Adaptation to Different Human Populations by HIV-1 Revealed by Codon-Based Analyses

Several codon-based methods are available for detecting adaptive evolution in protein-coding sequences, but to date none specifically identify sites that are selected differentially in two populations, although such comparisons between populations have been historically useful in identifying the action of natural selection. We have developed two fixed effects maximum likelihood methods: one for identifying codon positions showing selection patterns that persist in a population and another for detecting whether selection is operating differentially on individual codons of a gene sampled from two different populations. Applying these methods to two HIV populations infecting genetically distinct human hosts, we have found that few of the positively selected amino acid sites persist in the population; the other changes are detected only at the tips of the phylogenetic tree and appear deleterious in the long term. Additionally, we have identified seven amino acid sites in protease and reverse transcriptase that are selected differentially in the two samples, demonstrating specific population-level adaptation of HIV to human populations

Machine-learning to Stratify Diabetic Patients Using Novel Cardiac Biomarkers and Integrative Genomics

Background: Diabetes mellitus is a chronic disease that impacts an increasing percentage of people each year. Among its comorbidities, diabetics are two to four times more likely to develop cardiovascular diseases. While HbA1c remains the primary diagnostic for diabetics, its ability to predict long-term, health outcomes across diverse demographics, ethnic groups, and at a personalized level are limited. The purpose of this study was to provide a model for precision medicine through the implementation of machine-learning algorithms using multiple cardiac biomarkers as a means for predicting diabetes mellitus development. Methods: Right atrial appendages from 50 patients, 30 non-diabetic and 20 type 2 diabetic, were procured from the WVU Ruby Memorial Hospital. Machine-learning was applied to physiological, biochemical, and sequencing data for each patient. Supervised learning implementing SHapley Additive exPlanations (SHAP) allowed binary (no diabetes or type 2 diabetes) and multiple classifcation (no diabetes, prediabetes, and type 2 diabetes) of the patient cohort with and without the inclusion of HbA1c levels. Findings were validated through Logistic Regression (LR), Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes (NB), Support Vector Machine (SVM), and Classifcation and Regression Tree (CART) models with tenfold cross validation. Results: Total nuclear methylation and hydroxymethylation were highly correlated to diabetic status, with nuclear methylation and mitochondrial electron transport chain (ETC) activities achieving superior testing accuracies in the predictive model (~84% testing, binary). Mitochondrial DNA SNPs found in the D-Loop region (SNP-73G, -16126C, and -16362C) were highly associated with diabetes mellitus. The CpG island of transcription factor A, mitochondrial (TFAM) revealed CpG24 (chr10:58385262, P=0.003) and CpG29 (chr10:58385324, P=0.001) as markers correlating with diabetic progression. When combining the most predictive factors from each set, total nuclear methylation and CpG24 methylation were the best diagnostic measures in both binary and multiple classifcation sets. Conclusions: Using machine-learning, we were able to identify novel as well as the most relevant biomarkers associated with type 2 diabetes mellitus by integrating physiological, biochemical, and sequencing datasets. Ultimately, this approach may be used as a guideline for future investigations into disease pathogenesis and novel biomarker discover

The flavor puzzle in multi-Higgs models

We reconsider the flavor problem in the models with two Higgs doublets. By studying two generation toy models, we look for flavor basis independent constraints on Yukawa couplings that will give us the mass hierarchy while keeping all Yukawa couplings of the same order. We then generalize our findings to the full three generation Standard Model. We find that we need two constraints on the Yukawa couplings to generate the observed mass hierarchy, and a slight tuning of Yukawa couplings of order 10%, much less than the Standard Model. We briefly study how these constraints can be realized, and show how flavor changing currents are under control for $K-\bar{K}$ mixing in the near-decoupling limit.Comment: 26 pages, typos are corrected, references are added, the final versio

BioTIME: A database of biodiversity time series for the Anthropocene.

MotivationThe BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables includedThe database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.Spatial location and grainBioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).Time period and grainBioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.Major taxa and level of measurementBioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.Software format.csv and .SQL

Cytokine Combination Therapy with Erythropoietin and Granulocyte Colony Stimulating Factor in a Porcine Model of Acute Myocardial Infarction

PurposeErythropoietin (EPO) and granulocyte colony stimulating factor (GCSF) have generated interest as novel therapies after myocardial infarction (MI), but the effect of combination therapy has not been studied in the large animal model. We investigated the impact of prolonged combination therapy with EPO and GCSF on cardiac function, infarct size, and vascular density after MI in a porcine model.MethodsMI was induced in pigs by a 90 min balloon occlusion of the left anterior descending coronary artery. 16 animals were treated with EPO+GCSF, or saline (control group). Cardiac function was assessed by echocardiography and pressure-volume measurements at baseline, 1 and 6 weeks post-MI. Histopathology was performed 6 weeks post-MI.ResultsAt week 6, EPO+GCSF therapy stabilized left ventricular ejection fraction, (41 ± 1% vs. 33 ± 1%, p < 0.01) and improved diastolic function compared to the control group. Histopathology revealed increased areas of viable myocardium and vascular density in the EPO+GCSF therapy, compared to the control. Despite these encouraging results, in a historical analysis comparing combination therapy with monotherapy with EPO or GCSF, there were no significant additive benefits in the LVEF and volumes overtime using the combination therapy.ConclusionOur findings indicate that EPO+GCSF combination therapy promotes stabilization of cardiac function after acute MI. However, combination therapy does not seem to be superior to monotherapy with either EPO or GCSF