Online Interactive Teaching Modules Enhance Quantitative Proficiency of Introductory Biology Students

There is widespread agreement within the scientific and education communities that undergraduate biology curricula fall short in providing students with the quantitative and interdisciplinary problem-solving skills they need to obtain a deep understanding of biological phenomena and be prepared fully to contribute to future scientific inquiry. MathBench Biology Modules were designed to address these needs through a series of interactive, Web-based modules that can be used to supplement existing course content across the biological sciences curriculum. The effect of the modules was assessed in an introductory biology course at the University of Maryland. Over the course of the semester, students showed significant increases in quantitative skills that were independent of previous math course work. Students also showed increased comfort with solving quantitative problems, whether or not they ultimately arrived at the correct answer. A survey of spring 2009 graduates indicated that those who had experienced MathBench in their course work had a greater appreciation for the role of mathematics in modern biology than those who had not used MathBench. MathBench modules allow students from diverse educational backgrounds to hone their quantitative skills, preparing them for more complex mathematical approaches in upper-division courses

Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa

Correlates of protective cellular immunity revealed by analysis of population-level immune escape pathways in HIV-1

HLA class I-associated polymorphisms identified at the population level mark viral sites under immune pressure by individual HLA alleles. As such, analysis of their distribution, frequency, location, statistical strength, sequence conservation, and other properties offers a unique perspective from which to identify correlates of protective cellular immunity. We analyzed HLA-associated HIV-1 subtype B polymorphisms in 1,888 treatment-naïve, chronically infected individuals using phylogenetically informed methods and identified characteristics of HLA-associated immune pressures that differentiate protective and nonprotective alleles. Over 2,100 HLA-associated HIV-1 polymorphisms were identified, approximately one-third of which occurred inside or within 3 residues of an optimally defined cytotoxic T-lymphocyte (CTL) epitope. Differential CTL escape patterns between closely related HLA alleles were common and increased with greater evolutionary distance between allele group members. Among 9-mer epitopes, mutations at HLA-specific anchor residues representedthe most frequently detected escape type: these occurred nearly 2-fold more frequently than expected by chance and were computationally predicted to reduce peptide-HLA binding nearly 10-fold on average. Characteristics associated with protective HLA alleles (defined using hazard ratios for progression to AIDS from natural history cohorts) included the potential to mount broad immune selection pressures across all HIV-1 proteins except Nef, the tendency to drive multisite and/or anchor residue escape mutations within known CTL epitopes, and the ability to strongly select mutations in conserved regions within HIV's structural and functional proteins. Thus, the factors defining protective cellular immune responsesmay be more complex than simply targeting conserved viral regions. The results provide new information to guide vaccine design and immunogenicity studies