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Title:
A METHOD AND SYSTEM FOR ESTIMATION OF THE BREEDING VALUE OF AN ANIMAL FOR EATING QUALITY AND/OR COMMERCIAL YIELD PREDICTION
Document Type and Number:
WIPO Patent Application WO/2020/229641
Kind Code:
A1
Abstract:
The present invention is directed to a method and system to predict an estimation of the breeding value or genetic merit of an animal based on meat trait parameters such as eating quality and commercial yield and corresponding animal characteristics. A statistical model is generated based on meat trait data of said animals. The statistical model is compensated at least for genetic variance of said animals by using ancestry information of each animal. Further, the breeding value of the animal is estimated based on characteristics/traits of the animal, data in said generated database, the statistical model and the genetic variance. Also, the method and system provide ranking of animals based on an index value, which is in turn a weighted function of the animal traits or characteristics.

Inventors:
BERRY DONAGH (IE)
JUDGE MICHELLE (IE)
PABIOU THIERRY (IE)
CONROY STEPHEN (IE)
EVANS ROSS (IE)
Application Number:
PCT/EP2020/063558
Publication Date:
November 19, 2020
Filing Date:
May 14, 2020
Export Citation:
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Assignee:
AGRICULTURE AND FOOD DEV AUTHORITY TEAGASC (IE)
IRISH CATTLE BREEDING FEDERATION ICBF (IE)
International Classes:
G06Q10/04
Domestic Patent References:
WO2005078133A22005-08-25
WO2005078144A12005-08-25
Foreign References:
US20060053031A12006-03-09
CN108388765A2018-08-10
CN103914631A2014-07-09
CN109524059A2019-03-26
US20170362655A12017-12-21
CN105200148A2015-12-30
CN108388765A2018-08-10
CN103914631A2014-07-09
CN109524059A2019-03-26
Attorney, Agent or Firm:
PURDYLUCEY INTELLECTUAL PROPERTY (IE)
Download PDF:
Claims:
Claims

1. A computer implemented method to predict an estimation of a breeding value of an animal, comprising:

generating an informative database comprising trait parameters and corresponding animal characteristics from a plurality of animals, wherein trait parameters are selected from at least one of meat-eating quality parameter and/or a commercial yield parameter;

generating one or more statistical models based on meat-eating quality parameters and/or commercial yield parameters in said generated database; compensating the generated statistical model at least for genetic variance of animals wherein the genetic variance of the animal comprises ancestry information of each animal; and

outputting a predicted value indicative of the breeding value of the animal based on the characteristics/traits of the animal, data in said generated database, the statistical model and the genetic variance of the animal.

2. The method of claim 1 comprising the step of inputting the informative database to apply breeding values on eating quality traits for an entire national breeding index, for example Irish National Breeding Index.

3. The method of claim 1 or 2 wherein the predicted value is generated relative to a base population of informative ancestors of the animal.

4. The method of any preceding claim, wherein the predicted value is generated relative to a base population of informative ancestors of animal informed genomic sequences verifying the ancestry of the animal. 5. The method of any preceding claim wherein meat-eating quality parameters comprise tenderness, juiciness, chewiness and flavour.

6. The method of any preceding claim wherein the efficiency of the statistical model allows maintenance of the breeding index using a single sensory panellist measurement per animal of interest. 7. The method of any preceding claim wherein commercial yield parameters comprises data from 1 to 100 commercial cuts of meat.

8. The method of any preceding claim wherein the statistical model for determining traits is based on gender of the animal, age of the animal, heterosis, farm date and panel date.

9. The method of any preceding claim, further comprising ranking animals based on an index value wherein index value = wrtraiti + W2-trait2 + W3-trait3 + W4-trait4 + ... + wn traitn where, wi, W2, ... wn are predetermined weights; and

trait-i, trait2, ... traitn are various animal characteristics. 10. The method of claim 9, wherein wi, W2, ... wn is based on desired animal characteristics.

1 1 . A system to predict an estimation of a breeding value of an animal, comprising:

an informative database comprising trait parameters and corresponding animal characteristics from a plurality of animals wherein trait parameters are selected from at least one of a meat-eating quality parameter and/or a commercial yield parameter;

a processor operatively coupled to the database, said processor configured to:

generate a statistical model based on the trait parameters in said generated database; compensate the generated statistical model at least for genetic variance of animals wherein the genetic variance of the animal comprises ancestry information of each animal; and

output a prediction of an estimate of the breeding value of the animal based on characteristics/traits of the animal, data in said generated database, the statistical model and the genetic variance.

