bluuebunny/crossref_metadata_2025_split huggingface dataset:
ER-PM contacts regulate apical domain formation in hepatocytes
Authors: Chun Chung Gary Hong , Burden Jemima J. , Lorvellec Maëlle , Gissen Paul , Stefan Christopher J. 
Published: April 2020

AbstractApico-basal membrane polarity is fundamental for epithelial cell development and function. Polarity factors including the small GTPase Cdc42, the Par3/Par6/aPKC complex, and cytoskeletal proteins are recruited by the anionic lipids phosphatidylinositol 4,5-bisphosphate and phosphatidylserine. But how these lipids accumulate at polarised sites remains unclear. We have examined roles of contacts between the endoplasmic reticulum and plasma membrane (ER-PM contacts) in generating lipid gradients during apical domain formation. Comprehensive electron microscopy analyses in hepatocytes and epithelial spheroids revealed two distinct ER-PM contact architectures that are spatially linked to apical and baso-lateral domains. Moreover, apical domain formation was delayed in HepG2 cells upon modulating the ER-PM contact proteins E-Syt1 and ORP5. We propose ER-PM contacts regulate apico-basal polarity via the lipid transfer proteins E-Syt1 and ORP5. Importantly, our findings suggest that the spatial organisation of ER-PM contacts is a conserved feature of polarised epithelial cells.
Peptidoglycan editing provides immunity toAcinetobacter baumanniiduring bacterial warfare
Authors: Le Nguyen-Hung , Peters Katharina , Espaillat Akbar , Sheldon Jessica R. , Gray Joe , Venanzio Gisela Di , Lopez Juvenal , Djahanschiri Bardya , Mueller Elizabeth A. , Hennon Seth W. , Levin Petra Anne , Ebersberger Ingo , Skaar Eric P. , Cava Felipe , Vollmer Waldemar , Feldman Mario F. 
Published: April 2020

AbstractPeptidoglycan (PG) is essential in most bacteria. Thus, it is often targeted by various assaults, including the host immune response, antibiotic treatment and interbacterial attacks via the type VI secretion system (T6SS). Here, we report that the Gram-negative bacteriumAcinetobacter baumanniistrain ATCC 17978 produces, secretes and incorporates the non-canonical D-amino acid D-Lysine into its PG during stationary phase. We show that PG editing increases the competitiveness ofA. baumanniiduring bacterial warfare by providing immunity against peptidoglycan-targeting T6SS effectors from various bacterial competitors. We propose that PG editing has evolved as an effective strategy for bacteria to overcome T6SS attacks. In contrast, we found that D-Lys production is detrimental to pathogenesis due, at least in part, to the activity of the human enzyme D-amino acid oxidase (DAO), which degrades D-Lys producing H2O2toxic to bacteria. Phylogenetic analyses indicate that the last common ancestor ofA. baumanniipossessed the ability to produce D-Lys. However, this trait was independently lost multiple times, likely reflecting the evolution ofA. baumanniias a human pathogen.One sentence summaryAcinetobacter baumanniiattains immunity against nonkin competitors during T6SS warfare by incorporating D-Lysine into its peptidoglycan.
DeepArk: modelingcis-regulatory codes of model species with deep learning
Authors: Cofer Evan M. , Raimundo João , Tadych Alicja , Yamazaki Yuji , Wong Aaron K. , Theesfeld Chandra L. , Levine Michael S. , Troyanskaya Olga G. 
Published: April 2020

AbstractTo enable large-scale analyses of regulatory logic in model species, we developed DeepArk (https://DeepArk.princeton.edu), a set of deep learning models of thecis-regulatory codes of four widely-studied species:Caenorhabditis elegans, Danio rerio,Drosophila melanogaster, andMus musculus. DeepArk accurately predicts the presence of thousands of different context-specific regulatory features, including chromatin states, histone marks, and transcription factors. In vivo studies show that DeepArk can predict the regulatory impact of any genomic variant (including rare or not previously observed), and enables the regulatory annotation of understudied model species.
Attenuation of the extracellular matrix increases the number of synapses but suppresses synaptic plasticity
Authors: Dembitskaya Yulia , Gavrilov Nikolay , Kraev Igor , Doronin Maxim , Tyurikova Olga , Semyanov Alexey 
Published: April 2020

