Vpr enhances HIV-1 Env processing and virion infectivity in macrophages by modulating TET2-dependent IFITM3 expression

HIV-1 Vpr enhances viral replication in human macrophages via multiple mechanisms that are not clearly defined. It does not affect HIV-1 virion production during the first round of infection. We have recently discovered that Vpr targets the DNA demethylase TET2 for degradation, which leads to sustained interleukin-6 (IL-6) expression and elevated HIV-1 replication. We report here that Vpr enhanced Env processing in infected macrophages, associated with increased Env incorporation into virions with higher infectivity. Interestingly, IFITM3 was constitutively expressed in macrophages in a TET2-dependent fashion. We showed that Vprenhanced Env processing depended genetically on TET2 and IFITM3. We further showed that Vpr reduced IFITM3 expression by reducing demethylation of the IFITM3 promoter in macrophages, associated with degradation of TET2 and reduced TET2 binding to the IFITIM3 promoter. Our findings indicate that the Vpr-TET2 axis enhances HIV-1 replication in macrophages via two independent mechanisms: Reduced IFTIM3 expression to enhance Env processing and virion infectivity and sustained IL-6 expression to increase HIV-1 replication. The Vpr-TET2 axis may provide a novel target to develop therapeutics to inhibit HIV-1 infection and pathogenesis

AN INJECTABLE HYDROGEL SYSTEM WITH UNQIUE TUNABLE STIFFNESS FOR TISSUE ENGINEERING

Ph.DDOCTOR OF PHILOSOPH

Positive solutions for a class of higher-order singular semipositone fractional differential systems with coupled integral boundary conditions and parameters

In this paper, we study the existence of a class of higher-order singular semipositone fractional differential systems with coupled integral boundary conditions and parameters. By using the properties of the Green’s function and the Guo-Krasnosel’skii fixed point theorem, we obtain some existence results of positive solutions under some conditions concerning the nonlinear functions. The method of this paper is a unified method for establishing the existence of positive solutions for a large number of nonlinear differential equations with coupled boundary conditions. In the end, examples are given to demonstrate the validity of our main results

Positive Solutions of a Fractional Boundary Value Problem with Changing Sign Nonlinearity

We discuss the existence of positive solutions of a boundary value problem of nonlinear fractional differential equation with changing sign nonlinearity. We first derive some properties of the associated Green function and then obtain some results on the existence of positive solutions by means of the Krasnoselskii's fixed point theorem in a cone

Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification

Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive approach to examining abnormal brain connectivity associated with brain disorders. Graph neural network (GNN) gains popularity in fMRI representation learning and brain disorder analysis with powerful graph representation capabilities. Training a general GNN often necessitates a large-scale dataset from multiple imaging centers/sites, but centralizing multi-site data generally faces inherent challenges related to data privacy, security, and storage burden. Federated Learning (FL) enables collaborative model training without centralized multi-site fMRI data. Unfortunately, previous FL approaches for fMRI analysis often ignore site-specificity, including demographic factors such as age, gender, and education level. To this end, we propose a specificity-aware federated graph learning (SFGL) framework for rs-fMRI analysis and automated brain disorder identification, with a server and multiple clients/sites for federated model aggregation and prediction. At each client, our model consists of a shared and a personalized branch, where parameters of the shared branch are sent to the server while those of the personalized branch remain local. This can facilitate knowledge sharing among sites and also helps preserve site specificity. In the shared branch, we employ a spatio-temporal attention graph isomorphism network to learn dynamic fMRI representations. In the personalized branch, we integrate vectorized demographic information (i.e., age, gender, and education years) and functional connectivity networks to preserve site-specific characteristics. Representations generated by the two branches are then fused for classification. Experimental results on two fMRI datasets with a total of 1,218 subjects suggest that SFGL outperforms several state-of-the-art approaches

Existence and uniqueness of positive solutions for a class of nonlinear fractional differential equations with mixed-type boundary value conditions

In this article, we study a class of nonlinear fractional differential equations with mixed-type boundary conditions. The fractional derivatives are involved in the nonlinear term and the boundary conditions. By using the properties of the Green function, the fixed point index theory and the Banach contraction mapping principle based on some available operators, we obtain the existence of positive solutions and a unique positive solution of the problem. Finally, two examples are given to demonstrate the validity of our main results

