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Daniel Gianola - One of the best experts on this subject based on the ideXlab platform.
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assessment of poisson logit and linear models for genetic analysis of clinical Mastitis in norwegian red cows
Journal of Dairy Science, 2009Co-Authors: Ana I Vazquez, Douglas M Bates, K A Weigel, Daniel Gianola, Bjørg HeringstadAbstract:Abstract Clinical Mastitis is typically coded as presence/absence during some period of exposure, and records are analyzed with linear or binary data models. Because presence includes cows with multiple episodes, there is loss of information when a count is treated as a binary response. The Poisson model is designed for counting random variables, and although it is used extensively in epidemiology of Mastitis, it has rarely been used for studying the genetics of Mastitis. Many models have been proposed for genetic analysis of Mastitis, but they have not been formally compared. The main goal of this study was to compare linear (Gaussian), Bernoulli (with logit link), and Poisson models for the purpose of genetic evaluation of sires for Mastitis in dairy cattle. The response variables were clinical Mastitis (CM; 0, 1) and number of CM cases (NCM; 0, 1, 2, …). Data consisted of records on 36,178 first-lactation daughters of 245 Norwegian Red sires distributed over 5,286 herds. Predictive ability of models was assessed via a 3-fold cross-validation using mean squared error of prediction (MSEP) as the end-point. Between-sire variance estimates for NCM were 0.065 in Poisson and 0.007 in the linear model. For CM the between-sire variance was 0.093 in logit and 0.003 in the linear model. The ratio between herd and sire variances for the models with NCM response was 4.6 and 3.5 for Poisson and linear, respectively, and for model for CM was 3.7 in both logit and linear models. The MSEP for all cows was similar. However, within healthy animals, MSEP was 0.085 (Poisson), 0.090 (linear for NCM), 0.053 (logit), and 0.056 (linear for CM). For mastitic animals the MSEP values were 1.206 (Poisson), 1.185 (linear for NCM response), 1.333 (logit), and 1.319 (linear for CM response). The models for count variables had a better performance when predicting diseased animals and also had a similar performance between them. Logit and linear models for CM had better predictive ability for healthy cows and had a similar performance between them.
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genetic associations between clinical Mastitis and somatic cell score in early first lactation cows
Journal of Dairy Science, 2006Co-Authors: Bjørg Heringstad, Daniel Gianola, Y M Chang, Jorgen Odegard, G KlemetsdalAbstract:The objectives of this study were to examine genetic associations between clinical Mastitis and somatic cell score (SCS) in early first-lactation cows, to estimate genetic correlations between SCS of cows with and without clinical Mastitis, and to compare genetic evaluations of sires based on SCS or clinical Mastitis. Clinical Mastitis records from 15 d before to 30 d after calving and first test-day SCS records (from 6 to 30 d after calving) from 499,878 first-lactation daughters of 2,043 sires were analyzed. Results from a bivariate linear sire model analysis of SCS in cows with and without clinical Mastitis suggest that SCS is a heterogeneous trait. Heritability of SCS was 0.03 for mastitic cows and 0.08 for healthy cows, and the genetic correlation between the 2 traits was 0.78. The difference in rank between sire evaluations based on SCS of cows with and without clinical Mastitis varied from -994 to 1,125, with mean 0. A bivariate analysis with a threshold-liability model for clinical Mastitis and a linear Gaussian model for SCS indicated that heritability of liability to clinical Mastitis is at least as large as that of SCS in early lactation. The mean (standard deviation) of the posterior distribution of heritability was 0.085 (0.006) for liability to clinical Mastitis and 0.070 (0.003) for SCS. The posterior mean (standard deviation) of the genetic correlation between liability to clinical Mastitis and SCS was 0.62 (0.03). A comparison of sire evaluations showed that genetic evaluation based on SCS was not able to identify the best sires for liability to clinical Mastitis. The association between sire posterior means for liability to clinical Mastitis and sire predicted transmitting ability for SCS was far from perfect.