12. The system of claim 11 wherein the informative database comprises breeding values on eating quality traits for an entire national breeding index, for example Irish National Breeding Index.

13. The system of claim 11 or 12 wherein the predicted value is generated relative to a base population of informative ancestors of the animal. 14. The system of any of claims 11 to 13, wherein the predicted value is generated relative to a base population of informative ancestors of the animal informed genomic sequences verifying the ancestry of the animal.

15. The system of any of claims 11 to 14 wherein the meat-eating quality parameters comprise tenderness, juiciness, chewiness and flavour.

16. The system of any of claims 11 to 15 wherein commercial yield parameters comprises data from 1 to 100 commercial cuts of meat. 17. The system of any of claims 11 to 16 wherein the statistical model for determining tenderness is based on gender of the animal, age of the animal, heterosis, farm date and panel date.

18. The system of any of claims 11 to 17, wherein the degree of confidence of estimation of the breeding value is generated relative to a base population of informative ancestors of the animal.

Description:
Title

A METHOD AND SYSTEM FOR ESTIMATION OF THE BREEDING VALUE OF AN ANIMAL FOR EATING QUALITY AND/OR COMMERCIAL YIELD

PREDICTION Field

The present disclosure relates to a method and system to predict an estimation of breeding value of an animal. More specifically the present disclosure relates to estimation of breeding value of an animal for meat-eating quality and/or commercial meat yield prediction.

Background

Cattle breeders breed livestock with the objective to maximize profits by supplying livestock in accordance with the needs of the consumers. There exists in the art methods and systems which may predict or select a favourable breed or animals with desirable traits/characteristics.

For example, US Patent Application US2017362655 relate to methods, compositions, and systems are provided for managing bovine subjects in order to maximize their individual potential performance and edible meat value, and to maximize profits obtained in marketing the bovine subjects. The methods and systems draw an inference of a trait of a bovine subject by determining the nucleotide occurrence of at least one bovine Single Nucleotide Polymorphism (SNP) that is identified herein as being associated with the trait. Further, Chinese Patent Application CN105200148 relates to method for auxiliary detection of carcass composition traits of cattle with dual purposes of meat and dairy as well as kits and can apply to early selection of breeding beef cattle for genetic marker screening of carcass traits of the cattle with dual purposes of meat and dairy. Only a small amount of cattle blood or tissue (even at the tails) is collected for genomic DNA (deoxyribonucleic acid) extraction, molecular detection kits are used for detection, SNPs genetic markers of carcass and meat quality traits in beef cattle sample population can be identified after sequencing, haplotype analysis is performed, 6 SNPs loci in a PSAP (prosaposin) gene function action zone and haplotype combinations of the SNPs loci can serve as effective molecular genetic markers for auxiliary detection of the carcass traits of the cattle with the dual purposes of meat and dairy. Primer segments of PSAP genes and the SNPs locus haplotype combinations Hap2 and Hap6 can serve as special kits for detection and prediction of the carcass composition traits of the cattle with the dual purposes of meat and dairy. The method and the kits can be used for early selection of excellent individuals in germplasm resource development of the cattle with the dual purposes of meat and dairy as well as screening and production of fattening cattle before lairage in industrial production. Other patent publications in the art include W02005/078144, CN 108 388 765, CN 103 914 631 and CN 109 524 059.

However, none of the above prior art provides a method or system for selecting or predicting livestock having a desirable meat-eating quality or the optimised yield of selected commercial cuts of meat. While inter-breed differentiation on meat-eating quality is currently used, intra-breed variability in meat-eating quality is not currently considered. Furthermore, accurate assessment of meat- eating quality attributes typically requires experienced sensory scientists and a team of trained sensory panellists. Each animal assessment also requires multiple panellist measurements (6-10) for each quality trait. Consequently, generating databases of animal or genetic associated eating quality sensory traits is both costly and time consuming. Furthermore, to maintain accurate estimates of genetic merit, in for instance a National Breeding Index, continued and long term sensory measurements are required. The efficiency and minimum number of measurements from which a breeding index can be maintained is of critical importance.

