Genome-wide analysis of chromatin features identifies histone modification sensitive and insensitive yeast transcription factors

We propose a method to predict yeast transcription factor targets by integrating histone modification profiles with transcription factor binding motif information. It shows improved predictive power compared to a binding motif-only method. We find that transcription factors cluster into histone-sensitive and -insensitive classes. The target genes of histone-sensitive transcription factors have stronger histone modification signals than those of histone-insensitive ones. The two classes also differ in tendency to interact with histone modifiers, degree of connectivity in protein-protein interaction networks, position in the transcriptional regulation hierarchy, and in a number of additional features, indicating possible differences in their transcriptional regulation mechanisms

Augmented reality in medical education: a systematic review

Introduction: The field of augmented reality (AR) is rapidly growing with many new potential applications in medical education. This systematic review investigated the current state of augmented reality applications (ARAs) and developed an analytical model to guide future research in assessing ARAs as teaching tools in medical education. Methods: A literature search was conducted using PubMed, Embase, Web of Science, Cochrane Library, and Google Scholar. This review followed PRISMA guidelines and included publications from January 1, 2000 to June 18, 2018. Inclusion criteria were experimental studies evaluating ARAs implemented in healthcare education published in English. Our review evaluated study quality and determined whether studies assessed ARA validity using criteria established by the GRADE Working Group and Gallagher et al., respectively. These findings were used to formulate an analytical model to assess the readiness of ARAs for implementation in medical education. Results: We identified 100,807 articles in the initial literature search; 36 met inclusion criteria for final review and were categorized into three categories: Surgery (23), Anatomy (9), and Other (4). The overall quality of the studies was poor and no ARA was tested for all five stages of validity. Our analytical model evaluates the importance of research quality, application content, outcomes, and feasibility of an ARA to gauge its readiness for implementation. Conclusion: While AR technology is growing at a rapid rate, the current quality and breadth of AR research in medical training is insufficient to recommend the adoption into educational curricula. We hope our analytical model will help standardize AR assessment methods and define the role of AR technology in medical education

Deep Active Learning for Classifying Cancer Pathology Reports

Background: Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model. Results: We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes. Conclusions: Active learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling

Trypanocidal and leishmanicidal activity of six limonoids

Six limonoids [kotschyienone A and B (1, 2), 7-deacetylgedunin (3), 7-deacetyl-7-oxogedunin (4), andirobin (5) and methyl angolensate (6)] were investigated for their trypanocidal and leishmanicidal activities using bloodstream forms of Trypanosoma brucei and promastigotes of Leishmania major. Whereas all compounds showed anti-trypanosomal activity, only compounds 1–4 displayed anti-leishmanial activity. The 50% growth inhibition (GI 50) values for the trypanocidal and leishmanicidal activity of the compounds ranged between 2.5 and 14.9 μM. Kotschyienone A (1) was found to be the most active compound with a minimal inhibition concentration (MIC) value of 10 μM and GI 50 values between 2.5 and 2.9 μM. Only compounds 1 and 3 showed moderate cytotoxicity against HL-60 cells with MIC and GI 50 values of 100 μM and 31.5–46.2 μM, respectively. Compound 1 was also found to show activity against intracellular amastigotes of L. major with a GI 50 value of 1.5 μM. The results suggest that limonoids have potential as drug candidates for the development of new treatments against trypanosomiasis and leishmaniasis

CHARMM-GUI Membrane Builder Toward Realistic Biological Membrane Simulations

This is the peer reviewed version of the following article: Wu, E. L., Cheng, X., Jo, S., Rui, H., Song, K. C., Dávila-Contreras, E. M., … Im, W. (2014). CHARMM-GUI Membrane Builder Toward Realistic Biological Membrane Simulations. Journal of Computational Chemistry, 35(27), 1997–2004. http://doi.org/10.1002/jcc.23702, which has been published in final form at http://doi.org/10.1002/jcc.23702. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.CHARMM-GUI Membrane Builder, http://www.charmm-gui.org/input/membrane, is a web-based user interface designed to interactively build all-atom protein/membrane or membrane-only systems for molecular dynamics simulation through an automated optimized process. In this work, we describe the new features and major improvements in Membrane Builderthat allow users to robustly build realistic biological membrane systems, including (1) addition of new lipid types such as phosphoinositides, cardiolipin, sphingolipids, bacterial lipids, and ergosterol, yielding more than 180 lipid types, (2) enhanced building procedure for lipid packing around protein, (3) reliable algorithm to detect lipid tail penetration to ring structures and protein surface, (4) distance-based algorithm for faster initial ion displacement, (5) CHARMM inputs for P21 image transformation, and (6) NAMD equilibration and production inputs. The robustness of these new features is illustrated by building and simulating a membrane model of the polar and septal regions of E. coli membrane, which contains five lipid types: cardiolipin lipids with two types of acyl chains and phosphatidylethanolamine lipids with three types of acyl chains. It is our hope that CHARMM-GUI Membrane Builder becomes a useful tool for simulation studies to better understand the structure and dynamics of proteins and lipids in realistic biological membrane environments

Identification of Jun loss promotes resistance to histone deacetylase inhibitor entinostat through Myc signaling in luminal breast cancer

Abstract Background Based on promising phase II data, the histone deacetylase inhibitor entinostat is in phase III trials for patients with metastatic estrogen receptor-positive breast cancer. Predictors of sensitivity and resistance, however, remain unknown. Methods A total of eight cell lines and nine mouse models of breast cancer were treated with entinostat. Luminal cell lines were treated with or without entinostat at their IC50 doses, and MMTV/Neu luminal mouse tumors were untreated or treated with entinostat until progression. We investigated these models using their gene expression profiling by microarray and copy number by arrayCGH. We also utilized the network-based DawnRank algorithm that integrates DNA and RNA data to identify driver genes of resistance. The impact of candidate drivers was investigated in The Cancer Genome Atlas and METABRIC breast cancer datasets. Results Luminal models displayed enhanced sensitivity to entinostat as compared to basal-like or claudin-low models. Both in vitro and in vivo luminal models showed significant downregulation of Myc gene signatures following entinostat treatment. Myc gene signatures became upregulated on tumor progression in vivo and overexpression of Myc conferred resistance to entinostat in vitro. Further examination of resistance mechanisms in MMTV/Neu tumors identified a portion of mouse chromosome 4 that had DNA copy number loss and low gene expression. Within this region, Jun was computationally identified to be a driver gene of resistance. Jun knockdown in cell lines resulted in upregulation of Myc signatures and made these lines more resistant to entinostat. Jun-deleted samples, found in 17–23% of luminal patients, had significantly higher Myc signature scores that predicted worse survival. Conclusions Entinostat inhibited luminal breast cancer through Myc signaling, which was upregulated by Jun DNA loss to promote resistance to entinostat in our models. Jun DNA copy number loss, and/or high MYC signatures, might represent biomarkers for entinostat responsiveness in luminal breast cancer