 0.00551 -0.06122<BR> 17 0.00372 0.42205 5.92462 -0.00120 -0.00097 0.13391 -3.45946 0.00178 0.00599 -0.03085<BR> 85 0.00546 0.60639 12.66640 -0.00062 -0.00085 2.81506 -5.13533 -0.00063 0.00232 -0.31778<BR> 49* 0.00520 0.63175 11.42464 -0.00098 -0.00083 2.71669 -6.55758 0.00064 0.00509 -0.24685 365 f48<BR> 1 -0.16469<BR> 5 -0.37992<BR> 42 2.17193<BR> 16 1.39846<BR> 20 1.23493<BR> 69 -0.02798<BR> 17 0.16092<BR> 85 -3.46746<BR> 49* -1.18892 PREDICTED AND RESIDUAL VALUES ARE CALCULATED USING<BR> THE COEFFICIENTS BASED ON THESE 9 COMPONENTS, SINCE<BR> THOSE LISTED BELOW DO NOT PASS THE ENTRANCE LIMITS.<BR> <P>OBSERVED, PREDICTED, AND RESIDUAL VALUES<BR> OBSERVED PREDICTED RESIDUAL RESIDUAL<BR> NO. VALUE VALUE VALUE SQUARE<BR> 1 10.0000 11.6961 -1.6961 2.8767<BR> 2 10.0000 11.0137 -1.0137 1.0276<BR> 3 10.0000 14.2255 -4.2255 17.8545<BR> 4 10.0000 12.1996 -2.1996 4.8384<BR> 5 10.0000 14.3453 -4.3453 18.8818<BR> 6 10.0000 12.5640 -2.5640 6.5740<BR> 7 10.0000 10.4682 -0.4682 0.2192<BR> 8 10.0000 13.9291 -3.9291 15.4379<BR> 9 10.0000 13.7747 -3.7747 14.2482<BR> 10 10.0000 10.6705 -0.6705 0.4496<BR> 11 10.0000 12.7451 -2.7451 7.5358<BR> 12 10.0000 9.0096 0.9904 0.9809<BR> 13 10.0000 8.0497 1.9503 3.8037<BR> 14 10.0000 9.7803 0.2197 0.0482<BR> 15 10.0000 11.7403 -1.7403 3.0286<BR> 16 12.0000 13.5863 -1.5863 2.5163<BR> 17 12.0000 11.7610 0.2390 0.0571<BR> 18 12.0000 10.0415 1.9585 3.8359 19 12.0000 13.7015 -1.7015 2.8951<BR> 20 12.0000 12.0622 -0.0622 0.0039<BR> 21 12.0000 11.6887 0.3113 0.0969<BR> 22 12.0000 13.2823 -1.2823 1.6443<BR> 23 12.0000 13.4448 -1.4448 2.0874<BR> 24 12.0000 13.4282 -1.4282 2.0397<BR> 25 12.0000 15.7328 -3.7328 13.9339<BR> 26 12.0000 14.9074 -2.9074 8.4532<BR> 27 12.0000 14.0210 -2.0210 4.0845<BR> 28 12.0000 12.2286 -0.2286 0.0522<BR> 29 12.0000 14.1201 -2.1201 4.4947<BR> 30 12.0000 14.7667 -2.7667 7.6548<BR> 31 14.0000 12.1109 1.8891 3.5688<BR> 32 14.0000 15.7127 -1.7127 2.9334<BR> 33 14.0000 15.0839 -1.0839 1.1749<BR> 34 14.0000 15.6966 -1.6966 2.8784<BR> 35 14.0000 15.6241 -1.6241 2.6376<BR> 36 14.0000 14.5930 -0.5930 0.3516<BR> 37 14.0000 11.8007 2.1993 4.8370<BR> 38 14.0000 16.2138 -2.2138 4.9008<BR> 39 14.0000 13.2298 0.7702 0.5932<BR> 40 14.0000 13.5411 0.4589 0.2106<BR> 41 14.0000 17.1054 -3.1054 9.6435<BR> 42 14.0000 12.8173 1.1827 1.3989<BR> 43 14.0000 15.5940 -1.5940 2.5408<BR> 44 14.0000 15.3778 -1.3778 1.8984<BR> 45 16.0000 15.2710 0.7290 0.5314<BR> 46 16.0000 15.8985 0.1015 0.0103<BR> 47 16.0000 12.2898 3.7102 13.7658<BR> 48 16.0000 16.3874 -0.3874 0.1501<BR> 49 16.0000 16.7571 -0.7571 0.5733<BR> 50 16.0000 17.4957 -1.4957 2.2372<BR> 51 16.0000 17.1752 -1.1752 1.3810<BR> 52 16.0000 14.1024 1.8976 3.6011 53 16.0000 14.5729 1.4271 2.0365<BR> 54 16.0000 15.0075 0.9925 0.9851<BR> 55 16.0000 14.3397 1.6603 2.7565<BR> 56 16.0000 14.9363 0.0637 0.0041<BR> 57 16.0000 14.1178 1.8822 3.5427<BR> 58 16.0000 18.5820 -2.5820 6.6668<BR> 59 16.0000 14.9468 1.0532 1.1092<BR> 60 18.0000 17.3412 0.6588 0.4341<BR> 61 18.0000 16.4924 1.5076 2.2730<BR> 62 18.0000 19.3358 -1.3358 1.7844<BR> 63 18.0000 15.9235 2.0765 4.3118<BR> 64 18.0000 17.2066 0.7934 0.6295<BR> 65 18.0000 16.0229 1.9771 3.9088<BR> 66 18.0000 17.1888 0.8112 0.6581<BR> 67 18.0000 17.2358 0.7642 0.5840<BR> 68 18.0000 13.4253 4.5747 20.9279<BR> 69 18.0000 16.0157 1.9843 3.9376<BR> 70 18.0000 16.7060 1.2940 1.6744<BR> 71 18.0000 20.3059 -2.3059 5.3170<BR> 72 18.0000 17.1816 0.8184 0.6697<BR> 73 18.0000 17.1352 0.8648 0.7478<BR> 74 18.0000 16.6714 1.3286 1.7652<BR> 75 20.0000 18.7705 1.2295 1.5116<BR> 76 20.0000 18.5476 1.4524 2.1096<BR> 77 20.0000 22.1201 -2.1201 4.4949<BR> 78 20.0000 15.6271 4.3729 19.1221<BR> 79 20.0000 17.9372 2.0628 4.2551<BR> 80 20.0000 19.3522 0.6478 0.4197<BR> 81 20.0000 17.0462 2.9538 8.7250<BR> 82 20.0000 19.1850 0.8150 0.6642<BR> 83 20.0000 17.4616 2.5384 6.4437<BR> 84 20.0000 17.8576 2.1424 4.5897<BR> 85 20.0000 18.1655 1.8345 3.3655<BR> 86 20.0000 13.4085 6.5915 43.4480 87 20.0000 18.3196 1.6804 2.8237<BR> 88 20.0000 15.4201 4.5799 20.9759<BR> 89 20.0000 20.2287 -0.2287 0.0523 APPENDIX 2<BR> SELECTED RESULTS FROM FILE L4R10<BR> VARIABLE NAMES<BR> independent = area,<BR> r_mean ,r_max,<BR> r_var ,spher ,eccentr ,<BR> cell_or ,inertia ,compact ,<BR> elong ,r_fft_1 ,r_fft_h,<BR> fft_h1 ,fft_h2 ,fft_h3 ,<BR> fft_h4 ,fft_h5 ,fft_h6 ,<BR> fft_h7 ,fft_h8 ,fft_h9 ,<BR> fft_h10 ,fft_h11 ,fft_h12 ,<BR> fft_h13 ,fft_h14 ,fft_h15 ,<BR> fft_h16 ,fft_h17 ,fft_h18 ,<BR> fft_h19 ,fft_h20 ,fft_h21 ,<BR> DNA_ind ,DNA_amnt ,<BR> OD_max ,OD_var ,OD_skew ,<BR> OD_kurt ,DNA_ar_l ,DNA_ar_m ,<BR> DNA_ar_h ,DNA_am_l ,DNA_am_m , DNA_am_h ,DNA_c_l ,DNA_c_m ,<BR> DNA_c_h ,DNA_c_mh ,av_dst_l ,<BR> av_ds_m ,av_ds_h ,av_ds_mh ,<BR> DNA_lvm,DNA_lvh,DNA_lvmh ,<BR> l_den_o ,m_den_o ,h_den_o ,<BR> entropy ,energy,corr ,<BR> contrast ,homogen ,cl_shd,<BR> cl_prom ,den_l_s ,den_d_s ,<BR> range_ex ,range_av ,c_of_gr ,<BR> c_m_l ,c_m_m ,cr_m_h ,<BR> fract_di ,fract_a1 ,fract_a2 ,<BR> text_or ,sz_t_or,sh_run_0 ,<BR> sh_r_45 ,sh_r_90 ,sh_r_135,<BR> 1_r_0 ,1_r_45 ,1_r_90 ,<BR> 1_r_135 ,gr_1_0 ,gr_1_45 ,<BR> gr_1_90 ,gr_1_135 ,r_le_0 ,<BR> r_le_45 ,r_le_90 ,r_le_135 ,<BR> r_pc_0 ,r_pc_45 ,r_pc_90 ,<BR> r_pc_135, Varea ,<BR> Vr_mean ,Vr_max ,<BR> Vr_var ,Vspher ,Veccentr ,<BR> Vcell_or ,Vinertia ,Vcompact ,<BR> Velong ,Vr_fft_1,Vr_fft_h,<BR> Vfft_h1 ,Vfft_h2 ,Vfft_h3 ,<BR> Vfft_h4 ,Vfft_h5 ,Vfft_h6 ,<BR> Vfft_h7 ,Vfft_h8 ,Vfft_h9 ,<BR> Vfft_h10,Vfft_h11,Vfft_h12,<BR> Vfft_h13,Vfft_h14,Vfft_h15,<BR> Vfft_h16,Vfft_h17,Vfft_h18,<BR> Vfft_h19,Vfft_h20,Vfft_h21,<BR> VDNA_ind ,VNDA_amn ,<BR> VOD_max ,VOD_var ,VOD_skew ,<BR> VOD_kurt ,VDN_ar_l ,VDN_ar_m ,<BR> VDN_ar_h ,VDN_am_l ,VND_am_m ,<BR> VDN_am_h ,VDN_c_l ,VDN_c_m ,<BR> VDN_c_h ,VDN_c_mh ,Va_ds_l ,<BR> Va_ds_m ,Va_ds_h ,Va_ds_mh , VDN_lvm,VDN_lvh,VDN_lvmh ,<BR> Vl_den_o ,Vm_den_o ,Vh_den_o ,<BR> Ventropy ,Venergy ,Vcorr ,<BR> Vcontrast ,Vhomogen ,Vcl_shd ,<BR> Vcl_prom ,Vden_l_s ,Vden_d_s ,<BR> Vrange_ex ,Vrange_av ,Vc_of_gr ,<BR> Vc_m_l ,Vc_m_m ,Vcr_m_h,<BR> Vfract_di ,Vfrac_a1 ,Vfrac_a2,<BR> Vtext_or ,Vsz_t_or,Vsh_run_0 ,<BR> Vs_r_45 ,Vs_r_90 ,Vs_r_135,<BR> Vl_r_0 ,Vl_r_45 ,Vl_r_90 ,<BR> Vl_r_135 ,Vg_l_0 ,Vg_l_45 ,<BR> Vg_l_90 ,Vg_l_135,Vr_le_0 ,<BR> Vr_l_45 ,Vr_l_90 ,Vr_l_135 ,<BR> Vr_pc_0 ,Vr_pc_45,Vr_pc_90,<BR> Vr_p_135,<BR> small,<BR> large,<BR> leuk, j40,<BR> blurry,<BR> numcell, numjunk,<BR> smallthr, largethr,<BR> TSDnumS,TSDNUMSc,tsdsmen,ftsds,tsdnuml,tsdnumlc,tsdlmen,ftsd l,<BR> notnumS,notNUMSc,notsmen,fnots,notnuml,notnumlc,notlmen,fnot l,<BR> blrmS,blrNUMSc,blrsmen,fblrs,blrnuml,blrnumlc,blrlmen,fblrl, <BR> holnumS,holNUMSc,holsmen,fhols,holnuml,holnumlc,hollmen,fhol l,<BR> sptnorm,sptnrmc,sptnrmm,f44, sptabnm,sptabnmc,sptabnmm,f48.