Bjørg Heringstad - One of the best experts on this subject based on the ideXlab platform.
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assessment of poisson logit and linear models for genetic analysis of clinical Mastitis in norwegian red cows
Journal of Dairy Science, 2009Co-Authors: Ana I Vazquez, Douglas M Bates, K A Weigel, Daniel Gianola, Bjørg HeringstadAbstract:Abstract Clinical Mastitis is typically coded as presence/absence during some period of exposure, and records are analyzed with linear or binary data models. Because presence includes cows with multiple episodes, there is loss of information when a count is treated as a binary response. The Poisson model is designed for counting random variables, and although it is used extensively in epidemiology of Mastitis, it has rarely been used for studying the genetics of Mastitis. Many models have been proposed for genetic analysis of Mastitis, but they have not been formally compared. The main goal of this study was to compare linear (Gaussian), Bernoulli (with logit link), and Poisson models for the purpose of genetic evaluation of sires for Mastitis in dairy cattle. The response variables were clinical Mastitis (CM; 0, 1) and number of CM cases (NCM; 0, 1, 2, …). Data consisted of records on 36,178 first-lactation daughters of 245 Norwegian Red sires distributed over 5,286 herds. Predictive ability of models was assessed via a 3-fold cross-validation using mean squared error of prediction (MSEP) as the end-point. Between-sire variance estimates for NCM were 0.065 in Poisson and 0.007 in the linear model. For CM the between-sire variance was 0.093 in logit and 0.003 in the linear model. The ratio between herd and sire variances for the models with NCM response was 4.6 and 3.5 for Poisson and linear, respectively, and for model for CM was 3.7 in both logit and linear models. The MSEP for all cows was similar. However, within healthy animals, MSEP was 0.085 (Poisson), 0.090 (linear for NCM), 0.053 (logit), and 0.056 (linear for CM). For mastitic animals the MSEP values were 1.206 (Poisson), 1.185 (linear for NCM response), 1.333 (logit), and 1.319 (linear for CM response). The models for count variables had a better performance when predicting diseased animals and also had a similar performance between them. Logit and linear models for CM had better predictive ability for healthy cows and had a similar performance between them.
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genetic associations between clinical Mastitis and somatic cell score in early first lactation cows
Journal of Dairy Science, 2006Co-Authors: Bjørg Heringstad, Daniel Gianola, Y M Chang, Jorgen Odegard, G KlemetsdalAbstract:The objectives of this study were to examine genetic associations between clinical Mastitis and somatic cell score (SCS) in early first-lactation cows, to estimate genetic correlations between SCS of cows with and without clinical Mastitis, and to compare genetic evaluations of sires based on SCS or clinical Mastitis. Clinical Mastitis records from 15 d before to 30 d after calving and first test-day SCS records (from 6 to 30 d after calving) from 499,878 first-lactation daughters of 2,043 sires were analyzed. Results from a bivariate linear sire model analysis of SCS in cows with and without clinical Mastitis suggest that SCS is a heterogeneous trait. Heritability of SCS was 0.03 for mastitic cows and 0.08 for healthy cows, and the genetic correlation between the 2 traits was 0.78. The difference in rank between sire evaluations based on SCS of cows with and without clinical Mastitis varied from -994 to 1,125, with mean 0. A bivariate analysis with a threshold-liability model for clinical Mastitis and a linear Gaussian model for SCS indicated that heritability of liability to clinical Mastitis is at least as large as that of SCS in early lactation. The mean (standard deviation) of the posterior distribution of heritability was 0.085 (0.006) for liability to clinical Mastitis and 0.070 (0.003) for SCS. The posterior mean (standard deviation) of the genetic correlation between liability to clinical Mastitis and SCS was 0.62 (0.03). A comparison of sire evaluations showed that genetic evaluation based on SCS was not able to identify the best sires for liability to clinical Mastitis. The association between sire posterior means for liability to clinical Mastitis and sire predicted transmitting ability for SCS was far from perfect.
G Klemetsdal - One of the best experts on this subject based on the ideXlab platform.
