Two Independent Pathways for Self-Recognition in Proteus Mirabilis Are Linked by Type VI-Dependent Export

Swarming colonies of the bacterium Proteus mirabilis are capable of self-recognition and territorial behavior. Swarms of independent P. mirabilis isolates can recognize each other as foreign and establish a visible boundary where they meet; in contrast, genetically identical swarms merge. The ids genes, which encode self-identity proteins, are necessary but not sufficient for this territorial behavior. Here we have identified two new gene clusters: one (idr) encodes rhs-related products, and another (tss) encodes a putative type VI secretion (T6S) apparatus. The Ids and Idr proteins function independently of each other in extracellular transport and in territorial behaviors; however, these self-recognition systems are linked via this type VI secretion system. The T6S system is required for export of select Ids and Idr proteins. Our results provide a mechanistic and physiological basis for the fundamental behaviors of self-recognition and territoriality in a bacterial model system.Molecular and Cellular Biolog

The Complete Genome Sequence of Proteus mirabilis Strain BB2000 Reveals Differences from the P. mirabilis Reference Strain

We announce the complete genome sequence for Proteus mirabilis strain BB2000, a model system for self recognition. This opportunistic pathogen contains a single, circular chromosome (3,846,754 bp). Comparisons between this genome and that of strain HI4320 reveal genetic variations corresponding to previously unknown physiological and self-recognition differences.Molecular and Cellular Biolog

The Progress of CDAS

The Chinese Data Acquisition System (CDAS) based on FPGA techniques has been developed in China for the purpose of replacing the traditional analog baseband converter. CDAS is a high speed data acquisition and processing system with 1024 Msps sample rate for 512M bandwidth input and up to 16 channels (both USB and LSB) output with VSI interface compatible. The instrument is a flexible environment which can be updated easily. In this paper, the construction, the performance, the experiment results, and the future plans of CDAS will be reported

Discovery Of Genetic Pleiotropy For Obesity And Inflammation And Mediation In Identified Variants Through Obesity-Related Pathways

Genetic data suggest that obesity and inflammation shared a portion of their genetic architectures. Meanwhile, epidemiologic data suggest a causal link between obesity and inflammation. Therefore, we hypothesized that there are variants in the human genome that display pleiotropy for obesity and inflammation and that the effects of at least some of those variants could be mediated by obesity-related pathways. To test these hypotheses, we conducted univariate genome-wide association analyses of obesity-related traits (BMI and WHRadjBMI) and inflammation (CRP) in the UK Biobank (n= 291,396 Caucasian subjects), searched for overlapping signals, fine-mapped the regions to identify putative causal SNPs, and decomposed the total genetic effects of the SNPs using causal mediation analyses, with an adjustment of confounding of the obesity-inflammation relationship. We identified 16 SNPs (rs79113395, rs58048722, rs199956414, rs12203592, rs2049870, rs2721966, rs10086741, rs179444, rs3808478, rs6265, rs7926362, rs4922793, rs4755725, rs12577464, rs8047395 and rs11075987), residing in 8 distinct genetic loci, that display pleiotropy for CRP and BMI and 1 SNP (rs429358) that displays pleiotropy for CRP and WHRadjBMI. Among these SNPs, 4 (rs58048722, rs199956414, rs4755725, rs12577464) affected CRP only through obesity-related pathways. All other SNPs had both direct effects on CRP and indirect effects through obesity-related pathways. We found insufficient evidence that the indirect effects of SNPs on CRP through our obesity measures were moderated by sex, despite of sex differences in the phenotypic relationship between these two traits. These identified SNPs lays the foundation for mechanistically understanding the pathogenesis of obesity- and inflammation-associated diseases and may serve as targets for therapies that simultaneously treat these conditions

Mixed Model Selection Based on the Conceptual Predictive Statistic

Model selection plays an important role in statistical literature. The objective of model selection is to choose the most appropriate model from a potential large class of candidate models that balance the increase in fit against the increment in model complexity. To facilitate the selection process, a variety of model selection criteria are employed and have been developed for optimal selection of the most appropriate model. The most popular model selection criteria are the Akaike Information Criterion (AIC, 1973. 1974) and the Bayesian Information Criterion (BIC, 1976). Over the past several decades, a number of additional model selection criteria have been proposed and investigated. One important one among these is Cp from Mallow (1973), which is based on the Gauss discrepancy. In the dissertation, we focus on the development of variants of Cp in linear mixed models. Linear mixed model theory has expanded greatly in recent years, resulting in its widespread application in many areas of research. Therefore, the improvement of Cp in linear mixed model setting will significantly increase the efficiency and effectiveness of model selection. We propose the model selection criteria following Mallow\u27s Cp (1973) statistic in linear mixed models. The first proposed criterion is marginal Cp, called MCp. We first derive MCp based on the expected Gauss discrepancy. For the set of candidate models including the true model, we adopt a consistent estimator of correlation matrix of response data. Then we form and prove an idempotent matrix in linear mixed models, which leads to an asymptotically unbiased estimator of the expected Gauss discrepancy between a candidate model and the true model, called MCp. An improvement of MCp, called IMCp, is then proposed and proved, which is also an asymptotically unbiased estimator of the expected Gauss discrepancy. In the simulation study, a set of increasing correlation coefficients in the correlation matrix of the response variable is employed for demonstrating the performance of the proposed MCp and IMCp. The simulated data are generated in different sample sizes to investigate the effect of the sample size on the performance of the proposed criteria. The simulation results illustrate that under suitable conditions, the proposed criteria outperform AIC and BIC in selecting the correct model. The IMCp behaves best when the maximum likelihood estimator (MLE) is used. Additionally, the proposed criteria perform significantly better for highly correlated response data than for weakly correlated data. The second proposed criterion is conditional Cp, called CCp. We derive the CCp under the conditional mean of response variable. Corresponding to the case where the covariance matrix is known or unknown, we derive two versions of the conditional Cp, called TCCp and CCp, respectively, and they are proved based on the expected conditional Gauss discrepancy. When the covariance matrix is known, the TCCp is an unbiased estimator of the expected conditional Gauss discrepancy; when the covariance matrix is unknown, the CCp is an asymptotically unbiased estimator of the expected conditional Gauss discrepancy. In estimation, the best linear unbiased predictor (BLUP) is employed. The simulation results demonstrate that when the true model includes significant fixed effects variables, both TCCp and CCp perform effectively in selecting the correct model. When the variance components are unknown, the penalty term in CCp computed by the estimated effective degrees of freedom yields a very good approximation to the bias correction between the target discrepancy and the goodness-of-fit part in the proposed criteria