浅谈学报编辑的创新意识

阐述创新意识的内涵、创新意识的重要性及意义，提出具有创新意识的编辑，必须从学新、查新、更新、创新4个方面努力，才能走创新之路、立创新之业。     

基于抗体筛查的美国猪群流行性腹泻病毒真流行率分布推断

养殖家畜的冠状病毒,如猪流行性腹泻病毒(porcine epidemic diarrhea virus,PEDV),其基因序列与蝙蝠体内冠状病毒基因序列高度相似,可引起猪肠道感染并导致仔猪大量死亡,对动物健康危害极大.本研究基于对美国猪群PEDV流行毒株表观流行率的数据挖掘作为先验信息,以2019年美国生猪入境中国西南...     

Validation of the indirect fluorescent antibody and the complement fixation tests for the diagnosis of Theileria equi

The indirect fluorescent antibody (IFA) test for Theileria equi was evaluated to assess test's suitability for the serological diagnosis of equine piroplasmosis, to provide performance parameters for the purpose of test validation, and to compare it with the complement fixation (CF) test. Using a protocol that included Evan's blue, the specificity of the IFA test was estimated at 99.0% for T. equi by the classical method of analysis, and 96.6% by the Bayesian method. The use of Evan's blue in the test protocol increased test specificity and contributed to an excellent test agreement between two collaborating laboratories (kappa = 0.96). Using Bayesian analysis, the sensitivity estimate for the IFA test was 89.2%. The CF test sensitivity and specificity estimates for T. equi were 63.1 and 96.4%, respectively, as determined by Bayesian analysis. The IFA test was more sensitive than the CF test but the specificity estimates were similar.     

Bayes and diagnostic testing

Interpretation of the result of a diagnostic test depends not only on the actual test result(s) but also on information external to this result, namely the test's sensitivity and specificity. This external information (also called prior information) must be combined with the data to yield the so-called updated, posterior estimates of the true prevalence and the test characteristics. The Bayesian approach offers a natural, intuitive framework in which to carry out this estimation process. The influence of the prior information on the final result may not be ignored. Guidance for the choice of prior information not in conflict with the data can be obtained from a set of statistics and indices (DIC, p(D), Bayes-p).     

Forecasting clinical disease in pigs: comparing a naive and a Bayesian approach

In veterinary practice the clinician often evaluates and predicts herd health status over time according to clinical criteria. In this paper, we modeled three different clinical signs among pigs based on longitudinal clinical observations in 15 pig herds. We compared and discussed the outputs from two different approaches for making clinical forecasts in a herd: a naive approach using a simple time series model with previous disease observations as predictors and a Bayesian state space models approach, in which the time lag variable entered into the random component of the model. We used the Markov chain Monte Carlo technique to calculate posterior distributions of the forecasts. For the herd specific forecasts the results showed that there were only minor differences between the forecasts from the simple time series model and the median forecasts from the Bayesian model. However, the credibility intervals from the Bayesian model were wider than the forecasts from the simple model and, therefore the Bayesian model encompassed the variability in the forecasts better. Compared to the statistical model, the simple time series would be easier to implement in a practical setting. However, the latter lacks the inherent “generality” from the statistical model that allows the user to make statements about the distribution of the herds and to predict disease status based on the “average” correlation among the herds. The applicability of the Bayesian approach within a clinical decision-making framework was discussed, with special emphasis on the use of prior information and clinical forecasting.     

Bayesian structural equation models for inferring relationships between phenotypes: a review of methodology, identifiability, and applications

Structural equation models provide a general statistical modelling technique for estimating and testing relationships among variables. Such relationships are often not revealed by standard linear models, but are of importance for understanding mechanisms underlying e.g., production-related diseases, such as mastitis. This paper gives a review of Bayesian structural equation models concerning methodology and identifiability, focused on animal breeding and genetics modelling. Applications of this type of methods in animal breeding are also reviewed critically, with discussion on advantages and disadvantages of these approaches.     

Impact of relationships between test and training animals and among training animals on reliability of genomic prediction

One of the factors affecting the reliability of genomic prediction is the relationship among the animals of interest. This study investigated the reliability of genomic prediction in various scenarios with regard to the relationship between test and training animals, and among animals within the training data set. Different training data sets were generated from EuroGenomics data and a group of Nordic Holstein bulls (born in 2005 and afterwards) as a common test data set. Genomic breeding values were predicted using a genomic best linear unbiased prediction model and a Bayesian mixture model. The results showed that a closer relationship between test and training animals led to a higher reliability of genomic predictions for the test animals, while a closer relationship among training animals resulted in a lower reliability. In addition, the Bayesian mixture model in general led to a slightly higher reliability of genomic prediction, especially for the scenario of distant relationships between training and test animals. Therefore, to prevent a decrease in reliability, constant updates of the training population with animals from more recent generations are required. Moreover, a training population consisting of less‐related animals is favourable for reliability of genomic prediction.     