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genetic associations between clinical Mastitis and somatic cell score in early first lactation cows
Journal of Dairy Science, 2006Co-Authors: Bjørg Heringstad, Daniel Gianola, Y M Chang, Jorgen Odegard, G KlemetsdalAbstract:The objectives of this study were to examine genetic associations between clinical Mastitis and somatic cell score (SCS) in early first-lactation cows, to estimate genetic correlations between SCS of cows with and without clinical Mastitis, and to compare genetic evaluations of sires based on SCS or clinical Mastitis. Clinical Mastitis records from 15 d before to 30 d after calving and first test-day SCS records (from 6 to 30 d after calving) from 499,878 first-lactation daughters of 2,043 sires were analyzed. Results from a bivariate linear sire model analysis of SCS in cows with and without clinical Mastitis suggest that SCS is a heterogeneous trait. Heritability of SCS was 0.03 for mastitic cows and 0.08 for healthy cows, and the genetic correlation between the 2 traits was 0.78. The difference in rank between sire evaluations based on SCS of cows with and without clinical Mastitis varied from -994 to 1,125, with mean 0. A bivariate analysis with a threshold-liability model for clinical Mastitis and a linear Gaussian model for SCS indicated that heritability of liability to clinical Mastitis is at least as large as that of SCS in early lactation. The mean (standard deviation) of the posterior distribution of heritability was 0.085 (0.006) for liability to clinical Mastitis and 0.070 (0.003) for SCS. The posterior mean (standard deviation) of the genetic correlation between liability to clinical Mastitis and SCS was 0.62 (0.03). A comparison of sire evaluations showed that genetic evaluation based on SCS was not able to identify the best sires for liability to clinical Mastitis. The association between sire posterior means for liability to clinical Mastitis and sire predicted transmitting ability for SCS was far from perfect.
Rodrigo C. Bicalho - One of the best experts on this subject based on the ideXlab platform.
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longitudinal metagenomic profiling of bovine milk to assess the impact of intramammary treatment using a third generation cephalosporin
Scientific Reports, 2016Co-Authors: E K Ganda, Ynte Hein Schukken, R S Bisinotto, Svetlana Lima, Kristina Kronauer, Dean Harrison Decter, G Oikonomou, Rodrigo C. BicalhoAbstract:Antimicrobial usage in food animals has a direct impact on human health, and approximately 80% of the antibiotics prescribed in the dairy industry are used to treat bovine Mastitis. Here we provide a longitudinal description of the changes in the microbiome of milk that are associated with Mastitis and antimicrobial therapy. Next-generation sequencing, 16 S rRNA gene quantitative real-time PCR, and aerobic culturing were applied to assess the effect of disease and antibiotic therapy on the milk microbiome. Cows diagnosed with clinical Mastitis associated with Gram-negative pathogens or negative aerobic culture were randomly allocated into 5 days of Ceftiofur intramammary treatment or remained as untreated controls. Serial milk samples were collected from the affected quarter and the ipsilateral healthy quarter of the same animal. Milk from the mastitic quarter had a higher bacterial load and reduced microbial diversity compared to healthy milk. Resolution of the disease was accompanied by increases in diversity indexes and a decrease in pathogen relative abundance. Escherichia coli-associated mastitic milk samples had a remarkably distinct bacterial profile, dominated by Enterobacteriaceae, when compared to healthy milk. However, no differences were observed in culture-negative Mastitis samples when compared to healthy milk. Antimicrobial treatment had no significant effect on clinical cure, bacteriological cure, pathogen clearance rate or bacterial load.
