16 research outputs found

    Epidemiology of Exertional Rhabdomyolysis Susceptibility in Standardbred Horses Reveals Associated Risk Factors and Underlying Enhanced Performance

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    BACKGROUND: Exertional rhabdomyolysis syndrome is recognised in many athletic horse breeds and in recent years specific forms of the syndrome have been identified. However, although Standardbred horses are used worldwide for racing, there is a paucity of information about the epidemiological and performance-related aspects of the syndrome in this breed. The objectives of this study therefore were to determine the incidence, risk factors and performance effects of exertional rhabdomyolysis syndrome in Standardbred trotters and to compare the epidemiology and genetics of the syndrome with that in other breeds. METHODOLOGY/PRINCIPAL FINDINGS: A questionnaire-based case-control study (with analysis of online race records) was conducted following identification of horses that were determined susceptible to exertional rhabdomyolysis (based on serum biochemistry) from a total of 683 horses in 22 yards. Thirty six exertional rhabdomyolysis-susceptible horses were subsequently genotyped for the skeletal muscle glycogen synthase (GYS1) mutation responsible for type 1 polysaccharide storage myopathy. A total of 44 susceptible horses was reported, resulting in an annual incidence of 6.4 (95% CI 4.6-8.2%) per 100 horses. Female horses were at significantly greater risk than males (odds ratio 7.1; 95% CI 2.1-23.4; p = 0.001) and nervous horses were at a greater risk than horses with calm or average temperaments (odds ratio 7.9; 95% CI 2.3-27.0; p = 0.001). Rhabdomyolysis-susceptible cases performed better from standstill starts (p = 0.04) than controls and had a higher percentage of wins (p = 0.006). All exertional rhabdomyolysis-susceptible horses tested were negative for the R309H GYS1 mutation. CONCLUSIONS/SIGNIFICANCE: Exertional rhabdomyolysis syndrome in Standardbred horses has a similar incidence and risk factors to the syndrome in Thoroughbred horses. If the disorder has a genetic basis in Standardbreds, improved performance in susceptible animals may be responsible for maintenance of the disorder in the population