Yield prediction systems rely on carcass conformation and fat cover, rib-eye area measurements or individual back fat measurements. Recent data from Ireland suggests that, depending on the cut in question, 15-75% of the variability in carcass cut yields are due to genetic effects. It is technically challenging to provide an accurate estimation of breeding for multiple traits as these traits can be antagonistic.

Therefore, there is an unresolved and unfulfilled need for an estimation of breeding value of an animal having traits such as meat-eating quality and/or optimal commercial yield.

Summary

Embodiments of the present invention are directed to a method and system for predicting an estimation of breeding value or genetic merit of an animal, as set out in the appended claims, based on meat trait parameters such as eating quality, commercial yield and corresponding animal characteristics.

In one embodiment the invention provides a computer implemented method to predict an estimation of a breeding value or genetic merit of an animal, comprising:

generating an informative database comprising trait parameters and corresponding animal characteristics, wherein trait parameters are selected from at least one of meat-eating quality parameter and/or a commercial yield parameter;

generating one or more statistical models based on meat-eating quality parameters and/or commercial yield parameters in said generated database; compensating the generated statistical model at least for genetic variance of animals wherein the genetic variance of the animal comprises ancestry information of each animal; and

outputting a predicted value indicative of the breeding value or genetic merit of the animal based on the characteristics/traits of the animal, data in said generated database, the statistical model and the genetic variance. By configuring the generated model to take account of the ancestry information of each animal allows for a more computationally accurate model to be generated to enable an accurate prediction of the breeding value of an animal. Heretofore no system has been developed to accurately predict the estimated breeding value of an animal. The invention achieves this by compensating the values to take account of the ancestry data to provide a more accurate prediction. By employing an additive genetic variance and A as a numerator relationship matrix allows for the efficient identifying of particular traits associated with a particular animal in a simple and effective way. The matrix generation enables a much simpler solution to output an accurate predictor of the breeding value or genetic merit of an animal compared to other systems and methodologies. In addition the generated model normalizes all the non-genetic effects leaving the genetic component of the trait as the only variance.

There are a number of advantages of predicting accurate estimates of individual animal genetic merit for meat-eating quality and commercial yield, or traits, according to the invention:

1. Enabling year-on-year improvement in the traits of a herd population through the identification of genetically elite candidate parents of the next generation.

2. Quantifying the deviation in animals in the traits at a herd level thus facilitating the identification of herds that are achieving above or below their genetic potential and tailoring bespoke advice accordingly

3. Aid in the decision-making process for the procurement, sorting for preferred markets or customers (including any possible perimortem intervention to achieve a better sensory experience of meat) and marketing of meat (especially meat with a superior genetic potential for quality).

4. Estimates of individual animal genetic merit for meat quality for any downstream genomic analysis

5. To aid breeding-related entities (i.e. , breeders, breed societies, Ai companies) to make more informed decisions on germplasm (i.e. semen or embryos).

6. To aid the processing companies identify the characteristics of the supply pipelines to meet customer expectations. Moreover the statistical model can be further configured to take account of permanent environmental effects that have an influence on the accuracy of the predicted value. In one embodiment the method begins with generating a database comprising meat-eating quality parameters and corresponding animal characteristics, where meat-eating quality parameters comprise tenderness, chewiness, juiciness and flavour. These quality parameters are chosen to be non- antagonistic to carcass traits such as confirmation and fat class within the Irish Bovine herd.

In one embodiment there is provided the step of inputting the informative database to apply breeding values on eating quality traits for an entire national breeding index, for example Irish National Breeding Index.

Another database containing yield cut data for specific commercial cuts can be used to provide the necessary models allowing for the generation of real-time yield data. A statistical model is generated based on trait data in said generated database. For example, the trait can be at least one of meat-eating quality parameter and/or a commercial yield parameter. Further, the statistical model is compensated at least for genetic variance of said animals and/or nuisance effects (e.g. environmental). Finally, a predicted estimate of the breeding value or genetic merit of the animal is outputted based on characteristics/traits of the animal, data in said generated database, the statistical model and the genetic variance.

The system for estimation of breeding value of an animal comprises a database, where the data base comprises one or more of carcass traits, animal trait parameters and corresponding animal characteristics. The system further comprises a processor operatively coupled to the database. The processor is configured to generate a statistical model based on the traits in said generated database and compensate the generated statistical model at least for genetic variance of animals wherein the genetic variance of the animal comprises ancestry information of one or more animals. Further, the processor is configured to estimate, the Estimated Breeding Value (EBV) of the animal based on characteristics/traits of the animal, data in said generated database, the statistical model and the genetic variance.