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Microbial Diversity of Bovine Mastitic Milk as Described by Pyrosequencing of Metagenomic 16s rDNA
PloS one, 2012Co-Authors: Georgios Oikonomou, Vinicius Silva Machado, Carlos Santisteban, Ynte Hein Schukken, Rodrigo C. BicalhoAbstract:Dairy cow Mastitis is an important disease in the dairy industry. Different microbial species have been identified as causative agents in Mastitis, and are traditionally diagnosed by bacterial culture. The objective of this study was to use metagenomic pyrosequencing of bacterial 16S rRNA genes to investigate bacterial DNA diversity in milk samples of mastitic and healthy dairy cows and compare the results with those obtained by classical bacterial culture. One hundred and thirty-six milk samples were collected from cows showing signs of Mastitis and used for microbiological culture. Additionally, 20 milk samples were collected from healthy quarters. Bacterial DNA was isolated from the same milk samples and the 16S rRNA genes were individually amplified and pyrosequenced. Discriminant analysis showed that the groups of samples that were most clearly different from the rest and thus easily discriminated were the normal milk samples from healthy cows and those characterised by culture as Trueperella pyogenes and Streptococcus spp. The Mastitis pathogens identified by culture were generally among the most frequent organisms detected by pyrosequencing, and in some cases (Escherichia coli, Klebsiella spp. and Streptococcus uberis Mastitis) the single most prevalent microorganism. Trueperella pyogenes sequences were the second most prevalent sequences in Mastitis cases diagnosed as Trueperella pyogenes by culture, Streptococcus dysgalactiae sequences were the second most prevalent sequences in Mastitis cases diagnosed as Streptococcus dysgalactiae by culture, and Staphyloccocus aureus sequences were the third most prevalent in Mastitis cases diagnosed as Staphylococcus aureus by culture. In samples that were aerobic culture negative, pyrosequencing identified DNA of bacteria that are known to cause Mastitis, DNA of bacteria that are known pathogens but have so far not been associated with Mastitis, and DNA of bacteria that are currently not known to be pathogens. A possible role of anaerobic pathogens in bovine Mastitis is also suggested.
Andrea Scaloni - One of the best experts on this subject based on the ideXlab platform.
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ovine subclinical Mastitis proteomic analysis of whey and milk fat globules unveils putative diagnostic biomarkers in milk
Journal of Proteomics, 2013Co-Authors: Elisabetta Chiaradia, Andrea Valiani, Micaela Tartaglia, Fausto Scoppetta, Giovanni Renzone, Simona Arena, Luca Avellini, Simona Benda, Alberto Gaiti, Andrea ScaloniAbstract:Abstract Subclinical Mastitis is one of the main causes of alteration in milk content and has a major impact on both animal welfare and economy in the dairy industry. A better knowledge is needed to understand the ovine mammary gland metabolism and its response to bacterial infection. In this study, the proteomic changes in ovine milk as a result of subclinical Mastitis were investigated by comparing both whey and fat globule membrane profiles of samples from Staphylococcus chromogenes -positive individuals, with those from non-infected counterparts having high or low somatic cell count; the latter were used as control. 2-DE and combined MS procedures were utilized for this purpose. Although sample bromatological parameters were very similar, proteomic analysis highlighted significant differences between the three experimental groups. Most relevant changes were observed between samples of infected milk and control. Modifications related to the defense response of the mammary gland to the pathogen were evident, with important consequences on nutritional and technological properties of milk. On the other hand, quantitative protein changes between non-infected samples with low and high levels of somatic cells indicated that the latter may result as a consequence of a probable unpaired cellular metabolism due to cellular stress, hormonal variations or previous infections. Putative biomarkers useful for the monitoring of sheep mammary metabolism and for the careful management of ovine subclinical Mastitis to avoid its clinical degeneration are proposed and discussed. Biological significance Proteomics has been here applied to the differentiation of healthy and subclinical mastitic sheep milk samples, evidencing the response of the mammary gland to S. chromogenes infection. Presented results propose useful protein biomarkers for the detection of ewe mammary infection at its subclinical stages and, subsequently, Mastitis recognition and treatment. Differently from bovine, these data confirm that the increase in somatic cell count in sheep milk is not always associated with protein factors that characterize the mammary gland infection; accordingly, somatic cell count cannot be considered as a useful parameter to certainly diagnose subclinical Mastitis in ovine.
