Bayes框架下DNA证据的量化研究

针对DNA（脱氧核糖核酸）证据的量化过程中常用的插入算法存在的缺陷，即量化结果与样本大小无关，小样本时过分量化了DNA证据，本文考虑了样本大小的影响，引入了Bayes模型。给出了基于Bayes模型下的似然比的计算公式，结合实际案例，对比了两种方法下的计算结果，数据结果表明基于Bayes模型下的算法比插入算法更加精确和合理。     

Mitotic kinase oscillation governs the latching of cell cycle switches

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Molecular detection and phylogenetic analyses of Arsenophonus endosymbiont in wild specimens of phlebotomine sand flies from Colombia

Endosymbionts have gained prominence as a potential tool for biological control strategies in reducing vector-borne diseases. This study aimed to evaluate the presence of Arsenophonus, Spiroplasma, and Rickettsia endosymbionts in wild specimens of phlebotomine sand flies, as well as in culicids collected in different regions of Colombia. Analyses were conducted through conventional PCR, Sanger sequencing of the 16S rRNA gene, and phylogenetic analyses. Individuals from among 946 phlebotomine sand flies and 143 mosquitoes were selected for taxonomic identification confirmed through the analysis of the cytochrome oxidase subunit I gene sequences. Results showed the presence of Arsenophonus bacteria in samples of Lutzomyia longipalpis, Psychodopygus panamensis, and Pintomyia evansi. Arsenophonus sequences associated with Lu. longipalpis and Ps. panamensis are phylogenetically located near to sequences of louse flies, with K2P genetic distances of 0.006. In contrast, sequences obtained from Pi. evansi are phylogenetically located near Arsenophonus nasoniae (K2P 0.001–0.014). Other sequences of endosymbionts similar to Arsenophonus with high K2P genetic distances (0.056–0.097), when compared to different reference strains of this endosymbiont, were also found in other samples of Lu. longipalpis and Ae. aegypti. To the best of our knowledge, this is the first successful attempt to detect and elucidate the phylogenetic relationship of Arsenophonus in phlebotomine sand flies, yet its role within these insect vectors remains to be fully determined; therefore, the importance of entomological surveys that help better understand its behavior and potential use as a control agent is required to enable the proactive reduction of sand fly populations.     

Relationships among somatic cell count, California mastitis test, impedance and bacteriological status of milk in goats and sheep in early lactation

The objectives of this trial were to evaluate and compare the test characteristics of a number of indirect tests of bacteriological status of the milk from goats and sheep and to assess the affect of varying levels of prevalence of infection on the performance of those tests.The somatic cell count, California mastitis test (CMT) score, electrical impedance and the bacteriological status of 220 and 262 milk samples from the glands of lactating dairy goats and sheep, respectively, were determined. The sensitivity and specificity of indirect tests in predicting the bacteriological status were compared by analyzing the areas under the receiver operating characteristic curve and calculating the likelihood ratio at various cut-off values.Bacteria were isolated from 17.7 and 4.6% of glands from goats and sheep, respectively. Somatic cell count was a better predictor of bacteriological status than either the CMT score or impedance in both goats and sheep. However, knowledge of the CMT score in both sheep and goats and the impedance in goats increases the likelihood of predicting the presence of a bacterial pathogen compared to no testing at all (P<0.05). Knowledge of the age and days postpartum of the animals at the time of testing did not increase the probability that infection status could be predicted. Variation in cut-off levels to predict infection status reported in the literature may be due to variation in the prevalence of infection in the populations studied. Use of likelihood ratio allows comparison of tests over a range of prevalence’s as likelihood ratios are independent of prevalence. The prevalence of infection within a herd should be considered when selecting a cut-off value for indirect testing for the presence of bacteria in milk of small ruminants.     

Categorical variables with many categories are preferentially selected in bootstrap‐based model selection procedures for multivariable regression models

Automated variable selection procedures, such as backward elimination, are commonly employed to perform model selection in the context of multivariable regression. The stability of such procedures can be investigated using a bootstrap‐based approach. The idea is to apply the variable selection procedure on a large number of bootstrap samples successively and to examine the obtained models, for instance, in terms of the inclusion of specific predictor variables. In this paper, we aim to investigate a particular important problem affecting this method in the case of categorical predictor variables with different numbers of categories and to give recommendations on how to avoid it. For this purpose, we systematically assess the behavior of automated variable selection based on the likelihood ratio test using either bootstrap samples drawn with replacement or subsamples drawn without replacement from the original dataset. Our study consists of extensive simulations and a real data example from the NHANES study. Our main result is that if automated variable selection is conducted on bootstrap samples, variables with more categories are substantially favored over variables with fewer categories and over metric variables even if none of them have any effect. Importantly, variables with no effect and many categories may be (wrongly) preferred to variables with an effect but few categories. We suggest the use of subsamples instead of bootstrap samples to bypass these drawbacks.