Further, the system provides ranking of animals based on an index value which is in turn a weighted function of the animal traits or characteristics. Also, using the system a consumer or breeder may choose an animal with an appropriate index value having the balance of desired traits or characteristics.

Thereby, the trait of an animal may be predicted based on the method and system above and a breeder or a consumer may choose to breed or consume the desired animal with the desired meat-eating quality or optimal commercial yield.

In one embodiment the degree of confidence of estimation of the breeding value is generated relative to a base population of informative ancestors of the animal informed genomic sequences verifying the ancestry of the animal. In one embodiment there is provided a computer program product stored in a non-transitory storage medium, said storage medium operatively coupled to a processor and said computer program product causing the processor to carry out the method of any of claims described herein. Brief Description of the Drawings

The invention will be more clearly understood from the following description of an embodiment thereof, given by way of example only, with reference to the accompanying drawings, in which:- FIG. 1 exemplarily illustrates a method for estimation of breeding value of an animal; FIG. 2 is a functional block diagram illustrating the primary components of an apparatus for estimation of breeding value of an animal; and

FIG. 3 is a flow chart illustrating an exemplary embodiment according to one aspect of the invention.

Detailed Description of the Drawings

FIG. 1 exemplarily illustrates a method for estimation of breeding value of an animal. The method begins with generating 101 a database comprising one or more traits such as meat-eating quality parameters and commercial yield parameters. For example, each entry in the database of animals is rated for their meat tenderness, chewiness, juiciness and flavour.

A statistical model is individually generated 102 based on meat traits in said generated database. For example, a statistical model for determining the trait accounts for the influence of gender of the animal, age of the animal, heterosis, farm date and panel data.

Further, the statistical model is compensated 103 for genetic variance of the animals. The genetic variance of the animal comprises ancestry information of each animal. By employing an additive genetic variance and A as a numerator relationship matrix allows for the efficient identifying of particular traits associated with a particular animal. The matrix generation enables a much simpler solution to output an accurate predictor of the breeding value or genetic merit of an animal. Furthermore, the statistical model may be compensated for other genetic or environmental aspects, which affect the trait of the animal.

Finally, the breeding value of the animal is estimated 104 based on characteristics/traits of the animal, data in said generated database, the statistical model and the genetic variance. Further, the degree of confidence of estimation of the breeding value is generated relative to a base population of informative ancestors of the animal. Further, animals may be ranked based on an index value, which is in turn a weighted function of the cumulative animal traits or characteristics. index value = wrtraiti + W2-trait2 + W3-trait3 + W4-trait4 + ... + w n trait n where, wi, W2, ... w n are predetermined weights; and

trait-i, trait2, ... trait n are various animal characteristics

For example, the first trait may be feed conversion and the second trait may be eating quality the third trait may be yield and the above traits may be weighted for a ranking based on the desired eating quality outcome. In other words, if eating quality is the desired eating quality more weight is attached to the eating quality trait in comparison to other traits such that the animals may be ranked in accordance with the eating quality as desired.

Based on the predicted estimate breeding value or genetic merit of the animal a consumer or breeder is thereby enabled to choose an animal with an appropriate index value having the desired traits or characteristics. FIG. 2 is a functional block diagram illustrating the primary components of an apparatus for estimation of breeding value of an animal. The system for estimation of breeding value of an animal comprises an informative database 201 comprising meat traits and corresponding animal characteristics where meat traits comprise traits such as eating quality and commercial yield. The informative database can be built using information relating to a herd, for example a min 10 animals from the same farm, same date, same gender criteria etc.

The system further comprises a processor 202 operatively coupled to the database 201. The processor 202 is configured to generate a statistical model based on a combination of trait data in said generated database 201 and compensate the generated statistical model at least for genetic variance of animals. For example, a statistical model for determining eating quality is based on gender of the animal, age of the animal, heterosis, farm date and panel date. Further, the statistical model may be compensated for genetic or environmental aspects, which affect the meat-eating quality of the animal.