<BR> <P>CORR.<BR> <P>Limit = 0.05,3.0<BR> *************** EIGENVALUES, in order of value:<BR> 71.33028 32.23974 30.94120 18.27083 15.39462 10.85435 7.66954 6.18145 5.47879 4.76008<BR> 3.91405 3.25851 2.79417 2.42160 2.24172 2.11663 1.79695 1.57915 1.47290 1.35947<BR> 1.20042 1.12956 1.07051 0.93611 0.90029 0.82658 0.78055 0.71282 0.70711 0.64852<BR> 0.60405 0.56364 0.55948 0.51369 0.48017 0.47069 0.46124 0.43290 0.39927 0.37955<BR> 0.36181 0.34149 0.32396 0.30478 0.29242 0.28130 0.27298 0.25994 0.24401 0.23794<BR> 0.22361 0.21557 0.19996 0.18635 0.17822 0.17744 0.17185 0.16526 0.15142 0.14140<BR> 0.13923 0.12969 0.12560 0.11619 0.10982 0.10739 0.09951 0.09752 0.09251 0.08761<BR> 0.08470 0.07497 0.07472 0.07108 0.06767 0.06093 0.05828 0.05029 0.04886 0.04724<BR> 0.04602 0.04410 0.04184 0.04073 0.03990 0.03660 0.03472 0.03025 0.02784 0.02686<BR> 0.02435 0.02334 0.02172 0.02011 0.01912 0.01880 0.01691 0.01637 0.01494 0.01389<BR> 0.01216 0.01159 0.00989 0.00952 0.00940 0.00817 0.00801 0.00721 0.00659 0.00553<BR> 0.00491 0.00478 0.00393 0.00340 0.00318 0.00267 0.00246 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 Eigenvectors 1, 2, 4, 9, 10, 11, 22, 23, 42, 69, and 87 were selected and gave the following results:<BR> CORRELATION BETWEEN PRINCIPAL COMPONENTS AND DEPENDENT VARIABLE<BR> (those results correlating with Eigenvectors 1, 2, 4, 9, 10, 11, 22, 23, 42, 69, and 87 are in bold)<BR> -0.44812 -0.17110 -0.02805 -0.23778 -0.01812 -0.14156 -0.08413 0.13947 -0.25394 0.26115<BR> -0.19029 -0.01375 0.09051 -0.00650 -0.02159 0.06026 0.11019 -0.07673 -0.05921 -0.03769<BR> 0.02837 0.17501 0.18411 0.00796 -0.02586 -0.11309 0.05748 0.00540 -0.05848 -0.09799<BR> -0.08417 -0.07726 0.06310 -0.00117 -0.06789 0.08256 0.11394 -0.02449 -0.03086 0.11077<BR> 0.04692 0.14880 0.10932 -0.08501 0.01115 -0.02366 -0.02539 -0.01938 -0.02646 -0.00183<BR> -0.03616 -0.07857 0.07758 -0.00455 -0.05219 -0.01890 -0.03949 -0.00843 0.04725 -0.05220<BR> 0.06269 0.02134 -0.11738 0.02199 0.03509 -0.03905 0.09160 -0.04367 -0.15072 -0.06366<BR> 0.00702 -0.08959 -0.05336 -0.15885 0.04090 -0.02485 -0.03273 0.01159 0.08299 0.04217<BR> -0.01964 -0.06200 0.06862 -0.03073 -0.00859 0.01786 -0.20112 -0.07192 0.11830 0.05530<BR> -0.06916 -0.04555 0.05975 0.08019 0.05326 0.01988 -0.00138 0.05014 -0.00595 0.02004<BR> -0.01693 0.02421 -0.02960 -0.02495 0.06143 -0.07817 0.08211 -0.04232 0.02714 -0.01647<BR> 0.10152 -0.04331 0.03784 0.02744 0.00018 0.05253 0.00396 0.00342 0.00032 -0.00162<BR> -0.00008 -0.00332 0.00121 0.00544 0.00033 0.00685 -0.00874 0.00672 0.00678 0.00310<BR> -0.00580 0.01183 0.00137 -0.00436 0.00230 0.01039 -0.00734 -0.01111 -0.00614 0.00297<BR> 0.00150 0.00492 -0.00102 -0.00024 0.01126 -0.00474 0.00911 0.02058 -0.00999 -0.00908<BR> 0.00675 -0.00716 0.01048 -0.01631 -0.00781 -0.00135 -0.00564 0.02175 0.01838 -0.01184<BR> -0.01218 0.00922 -0.00411 -0.01539 -0.00683 -0.01416 -0.03587 0.00442 -0.00566 0.00220<BR> -0.00364 -0.00062 0.02689 0.02394 0.00240 0.00562 0.01917 -0.01332 0.00169 0.00611<BR> 0.00809 -0.02782 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 REGRESSION COEFFICIENTS OF PRINCIPAL COMPONENTS CONSTANT COMPONENTS<BR> (MEAN OF Y; those results correlating with Eigenvectors 1, 2, 4, 9, 10, 11, 22, 23, 42, 69, and 87 are in bold)<BR> 28.98305 -0.24458 -0.13890 -0.02325 -0.25642 -0.02129 -0.19806 -0.14003 0.25857 -0.50008<BR> 0.55175 -0.44336 -0.03511 0.24958 -0.01927 -0.06648 0.19095 0.37889 -0.28146 -0.22490<BR> -0.14902 0.11937 0.75905 0.82023 0.03790 -0.12562 -0.57338 0.29990 0.02949 -0.32055<BR> -0.56090 -0.49919 -0.47433 0.38884 -0.00754 -0.45164 0.55471 0.77332 -0.17160 -0.22511<BR> 0.82878 0.35956 1.17371 0.88537 -0.70983 0.09503 -0.20565 -0.22401 -0.17523 -0.24693<BR> -0.01731 -0.35244 -0.78007 0.79971 -0.04862 -0.56983 -0.20680 -0.43906 -0.09563 0.55960<BR> -0.63992 0.77447 0.27318 -1.52667 0.29736 0.48808 -0.54933 1.33855 -0.64459 -2.28413<BR> -0.99141 0.11125 -1.50832 -0.89983 -2.74629 0.72483 -0.46395 -0.62492 0.23832 1.73070<BR> 0.89428 -0.42199 -1.36083 1.54635 -0.70195 -0.19826 0.43040 -4.97499 -1.90602 3.26823<BR> 1.55547 -2.04297 -1.37446 1.86891 2.60702 1.77546 0.66835 -0.04893 1.80651 -0.22453<BR> 0.78376 -0.70774 1.03694 -1.37213 -1.17865 2.92099 -3.98565 4.22766 -2.29809 1.54137<BR> -1.02138 6.67550 -2.88750 2.78107 2.16988 0.01448 4.68543 0.36795 21.73565 2.33185<BR> -13.72568 -0.65410 -29.72441 10.92369 49.24777 2.96004 62.37509 -80.03191 63.04469 63.76741<BR> 29.17001 -55.39402 114.15483 13.62004 -43.65670 23.61944 109.11919 -77.53935 -120.10095 -67.84739<BR> 33.68808 17.10762 57.81195 -12.54928 -3.02104 140.79829 -60.47807 116.67670 264.74973 -133.41080<BR> -121.28030 93.14059 -99.64044 148.61188 -232.19316 -120.23753 -21.08130 -88.45646 348.23605 309.44754<BR> -205.85442 -214.52692 169.23039 -75.82188 -283.82574 -140.67709 -294.47009 -772.94635 95.92555 -124.63916<BR> 48.45857 -86.40694 -15.18354 670.39685 602.03229 67.82990 163.25047 566.09558 -405.31006 56.75330<BR> 209.54150 353.85907 -2846.23584 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000<BR> 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 COEFFICIENTS OF VARIABLES OBTAINED FROM PRINCIPAL COMPONENTS REGRESSION INDEX<BR> OF RESIDUAL F-VALUES COMPONENTS SUM OF REGRESSION COMPONENT ENTERING SQUARES MODEL<BR> TO ENTER R2 CONSTANT VARIABLES<BR> 13 area 18 r_mean 19 r_max 