Comparison of Different Sampling Strategies and Laboratory Methods for the Detection of C. jejuni and C. coli from Broiler Flocks at Primary Production

Summary The objective of the study was to evaluate the performance of different combinations of sample type, transport medium and culture methods for the recovery of Campylobacter jejuni and C. coli from broiler flocks at primary production. Boot swabs moistened with one of four different transport media [maximum recovery diluent (n = 120), Exeter broth (EX) (n = 120), buffered peptone water (n = 120) and modified semi‐solid Cary‐Blair (n = 120)], caecal samples (n = 40) and faecal samples (n = 120) from 40 broiler flocks were compared and sensitivity estimates obtained using a Bayesian model. Samples were cultured onto mCCDA before and after enrichment in EX and incubated microaerobically at 41.5°C. Campylobacter suspect colonies were identified to the species level by multiplex PCR. Results from the Bayesian model indicated that boot swabs after enrichment had higher sensitivity (90–94%) than caecal contents before or after enrichment (84% and 89%, respectively) and faecal samples after enrichment (82%) for the detection of Campylobacter spp., although these differences were not statistically significant. Enrichment significantly increased the sensitivity of boot swab and caecal samples for detection of Campylobacter spp. and C. jejuni, respectively. However, the enrichment of caecal samples resulted in a significant decrease in the sensitivity of these samples for detection of C. coli. There was much greater variation in the sensitivity estimates of the methods for detecting C. coli than for C. jejuni, and the ranking of methods was different between the two species. Boot swabs gave the best sensitivity values for detection of C. jejuni, and enrichment culture of faecal samples was the most sensitive method for detection of C. coli.     

Bayesian estimation of diagnostic accuracy of a new bead-based antibody detection test to reveal Toxoplasma gondii infections in pig populations

The success of a Toxoplasma gondii surveillance program in European pig production systems depends partly on the quality of the test to detect infection in the population. The test accuracy of a recently developed serological bead-based assay (BBA) was investigated earlier using sera from experimentally infected animals. In this study, the accuracy of the BBA was determined by the use of sera from animals from two field subpopulations. As no T. gondii infection information of these animals was available, test accuracy was determined through a Bayesian approach allowing for conditional dependency between BBA and an ELISA test. The priors for prevalence were based on available information from literature, whereas for specificity vague non-informative priors were used. Priors for sensitivity were based either on available information or specified as non-informative. Posterior estimates for BBA sensitivity and specificity were (mode) 0.855 (Bayesian 95% credibility interval (bCI) 0.702–0.960) and 0.913 (bCI 0.893–0.931), respectively. Comparing the results of BBA and ELISA, sensitivity was higher for the BBA while specificity was higher for ELISA. Alternative priors for the sensitivity affected posterior estimates for sensitivity of both BBA and ELISA, but not for specificity. Because the difference in prevalence between the two subpopulations is small, and the number of infected animals is small as well, the precision of the posterior estimates for sensitivity may be less accurate in comparison to the estimates for specificity. The estimated value for specificity of BBA is at least optimally defined for testing pigs from conventional and organic Dutch farms.     

Bayesian methods for estimating pathogen prevalence within groups of animals from faecal-pat sampling

Pathogens such as Escherichia coli O157:H7 and Campylobacter spp. have been implicated in outbreaks of food poisoning in the UK and elsewhere. Domestic animals and wildlife are important reservoirs for both of these agents, and cross-contamination from faeces is believed to be responsible for many human outbreaks. Appropriate parameterisation of quantitative microbial-risk models requires representative data at all levels of the food chain. Our focus in this paper is on the early stages of the food chain-specifically, sampling issues which arise at the farm level. We estimated animal–pathogen prevalence from faecal-pat samples using a Bayesian method which reflected the uncertainties inherent in the animal-level prevalence estimates. (Note that prevalence here refers to the percentage of animals shedding the bacteria of interest). The method offers more flexibility than traditional, classical approaches: it allows the incorporation of prior belief, and permits the computation of a variety of distributional and numerical summaries, analogues of which often are not available through a classical framework. The Bayesian technique is illustrated with a number of examples reflecting the effects of a diversity of assumptions about the underlying processes. The technique appears to be both robust and flexible, and is useful when defecation rates in infected and uninfected groups are unequal, where population size is uncertain, and also where the microbiological-test sensitivity is imperfect. We also investigated the determination of the sample size necessary for determining animal-level prevalence from pat samples to within a pre-specified degree of accuracy.