AbstractThe brain extracellular matrix (ECM) is a proteoglycan complex that occupies the extracellular space between brain cells and regulates brain development, brain wiring, and synaptic plasticity. However, the action of the ECM on synaptic plasticity remains controversial. Here, we employed serial section electron microscopy to show that enzymatic attenuation of ECM with chondroitinase ABC (ChABC) triggers the appearance of new glutamatergic synapses onto thin dendritic spines of CA1 pyramidal neurons. The appearance of new synapses increased the ratio of the field excitatory postsynaptic potential (fEPSP) to presynaptic fiber volley (PrV), suggesting that these new synapses are formed on existing axonal fibers. However, both the mean miniature excitatory postsynaptic current (mEPSC) amplitude and AMPA/NMDA ratio were decreased, suggesting that ECM attenuation increased the proportion of ‘unpotentiated’ synapses. A higher proportion of unpotentiated synapses would be expected to promote long-term potentiation (LTP). Surprisingly, theta-burst induced LTP was suppressed by ChABC treatment. The suppression of LTP was accompanied by decreased excitability of CA1 pyramidal neurons due to the upregulation of small conductance Ca2+-activated K+(SK) channels. A pharmacological blockade of SK channels restored cell excitability and, expectedly, enhanced LTP above the level of control. This enhancement of LTP was abolished by a blockade of Rho-associated protein kinase (ROCK), which is involved in the maturation of dendritic spines. Thus, ECM attenuation enables the appearance of new synapses in the hippocampus, which is compensated for by a reduction in the excitability of postsynaptic neurons, thereby preventing network overexcitation at the expense of synaptic plasticity.
Computationally replicating the Smith et al. (2015) positive-negative mode linking functional connectivity and subject measures
Authors: Goyal Nikhil , Moraczewski Dustin , Bandettini Peter , Finn Emily S. , Thomas Adam 
Published: April 2020

AbstractUnderstanding brain functionality and predicting human behavior based on functional brain activity is a major goal of neuroscience. Numerous studies have been conducted to investigate the relationship between functional brain activity and attention, subject characteristics, autism, psychiatric disorders, and more. By modeling brain activity data as networks, researchers can leverage the mathematical tools of graph and network theory to probe these relationships. In their landmark study, Smith et al. (2015) analyzed the relationship of young adult connectomes and subject measures, using data from the Human Connectome Project (HCP). Using canonical correlation analysis (CCA), Smith et al. found that there was a single prominent CCA mode which explained a statistically significant percentage of the observed variance in connectomes and subject measures. They also found a strong positive correlation of 0.87 between the primary CCA mode connectome and subject measure weights. In this study, we computationally replicate the findings of the original study in both the HCP 500 and HCP 1200 subject releases. The exact computational replication in the HCP 500 dataset was a success, validating our analysis pipeline for extension studies. The extended replication in the larger HCP 1200 dataset was partially successful and demonstrated a dominant primary mode.
Prediction of protein-protein interactions based on elastic net and deep forest
Authors: Yu Bin , Chen Cheng , Yu Zhaomin , Ma Anjun , Liu Bingqiang , Ma Qin 
Published: April 2020

AbstractPrediction of protein-protein interactions (PPIs) helps to grasp molecular roots of disease. However, web-lab experiments to predict PPIs are limited and costly. Using machine-learning-based frameworks can not only automatically identify PPIs, but also provide new ideas for drug research and development from a promising alternative. We present a novel deep-forest-based method for PPIs prediction. First, pseudo amino acid composition (PAAC), autocorrelation descriptor (Auto), multivariate mutual information (MMI), composition-transition-distribution (CTD), and amino acid composition PSSM (AAC-PSSM), and dipeptide composition PSSM (DPC-PSSM) are adopted to extract and construct the pattern of PPIs. Secondly, elastic net is utilized to optimize the initial feature vectors and boost the predictive performance. Finally, GcForest-PPI model based on deep forest is built up. Benchmark experiments reveal that the accuracy values ofSaccharomyces cerevisiaeandHelicobacter pyloriare 95.44% and 89.26%. We also apply GcForest-PPI on independent test sets and CD9-core network, crossover network, and cancer-specific network. The evaluation shows that GcForest-PPI can boost the prediction accuracy, complement experiments and improve drug discovery. The datasets and code of GcForest-PPI could be downloaded athttps://github.com/QUST-AIBBDRC/GcForest-PPI/.
StackRAM: a cross-species method for identifying RNA N6-methyladenosine sites based on stacked ensemble
Authors: Yu Zhaomin , Tian Baoguang , Liu Yaning , Zhang Yaqun , Ma Qin , Yu Bin 
Published: April 2020