Further, the processor 202 is configured to estimate, breeding value of the animal based on characteristics/traits of the animal, data in said generated database, the statistical model and the genetic variance. Further, the degree of confidence of estimation of the breeding value is generated, by the processor 202, relative to a base population of informative ancestors of the animal.

Further, the processor 202 is configured for providing ranking of animals based on an index value which is in turn a weighted function of the animal traits or characteristics. index value = wrtraiti + W2-trait2 + W3-trait3 + W4-trait4 + ... + w n trait n where, wi, W2, ... w n are predetermined weights; and

trait-i, trait2, ... trait n are various animal characteristics.

For example, the first trait may be feed conversion and the second trait may be eating quality a third trait may be commercial yield and the above traits may be weighted for a ranking based on the desired trait. In other words, if eating quality is the desired trait more weight is attached to the eating quality trait in comparison to other traits such that the animals may be ranked in accordance with the eating quality as desired.

Thereby, the meat-eating quality of an animal may be predicted based on the method and system above and a breeder or a consumer may choose to breed or consume the desired animal with the desired meat-eating quality.

In an embodiment, a memory 203 may be operably coupled to the processor 202. The memory 203 stores a computer program product, which causes the processor to carry out the above recited method steps or the functions of the system disclosed above.

Example Embodiment

FIG. 3 is a flow chart illustrating an exemplary embodiment according to one aspect of the invention. In the example shown a predictive model employed for Bovine Animal Ranking based on desired traits such as eating quality or commercial yield. It will be appreciated that other animals such as sheep, chickens, pigs and the like can be estimated in accordance with the invention.

The starting point as shown in FIG. 3 for eating quality is inputting an existing representative dataset from a trained sensory panel, comprising a number of people (preferably six or more) and eating quality assessments from animals identified and sourced to reflect the modern-day germplasm used on a typical farm. The animal’s pedigree is confirmed from a captured genomic sequence, as well as any nuisance effects. An updated and expanded database, where the Trait(s) reflecting meat-eating quality, is defined and stored. The traits are defined as follows: Eating Quality; Tenderness; Juiciness, Chewiness and/or Beef Flavour.

In an alternative embodiment the starting point as shown in FIG. 3 for eating quality is inputting an existing representative dataset of a trained sensory panel consisting of one panellist and eating quality assessments from animals identified and sourced to reflect the modern-day germplasm used on a typical farm. The animal’s pedigree is confirmed from a captured genomic sequence as well as any nuisance effects. An updated and expanded database, where the Trait(s) reflecting meat-eating quality are defined. The traits are defined as follows: Eating Quality; Tenderness; Juiciness, chewiness and/or Meat flavour.

The starting point as shown in FIG. 3 for commercial yield prediction is inputting existing commercial yield data from processed animals in either untrimmed greenweight form or trimmed to a commercial specification, identified and sourced to reflect the modern-day germplasm used on a typical farm. An updated and expanded database, where the Trait(s) reflecting meat-eating quality, can be defined and stored. The traits can be defined as follows: a range from 1 to 100 commercial cuts to a defined commercial specification.

A parsimonious and pertinent statistical model is developed based on the dataset, for example Tender = gender*month_of_age + heterosis + farm*date + panel*date. Unique coefficients representing a herd of cattle, for example an Irish herd, can be generated and are used in the statistical model. The contribution of genetic differences to the observed variability (i.e. , heritability) is quantified in the sample population. The data, trait definition, statistical model and estimated variance components are used to estimate measures of genetic merit for individuals and the relatives using of the animal. The estimated genetic merit (estimated breeding value) as well as the estimated degree of confidence in these predictions (i.e. reliability) are generated and re-based to be relative to a base population of informative ancestors. The ancestor information relating to an animal can be used as a genetic variance model configured to compensate the generated statistical model. The genetic variance can be gathered information relating to the animals parents, grandparents, siblings or relations and used to compensate the statistical model to provide a more accurate predicted value of the estimate breeding value of an animal, as set out below in more detail.