Functional Brain Network Estimation With Time Series Self-Scrubbing

Functional brain network (FBN) is becoming an increasingly important measurement for exploring cerebral mechanisms and mining informative biomarkers that assist diagnosis of some neurodegenerative disorders. Despite its effectiveness to discover valuable hidden patterns in the human brain, the estimated FBNs are often heavily influenced by the quality of the observed data (e.g., blood oxygen level dependent signal series). In practice, a preprocessing pipeline is usually employed for improving data quality. With this in mind, some data points (volumes or time course in the time series) are still not clean enough, due to artifacts including spurious resting-state processes (head movement, mind-wandering). Therefore, not all volumes in the fMRI time series can contribute to the subsequent FBN estimation. To address this issue, we propose a novel FBN estimation method by introducing a latent variable as an indicator of the data quality, and develop an alternating optimization algorithm for jointly scrubbing the data and estimating FBN simultaneously. To further illustrate the effectiveness of the proposed method, we conduct experiments on two public datasets to identify subjects with mild cognitive impairment from normal controls based on the estimated FBNs, and achieve improved accuracies than the baseline methods

Experimental Study of Oblique Pedestrian Streams

The intersecting of pedestrian streams is a common phenomenon which would lead to the pedestrian deceleration, stopping, and even threat to the safety of walking. The organization of pedestrian flow is a critical factor which influences the intersection traffic. The aim of this paper is to study the characteristics of oblique pedestrian streams by a set of pedestrian experiments. Two groups of experiment participants, three volume levels and five intersecting angles were tested. The qualitative analysis and quantitative analysis methods were applied to find out the relationship between the pedestrian streams angle and pedestrian characteristics. The results indicated that the mean and median speed, exit traffic efficiency decreased initially and increased afterwards with the increase of intersecting angles when the volume was 1,000 p/h/m and 3,000 p/h/m, while the speed standard deviation changing inversely. However, these four factors show the opposite variation tendency in volume 5,000 p/h/m. Meanwhile, the quadratic function was selected to fit them. It is found that the worst speeds of pedestrian streams were 131° and 122° in volume 1,000 p/h/m and 3,000 p/h/m, respectively, and the greatest influence on pedestrian streams was 125° in volume 5,000 p/h/m. The results of this research can help establish the foundation for the organization and optimization of intersecting pedestrian streams.</p

Remodeling Pearson's Correlation for Functional Brain Network Estimation and Autism Spectrum Disorder Identification

Functional brain network (FBN) has been becoming an increasingly important way to model the statistical dependence among neural time courses of brain, and provides effective imaging biomarkers for diagnosis of some neurological or psychological disorders. Currently, Pearson's Correlation (PC) is the simplest and most widely-used method in constructing FBNs. Despite its advantages in statistical meaning and calculated performance, the PC tends to result in a FBN with dense connections. Therefore, in practice, the PC-based FBN needs to be sparsified by removing weak (potential noisy) connections. However, such a scheme depends on a hard-threshold without enough flexibility. Different from this traditional strategy, in this paper, we propose a new approach for estimating FBNs by remodeling PC as an optimization problem, which provides a way to incorporate biological/physical priors into the FBNs. In particular, we introduce an L1-norm regularizer into the optimization model for obtaining a sparse solution. Compared with the hard-threshold scheme, the proposed framework gives an elegant mathematical formulation for sparsifying PC-based networks. More importantly, it provides a platform to encode other biological/physical priors into the PC-based FBNs. To further illustrate the flexibility of the proposed method, we extend the model to a weighted counterpart for learning both sparse and scale-free networks, and then conduct experiments to identify autism spectrum disorders (ASD) from normal controls (NC) based on the constructed FBNs. Consequently, we achieved an 81.52% classification accuracy which outperforms the baseline and state-of-the-art methods

ReCGiP, a database of reproduction candidate genes in pigs based on bibliomics

<p>Abstract</p> <p>Background</p> <p>Reproduction in pigs is one of the most economically important traits. To improve the reproductive performances, numerous studies have focused on the identification of candidate genes. However, it is hard for one to read all literatures thoroughly to get information. So we have developed a database providing candidate genes for reproductive researches in pig by mining and processing existing biological literatures in human and pigs, named as ReCGiP.</p> <p>Description</p> <p>Based on text-mining and comparative genomics, ReCGiP presents diverse information of reproduction-relevant genes in human and pig. The genes were sorted by the degree of relevance with the reproduction topics and were visualized in a gene's co-occurrence network where two genes were connected if they were co-cited in a PubMed abstract. The 'hub' genes which had more 'neighbors' were thought to be have more important functions and could be identified by the user in their web browser. In addition, ReCGiP provided integrated GO annotation, OMIM and biological pathway information collected from the Internet. Both pig and human gene information can be found in the database, which is now available.</p> <p>Conclusions</p> <p>ReCGiP is a unique database providing information on reproduction related genes for pig. It can be used in the area of the molecular genetics, the genetic linkage map, and the breeding of the pig and other livestock. Moreover, it can be used as a reference for human reproduction research.</p