ABSTRACTN6-methyladenosine is a prevalent RNA methylation modification, which plays an important role in various biological processes. Accurate identification of the m6A sites is fundamental to deeply understand the biological functions and mechanisms of the modification. However, the experimental methods for detecting m6A sites are usually time-consuming and expensive, and various computational methods have been developed to identify m6A sites in RNA. This paper proposes a novel cross-species computational method StackRAM using machine learning algorithms to identify the m6A sites in S. cerevisiae、H. sapiensand A. thaliana. First, the RNA sequences features are extracted through binary encoding, chemical property, nucleotide frequency, k-mer nucleotide frequency, pseudo dinucleotide composition, and position-specific trinucleotide propensity, and the initial feature set is obtained by feature fusion. Secondly, the Elastic Net is used for the first time to filter redundant and noisy information and retain important features for m6A sites classification. Finally, the base-classifiers output probabilities are combined with the optimal feature subset corresponding to the Elastic Net, and the combination feature input the second-stage meta-classifier SVM. The jackknife test on training dataset S.cerevisiaeindicates that the prediction performance of StackRAM is superior to the current state-of-the-art methods. StackRAM prediction accuracy for independent test datasets H.sapiensand A.thalianareach 92.30% and 87.06%, respectively. Therefore, StackRAM has development potential in cross-species prediction and can be a useful method for identifying m6A sites. The source code and all datasets are available athttps://github.com/QUST-AIBBDRC/StackRAM/.
DriverGroup: A novel method for identifying driver gene groups
Authors: Pham Vu VH , Liu Lin , Bracken Cameron P , Goodall Gregory J , Li Jiuyong , Le Thuc D 
Published: April 2020

AbstractMotivationIdentifying cancer driver genes is a key task in cancer informatics. Most exisiting methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesise that there are driver gene groups that work in concert to regulate cancer and we develop a novel computational method to detect those driver gene groups.ResultsWe develop a novel method namedDriverGroupto detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (1) Constructing the gene network, (2) Discovering critical nodes of the constructed network, and (3) Identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance ofDriverGroupin detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step ofDriverGroup. The application ofDriverGroupto DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then applyDriverGroupto the BRCA dataset to identify coding and non-coding driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness ofDriverGroup.Availability and implementationDriverGroupis available athttps://github.com/pvvhoang/DriverGroupContactThuc.Le@unisa.edu.auSupplementary informationSupplementary data are available atBioinformaticsonline.
Identification of simplified microbial communities that inhibitClostridioides difficileinfection through dilution/extinction
Authors: Auchtung Jennifer M. , Preisner Eva C. , Collins James , Lerma Armando I. , Britton Robert A. 
Published: April 2020

AbstractThe gastrointestinal microbiome plays an important role in limiting susceptibility to infection withClostridioides difficile. To better understand the ecology of bacteria important forC. difficilecolonization resistance, we developed an experimental platform to simplify complex communities of fecal bacteria through dilution and rapidly screen for their ability to inhibitC. difficile in vitro. We simplified complex communities from six fecal donors and found that 17% of simplified communities inhibitedC. difficilegrowth when initially isolated and when re-cultured from frozen stocks. Composition varied between simplified communities based upon fecal donor used for dilution; complexity ranged from 19-67 OTUs. One simplified community could be further simplified through dilution and retain the ability to inhibitC. difficile. We tested efficacy of seven simplified communities in a humanized microbiota mouse model and found that four communities were able to significantly reduce the severity of the initialC. difficileinfection and limit susceptibility to disease relapse. Analysis of fecal microbiomes from treated mice demonstrated that simplified communities accelerated recovery of endogenous bacteria and led to stable engraftment of at least 20% of bacteria from simplified communities. Overall, the insights gained through the identification and characterization of these simplified communities increase our understanding of the microbial dynamics ofC. difficileinfection and recovery.ImportanceClostridioides difficileis the leading cause of antibiotic-associated diarrhea and a significant healthcare burden. While fecal microbiota transplantation is highly effective at treating recurrentC. difficiledisease, uncertainties about the undefined composition of fecal material and potential long-term unintended health consequences have motivated studies to identify new communities of simple microbes that will be effective at treating disease. This work describes a platform for rapidly identifying and screening new simplified communities of microbes for efficacy in treatingC. difficileinfection and identifies four new simplified communities of microbes with potential for development of new therapies to treatC. difficiledisease in humans. While this platform was developed and validated to model infection withC. difficile, the underlying principles described in the paper could be easily modified to develop therapeutics to treat other gastrointestinal diseases.
Predictive modeling of secondary pulmonary hypertension in left ventricular diastolic dysfunction
Authors: Harrod Karlyn K. , Rogers Jeffrey L. , Feinstein Jeffrey A. , Marsden Alison L. , Schiavazzi Daniele E. 
Published: April 2020