    Agribusiness Sheep Updates - 2004 part 2

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    Precision Pastures Using Species Diversity to Improve Pasture Performance Anyou Liu and Clinton Revell, Department of Agriculture, Western Australia New Annual Pasture Legumes for Sheep Graziers Phil Nichols, Angelo Loi, Brad Nutt and Darryl McClements Department of Agriculture Western Australia Pastures from Space – Can Satellite Estimates of Pasture Growth Rate be used to Increase Farm Profit? Lucy Anderton, Stephen Gherardi and Chris Oldham Department of Agriculture Western Australia Summer-active Perennial Grasses for Profitable Sheep Production Paul Sanford and John Gladman, Department of Agriculture, Western Australia Pastures From Space – Validation Of Predictions Of Pasture Growth Rates DONALD, G.E.A, EDIRISINGHE, A.A, HENRY, D.A.A, MATA, G.A, GHERARDI, S.G.B, OLDHAM, C.M.B, GITTINS, S.P.B AND SMITH, R. C. G.C ACSIRO, Livestock Industries, PMB 5, Wembley, WA, 6913. BDepartment of Agriculture Western Australia, Bentley, WA, 6983. C Department of Land Information Western Australia, Floreat, WA, 6214. Production and Management of Biserrula Pasture - Managing the Risk of Photosensitivity Dr Clinton Revell and Roy Butler, Department of Agriculture Western Australia Meat Quality of Sheep Grazed on a Saltbush-based Pasture Kelly Pearce1,2, David Masters1, David Pethick2, 1 CSIRO LIVESTOCK INDUSTRIES, WEMBLEY, WA 2 SCHOOL OF VETERINARY AND BIOMEDICAL SCIENCE, MURDOCH UNIVERSITY, MURDOCH, WA Precision Sheep Lifetime Wool – Carryover Effects on Subsequent Reproduction of the Ewe Flock Chris Oldham, Department of Agriculture Western Australia Andrew Thompson, Primary Industries Research Victoria (PIRVic), Dept of Primary Industries, Hamilton, Vic Ewe Productivity Trials - a Linked Analysis Ken Hart, Johan Greeff, Department of Agriculture Western Australia, Beth Paganoni, School of Animal Biology, Faculty of Natural and Agricultural Sciences, University of Western Australia. Grain Finishing Systems For Prime Lambs Rachel Kirby, Matt Ryan, Kira Buttler, Department of Agriculture, Western Australia The Effects of Nutrition and Genotype on the Growth and Development, Muscle Biochemistry and Consumer Response to Lamb Meat David Pethick, Department of Veterinary Science, Murdoch University, WA, Roger Heggarty and David Hopkins, New South Wales Agriculture ‘Lifetime Wool’ - Effects of Nutrition During Pregnancy and Lactation on Mortality of Progeny to Hogget Shearing Samantha Giles, Beth Paganoni and Tom Plaisted, Department of Agriculture Western Australia, Mark Ferguson and Darren Gordon, Primary Industries Research Victoria (PIRVic), Dept of Primary Industries, Hamilton, Vic Lifetime Wool - Target Liveweights for the Ewe Flock J. Young, Farming Systems Analysis Service, Kojonup, C. Oldham, Department of Agriculture Western Australia, A. Thompson, Primary Industries Research Victoria (PIRVic), Hamilton, VIC Lifetime Wool - Effects of Nutrition During Pregnancy and Lactation on the Growth and Wool Production of their Progeny at Hogget Shearing B. Paganoni, University of Western Australia, Nedlands WA, C. Oldham, Department of Agriculture Western Australia, M. Ferguson, A. Thompson, Primary Industries Research Victoria (PIRVic), Hamilton, VIC RFID Technology – Esperance Experiences Sandra Brown, Department of Agriculture Western Australia The Role of Radio Frequency Identification (RFID) Technology in Prime Lamb Production - a Case Study. Ian McFarland, Department of Agriculture, Western Australia. John Archer, Producer, Narrogin, Western Australia Win with Twins from Merinos John Milton, Rob Davidson, Graeme Martin and David Lindsay The University of Western Australia Precision Sheep Need Precision Wool Harvesters Jonathan England, Castle Carrock Merinos, Kingston SE, South Australia Business EBVs and Indexes – Genetic Tools for your Toolbox Sandra Brown, Department of Agriculture Western Australia Green Feed Budget Paddock Calculator Mandy Curnow, Department of Agriculture Western Australia Minimising the Impact of Drought - Evaluating Flock Recovery Options using the ImPack Model Karina P. Wood, Ashley K. White, B. Lloyd Davies, Paul M. Carberry, NSW Department of Primary Industries (NSW DPI), Lifetime Wool - Modifying GrazFeed® for WA Mike Hyder, Department of Agriculture Western Australia , Mike Freer, CSIRO Plant Industry, Canberra, A.C.T. , Andrew van Burgel, and Kazue Tanaka, Department of Agriculture Western Australia Profile Calculator – A Way to Manage Fibre Diameter Throughout the Year to Maximise Returns Andrew Peterson, Department of Agriculture, Western Australia Pasture Watch - a Farmer Friendly Tool for Downloading and Analysing Pastures from Space Data Roger Wiese,Fairport Technologies International, South Perth, WA, Stephen Gherardi, BDepartment of Agriculture Western Australia, Gonzalo Mata, CCSIRO, Livestock Industries, Wembley, Western Australia, and Chris Oldham, Department of Agriculture Western Australia Sy Sheep Cropping Systems An Analysis of a Cropping System Containing Sheep in a Low Rainfall Livestock System. Evan Burt, Amanda Miller, Anne Bennett, Department of Agriculture, Western Australia Lucerne-based Pasture for the Central Wheatbelt – is it Good Economics? Felicity FluggeA, Amir AbadiA,B and Perry DollingA,B,A CRC for Plant-based Management of Dryland Salinity: BDept. of Agriculture, WA Sheep and Biserrula can Control Annual Ryegrass Dean Thomas, John Milton, Mike Ewing and David Lindsay, The University of WA, Clinton Revell, Department of Agriculture, Western Australia Sustainable Management Pasture Utilisation, Fleece Weight and Weaning Rate are Integral to the Profitability of Dohnes and SAMMs. Emma Kopke,Department of Agriculture Western Australia, John Young, Farming Systems Analysis Service Environmental Impact of Sheep Confinement Feeding Systems E A Dowling and E K Crossley, Department of Agriculture, Western Australia Smart Grazing Management for Production and Environmental Outcomes Dr Brien E (Ben) Norton, Centre for the Management of Arid Environments, Curtin University of Technology, WA Common Causes of Plant Poisoning in the Eastern Wheatbelt of Western Australia. Roy Butler, Department of Agriculture, Western Australia Selecting Sheep for Resistance to Worms and Production Trait Responses John Karlsson, Johan Greeff, Department of Agriculture, Western Australia, Geoff Pollott, Imperial College, London UK Production and Water Use of Lucerne and French Serradella in Four Soil Types, Diana Fedorenko1,4, Darryl McClements2,4 and Robert Beard3,4, 12Department of Agriculture, Western Australia; 3Farmer, Meckering; 4CRC for Plant-based Management of Dryland Salinity. Worm Burdens in Sheep at Slaughter Brown Besier, Department of Agriculture Western Australia, Una Ryan, Caroline Bath, Murdoch Universit