In order to rank animals, a weight (i.e. monetary or otherwise) is assigned to the eating quality traits. The structure of an overall ranking index is developed as: o Index value = wrtraiti + W2-trait2 + W3-trait3 + W4-trait4 + ... . + Wn traitn

where w x is the weighting factor on trait x

An important aspect to the invention is the ability to build the most pertinent and parsimonious statistical model to allow for generation of both the estimates of the necessary contribution of the random effects to the underlying variability but also the coefficients on each of the fixed and random effects. This requires not only the large unique dataset globally on sensory information coupled with information on the likely contributing features, but also in-depth knowledge and experience on the effects (and, where relevant their interactions) that associate with the different sensory traits. A similar approach can be adopted for yield where a large commercial yield dataset is required to develop the yield statistical model

An important technical advancement is the generation of genomic evaluations for each sensory trait using a highly informative genotype panel bespoke to the cattle herd in Ireland which includes DNA variants of particular informativeness to Ireland including all known DNA variants in genes for the purpose of verifying animal parentage or ancestry thereby improving the accuracy of the prediction. The genomic evaluations for one or more sensory traits can be used when calculating the additive genetic variance below.

The statistical model was uniquely developed and can be tailored to represent a national herd, for example a model of the Irish national herd as described herein. The methodology requires quality control measures on both the statistical model terms but also, using the parentage of each animal and genomic information. This therefore requires a combination of both skillsets and data. A statistical model fitted, according to a exemplary embodiment of the invention, is: Y = HSD + DL + GENDER + a + e where Y is the dependent variable of tenderness, flavour, juiciness or chewiness,

HSD is the fixed effect of herd-by-date of slaughter,

DL is the fixed effect of date-by-location of sensory analysis, The DL can be the herd number and the date of slaughter joined together so the model knows that all animals slaughtered from that farm on that day can be treated the same, GENDER is the gender of the animal (i.e. , bull, steer or heifer), a is the additive genetic effect (N(0,Aaa2)

where aa2 is the additive genetic variance and A is the numerator relationship matrix, and

e represents the residual term

where N(0,lae2) with ae2 representing the residual variance and I an identity matrix.

A key differentiator is that the inclusion the additive genetic variance (aa2) having ancestry information and A as the numerator relationship matrix allows for a more accurate prediction of an estimation of a breeding value or genetic merit of an animal. The predicted breeding value or genetic merit of the animal enables a consumer or breeder is thereby to choose an animal with an appropriate index value having the desired traits or characteristics.

It will be appreciated that the term e to factor in the residual variance can be optionally included to further refine the accuracy when outputting the predicted estimate of breeding value. In addition a repeatability model can also be used where a permanent environmental effect is included as a random effect where N(0,l sRE2) with sRE2 being the permanent environmental variance and I the identity matrix.

While genetic evaluations and associated reliability measures were generated, this was converted mathematically to estimates of the likely true sensory value with a predicted probability for each animal of surpassing a minimum threshold on the sensory scale. A novel approach to the validation of the genetic evaluation, is whereby rather than simply choosing a validation population of the youngest animals or using a statistical technique like k-means, only animals with the same mean, median and model sensory value can be considered.

In operation, ear tag numbers of animals are entered into a system, the animals may exist in a feedlot or lairage or may even have just been slaughtered. The system confirms, through a database, that animal identification is correct and fetches the pre-generated measures of genetic merit, as well as ancillary information (e.g. gender, number of movements), for that animal. Animals are then ranked in accordance with the above methodology. The benefit of the invention is to facilitate differentiation of animals genetically divergent for a range of traits combined or individually.

Further, a person ordinarily skilled in the art will appreciate that the various illustrative logical/functional blocks, modules, circuits, and process steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or a combination of hardware and software. To clearly illustrate this interchangeability of hardware and a combination of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or a combination of hardware and software depends upon the design choice of a person ordinarily skilled in the art. Such skilled artisans may implement the described functionality in varying ways for each particular application, but such obvious design choices should not be interpreted as causing a departure from the scope of the present invention.

The process described in the present disclosure may be implemented using various means. For example, the apparatus described in the present disclosure may be implemented in hardware, firmware, software, or any combination thereof. For a hardware implementation, the processing units, or processors(s) or controller(s) may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, or a combination thereof.

For a firmware and/or software implementation, software codes may be stored in a memory and executed by a processor. Memory may be implemented within the processor unit or external to the processor unit. As used herein the term “memory” refers to any type of volatile memory or nonvolatile memory.

In the specification the terms "comprise, comprises, comprised and comprising" or any variation thereof and the terms include, includes, included and including" or any variation thereof are considered to be totally interchangeable and they should all be afforded the widest possible interpretation and vice versa.

The invention is not limited to the embodiments hereinbefore described but may be varied in both construction and detail.