20 r_var 21 spher 22 eccentr<BR> 1 1986.75513 29.15 29.15 0.2008 15.1542 -0.0004 -0.0389 -0.0327 -0.0233 -0.4056 0.4206<BR> 10 1817.21204 21.16 10.73 0.2690 12.6762 -0.0003 -0.0284 -0.0121 0.4201 -2.2218 5.1439<BR> 9 1656.90479 19.01 11.03 0.3335 20.8831 -0.0004 -0.0399 -0.0270 0.2571 -1.4064 3.5168<BR> 4 1516.34875 18.06 10.47 0.3900 -4.1686 -0.0004 -0.0390 -0.0245 0.3153 -1.6391 4.0539<BR> 87 1415.79370 16.93 7.95 0.4305 -16.8570 -0.0034 -0.2018 -0.1455 0.4984 0.8154 4.5858<BR> 11 1325.77771 16.19 7.54 0.4667 -41.9344 -0.0034 -0.2102 -0.1523 0.4920 1.0658 5.1381<BR> 23 1241.51282 15.75 7.47 0.5006 -28.8066 -0.0033 -0.2072 -0.1580 0.3486 2.0133 3.5948<BR> 22 1165.36890 15.44 7.12 0.5312 -62.9996 -0.0033 -0.2043 -0.1425 0.7885 0.3061 6.1209<BR> 2 1092.59204 15.30 7.19 0.5605 -45.8285 -0.0034 -0.2137 -0.1557 0.6555 1.3196 5.5286<BR> 74 1029.86572 15.13 6.52 0.5857 -88.0782 -0.0039 -0.2911 -0.2369 -0.2638 5.1650 6.5088<BR> 69 973.39307 14.97 6.15 0.6084 -100.4466 -0.0042 -0.3170 -0.2492 -0.2521 4.2707 1.6744<BR> 42* 918.35266 14.94 6.29 0.6306 -113.5530 -0.0039 -0.2913 -0.2219 0.0910 4.7798 2.0601 23 cell_or 24 inertia 25 compact 26 elong 27 r_fft_l 28r_fft_h 29 fft_h1 30 fft_h2 31 fft_h3 32 fft_h4<BR> 1 -0.00014 2.39578 -0.59325 0.40766 0.00039 0.23719 0.18298 0.01463 -0.00412 -0.00304<BR> 10 -0.00439 23.34806 -1.98718 4.80948 -0.03426 0.28693 -0.75968 0.17209 -0.25457 -0.42621<BR> 9 -0.00369 15.31147 -1.18442 3.27522 -0.02608 0.43613 -1.02414 0.11706 -0.22506 -0.32992<BR> 4 -0.00450 17.80728 -0.52523 3.76882 -0.02897 0.42427 -1.07594 0.13485 -0.25425 -0.36245<BR> 87 -0.00430 53.69419 27.09743 0.04511 -0.02671 2.37862 -21.80129 -0.02263 -1.08385 -0.74957<BR> 11 -0.00278 53.95402 26.67413 0.51162 -0.04086 2.56816 -22.66800 -0.00598 -1.17842 -0.92412<BR> 23 -0.00195 47.38270 24.72631 -0.86501 -0.03174 2.56592 -22.31400 -0.05654 -1.09696 -0.94741<BR> 22 -0.00840 64.41065 26.70916 1.43000 -0.02312 2.64903 -22.60423 0.02603 -1.06739 -0.80955<BR> 2 -0.00843 58.63926 23.62866 0.85233 -0.03292 2.60562 -23.27325 0.00523 -1.13908 -0.92975<BR> 74 -0.00933 48.14479 7.52188 1.16957 -0.08102 2.26499 -28.33220 0.02318 -1.60627 -0.26044<BR> 69 -0.02487 45.54356 42.01500 -3.62111 -0.04146 2.85420 -27.44777 -0.16398 -1.46603 1.96270<BR> 42* -0.02895 54.44567 46.13166 -3.12260 -0.03300 3.06026 -27.24798 -0.14940 -1.49111 2.01858 33 fft_h5 34 fft_h6 35 fft_h7 36 fft_h8 37 fft_h9 38 fft_h10 39 fft_h11 40 fft_h12 41 fft_h13<BR> 42 fft_h14<BR> 1 0.01847 0.07324 0.14916 0.27913 0.37786 0.65232 0.79841 1.08272 1.28539 1.46382<BR> 10 -0.70242 -0.92015 -0.93725 -0.33994 -0.26561 1.53537 0.72962 1.33349 1.56358 3.64162<BR> 9 -0.39406 -0.50485 -0.11456 0.60989 1.01472 2.53306 1.79908 3.16516 3.55163 4.60824<BR> 4 -0.44101 -0.50476 -0.12687 0.55883 0.88248 2.49893 1.74221 3.09778 3.27100 4.61440<BR> 87 -4.85683 -3.84680 5.66943 -4.32474 24.78056 -18.15130 -68.03307 79.60461 23.039851 -10.09554<BR> 11 -5.16628 -4.20963 5.39304 -4.54246 24.44120 -17.79695 -67.60872 80.51752 23.32756 -8.21639<BR> 23 -4.94293 -4.40332 4.38084 -5.07214 21.91654 -20.79870 -67.48130 82.50224 19.13993 -11.40064<BR> 22 -4.49644 -4.12064 4.72255 -5.18480 22.43138 -19.99465 -68.46874 84.29064 18.10549 -7.67652<BR> 2 -4.68979 -4.41965 4.32564 -5.67891 21.88838 -20.56764 -69.05540 83.71240 17.49162 -8.23077<BR> 74 -7.69736 -2.63385 0.45994 -0.12797 19.93004 -26.73374 -69.57316 60.25452 4.96559 -16.08179<BR> 69 -7.45570 -3.51742 -0.30594 4.59231 18.08501 -36.29334 -65.61973 79.20781 -9.86347 -42.36713<BR> 42* -7.66288 -4.49875 -2.79344 5.33690 22.07887 -40.48370 -57.11639 90.14920 -16.66207 -42.60194 43 fft_h15 44 fft_h16 45 fft_h17 46 fft_h18 47 fft_h19 48 fft_h20 49 fft_h21 63 DNA_ind<BR> 64 DNA_amnt 65 OD_max<BR> 1 1.56297 1.84889 2.08292 2.19349 2.21963 2.61210 2.49553 -0.04301 -0.00083 0.14540<BR> 10 2.21881 1.57079 1.91397 2.65757 2.66373 3.97222 2.31360 -0.04198 -0.00167 0.54080<BR> 9 2.34848 2.14548 3.71598 3.56237 4.47452 4.89734 3.35723 -0.08267 -0.00056 0.69712<BR> 4 2.10611 1.89876 3.62927 3.26010 4.25401 4.38692 2.88848 -0.06319 0.00012 0.96612<BR> 87 19.41859 -35.17314 14.47245 -25.92324 -68.09641 -1.01626 18.54206 2.14981 0.01797 3.06474<BR> 11 21.34963 -34.53693 17.11508 -25.88559 -65.55521 -1.00859 18.96141 2.11604 0.01975 2.96964<BR> 23 23.73109 -47.03261 17.57797 -25.29914 -64.59228 -6.64101 14.76327 1.89499 0.01507 2.78060<BR> 22 22.25302 -53.03665 21.51764 -15.23043 -67.56295 -2.67966 20.32400 1.76796 0.01461 1.75291<BR> 2 21.78922 -53.47830 21.25835 -15.59251 -67.52314 -2.62884 20.25880 1.69491 0.01386 1.64678<BR> 74 44.96548 -87.48573 31.14426 -9.52985 -91.91827 -21.84790 -5.82632 0.85587 -0.00628 2.11935<BR> 69 69.91273 -79.06959 -1.46483 29.42960 -147.35609 -40.00616 4.48317 0.93166 0.00256 1.95979<BR> 42* 86.39620 -72.80610 4.05846 36.63133 -130.05481 -48.49694 -21.68953 1.20612 0.00465 1.86979 66 OD_var 67 OD_skew 68 OD_kurt 69 DNA_ar_170 DNA_ar_m 71 DNA_ar_h 72 DNA_am_l 73 DNA_am_m 74 DNA_am_h 75<BR> DNA_c_l<BR> 1 -0.55412 -0.08813 -0.02676 -0.14506 0.08883 0.20522 -0.16129 -0.32460 0.14492 0.02548<BR> 10 0.35399 0.37212 0.10981 -0.16038 0.05080 0.23850 -0.18103 -0.54054 0.18907 0.08870<BR> 9 0.83860 0.45925 0.16768 -0.11135 0.88195 -0.04152 -0.08380 0.55150 -0.03263 0.23708<BR> 4 1.54762 0.58321 0.18854 -0.08089 0.88392 -0.08965 -0.08091 0.54615 -0.03356 0.18550<BR> 87 -4.31997 2.64856 0.46002 1.65314 7.65120 -4.45742 2.74362 6.77086 -2.65187 -1.81096<BR> 11 -4.02917 2.53263 0.34122 1.50539 9.20748 -4.60692 2.54294 8.12613 -2.73520 -1.90720<BR> 23 -3.70987 2.51885 0.49189 1.28072 9.26086 -4.26844 2.08844 7.75285 -2.40797 -1.90244<BR> 22 -4.90376 2.29273 0.34658 1.31889 8.72398 -4.19685 2.07459 7.35590 -2.34021 -1.84667<BR> 2 -4.99783 2.32379 0.36571 1.33894 8.86030 -4.26156 2.10319 7.61377 -2.39594 -1.83875<BR> 74 -2.88321 3.06006 1.01991 1.90959 3.16722 -3.76089 2.44455 3.97285 -2.05415 -1.85066<BR> 69 -4.96654 3.17346 1.30996 0.87824 6.82941 -3.04325 1.29712 5.45848 -1.59199 -2.10347<BR> 42* -5.01881 2.84794 1.18260 0.99213 