    Genome-wide association for milk production and lactation curve parameters in Holstein dairy cows

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    The aim of this study was to identify genomic regions associated with 305-day milk yield and lactation curve parameters on primiparous (n = 9,910) and multiparous (n = 11,158) Holstein cows. The SNP solutions were estimated using a weighted single-step genomic BLUP approach and imputed high-density panel (777k) genotypes. The proportion of genetic variance explained by windows of 50 consecutive SNP (with an average of 165 Kb) was calculated, and regions that accounted for more than 0.50% of the variance were used to search for candidate genes. Estimated heritabilities were 0.37, 0.34, 0.17, 0.12, 0.30 and 0.19, respectively, for 305-day milk yield, peak yield, peak time, ramp, scale and decay for primiparous cows. Genetic correlations of 305-day milk yield with peak yield, peak time, ramp, scale and decay in primiparous cows were 0.99, 0.63, 0.20, 0.97 and -0.52, respectively. The results identified three windows on BTA14 associated with 305-day milk yield and the parameters of lactation curve in primi- and multiparous cows. Previously proposed candidate genes for milk yield supported by this work include GRINA, CYHR1, FOXH1, TONSL, PPP1R16A, ARHGAP39, MAF1, OPLAH and MROH1, whereas newly identified candidate genes are MIR2308, ZNF7, ZNF34, SLURP1, MAFA and KIFC2 (BTA14). The protein lipidation biological process term, which plays a key role in controlling protein localization and function, was identified as the most important term enriched by the identified genes

    Expert-based development of a generic HACCP-based risk management system to prevent critical negative energy balance in dairy herds

