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

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 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

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

Data Preprocessing.

<p>Data Preprocessing.</p

Self-Consistent Implementation of a Solvation Free Energy Framework to Predict the Salt Solubilities of Six Alkali Halides

To assess the salt solubilities of six alkali halides in aqueous systems, we proposed a thermodynamic cycle and an efficient molecular modeling methodology. The Gibbs free energy changes for vaporization, dissociation, and dissolution were calculated using the experimental data of ionic thermodynamic properties obtained from the NBS tables. Additionally, the Marcus’ and Tissandier’s solvation free energy data for Li+, Na+, K+, Cl–, and Br– ions were compared with the conventional solvation free energies by substituting into our self-consistent thermodynamic cycle. Furthermore, Tissandier’s absolute solvation free energy data were used as the training set to refit the Lennard-Jones parameters of OPLS-AA force field for ions. To predict salt solubilities, an assumption of a pseudo-solvent was proposed to characterize the coupling work of a solute with its environment from infinitely diluted to saturated solutions, indicating that the Gibbs energy change of solvation process is a function of ionic strength. Following the self-consistency of the cycle, the newly derived formulas were used to determine the salt solubilities by interpolating the intersection of Gibbs free energy of dissolution and the zero free energy line. The refined ion parameters can also predict the structure and thermodynamic properties of aqueous electrolyte solutions, such as densities, pair correlation functions, hydration numbers, mean activity coefficients, vapor pressures, and the radial dependences of the net charge at 298.15 K and 1 bar. Our method can be used to characterize the solid–liquid equilibria of ions or charged particles in aqueous systems. Furthermore, for highly concentrated strong electrolyte systems, it is essential to introduce accurate water models and polarizable force fields

Graphic representation of the SPARQL query for finding the compound similar to Dexamethasone.

<p>Graphic representation of the SPARQL query for finding the compound similar to Dexamethasone.</p