Pan-Genomic Study of Mycobacterium tuberculosis Reflecting the Primary/Secondary Genes, Generality/Individuality, and the Interconversion Through Copy Number Variations

Tuberculosis (TB) has surpassed HIV as the leading infectious disease killer worldwide since 2014. The main pathogen, Mycobacterium tuberculosis (Mtb), contains ~4,000 genes that account for ~90% of the genome. However, it is still unclear which of these genes are primary/secondary, which are responsible for generality/individuality, and which interconvert during evolution. Here we utilized a pan-genomic analysis of 36 Mtb genomes to address these questions. We identified 3,679 Mtb core (i.e., primary) genes, determining their phenotypic generality (e.g., virulence, slow growth, dormancy). We also observed 1,122 dispensable and 964 strain-specific secondary genes, reflecting partially shared and lineage-/strain-specific individualities. Among which, five L2 lineage-specific genes might be related to the increased virulence of the L2 lineage. Notably, we discovered 28 Mtb “Super Core Genes” (SCGs: more than a copy in at least 90% strains), which might be of increased importance, and reflected the “super phenotype generality.” Most SCGs encode PE/PPE, virulence factors, antigens, and transposases, and have been verified as playing crucial roles in Mtb pathogenicity. Further investigation of the 28 SCGs demonstrated the interconversion among SCGs, single-copy core, dispensable, and strain-specific genes through copy number variations (CNVs) during evolution; different mutations on different copies highlight the delicate adaptive-evolution regulation amongst Mtb lineages. This reflects that the importance of genes varied through CNVs, which might be driven by selective pressure from environment/host-adaptation. In addition, compared with Mycobacterium bovis (Mbo), Mtb possesses 48 specific single core genes that partially reflect the differences between Mtb and Mbo individuality

Global Solutions to an initial boundary problem for the compressible 3-D MHD equations with Navier-slip and perfectly conducting boundary conditions in exterior domains

An initial boundary value problem for compressible Magnetohydrodynamics (MHD) is considered on an exterior domain (with the first Betti number vanishes) in $R^3$ in this paper. The global existence of smooth solutions near a given constant state for compressible MHD with the boundary conditions of Navier-slip for the velocity filed and perfect conduction for the magnetic field is established. Moreover the explicit decay rate is given. In particular, the results obtained in this paper also imply the global existence of classical solutions for the full compressible Navier-Stokes equations with Navier-slip boundary conditions on exterior domains in three dimensions, which is not available in literature, to the best of knowledge of the authors'

Optimizing the magnetization-prepared rapid gradient-echo (MP-RAGE) sequence.

The three-dimension (3D) magnetization-prepared rapid gradient-echo (MP-RAGE) sequence is one of the most popular sequences for structural brain imaging in clinical and research settings. The sequence captures high tissue contrast and provides high spatial resolution with whole brain coverage in a short scan time. In this paper, we first computed the optimal k-space sampling by optimizing the contrast of simulated images acquired with the MP-RAGE sequence at 3.0 Tesla using computer simulations. Because the software of our scanner has only limited settings for k-space sampling, we then determined the optimal k-space sampling for settings that can be realized on our scanner. Subsequently we optimized several major imaging parameters to maximize normal brain tissue contrasts under the optimal k-space sampling. The optimal parameters are flip angle of 12°, effective inversion time within 900 to 1100 ms, and delay time of 0 ms. In vivo experiments showed that the quality of images acquired with our optimal protocol was significantly higher than that of images obtained using recommended protocols in prior publications. The optimization of k-spacing sampling and imaging parameters significantly improved the quality and detection sensitivity of brain images acquired with MP-RAGE

Socio-economic factors of bacillary dysentery based on spatial correlation analysis in Guangxi Province, China.

BACKGROUND: In the past decade, bacillary dysentery was still a big public health problem in China, especially in Guangxi Province, where thousands of severe diarrhea cases occur every year. METHODS: Reported bacillary dysentery cases in Guangxi Province were obtained from local Centers for Diseases Prevention and Control. The 14 socio-economic indexes were selected as potential explanatory variables for the study. The spatial correlation analysis was used to explore the associations between the selected factors and bacillary dysentery incidence at county level, which was based on the software of ArcGIS10.2 and GeoDA 0.9.5i. RESULTS: The proportion of primary industry, the proportion of younger than 5-year-old children in total population, the number of hospitals per thousand persons and the rates of bacillary dysentery incidence show statistically significant positive correlation. But the proportion of secondary industry, per capital GDP, per capital government revenue, rural population proportion, popularization rate of tap water in rural area, access rate to the sanitation toilets in rural, number of beds in hospitals per thousand persons, medical and technical personnel per thousand persons and the rate of bacillary dysentery incidence show statistically significant negative correlation. The socio-economic factors can be divided into four aspects, including economic development, health development, medical development and human own condition. The four aspects were not isolated from each other, but interacted with each other

Mapping the habitat for the moose population in Northeast China by combining remote sensing products and random forests

Many wildlife species face the risks of habitat loss, habitat fragmentation or local extinction in response to climate change and anthropogenic disturbance. Moose (Alces alces) in Northeast China is on the southernmost edge of the geographical range of Eurasian moose, the distribution of this population is retreating, and population number has been declining for the last several decades. However, little is known about its habitat suitability over a large spatial scale, which hinders further effective conservation of the moose in China. It is critical to explore the moose-habitat relationships and habitat suitability to understand moose habitat requirements, potential land use impacts, and effective management. In this paper, we combined remote sensing-derived predictors and machine learning methods (down-sampling random forests) to explore the moose-habitat associations and map moose habitat suitability. Results showed that our model performed well to excellently in terms of three evaluation metrics (AUCROC, AUCPR, CBI), which indicates the advantages of the combination of remote sensing and machine learning methods in predicting moose habitat. We identified the main factors driving moose distribution in Northeast China are the human footprint index, the mean monthly maximum temperature of the late spring, the percentage of coniferous forest, the minimum dynamic habitat index, the minimum temperature of the coldest month, and the distance from town. Moose responds to these variables nonlinearly. Generally, variables related to human disturbance and heat stress are the main drivers of moose occurrence and are negatively associated with moose occurrence probability. High suitability areas are mainly distributed in eastern and northern Greater Khingan Mountains. Highly suitable habitat covers only a small proportion of the study area. We identified 67,400 km2 of suitable habitat covering 13.6% of the study area. Our study can provide critical information for decision-makers when designing conservation and management strategies for the moose in China and other regions of the world with similar conditions

A Hierarchical Adaptive Approach to the Optimal Design of Experiments

Experimentation is at the core of research in cognitive science, yet observations can be expensive and time-consuming to acquire. A major interest of researchers is designing experiments that lead to maximal accumulation of information about the phenomenon under study with the fewest possible number of observations. In addressing this challenge, statisticians have developed adaptive design optimization methods. This paper introduces a hierarchical Bayes extension of adaptive design optimization that provides a judicious way to exploit two complementary schemes of inference (with past and future data) to achieve even greater accuracy and efficiency in information gain. We demonstrate the method in a simulation experiment in the field of visual perception