5.87284 -2.98741 1.34082 4.61351 -1.49149 -2.04937 76 DNA_c_m 77 DNA_c_h 78 DNA_c_mh 79 av_dst_l 80 av_ds_m 81 av_ds_h 82 av_ds_mh 83 DNA_lvm 84 DNA_lvh 85<BR> DNA_lvmh<BR> 1 -0.01469 0.00107 -0.01469 0.32867 -0.45247 -0.06932 -1.22729 -0.11794 -0.01772 -0.00210<BR> 10 -0.02097 -0.00569 -0.02149 0.82208 5.56512 -1.23432 -2.42525 0.11311 0.03898 0.06075<BR> 9 -0.03602 -0.03271 -0.03828 1.34107 9.52172 -3.75061 -6.40982 0.74998 0.15630 0.21145<BR> 4 -0.02679 -0.02348 -0.02946 1.09500 10.60462 -3.49672 -5.19505 0.88885 0.20282 0.26597<BR> 87 0.11088 0.03827 0.13283 -4.53022 7.99241 -19.96289 14.45108 -0.30068 -0.48868 -0.15500<BR> 11 0.10916 0.03748 0.12965 -4.67866 5.99497 -19.48718 15.20819 0.12885 -0.37384 -0.05996<BR> 23 0.13121 0.08407 0.14595 -4.30460 19.42750 -20.96250 13.01847 0.27344 -0.32584 0.02706<BR> 22 0.14064 0.09424 0.15118 -4.64023 13.61395 -20.22830 10.77000 -0.30337 -0.44204 -0.08634<BR> 2 0.14025 0.09023 0.15174 -4.62422 13.40673 -20.41808 10.46473 -0.30864 -0.44982 -0.10715<BR> 74 0.19072 0.07998 0.18087 -5.18143 -3.99647 -24.18745 -4.94137 0.47279 -0.40024 0.25451<BR> 69 0.17359 0.10615 0.14885 -5.53898 -10.00746 -20.90989 -10.76253 -1.06389 -0.50041 0.01603<BR> 42* 0.20896 0.11795 0.17520 -6.10510 -6.94500 -22.44129 -10.47224 -1.43358 -0.59862 -0.02054 86 l_den_o 87 m_den_o 88 h_den_o 89 entropy 90 energy 91 corr 92 contrast 93 homogen 94 cl_shd 95 cl_prom<BR> 1 0.00084 0.00492 -0.03437 0.49014 -6.65755 -0.00237 0.00958 -2.55213 -0.01186 -0.02907<BR> 10 -0.00393 -0.02080 -0.01207 -1.59453 9.09981 -0.00160 -0.01620 0.51079 -0.62269 0.17743<BR> 9 -0.02704 -0.13495 -0.00665 -2.92287 16.66657 0.00051 -0.03387 0.85957 -0.60547 0.33993<BR> 4 -0.02191 -0.12923 0.02815 -1.02086 -1.33324 0.00279 -0.02098 -3.93468 -0.48034 0.25860<BR> 87 -0.13974 -0.03492 1.23124 -1.46252 -42.12260 -0.00619 -0.06572 -9.60312 -3.32255 -0.48562<BR> 11 -0.14658 -0.16176 1.25181 0.99002 -56.11326 -0.00227 -0.04327 -11.13957 -2.76276 -0.87220<BR> 23 -0.09715 -0.10805 1.38200 -2.08037 -46.88458 -0.01241 -0.06076 -14.07368 -3.02176 -0.50420<BR> 22 -0.08447 -0.04636 1.43827 -1.53810 -55.55853 -0.01267 -0.05968 -16.79366 -3.20511 -0.62201<BR> 2 -0.08588 -0.05383 1.44370 -1.88387 -54.21935 -0.01375 -0.05975 -17.05964 -3.27591 -0.53885<BR> 74 -0.15309 -0.22508 1.58039 -5.62561 -47.93876 -0.01536 -0.07292 -18.41932 -3.13862 -0.19618<BR> 69 -0.06754 -0.22130 1.49311 -5.58715 -50.58813 -0.00954 -0.06991 -21.80220 -1.77698 0.01838<BR> 42* -0.01127 -0.15869 1.42427 -5.26725 -55.31086 -0.00869 -0.07956 -22.34267 -1.39398 -0.15891 96 den_1_s 97 den_d_s 98 range_ex 99 range_av100 c_of_gr 101 c_m_1 102 c_m_m 103 cr_m_h 104 fract_di105 fract_al<BR> 1 -9.35546 7.23420 -0.00018 -0.00029 1.18976 0.75989 0.75989 0.14588 -0.33041 0.85449 -0.00001<BR> 10 -2.02621 -55.00179 -0.00022 -0.00026 6.51948 1.16560 1.39921 -0.49465 0.24008 -0.00001<BR> 9 -35.61385 -104.32639 -.00049 -0.00054 -1.03504 0.49961 0.02295 -0.44988 0.99816 0.00001<BR> 4 -31.39001 -93.48791 -0.00034 0.00003 -2.35308 0.07221 -0.89069 -0.63098 0.97702 0.00002<BR> 87 -1.14774 -431.54929 -0.00201 0.00872 31.57230 5.41402 17.14449 -9.10154 8.54906 -0.00003<BR> 11 -14.05061 -411.45175 -0.00262 0.0815 38.80146 6.86616 14.92696 -8.77547 8.55482 -0.00002<BR> 23 61. 61813 -377.27954 -0.00265 0.00682 52.24400 7.16915 16.48841 -10.21294 12.94800 -0.00003<BR> 22 84.19618 -4149.91888 -0.00269 0.00644 51.60872 5.56793 17.05678 -10.80299 12.65559 -0.00004<BR> 2 87.38760 -408.48447 -0.00273 0.00627 48.58872 5.25209 16.58864 -10.75368 12.90250 -0.00004<BR> 74 98.48270 -314.09415 -0.00063 0.01199 80.92771 8.54001 30.00205 -12.78070 23.32765 -0.00009<BR> 69 64.45259 -460.79401 -0.00148 0.01185 88.14262 10.25950 37.39706 -11.67723 26.89519 0.00001<BR> 42* 48.68956 -394.59109 -00.153 0.01118 85.17691 8.65931 39.59583 -12.64928 24.61291 0.00002 106 fract_a2107 text_or 108 sz_t_or 109 sh_run_0110 sh_r_45 111 sh_r_90 112 sh_r_135113 1_r_0 114 1_r_45 115 1_r_90<BR> 1 -0.00006 0.00043 0.10827 1.97744 2.07022 4.82157 5.53961 -0.07547 -0.07692 -0.06520<BR> 10 -0.00004 -0.01362 0.28835 -1.20503 -0.56724 0.41421 -4.31367 0.03196 0.02839 0.00563<BR> 9 0.0002 -0.01251 0.56874 -2.80395 -0.56112 -0.61851 -4.94392 0.07425 0.02797 0.02313<BR> 4 0.00010 -0.01608 0.70994 0.62055 3.18737 8.07051 3.33298 -0.04156 -0.09757 -0.08735<BR> 87 -0.00026 -0.06272 1.55839 -10.44449 28.39096 -52.76916 40.63438 0.00913 -0.57728 0.41134<BR> 11 -0.00022 -0.05603 1.35357 -7.53921 30.88550 -48.29991 45.64971 -0.01671 -0.58764 0.40364<BR> 23 -0.00028 -0.06679 4.14884 -9.22058 30.20188 -45.93955 42.64612 -0.03975 -0.68660 0.34968<BR> 22 -0.00032 -0.06613 3.97971 -6.98066 32.75071 -36.75086 45.33275 -0.08827 -0.75628 0.28008<BR> 2 -0.00035 -0.06684 3.94432 -6.71745 32.72784 -36.26966 45.74046 -0.10088 -0.76308 0.27169<BR> 74 -0.00063 -0.08724 3.78202 11.25456 55.57318 14.09343 78.18546 0.41706 -0.36453 0.74892<BR> 69 -0.00022 -0.09487 2.45741 1.25656 65.66628 20.82033 75.50686 0.40762 -0.61204 0.67606<BR> 42* -0.00010 -0.04653 2.03227 2.30698 65.81676 13.68593 77.00262 0.34144 -0.61451 0.66091 116 1_r_135 117 gr_1_0 118 gr_1_45 119 gr_1_90 120 gr_1_135121 r_le_0 122 r_le_45 123 r_le_90 124 r_le_135125 r_pc_0<BR> 1 -0.07311 -0.00470 -0.00459 -0.00400 -0.00400 -0.00200 -0.00191 -0.00145 -0.00147 1.51256<BR> 10 0.04859 -0.00355 -0.00333 -0.00252 -0.00288 -0.00313 -0.00249 -0.00153 -0.00203 -0.26519<BR> 9 0.06297 -0.00587 -0.00524 -0.00399 -0.00436 -0.00482 -0.00329 -0.00224 -0.00270 -1.08816<BR> 4 -0.04038 -0.00465 -0.00408 -0.00336 -0.00391 -0.00269 -0.00141 -0.00110 -0.00172 1.44254<BR> 87 -0.47857 -0.3356 -0.02607 -0.03363 -0.02610 -0.01779 -0.00503 -0.01914 -0.00818 -4.00935<BR> 11 -0.49760 -0.003530 -0.02793 -0.03536 -0.02780 -0.01729 -0.00506 -0.01924 -0.00824 -2.73481<BR> 23 -0.50993 -0.02922 -0.02170 -0.03033 -0.02319 -0.01721 -0.00428 -0.01863 -0.00814 -1.78919<BR> 22 -0.53403 -0.02896 -0.02139 -0.03006 -0.02338 -0.01584 -0.00276 -0.01758 -0.00763 -0.38453<BR> 2 -0.54067 -0.02963 -0.02210 -0.03064 -0.02397 -0.01638 -0.00338 -0.01798 -0.00806 -0.13435<BR> 74 -0.1735 -0.03774 -0.03009 -0.03644 -0.02968 -0.01421 -0.00171 -0.01832 -0.00990 2.55566<BR> 69 -0.08949 -0.03988 -0.02588 -0.03647 -0.02979 -0.01892 0.00280 -0.01809 -0.01028 -0.92094<BR> 42* -0.17713 -0.03571 -0.02338 -0.03434 -0.02727 -0.01622 0.00430 -0.01711 -0.00876 -0.17044 126 r_pc_45 127 r_pc_90 128 r_pc_135138 Varea 143 Vr_mean 144 Vr_max 145 Vr_var 146 Vspher 147 Veccentr 148 Vcell_or<BR> 1 1.56986 1.65360 1.89642 -0.00094 -0.10456 -0.09786 -0.07499 -0.17905 0.90727 0.00034<BR> 10 -0.06817 0.36631 -0.91163 -0.00113 -0.15382 -0.08654 0.93687 8.94090 13.12274 -0.00926<BR> 9 0.06269 0.10109 -1.06755 -0.00147 -0.19661 -0.16781 0.51024 3.68900 8.22977 -0.01095<BR> 4 2.80758 3.07206 1.75133 -0.00140 -0.18580 -0.14252 0.64066 3.29689 9.02805 -0.01133<BR> 87 10.09467 -17.23764 11.78179 0.00281 1.02084 1.06819 -2.72303 -87.43463 68.38710 -0.03776<BR> 11 11.00514 -16.38227 12.93906 0.00305 1.05688 1.12744 -2.68847 -94.07480 70.28796 -0.03464<BR> 23 13.56103 -14.34221 13.95396 0.00237 0.91260 0.94731 -2.85599 -80.06860 69.40712 -0.02170<BR> 22 15.29390 -11.98087 14.86266 0.00309 0.99695 1.03850 -2.74887 -151.23146 66.35151 -0.01867<BR> 2 15.39153 -11.77695 15.02687 0.00289 0.97495 1.00891 -2.94496 -155.05748 64.49918 -0.01837<BR> 74 18.83357 -9.23274 17.00159 0.00461 1.36196 0.60424 -6.73057 -147.96873 83.37253 -0.00376<BR> 69 26.25803 -6.09145 15.63445 0.00321 1.20764 1.00023 -5.29276 -154.11649 89.12291 0.01677<BR> 42* 24.75760 -7.29949 16.42604 0.00067 0.88743 0.67788 -4.37527 -161.28751 94.43922 0.01244 149 Vinertia 150 Vcompact 151 Velong 152 Vr_fft_1153 Vr_fft_h154 Vfft_h1 155 Vfft_h2 156 Vfft_h3 157 Vfft_h4 158 Vfft_h5<BR> 1 4.26705 -1.57122 0.86829 -0.00139 -0.17276 0.11232 0.03367 -0.02241 -0.03015 0.05734<BR> 10 62.68205 -3.64718 11.90385 -0.07886 0.29856 -1.65359 0.45946 -0.65975 -0.95456 -1.46898<BR> 9 39.18447 6.07884 7.49369 -0.06544 0.79126 -2.07145 0.28237 -0.64557 -0.75488 -0.43628<BR> 4 43.27712 7.90807 8.11301 -0.06817 0.88064 -2.09593 0.30422 -0.69674 -0.81914 -0.50966<BR> 87 100.81413 152.56296 19.07251 0.84580 -26.75653 -41.18267 0.90412 1.52817 -1.0930 7.30973<BR> 11 103.07240 150.45940 20.58435 0.81429 -26.16781 -43.01097 0.96656 1.27453 -1.60627 6.62308<BR> 23 98.86678 153.22916 20.79619 0.81816 -26.01278 -44.35555 0.98447 1.34572 -1.43031 8.13869<BR> 22 77.92241 100.68987 17.03569 0.77550 -26.49974 -48.07672 0.79139 1.07842 -1.40347 7.96948<BR> 2 66.57063 93.44411 15.20038 0.75947 -26.69216 -48.94823 0.71755 0.93999 -1.64607 7.58180<BR> 74 150.52876 -242.06836 33.82732 0.84623 -18.12708 -49.18948 1.53053 3.79053 -1.59744 6.68966<BR> 69 108.13197 -85.98094 34.46703 0.87117 -13.59164 -51.35224 1.68593 3.13310 -0.15893 2.50399<BR> 42* 14487663 -96.69802 40.05738 0.89607 -13.66348 -52.11329 1.91499 3.43159 0.76143 0.16196 159 Vfft_h6 160 Vfft_h7 161 Vfft_h8 162 Vfft_h9 163 Vfft_h10164 Vfft_h11165 Vfft_h12166 Vfft_h13167 Vfft_h14168 Vfft_h15<BR> 1 0.13495 0.23550 0.26876 0.23743 0.76632 0.67497 0.95877 1.11224 1.48092 1.78912<BR> 10 -1.78398 -1.70355 -0.86436 1.32639 2.89286 0.65951 2.55198 4.28076 4.08296 6.73713<BR> 9 -0.48706 0.57993 1.63068 4.21648 5.42745 2.95458 7.41973 7.68240 5.38273 6.10386<BR> 4 -0.46951 0.69145 1.69340 4.01774 5.37573 2.98538 7.19208 7.20666 5.64656 5.04110<BR> 87 12.10582 7.39085 -3.48222 -22.08649 -6.82887 27.83117 -17.52382 56.83738 48.73734 26.93043<BR> 11 10.60879 6.16757 -4.34894 -23.25189 -6.78846 27.29089 -16.55965 57.78080 54.05794 31.36532<BR> 23 10.74155 3.55646 -6.46845 -28.91087 -16.79446 22.51773 -9.04204 53.97602 48.37152 45.38147<BR> 22 9.51513 4.00127 -6.86973 -27.55742 -15.92947 14.78228 -1.77249 58.08395 57.31110 30.29937<BR> 2 8.99956 3.45660 -7.58845 -28.23385 -16.81959 14.04492 -2.59109 57.34152 56.47300 29.75246<BR> 74 10.98562 6.83537 4.72781 -10.95223 13.02832 18.86419 -2.97352 47.35118 68.06789 11.62714<BR> 69 -3.80655 3.24866 12.77506 -11.33705 39.69277 21.72697 -16.12794 47.64566 50.32424 -32.80860<BR> 42* -7.62320 -6.16343 16.38728 4.06962 19.74702 39.22703 -3.00466 41.31390 54.63694 -33.73012 169 Vfft_h16170 Vfft_h17171 Vfft_h18172 Vfft_h19173 Vfft_h20174 Vfft_h21188 VDNA_ind189 VDNA_amn190 VOD_max 191 VOD_var 1 2.31956 2.93749 3.47109 3.36245 3.84391 3.41364 -0.12263 -0.00176 0.05449 -0.85762<BR> 10 -0.25484 5.40605 1.50735 3.82630 1.94262 7.20088 -0.46977 -0.00652 -0.73396 -0.69209<BR> 9 0.38941 8.46323 3.06873 5.93230 1.88277 1.70899 -0.46288 -0.00438 0.64759 2.15108<BR> 4 -0.41659 7.65324 3.29531 5.87072 1.04922 0.87556 -0.43451 -0.00350 1.18030 2.82127<BR> 87 -12.84592 26.75196 94.88079 33.24755 -39.30532 15.74143 -0.49233 -0.01117 -24.58943 -0.46625<BR> 11 -14.55052 32.39792 92.02084 38.48108 -34.07346 19.19498 -0.56210 -0.00926 -23.71371 1.21644<BR> 23 -31.17985 43.90747 98.16367 37.84852 -54.01455 36.84663 -1.63427 -0.02377 -24.92730 3.76845<BR> 22 -62.36590 59.33813 145.16170 36.52983 -47.08738 51.96387 -1.28055 -0.01905 -24.69983 2.52798<BR> 2 -62.88351 58.80287 145.08206 37.13258 -46.93314 51.82630 -1.35417 -.01985 -24.74450 2.51739<BR> 74 -19.65018 97.08630 96.57864 20.03622 14.02529 101.03030 -2.24210 -0.03931 -15.27391 -29.03440<BR> 69 -24.00671 92.36275 63.90499 15.74218 69.06335 104.94826 -3.34698 -0.04340 -12.88337 -25.89991<BR> 42* -40.58402 76.58200 16.85871 8.94528 53.36829 123.71467 -3.71585 -0.04867 -6.87718 -22.44360 192 VOD_skew193 VOD_kurt194 VDN_ar_1195 VDN_ar_m196 VDN_ar_h197 VDN_am_1198 VDN_am_m199 VDN_am_h200 VDN_c_1201<BR> VDN_c_m 1 -0.30646 -0.05644 -0.52612 0.32524 0.44212 -0.36718 0.56758 0.24164 -0.02290 -0.04322<BR> 10 -0.52690 -0.03074 -1.78117 0.06117 -0.62439 -0.99916 -0.94767 -1.46325 -0.02319 -0.05708<BR> 9 0.26598 0.00916 -2.39552 6.79333 -1.28197 -1.04571 2.00342 -1.80512 0.58435 -0.11698<BR> 4 0.25374 0.03055 -2.55677 5.57809 -1.69769 -1.13662 1.17244 -2.16916 0.47456 -0.10170<BR> 87 4.74424 -0.26728 7.60297 30.87803 -5.84078 5.85038 10.45059 -1.68736 -1.10618 -0.69734<BR> 11 4.13615 -0.42874 7.82664 27.92881 -5.68060 6.60751 10.65440 -1.34669 -1.22554 -0.64883<BR> 23 2.99976 -0.44297 5.98526 23.12588 -6.51375 5.37963 6.25360 -1.98658 -1.79191 -0.65215<BR> 22 2.15095 -0.53828 6.57987 24.64655 -5.01814 5.48524 7.06580 0.43753 -1.65888 -0.63692<BR> 2 2.3887 -0.51168 6.62467 25.28786 -5.01858 5.55333 7.42873 0.54537 -1.64666 -0.64030<BR> 74 3.89356 -0.36001 8.47702 5.36749 3.29621 6.16265 13.68868 9.10416 -0.14633 -0.58291<BR> 69 6.49168 -0.02010 -3.31354 12.38836 -0.65476 0.01454 26.04001 3.71646 -1.05415 -0.52616<BR> 42* 6.32129 -0.11946 -1.08692 16.73291 -0.28347 1.40043 25.50239 4.98501 -0.15430 -0.51788 202 VDN_c_h 203 VDN_c_mh204 Va_ds_1 205 Va_ds_m 206 Va_ds_h 207 Va_ds_mh208 VDN_1vm 209 VDN_1vh 210 VDN_1vmh211<BR> V1_den_o 1 -0.02878 -0.04037 0.13097 -0.67177 -0.41714 -1.47174 -0.11041 -0.05045 -0.03774 -0.01889<BR> 10 -0.07789 -0.06860 -0.58310 2.47014 -0.48969 -1.27009 -0.08142 -0.05692 -0.05688 -0.10030<BR> 9 -0.09325 -0.14424 -0.04575 5.67076 0.89913 0.87336 0.18876 0.13814 0.0885 -0.21426<BR> 4 -0.07249 -0.13055 -0.09743 4.25719 0.82241 1.10047 0.25656 0.16753 0.13428 -0.19146<BR> 87 0.04173 0.19176 7.38267 -14.63233 1.18891 12.81849 2.60190 0.98075 1.09895 0.10879<BR> 11 0.05866 0.24855 8.21312 -16.86062 1.26641 11.29467 2.82199 1.07780 1.18507 0.12621<BR> 23 0.12713 0.23989 5.60581 -17.05558 -0.28996 8.70310 3.05583 1.08780 1.24392 0.21384<BR> 22 0.15546 0.24193 6.54638 -21.12947 -1.40674 8.48468 3.15988 1.20263 1.42225 0.22148<BR> 2 0.16224 0.24140 6.51733 -23.06554 -1.25491 8.75263 3.15212 1.19753 1.41696 0.21624<BR> 74 -0.12949 0.30734 1.47220 7.45505 5.46416 17.91813 2.62466 1.57329 1.94871 -0.03242<BR> 69 -0.46131 0.07269 -1.74375 -14.92184 4.19682 6.81732 1.11092 1.28268 1.89332 0.27895<BR> 42* -0.44414 0.07730 -3.07956 -7.52305 4.78089 8.90018 1.61927 1.50277 2.03754 0.47772 212 Vm_den_o213 Vh_den_o214 Ventropy215 Venergy 216 Vcorr 217 Vcontras218 Vhomogen219 Vcl_shd 220 Vcl_prom221 Vden_1_S 1 0.00638 -0.08683 -1.86691 -20.12004 -0.00410 0.01029 -10.83819 -0.29967 -0.10852 -21.89182<BR> 10 -0.01805 -0.11563 -3.63092 -26.49584 -0.00535 -0.24938 -9.21821 -0.65975 -0.04596 -59.56893<BR> 9 -0.08375 -0.15382 -6.08202 -51.73222 0.00465 -0.42746 -23.21567 0.05689 -0.08052 -134.77995<BR> 4 -0.07385 -0.09034 -7.41449 -76.57237 0.00649 -0.46325 -35.41475 0.22249 -0.14876 -177.41220<BR> 87 0.76021 -4.12137 -13.73705 -269.75119 -0.04959 -0.19028 -85.81966 -2.49107 -0.29132 -1014.30298<BR> 11 0.59471 -4.10528 -18.85522 -292.80823 -0.04951 -0.16740 -64.23193 -3.31609 -0.66837 -1028.33105<BR> 23 0.66193 -4.12347 -25.59889 -345.32806 -0.05561 -0.36290 -72.39880 -3.81070 -0.75244 -871.90448<BR> 22 0.77209 -3.94161 -25.20407 -344.29993 -0.04974 -0.34028 -63.48359 -3.77882 -0.89869 -490.28165<BR> 2 0.77305 -3.93105 -24.27131 -337.74475 -0.04943 -0.32541 -63.61818 -3.62158 -0.83747 -468.85812<BR> 74 0.27365 -3.60523 -57.31804 -639.05804 -0.10188 0.13771 -61.42859 -5.07091 -2.84037 -907.97455<BR> 69 0.03851 -3.55279 -51.91123 -587.31665 -0.02658 -0.39559 6.32283 -4.77075 -2.30567 -595.67639<BR> 42* 0.23206 -4.09840 -46.17784 -582.23376 -0.01352 -0.09699 -16.12800 -4.54841 -2.84388 -500.77136 222 Vden_d_s223 Vrange_e224 Vrange_a225 Vc_of_gr226 Vc_m_1 227 Vc_m_m 228 Vcr_m_h 229 Vfract_d230 Vfrac_a1231 Vfrac_a2<BR> 1 27.69567 -0.00041 -0.00055 1.27509 0.94124 -0.07314 -0.55450 1.22504 -0.00002 -0.00011<BR> 10 -73.38484 -0.00110 -0.00094 8.21875 0.70969 1.35095 -1.78450 -1.24852 -0.00005 -0.00022<BR> 9 -198.98393 0.00256 0.00149 -2.59815 -0.96889 2.17636 -1.83802 7.22022 -0.00002 -0.00011<BR> 4 -220.02757 0.00352 0.00196 -3.70288 -1.55606 1.23397 -2.20220 5.93021 0.00000 0.00002<BR> 87 60.85254 -0.01907 -0.01071 9.08044 12.33767 6.58746 -24.95790 -5.08009 -0.00029 -0.00149<BR> 11 97.54432 -0.02036 -0.01245 27.43414 16.01615 4.02722 -24.29801 -3.40907 -0.00029 -0.00145<BR> 23 77.87082 -0.01987 -0.01298 43.06069 13.49958 3.18994 -28.16086 0.14873 -0.00033 -0.00168<BR> 22 -290.84592 -0.01816 -0.01072 21.57058 7.92022 3.36572 -28.40673 -4.04036 -0.00031 -0.00156<BR> 2 -265.69980 -0.01824 -0.01057 16.41261 7.33701 3.10561 -28.26362 -2.44764 -0.00031 -0.00159<BR> 74 -1093.03589 -0.02806 -0.00442 -34.19225 17.36572 -22.69934 -27.10113 -8.23521 -0.00032 -0.00188<BR> 69 -1499.92712 -0.02761 -0.00369 -3.04341 13.99082 -13.41447 -31.42359 2.39528 -0.00013 -0.0010<BR> 42* -1765.48901 -0.02212 -0.00024 -27.32422 17.88881 -9.10571 -32.04485 7.80874 -0.00015 -0.0111 232 Vtext_of233 Vxz_t_or234 Vsh_run_235 Vs_r_45 236 Vs_r_90 237 Vs_r_135238 Vl_r_0 239 vl_r_45 240 vl_r_90 241 vl_r_135<BR> 1 -0.00150 0.00171 -7.88976 -9.l4284 -20.25967 -21.62633 -0.15351 -0.15538 -0.12926 -0.13202<BR> 10 -0.00202 0.00251 -15.55473 -14.15098 -37.79883 -33.70279 0.08473 0.11282 0.06493 0.05736<BR> 9 0.01320 0.01350 -23.62698 -33.48152 -88.52801 -89.11386 0.18049 0.07956 0.11565 0.10381<BR> 4 0.01171 0.01949 -37.71206 -44.60531 -131.86551 -125.66708 0.00737 -0.11547 -0.04516 -0.03345<BR> 87 -0.08594 0.05918 -68.91315 -185.02190 -343.19510 -1095.41724 0.71778 0.62505 1.80609 -0.56854<BR> 11 -0.09174 0.04564 -76.64258 -12.25885 -323.95657 -1057.05664 0.91690 0.88957 2.00143 -0.34738<BR> 23 -0.05452 0.14830 -93.41580 -214.07246 -332.91672 -1060.14050 1.15693 0.93029 1.99940 -0.15773<BR> 22 -0.06284 0.15290 -60.16879 -195.18285 -323.83771 -971.17981 1.32119 0.86786 2.02601 0.09540<BR> 2 -0.06529 0.15212 -59.07008 -194.00833 -324.02179 -969.34186 1.29626 0.84875 2.00609 0.08018<BR> 74 -0.03396 0.20493 196.98929 -101.41352 -674.00336 -960.16656 0.70775 1.96548 3.49111 -0.70803<BR> 69 0.06394 0.18233 199.59833 -303.97961 -543.86865 -1104.20850 -1.33468 3.60139 4.56803 -0.62004<BR> 42* 0.00370 0.17250 208.28133 -276.12299 -383.72711 -1211.97021 -1.32399 3.31291 4.65047 -0.87999 242 Vg_l_0 243 Vg_l_45 244 Vg_l_90 245 Vg_l_135246 Vr_le_0 247 Vr_l_45 248 Vr_l_90 249 Vr_l_135250 Vr_pc_0 251 vr_pc_45<BR> 1 -0.01134 -0.0118 -0.00963 -0.00963 -0.00431 -0.00420 -0.00420 -0.00323 -0.00314 -7.28950 -8.21956<BR> 10 -0.01752 -0.01713 -0.01543 -0.01510 -0.00717 -0.00565 -0.00471 -0.00461 -14.95726 -10.60654<BR> 9 -0.02659 -0.02543 -0.02264 -0.02276 -0.00970 -0.00696 -0.00618 -0.00602 -25.05242 -35.10679<BR> 4 -0.02350 -0.02235 -0.02085 -0.02126 -0.00650 -0.00396 -0.00422 -0.00427 -35.88666 -43.94158<BR> 87 0.05349 0.00453 -0.00813 -0.01537 0.02271 -0.00381 -0.00645 -0.01874 47.56613 200.20309<BR> 11 0.06017 0.00866 -0.00326 -0.01081 0.02231 -0.00682 -0.00698 -0.01919 65.40279 222.80186<BR> 23 0.06757 0.01976 0.0155 -0.00657 0.01883 -0.00761 -0.00936 -0.02183 55.61045 208.80795<BR> 22 0.07672 0.02965 0.00895 0.00114 0.02161 -0.00432 -0.00866 -0.01947 79.99532 218.85072<BR> 2 0.07454 0.02742 0.00701 -0.00082 0.02138 -0.00467 -0.00907 -0.01987 79.50935 218.50247<BR> 74 0.09127 0.06438 0.03243 -0.01241 0.03531 0.01427 0.00580 -0.03722 72.64172 304.63104<BR> 69 0.06188 0.06220 -0.00474 -0.02226 0.01520 0.02190 -0.00213 -0.03711 -51.19676 379.48703<BR> 42* 0.05615 0.04299 -0.01803 -0.03473 0.00971 0.00515 -0.00873 -0.04552 -31.52683 357.91962 252 Vr_pc_90253 Vr_p_135254 small 262 large 270 leuk 286 j40 294 blurry 302 numcell306 numjunk 310 smallthr<BR> 1 -7.73060 -8.22842 0.00004 -0.00007 0.00027 -0.00006 0.00045 0.00003 0.00007 0.00003<BR> 10 -8.17965 -12.58520 0.00007-0.00093 0.00072 -0.00236 -0.00088 -0.00005 -0.00212 0.0000<BR> 9 -19.59473 -22.97317 0.00008 -0.00051 -0.00027 -0.00361 0.00130 0.00002 -0.00320 0.00007<BR> 4 -31.51925 -32.57619 0.00014 -0.00041 -0.00011 -0.00359 0.00174 0.00008 -0.00332 0.000111<BR> 87 59.37930 -79.25972 0.00077 -0.00418 0.00518 -0.00330 -0.00349 0.00015 -0.01140 -0.00103<BR> 11 79.67919 -52.69867 0.00065 -0.00384 0.00508 -0.00332 0.00145 0.00012 -0.01146 -0.00104<BR> 23 61.38136 -51.26807 0.00059 -0.00330 0.00511 -0.00079 0.00635 0.00016 -0.00953 -0.00090<BR> 22 64.91979 -18.33139 0.00054 -0.00362 -0.00116 -0.00244 0.00914 0.00011 -0.01272 -0.00091<BR> 2 63.67289 -18.89434 0.00063 -0.00360 -0.00108 -0.00271 0.00890 0.00018 -0.01290 -0.00083<BR> 74 136.17209 -121.98172 0.00023 -0.00036 0.00330 0.00005 0.01273 0.00024 -0.01393 -0.00071<BR> 69 259.93484 -65.18663 0.00022 0.00194 -0.00032 -0.00434 0.01992 0.00054 -0.01113 0.00000<BR> 42* 304.81378 -109.63728 0.00042 0.00076 -0.00295 -0.00323 0.01462 0.00054 -0.01300 -0.00020 314 largethr326 TSDnumS 327 TSDNUMSc328 tsdsmen 329 ftsds 330 tsdnuml 331 tsdnumlc332 tsdlmen 333 ftsdl 334 notnumS<BR> 1 0.00001 -0.00006 -0.00012 -0.08542 -0.11568 0.00010 0.00017 -0.07701 0.18351 0.00006<BR> 10 -0.00050 -0.00023 -0.00028 -0.14575 -0.39130 0.00025 0.00061 -0.26530 0.45423 -0.001305<BR> 9 -0.00044 -0.00014 0.00017 0.04158 -0.19951 0.00020 0.00065 -0.29683 0.38048 -0.00017<BR> 4 -0.00017 -0.00011 0.00010 -0.00554 -0.15651 0.00022 0.00061 -0.26548 0.39876 -0.00074<BR> 87 0.01119 -0.00060 0.00272 0.21532 -1.64185 0.00092 -0.00335 -0.10748 0.98727 0.04184<BR> 11 0.01098 -0.00049 0.00324 0.58671 -1.45303 0.00075 -0.00348 -0.15851 0.64637 0.01644<BR> 23 0.01012 -0.00026 0.00396 0..57848 -1.15512 0.00050 -0.00355 -0.26342 0.00556 0.01525<BR> 22 0.00963 -0.00040 0.00359 0.32508 -1.40198 0.00062 -0.00398 -0.05673 0.36105 0.01497<BR> 2 0.00966 -0.00034 0.00367 0.36075 -1.29370 0.00062 -0.00406 0.00376 0.30888 0.01552<BR> 74 0.00911 -0.00056 0.00870 -1.66975 -2.39482 0.00097 -0.00597 1.24393 1.56574 0.01108<BR> 69 0.00553 0.00007 0.00946 -1.93057 -1.49594 0.00047 -0.00662 1.59126 0.73586 0.01648<BR> 42* 0.00739 0.000360.00823 -2.13594 -1.03608 0.00008 -0.00846 2.00022 0.53992 0.01808 335 notNUMSc336 notsmen 337 fnots 338 notnuml 339 notnumlc 340 notlmen 341 fnotl 342 blrmS 343 blrNUMSc344 blrsmen<BR> 10.00007 0.01378 0.04089 0.00003 0.00003 0.03660 0.08048 0.00003 0.00003 -0.00044<BR> 10 -0.00145 -0.01179 -2.24572 -0.00002 0.00001 -0.03840 0.00653 0.00002 0.00003 0.00548<BR> 9 0.00061 0.05196 0.20885 0.00003 0.00004 -0.03555 0.29492 -0.00001 -0.00008 -0.16058<BR> 4 -0.00016 0.05578 -0.89955 0.00011 0.00015 -0.07811 0.56424 0.00012 0.00009 -0.07395<BR> 87 -0.00533 0.42147 .54134 -0.00025 0.00031 -0.89056 -3.66488 -0.00005 0.00004 -1.10537<BR> 11 -0.00196 0.58840 12.39827 -0.00032 0.00022 -0.89661 -4.10644 -0.00016 -0.00007 -1.01986<BR> 23 -0.00436 0.55255 10.88213 -0.00024 0.00031 -0.90570 -4.65138 -0.00032 -0.00033 -0.96893<BR> 22 -0.00614 0.38030 11.12704 -0.00030 0.00027 -0.89698 -4.58410 -0.00041 -0.00045 -0.93659<BR> 2 -0.00535 0.38824 12.02977 -0.00023 0.00034 -0.88856 -4.41502 -0.00033 -0.00036 -0.91409<BR> 74 -0.01778 0.18420 1154423 -0.00003 0.00054 -1.13015 -4.85631 -0.00030 -0.00079 -0.81751<BR> 69 -0.02417 -0.11661 22.00106 0.00015 0.00063 -0.82144 -4.68632 -0.00015 -0.00095 -1.02334<BR> 42* -0.02050 0.13198 24.4733 0.00010 0.00051 -0.85805 -3.80733 -0.00022 -0.00104 -1.16608 345 fblrs 346 blrnuml 347 blmumlc348 blrlmen 349 fblrl 350 holnumS 351 holNUMSc352 holsmen 353 fhols 354 holnuml<BR> 1 0.05856 0.00011 0.00023 -0.01452 0.13331 0.00009 0.00010 0.02562 0.25389 -0.00025<BR> 10 0.13245 -0.00070 -0.00236 0.20433 -1.01168 0.00005 0.00009 0.05109 0.23822 -0.00015<BR> 9 0.09186 0.00076 0.00041 0.09945 1.47131 -0.00006 -0.00003 0.05301 -0.11840 0.00022<BR> 4 0.51083 0.00000 -0.00130 0.13922 0.13185 0.00001 0.00004 0.05102 0.05282 0.00049<BR> 87 -2.40532 -0.00429 -0.01836 -0.68277 -8.16351 -0.00082 -0.00071 0.11579 -2.00552 0.00318<BR> 11 -2.91821 -0.00360 -0.01654 -0.66981 -7.36080 -0.00091 -0.00083 0.01432 -2.32425 0.00304<BR> 23 -4.32553 0.00116 -0.00356 -0.90973 -0.16993 -0.00105 -0.00094 0.12862 -3.39728 0.00290<BR> 22 -4.42860 0.00198 -0.00155 -1.01164 1.15885 -0.00105 -0.00092 0.18928 -3.13126 0.00245<BR> 2 -4.22379 0.00181 -0.00200 -0.98106 0.78411 -0.00098 -0.00086 0.19958 -2.99826 0.00253<BR> 74 -5.01215 0.00517 -0.02195 -0.49085 3.79970 -0.00156 -0.00090 0.54399 -5.41558 0.00509<BR> 69 -4.55980 0.00597 -0.02772 -0.31217 1.44890 -0.00139 -0.00055 0.89817 -5.18388 0.00506<BR> 42* -3.89759 0.00632 -0.03067 -0.72576 2.72408 -0.00136 -0.00053 0.92168 -4.37894 0.00464 355 holnumlc356 hollmen 357 fholl 358 splnorm 359 spinrmc360 sptnrmm 361 f44 362 sptabnm 363 sptabnmc364 sptabnmm<BR> 1 -0.00027 0.00610 -0.45592 0.00014 0.00016 