A Computational Study Identifies HIV Progression-Related Genes Using mRMR and Shortest Path Tracing

<div><p>Since statistical relationships between HIV load and CD4+ T cell loss have been demonstrated to be weak, searching for host factors contributing to the pathogenesis of HIV infection becomes a key point for both understanding the disease pathology and developing treatments. We applied Maximum Relevance Minimum Redundancy (mRMR) algorithm to a set of microarray data generated from the CD4+ T cells of viremic non-progressors (VNPs) and rapid progressors (RPs) to identify host factors associated with the different responses to HIV infection. Using mRMR algorithm, 147 gene had been identified. Furthermore, we constructed a weighted molecular interaction network with the existing protein-protein interaction data from STRING database and identified 1331 genes on the shortest-paths among the genes identified with mRMR. Functional analysis shows that the functions relating to apoptosis play important roles during the pathogenesis of HIV infection. These results bring new insights of understanding HIV progression.</p></div

PPI network of shortest paths among 86 mRMR identified proteins.

<p>Shortest paths between each pair of the 86 mRMR selected proteins were identified in the STRING PPI network. Proteins are presented using their Ensemble IDs. Proteins in yellow are the 86 identified using mRMR; in blue and green are located only on shortest paths; in blue are annotated in Ensemble Biomart; and in green are not annotated in Ensemble Biomart.</p

Expression differences of <i>SRP14P1</i>, <i>SLC45A2</i>, and <i>DNAJB1</i> between VNPs and RPs.

<p>This figure show the expression differences of <i>SRP14P1</i> (A), <i>SLC45A2</i> (B), and <i>DNAJB1</i> (C) between VNPs and RPs, separately. Error bars indicate standard errors.</p

Novel Two-Dimensional Silicon Dioxide with in-Plane Negative Poisson’s Ratio

Silicon dioxide or silica, normally existing in various bulk crystalline and amorphous forms, was recently found to possess a two-dimensional structure. In this work, we use ab initio calculation and evolutionary algorithm to unveil three new two-dimensional (2D) silica structures whose thermal, dynamical, and mechanical stabilities are compared with many typical bulk silica. In particular, we find that all three of these 2D silica structures have large in-plane negative Poisson’s ratios with the largest one being double of penta graphene and three times of borophenes. The negative Poisson’s ratio originates from the interplay of lattice symmetry and SiO tetrahedron symmetry. Slab silica is also an insulating 2D material with the highest electronic band gap (>7 eV) among reported 2D structures. These exotic 2D silica with in-plane negative Poisson’s ratios and widest band gaps are expected to have great potential applications in nanomechanics and nanoelectronics

Additional file 1: of Single-cell genome-wide bisulfite sequencing uncovers extensive heterogeneity in the mouse liver methylome

Supplementary materials. The supplementary materials include Figures S1–S3, Tables S1–S3, and Supplementary Experimental Procedures. (PDF 473 kb

Top 20 of the 1290 genes by betweenness in the shortest paths among the 86 mRMR identified genes.

<p>Top 20 of the 1290 genes by betweenness in the shortest paths among the 86 mRMR identified genes.</p

Top 20 of the 147 genes by mRMR score.

<p>Top 20 of the 147 genes by mRMR score.</p

IFS curve to determine the number of features used in prediction.

<p>We used an IFS curve to determine the number of features finally used in mRMR selection. Prediction accuracy reached its maximum value when 147 genes were included. The ‘predict1’, ‘predict2’ and ‘predict3’ refer to the three prediction methods we used – a vote of the top five nearest neighbor, the first nearest neighbor and nearest clustering center of each phenotype group, seperately.</p

Electrocatalytic-Induced Electrochemical Sensor Based on the Heterojunction Cu–Ni/Ni(OH)₂ for the Detection of Hydrogen Sulfide

The sensitive and specific analytic assay of hydrogen sulfide (H2S) is beneficial to early H2S-related disease diagnosis and prevention. Herein, an electrocatalytic-assisted electrochemical sensing platform with excellent precision is proposed to detect H2S based on heterojunction Cu–Ni/Ni(OH)2 nanoflowers by coupling the vulcanization reaction of copper ions, where a series of Cu–Ni/Ni(OH)2 nanohybrids with diverse amounts of Cu2+ ions modified on a bare electrode have been prepared via a facile ion-exchange approach. Taking advantage of the unique nanostructure, the integrated electroactive Cu–Ni/Ni(OH)2, the optimal interface effect, and the excellent specific surface area with an opportunity for high surface active sites, the designed Cu–Ni/Ni(OH)2 nanohybrids exhibit an outstanding current response. Under optimal experimental conditions, the electrochemical sensor reveals superb analysis performance with a wider linear range from 0.1 to 230 μM and a low limit of detection of 0.091 μM for H2S detection. This approach provides an avenue to synthesize copper-based hydroxide nanomaterials as a robust electrochemical indicator and an idea for highly specific detection of H2S for early disease screening and biomedical detection

Data_Sheet_1_A radiomics-based study of deep medullary veins in infants: Evaluation of neonatal brain injury with hypoxic-ischemic encephalopathy via susceptibility-weighted imaging.docx

ObjectiveThe deep medullary veins (DMVs) can be evaluated using susceptibility-weighted imaging (SWI). This study aimed to apply radiomic analysis of the DMVs to evaluate brain injury in neonatal patients with hypoxic-ischemic encephalopathy (HIE) using SWI.MethodsThis study included brain magnetic resonance imaging of 190 infants with HIE and 89 controls. All neonates were born at full-term (37+ weeks gestation). To include the DMVs in the regions of interest, manual drawings were performed. A Rad-score was constructed using least absolute shrinkage and selection operator (LASSO) regression to identify the optimal radiomic features. Nomograms were constructed by combining the Rad-score with a clinically independent factor. Receiver operating characteristic curve analysis was applied to evaluate the performance of the different models. Clinical utility was evaluated using a decision curve analysis.ResultsThe combined nomogram model incorporating the Rad-score and clinical independent predictors, was better in predicting HIE (in the training cohort, the area under the curve was 0.97, and in the validation cohort, it was 0.95) and the neurologic outcomes after hypoxic-ischemic (in the training cohort, the area under the curve was 0.91, and in the validation cohort, it was 0.88).ConclusionBased on radiomic signatures and clinical indicators, we developed a combined nomogram model for evaluating neonatal brain injury associated with perinatal asphyxia.</p