Seasonal variation of bulk milk somatic cell counts in UK dairy herds: investigations of the summer rise

Individual cow somatic cell count (SCC) patterns were explored over a one year period in 33 dairy herds to investigate the reason for a summer rise in bulk milk somatic cell counts (BMSCC). Cow test day somatic cell counts were categorised according to the magnitude of change since the previous test day reading, to examine which categories were responsible for the summer increase. Multilevel models using Markov chain Monte Carlo methods were specified to estimate the number of somatic cells/ml produced by different cell count categories. Stage of lactation and parity were accounted for in the models. There was an increase in the proportion of cows that remained above 200,000 cells/ml for two consecutive recordings in summer and this group of cows were responsible for 70.8% of the increase in somatic cells/ml produced from May to September compared with October to March. There was no evidence that a greater new infection rate (somatic cell counts moving from below 100,000 cells/ml to over 200,000 cells/ml) contributed to the increased summer bulk milk somatic cell counts. There was no indication that a general small increase in all somatic cell counts played an important role in the increased summer somatic cell counts. Markov chain Monte Carlo methods provided a valuable and flexible platform for parameter estimation in reasonably complex multilevel models.     

Assessing the convergence of Markov Chain Monte Carlo methods: an example from evaluation of diagnostic tests in absence of a gold standard

The accessibility of Markov Chain Monte Carlo (MCMC) methods for statistical inference have improved with the advent of general purpose software. This enables researchers with limited statistical skills to perform Bayesian analysis. Using MCMC sampling to do statistical inference requires convergence of the MCMC chain to its stationary distribution. There is no certain way to prove convergence; it is only possible to ascertain when convergence definitely has not been achieved. These methods are rather subjective and not implemented as automatic safeguards in general MCMC software. This paper considers a pragmatic approach towards assessing the convergence of MCMC methods illustrated by a Bayesian analysis of the Hui–Walter model for evaluating diagnostic tests in the absence of a gold standard. The Hui–Walter model has two optimal solutions, a property which causes problems with convergence when the solutions are sufficiently close in the parameter space. Using simulated data we demonstrate tools to assess the convergence and mixing of MCMC chains using examples with and without convergence. Suggestions to remedy the situation when the MCMC sampler fails to converge are given. The epidemiological implications of the two solutions of the Hui–Walter model are discussed.     

The use of Markov chain Monte Carlo for analysis of correlated binary data: patterns of somatic cells in milk and the risk of clinical mastitis in dairy cows

Two analytical approaches were used to investigate the relationship between somatic cell concentrations in monthly quarter milk samples and subsequent, naturally occurring clinical mastitis in three dairy herds. Firstly, cows with clinical mastitis were selected and a conventional matched analysis was used to compare affected and unaffected quarters of the same cow. The second analysis included all cows, and in order to overcome potential bias associated with the correlation structure, a hierarchical Bayesian generalised linear mixed model was specified. A Markov chain Monte Carlo (MCMC) approach, that is Gibbs sampling, was used to estimate parameters.

The results of both the matched analysis and the hierarchical modelling suggested that quarters with a somatic cell count (SCC) in the range 41,000–100,000 cells/ml had a lower risk of clinical mastitis during the next month than quarters <41,000 cell/ml. Quarters with an SCC >200,000 cells/ml were at the greatest risk of clinical mastitis in the next month. There was a reduced risk of clinical mastitis between 1 and 2 months later in quarters with an SCC of 81,000–150,000 cells/ml compared with quarters below this level. The hierarchical modelling analysis identified a further reduced risk of clinical mastitis between 2 and 3 months later in quarters with an SCC 61,000–150,000 cells/ml, compared to other quarters.

We conclude that low concentrations of somatic cells in milk are associated with increased risk of clinical mastitis, and that high concentrations are indicative of pre-existing immunological mobilisation against infection. The variation in risk between quarters of affected cows suggests that local quarter immunological events, rather than solely whole cow factors, have an important influence on the risk of clinical mastitis. MCMC proved a useful tool for estimating parameters in a hierarchical Bernoulli model. Model construction and an approach to assessing goodness of model fit are described.     

性别比例和性状遗传力对闭锁群体BLUP选择效果的影响

采用MonteCarlo方法模拟研究了性别比例和性状遗传力对闭锁群体动物模型BLUP选择效果的影响 ,选育过程中世代不重叠 ,共进行了 1 5个世代的选择。结果表明性别比例对群体育种值和近交系数的变化都有明显的影响。在育种值达到最大值以前 ,群体平均育种值提高的速度随着公畜比例的增加而有所减慢 ,但会使育种值达到最大值的时间后移 ,在育种值达到最大值后 ,其下降的速度则随着公畜比例的降低而加快。随着公畜比例的增加 ,群体近交系数的上升速度会明显变慢。高遗传力性状的选择效果要优于低遗传力性状     

Prevalence and age-related infection of Cryptosporidium suis, C. muris and Cryptosporidium pig genotype II in pigs on a farm complex in the Czech Republic

A total of 413 pig faecal samples were collected from pre-weaners (119), starters (131), pre-growers (123) and sows (40) from a farm with a closed breeding system segmented into two breeding complexes and a growing complex in the region of Vysočina, Czech Republic and screened for the presence of Cryptosporidium using staining methods and genotyping (SSU rRNA). Cryptosporidium oocysts were detected by microscopy in the faeces of 21.1% of the samples (87/413). Sequence analyses and RFLP identified C. suis in 44, Cryptosporidium pig genotype II in 23 and C. muris in 2 samples. No mixed infections were found.Pigs under 7 weeks of age were infected with C. suis only. Cryptosporidium pig genotype II was found in animals from 7 weeks of age. No relationship was found between diarrhoea and any Cryptosporidium infection in any of the different age groups (P < 0.05). The pre-weaned pigs shed significantly more Cryptosporidium oocysts than older pigs and it was associated with C. suis infection.