Effect of Feed Cost on the Economic Impact of PRRS

Economic impacts based on PRRS associated losses as reported in a previous study were modeled with varying grain prices. Sensitivity tables show that as grain (feed) prices rise, the economic impact of disease events increases. As corn prices rise from $2.25/bu up to$5.00/bu, there is a $92.6 million increase in the cost of PRRS to US pork producers. Every$0.50/bu increase in corn price costs the pork industry $18.52 million in PRRS associated losses. In the PRRS-affected farm, for every$0.50/bu increase, the cost per litter increases $0.886, the cost per nursery pig increases$0.072/hd and the cost per finisher pig increases $0.405/hd. With corn at$2.50 to $5.00/bu the national impact is estimated at$594.19 to $686.77 million annually, or$5.94 to $6.87/hd marketed in the US. As feed prices rise, the value of improved health care also rises. As costs rise, it is imperative to continue efforts on disease control and prevention

Economic Analysis of Increased Levels of Intramuscular Fat in Pork: Producer and Industry Opportunities

Ultrasound technology is available for accurately measuring intramuscular fat (IMF) in live pigs. This report provides information on the costs for pig producers and processors to implement this technology and what consumers are willing to pay for pork with improved levels of intramuscular fat. About half the participants in the willingness to pay study preferred the high IMF chop. They paid a premium of 25 percent over the low IMF chop.ultrasound technology, measure intramuscular fat live pigs, pig producer cost, pig processor costs, consumer willingness to pay, Agribusiness, Agricultural and Food Policy, Consumer/Household Economics, Demand and Price Analysis, Farm Management, Food Consumption/Nutrition/Food Safety, Livestock Production/Industries, Marketing,

A Systems Biology Approach Identifies a R2R3 MYB Gene Subfamily with Distinct and Overlapping Functions in Regulation of Aliphatic Glucosinolates

BACKGROUND: Glucosinolates are natural metabolites in the order Brassicales that defend plants against both herbivores and pathogens and can attract specialized insects. Knowledge about the genes controlling glucosinolate regulation is limited. Here, we identify three R2R3 MYB transcription factors regulating aliphatic glucosinolate biosynthesis in Arabidopsis by combining several systems biology tools. METHODOLOGY/PRINCIPAL FINDINGS: MYB28 was identified as a candidate regulator of aliphatic glucosinolates based on its co-localization within a genomic region controlling variation both in aliphatic glucosinolate content (metabolite QTL) and in transcript level for genes involved in the biosynthesis of aliphatic glucosinolates (expression QTL), as well as its co-expression with genes in aliphatic glucosinolate biosynthesis. A phylogenetic analysis with the R2R3 motif of MYB28 showed that it and two homologues, MYB29 and MYB76, were members of an Arabidopsis-specific clade that included three characterized regulators of indole glucosinolates. Over-expression of the individual MYB genes showed that they all had the capacity to increase the production of aliphatic glucosinolates in leaves and seeds and induce gene expression of aliphatic biosynthetic genes within leaves. Analysis of leaves and seeds of single knockout mutants showed that mutants of MYB29 and MYB76 have reductions in only short-chained aliphatic glucosinolates whereas a mutant in MYB28 has reductions in both short- and long-chained aliphatic glucosinolates. Furthermore, analysis of a double knockout in MYB28 and MYB29 identified an emergent property of the system since the absence of aliphatic glucosinolates in these plants could not be predicted by the chemotype of the single knockouts. CONCLUSIONS/SIGNIFICANCE: It seems that these cruciferous-specific MYB regulatory genes have evolved both overlapping and specific regulatory capacities. This provides a unique system within which to study the evolution of MYB regulatory factors and their downstream targets

Genetic Classification of Populations using Supervised Learning

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case--control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed \emph{unsupervised}. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available. In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.Comment: Accepted PLOS On