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    The objective of this study was to develop a generic risk management system based on the Hazard Analysis and Critical Control Point (HACCP) principles for the prevention of critical negative energy balance (NEB) in dairy herds using an expert panel approach. In addition, we discuss the advantages and limitations of the system in terms of implementation in the individual dairy herd. For the expert panel, we invited 30 researchers and advisors with expertise in the field of dairy cow feeding and/or health management from eight European regions. They were invited to a Delphi-based set-up that included three inter-correlated questionnaires in which they were asked to suggest risk factors for critical NEB and to score these based on 'effect' and 'probability'. Finally, the experts were asked to suggest critical control points (CCPs) specified by alarm values, monitoring frequency and corrective actions related to the most relevant risk factors in an operational farm setting. A total of 12 experts (40 %) completed all three questionnaires. Of these 12 experts, seven were researchers and five were advisors and in total they represented seven out of the eight European regions addressed in the questionnaire study. When asking for suggestions on risk factors and CCPs, these were formulated as 'open questions', and the experts' suggestions were numerous and overlapping. The suggestions were merged via a process of linguistic editing in order to eliminate doublets. The editing process revealed that the experts provided a total of 34 CCPs for the 11 risk factors they scored as most important. The consensus among experts was relatively high when scoring the most important risk factors, while there were more diverse suggestions of CCPs with specification of alarm values and corrective actions. We therefore concluded that the expert panel approach only partly succeeded in developing a generic HACCP for critical NEB in dairy cows. We recommend that the output of this paper is used to inform key areas for implementation on the individual dairy farm by local farm teams including farmers and their advisors, who together can conduct herd-specific risk factor profiling, organise the ongoing monitoring of herd-specific CCPs, as well as implement corrective actions when CCP alarm values are exceeded

    Analysing genetic variation in farm animals

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    In this chapter, topics focused on how to quantify the extent to which genes affect measured traits and how to use this information in breeding programmes. Highlights include: estimating heritability; estimating non-additive parameters, correlations, and genotype by environment interactions, molecular genetics and trait variations; and calculating inbreeding using SNP markers

    Genetic improvement of farmed animals

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    Genetic Improvement of Farmed Animals provides a thorough grounding in the basic sciences underpinning farmed animal breeding. Relating science to practical application, it covers all the major farmed animal species: cattle, sheep, goats, poultry, pigs and aquaculture species. The book: - Provides comprehensive coverage - from fundamental genetics, analysis of variation, prediction of breeding values and response to selection, through to application of modern genomics and biotechnology. - Illustrates the practical application of theory in temperate and tropical systems. - Outlines current practice and explores future directions, including sustainability and ethical implications, to leave readers completely up to date. Based on the previous bestseller, Genetic Improvement of Cattle and Sheep, this book has been completely revised, expanded and redesigned to be an essential textbook for undergraduate, masters and other early postgraduate-level students in agriculture, animal and veterinary science, as well as breeders, farmers, industry technical staff, advisors and extension workers

    A Comparison of Different Methodologies for the Measurement of Extracellular Vesicles and Milk-derived Particles in Raw Milk from Cows

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    Cow's milk is economically important to the agricultural industry with the nutritive value of milk being routinely measured. This does not give full insight into normal mammary tissue turnover during the course of lactation, which could be important for both an understanding of milk production and animal welfare. We have previously demonstrated that submicron particles, including extracellular vesicles (EVs), can be measured in unprocessed cow's milk by flow cytometry and that they correlate with stage of lactation. A number of different techniques are available to measure EVs and other milk-derived particles. The purpose of this study was to compare two different methodologies and the value of fluorescent staining for the phospholipid phosphatidylserine (PS), which is exposed on the surface of EVs (but not other milk-derived particles). We used two different flow cytometers and nanotracker analysis to detect milk-derived particles in whole and skimmed milk samples. Our findings indicate significant correlation, after staining for PS, suggesting potential for larger multicenter studies in the future

    Results of multivariable conditional logistic regression analysis of variables associated with exertional rhabdomyolysis syndrome in Swedish Standardbred trotters.

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    <p>Models were derived using a stepwise forward selection approach with variables retained in the model if they were significantly associated with ERS (P<sub>Wald</sub> <0.05) and/or improved model fit (P<sub>LRS</sub> <0.05). Pwald =  Wald test P-value; P<sub>LRS</sub>  =  Likelihood Ratio Statistic P-value.</p
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