AbstractDiastolic dysfunction is a common pathology occurring in about one third of patients affected by heart failure. This condition is not associated with a marked decrease in cardiac output or systemic pressure and therefore is more difficult to diagnose than its systolic counterpart. Compromised relaxation or increased stiffness of the left ventricle with or without mitral valve stenosis induces an increase in the upstream pulmonary pressures, and is classified as secondary or group II (2018 Nice classification) pulmonary hypertension. This may result in an increase in the right ventricular afterload leading to right ventricular failure. Elevated pulmonary pressures are therefore an important clinical indicator of diastolic heart failure (sometimes referred to asheart failure with preserved ejection fraction, HFpEF), showing significant correlation with associated mortality. Accurate measurements of this quantity, however, are typically obtained through invasive catheterization, and after the onset of symptoms. In this study, we use the hemodynamic consistency of a differential-algebraic circulation model to predict pulmonary pressures in adult patients from other, possibly non-invasive, clinical data. We investigate several aspects of the problem, including the ability of model outputs to represent a sufficiently wide pathologic spectrum, identifiability of its parameters, to the accuracy of the predicted pulmonary pressures. We also find that a classifier using the assimilated model parameters as features is free from the problem of missing data and is able to detect pulmonary hypertension with sufficiently high accuracy. For a cohort of 82 patients suffering from various degrees of heart failure severity we show that systolic, diastolic and wedge pulmonary pressures can be estimated on average within 8, 6 and 6 mmHg, respectively. We also show that, in general, increased data availability leads to improved predictions.
A Novel Transfer Learning Model for Predictive Analytics using Incomplete Multimodality Data
Authors: Liu Xiaonan , Chen Kewei , Wu Teresa , Weidman David , Lure Fleming Y. M. , Li Jing ,   The Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Published: April 2020

AbstractMultimodality datasets are becoming increasingly common in various domains to provide complementary information for predictive analytics. In health care, diagnostic imaging of different kinds contains complementary information about an organ of interest, which allows for building a predictive model to accurately detect a certain disease. In manufacturing, multi-sensory datasets contain complementary information about the process and product, allowing for more accurate quality assessment. One significant challenge in fusing multimodality data for predictive analytics is that the multiple modalities are not universally available for all samples due to cost and accessibility constraints. This results in a unique data structure called Incomplete Multimodality Dataset (IMD) for which existing statistical models fall short. We propose a novel Incomplete-Multimodality Transfer Learning (IMTL) model that builds a predictive model for each sub-cohort of samples with the same missing modality pattern, and meanwhile couples the model estimation processes for different sub-cohorts to allow for transfer learning. We develop an Expectation-Maximization (EM) algorithm to estimate the parameters of IMTL and further extend it to a collaborative learning paradigm that is specifically valuable for patient privacy preservation in health care applications of the IMTL. We prove two advantageous properties of IMTL: the ability for out-of-sample prediction and a theoretical guarantee for a larger Fisher information compared with models without transfer learning. IMTL is applied to diagnosis and prognosis of the Alzheimer’s Disease (AD) at an early stage of the disease called Mild Cognitive Impairment (MCI) using incomplete multimodality imaging data. IMTL achieves higher accuracy than competing methods without transfer learning.
HiTaC: a hierarchical taxonomic classifier for fungal ITS sequences compatible with QIIME2
Authors: Miranda Fábio M. , Azevedo Vasco C. , Ramos Rommel J. , Renard Bernhard Y. , Piro Vitor C. 
Published: April 2020

AbstractBackgroundFungi play a key role in several important ecological functions, ranging from organic matter decomposition to symbiotic associations with plants. Moreover, fungi naturally inhabit the human body and can be beneficial when administered as probiotics. In mycology, the internal transcribed spacer (ITS) region was adopted as the universal marker for classifying fungi. Hence, an accurate and robust method for ITS classification is not only desired for the purpose of better diversity estimation, but it can also help us gain a deeper insight into the dynamics of environmental communities and ultimately comprehend whether the abundance of certain species correlate with health and disease. Although many methods have been proposed for taxonomic classification, to the best of our knowledge, none of them fully explore the taxonomic tree hierarchy when building their models. This in turn, leads to lower generalization power and higher risk of committing classification errors.ResultsHere we introduce HiTaC, a robust hierarchical machine learning model for accurate ITS classification, which requires a small amount of data for training and can handle imbalanced datasets. HiTaC was thoroughly evaluated with the established TAXXI benchmark and could correctly classify fungal ITS sequences of varying lengths and a range of identity differences between the training and test data. HiTaC outperforms state-of-the-art methods when trained over noisy data, consistently achieving higher F1-score and sensitivity across different taxonomic ranks, improving sensitivity by 6.9 percentage points over top methods in the most noisy dataset available on TAXXI.ConclusionsHiTaC is publicly available at the Python package index, BIO-CONDA and Docker Hub. It is released under the new BSD license, allowing free use in academia and industry. Source code and documentation, which includes installation and usage instructions, are available athttps://gitlab.com/dacs-hpi/hitac.