0.02341 0.32921 -0.00010 -0.00023 0.05202<BR> 10 -0.00007 0.00402 -0.27483 0.00005 0.00003 -0.16061 0.18381 0.00007 0.00003 0.12218<BR> 9 0.00041 -0.03763 0.44779 -0.00020 -0.00032 -0.41591 -0.44790 0.00048 0.00074 0.14764<BR> 4 0.00071 -0.04209 0.88917 -0.00024 -0.00048 -0.58577 -0.61318 0.00103 0.00150 0.08858<BR> 87 0.00593 0.37161 -0.23371 -0.00181 0.00144 0.47954 -3.28323 0.00332 0.00223 0.10500<BR> 11 0.00574 0.35809 -0.52843 -0.00188 0.00142 0.63493 -3.48654 0.00311 0.00189 0.06501<BR> 23 0.00559 0.35155 -0.89035 -0.00208 0.00121 0.36382 -4.45687 0.00322 0.00261 -0.05408<BR> 22 0.00520 0.28944 -1.69249 -0.00206 0.00130 0.37403 -4.19117 0.00317 0.00284 -0.08920<BR> 2 0.00529 0.28983 -1.58769 -0.00200 0.00136 0.35750 -4.08387 0.00324 0.00289 -0.07293<BR> 74 0.00575 0.34771 2.29959 -0.00319 0.00058 1.68366 -6.95728 0.00523 0.00418 -0.08702<BR> 69 0.00835 0.58911 2.67044 -0.00254 0.00214 1.81851 -6.35336 0.00342 0.00438 -0.56804<BR> 42* 0.00799 0.58536 2.16541 -0.00260 0.00245 1.97813 -6.02041 0.00381 0.00585 -0.45237 365 f48<BR> 1 -0.21465<BR> 10 0.15662<BR> 9 0.94274<BR> 4 1.90661<BR> 87 3.62779<BR> 11 3.29623<BR> 23 2.99823<BR> 22 3.00011<BR> 2 3.07416<BR> 74 4.37296<BR> 69 3.30860<BR> 42* 4.66277<BR> *PREDICTED AND RESIDUAL VALUES ARE CALCULATED USING THE<BR> COEFFICIENTS BASED ON THESE 12 COMPONENTS, SINCE<BR> THOSE LISTED BELOW DO NOT PASS THE ENTRANCE LIMITS.

TABLE OF OBSERVED,K PREDICTED, AND RESIDUAL VALUES<BR> OBSERVED PREDICTED RESIDUAL RESIDUAL<BR> OBSERVED PREDICTED RESIDUAL RESIDUAL<BR> NO. VALUE VALUE VALUE SQUARE<BR> 1 22.0000 25.0312 -3.0312 9.1883<BR> 2 22.0000 22.5708 -0.5708 0.3258<BR> 3 22.0000 25.6453 -3.6453 13.2882<BR> 4 22.0000 24.5778 -2.5778 6.6453<BR> 5 22.0000 24.1182 -2.1182 4.4870<BR> 6 22.0000 21.7865 0.2135 0.0456<BR> 7 22.0000 27.7999 -5.7999 33.6391<BR> 8 22.0000 25.4260 -3.4260 11.7378<BR> 9 22.0000 24.0348 -2.0348 4.1403<BR> 10 22.0000 26.6073 -4.6073 21.2274<BR> 11 22.0000 23.3843 -1.3843 1.9162<BR> 12 22.0000 26.1209 -4.1209 16.9820<BR> 13 22.0000 25.3890 -3.3890 11.4850<BR> 14 22.0000 23.1710 -1.1710 1.3713<BR> 15 22.0000 20.1868 1.8132 3.2876<BR> 16 24.0000 28.0183 -4.0183 16.1466<BR> 17 24.0000 25.6399 -1.6399 2.6892<BR> 18 24.0000 26.9551 -2.9551 8.7326<BR> 19 24.0000 26.9322 -2.9322 8.5976<BR> 20 24.0000 25.8737 -1.8737 3.5106<BR> 21 24.0000 29.1547 -5.1547 26.5714<BR> 22 24.0000 25.0571 -1.0571 1.1174<BR> 23 24.0000 26.5215 -2.5215 6.3579<BR> 24 24.0000 26.7948 -2.7948 7.8107<BR> 25 24.0000 23.9675 0.0325 0.0011 26 24.0000 24.8588 -0.8588 0.7376<BR> 27 24.0000 23.3131 0.6869 0.4718<BR> 28 24.0000 22.7798 1.2202 1.488<BR> 289 24.0000 28.4038 -4.4038 19.3933<BR> 30 24.0000 26.0751 -2.0751 4.3061<BR> 31 26.0000 25.5088 0.4912 0.2412<BR> 32 26.0000 28.8348 -2.8348 8.0359<BR> 33 26.0000 27.7813 -1.7813 3.1730<BR> 34 26.0000 25.7471 0.2529 0.0640<BR> 35 26.0000 29.9124 -3.9124 15.3070<BR> 36 26.0000 31.0932 -5.0932 25.9411<BR> 37 26.0000 30.9932 -4.9932 24.9319<BR> 38 26.0000 26.3461 -0.2461 0.0606<BR> 39 26.0000 22.9491 3.0509 9.3081<BR> 40 26.0000 27.1361 -1.1361 1.2907<BR> 41 26.0000 28.7800 -2,.7800 7.7285<BR> 42 26.0000 22.0800 3.9200 15.3663<BR> 43 26.0000 25.9019 0.0981 0.0096<BR> 44 26.0000 26.4476 -0.4476 0.2004<BR> 45 28.0000 30.2410 -2.2410 5.0222<BR> 46 28.0000 28.9885 -0.9885 0.9772<BR> 47 28.0000 29.3896 -1.3896 1.9310<BR> 48 28.0000 28.3273 -0.3273 0.1071<BR> 49 28.0000 30.0150 -2.0150 4.0603<BR> 50 28.0000 30.8845 -2.8845 8.3202<BR> 51 28.0000 26.0518 1.9482 3.7957<BR> 52 28.0000 26.9665 1.0335 1.0682<BR> 53 28.0000 28.7850 -0.7850 0.6161<BR> 54 28.0000 29.8529 -1.8529 3.4331<BR> 55 28.0000 29.3206 -1.3206 1.7440<BR> 56 28.0000 27.7923 0.2077 0.0431<BR> 57 28.0000 25.3869 2.6131 6.8283<BR> 58 28.0000 29.7153 -1.71532.9423<BR> 59 28.0000 30.0290 -2.0290 4.1170 60 30.0000 30.1295 -0.1295 0.0168<BR> 61 30.0000 27.7496 2.2504 5.0642<BR> 62 30.0000 29.8108 0.1892 0.0358<BR> 63 30.0000 28.4190 1.5810 2.4997<BR> 64 30.0000 31.6810 -1.6810 2.8258<BR> 65 30.0000 31.9916 -1.9916 3.9665<BR> 66 30.0000 28.8788 1.1212 1.2571<BR> 67 30.0000 28.6669 1.3331 1.7772<BR> 68 30.0000 28.4670 1.5330 2.3501<BR> 69 30.0000 31.7409 -1.7409 3.0308<BR> 70 30.0000 32.0815 -2.0815 4.3328<BR> 71 30.0000 30.0390 -0.0390 0.0015<BR> 72 30.0000 29.9995 0.0005 0.0000<BR> 73 30.0000 24.9125 5.0875 5.8827<BR> 74 30.0000 31.3477 -1.3477 1.8162<BR> 75 32.0000 29.5232 2.4768 6.1345<BR> 76 32.0000 26.5234 5.4766 9.99333<BR> 77 32.0000 33.7329 -1.7329 3.0028<BR> 78 32.0000 33.1167 -1.1167 1.2470<BR> 79 32.0000 29.7598 2.2402 5.0185<BR> 80 32.0000 32.3126 -0.3126 0.0977<BR> 81 32.0000 29.4255 2.5745 6.6283<BR> 82 32.0000 24.5684 7.4316 5.2280<BR> 83 32.0000 31.4822 0.5178 0.2681<BR> 84 32.0000 28.2404 3.7596 14.1343<BR> 85 32.0000 32.9615 -0.9615 0.9244<BR> 86 32.0000 31.2857 0.7143 0.5102<BR> 87 32.0000 28.9930 3.0070 9.0419<BR> 88 32.0000 27.3749 4.6251 21.3920<BR> 89 32.0000 32.9113 -0.9113 0.8305<BR> 90 34.0000 26.3958 7.6042 57.8238<BR> 91 34.0000 31.7524 2.2476 5.0518<BR> 92 34.0000 34.7561 -0.7561 0.5718<BR> 93 34.0000 29.5899 4.4101 19.4486 94 34.0000 32.8228 1.1772 1.3857<BR> 95 34.0000 33.8645 0.1355 0.0184<BR> 96 34.0000 30.3683 3.6317 13.1894<BR> 97 34.0000 31.8744 2.1256 4.5180<BR> 98 34.0000 30.2394 3.7606 14.1422<BR> 99 34.0000 38.4826 -4.4826 20.0935<BR> 100 34.0000 30.3533 3.6467 13.2981<BR> 101 34.0000 31.1756 2.8244 7.9772<BR> 102 34.0000 29.3021 4.6979 22.0701<BR> 103 34.0000 34.9564 -0.9564 0.9148<BR> 104 36.0000 34.7368 1.2632 1.5956<BR> 105 36.0000 32.4015 3.5985 12.9494<BR> 106 36.0000 32.1800 3.8200 14.5927<BR> 107 36.0000 30.5498 5.4502 29.7045<BR> 108 36.0000 35.1182 0.8818 0.7776<BR> 109 36.0000 34.0978 1.9022 3.6183<BR> 110 36.0000 31.5968 4.4032 19.3882<BR> 111 36.0000 33.0073 2.9927 8.9563<BR> 112 36.0000 34.2308 1.7692 3.1301<BR> 113 36.0000 37.2020 -1.2020 1.4447<BR> 114 36.0000 34.7871 1.2129 1.4710<BR> 115 36.0000 37.0750 -1.0750 1.1555<BR> 116 36.000033.9047 2.0953 4.3902<BR> 117 36.0000 31.4249 4.5751 20.9316<BR> 118 36.000036.2707 -0.2707 0.0733