Inequalities and Duality in Gene Coexpression Networks of HIV-1 Infection Revealed by the Combination of the Double-Connectivity Approach and the Gini's Method

The symbiosis (Sym) and pathogenesis (Pat) is a duality problem of microbial infection, including HIV/AIDS. Statistical analysis of inequalities and duality in gene coexpression networks (GCNs) of HIV-1 infection may gain novel insights into AIDS. In this study, we focused on analysis of GCNs of uninfected subjects and HIV-1-infected patients at three different stages of viral infection based on data deposited in the GEO database of NCBI. The inequalities and duality in these GCNs were analyzed by the combination of the double-connectivity (DC) approach and the Gini's method. DC analysis reveals that there are significant differences between positive and negative connectivity in HIV-1 stage-specific GCNs. The inequality measures of negative connectivity and edge weight are changed more significantly than those of positive connectivity and edge weight in GCNs from the HIV-1 uninfected to the AIDS stages. With the permutation test method, we identified a set of genes with significant changes in the inequality and duality measure of edge weight. Functional analysis shows that these genes are highly enriched for the immune system, which plays an essential role in the Sym-Pat duality (SPD) of microbial infections. Understanding of the SPD problems of HIV-1 infection may provide novel intervention strategies for AIDS

Deformable Kernel Expansion Model for Efficient Arbitrary-shaped Scene Text Detection

Scene text detection is a challenging computer vision task due to the high variation in text shapes and ratios. In this work, we propose a scene text detector named Deformable Kernel Expansion (DKE), which incorporates the merits of both segmentation and contour-based detectors. DKE employs a segmentation module to segment the shrunken text region as the text kernel, then expands the text kernel contour to obtain text boundary by regressing the vertex-wise offsets. Generating the text kernel by segmentation enables DKE to inherit the arbitrary-shaped text region modeling capability of segmentation-based detectors. Regressing the kernel contour with some sampled vertices enables DKE to avoid the complicated pixel-level post-processing and better learn contour deformation as the contour-based detectors. Moreover, we propose an Optimal Bipartite Graph Matching Loss (OBGML) that measures the matching error between the predicted contour and the ground truth, which efficiently minimizes the global contour matching distance. Extensive experiments on CTW1500, Total-Text, MSRA-TD500, and ICDAR2015 demonstrate that DKE achieves a good tradeoff between accuracy and efficiency in scene text detection

Lactobacillus rhamnosus GG Suppresses Meningitic E. coli K1 Penetration across Human Intestinal Epithelial Cells In Vitro and Protects Neonatal Rats against Experimental Hematogenous Meningitis

The purpose of this study was to examine prophylactic efficacy of probiotics in neonatal sepsis and meningitis caused by E. coli K1. The potential inhibitory effect of Lactobacillus rhamnosus GG (LGG) on meningitic E. coli K1 infection was examined by using (i) in vitro inhibition assays with E44 (a CSF isolate from a newborn baby with E. coli meningitis), and (ii) the neonatal rat model of E. coli sepsis and meningitis. The in vitro studies demonstrated that LGG blocked E44 adhesion, invasion, and transcytosis in a dose-dependent manner. A significant reduction in the levels of pathogen colonization, E. coli bacteremia, and meningitis was observed in the LGG-treated neonatal rats, as assessed by viable cultures, compared to the levels in the control group. In conclusion, probiotic LGG strongly suppresses meningitic E. coli pathogens in vitro and in vivo. The results support the use of probiotic strains such as LGG for prophylaxis of neonatal sepsis and meningitis

A Novel Interface Database of Graphene Nanoribbon from Density Functional Theory

Interfaces play a crucial role in determining the overall performance and functionality of electronic devices and systems. Driven by the data science, machine learning (ML) reveals excellent guidance for material selection and device design, in which an advanced database is crucial for training models with state-of-the-art (SOTA) precision. However, a systematic database of interfaces is still in its infancy due to the difficulties in collecting raw data in experiment and the expensive first-principles computational cost in density functional theory (DFT). In this paper, we construct ample interface structures of graphene nanoribbons (GNR), whose interfacial morphology can be precisely fabricated based on specific molecular precursors. The GNR interfaces serve as promising candidates since their bandgaps can be modulated. Their physical properties including energy bands and density of states (DOS) maps are obtained under reasonable calculation parameters. This database can provide theoretical guidance for the design of electronic devices and accelerate the ML study of various physical quantities

Ruhm Meets GHH

This paper …rst documents several important business cycle properties of health status and health expenditures in the US. We …nd that health expenditures are pro-cyclical while health status is counter-cyclical. We then develop a stochastic dynamic general equilibrium model with endogenous health accumulation. The model has four distinct features: 1) Both medical expenditures and leisure time are used to produce health stock; 2) Health enters into production function; 3) Depreciation rate of health stock negatively depends on working hours; 4) Health enters into utility function. We calibrate the model to US economy. The results show that the model can jointly rationalize the counter-cyclicality of health status and pro-cyclicality of medical expenditure. We also investigate the relative importance of each feature in a¤ecting th