bluuebunny/crossref_metadata_2025_split huggingface dataset:
Learning a genome-wide score of human-mouse conservation at the functional genomics level
Authors: Kwon Soo Bin , Ernst Jason 
Published: September 2020

AbstractIdentifying genomic regions with functional genomic properties that are conserved between human and mouse is an important challenge in the context of mouse model studies. To address this, we take a novel approach and learn a score of evidence of conservation at the functional genomics level by integrating large-scale information in a compendium of epigenomic, transcription factor binding, and transcriptomic data from human and mouse. The computational method we developed to do this, Learning Evidence of Conservation from Integrated Functional genomic annotations (LECIF), trains a neural network, which is then used to generate a genome-wide score in human and mouse. The resulting LECIF score highlights human and mouse regions with shared functional genomic properties and captures correspondence of biologically similar human and mouse annotations even though it was not explicitly given such information. LECIF will be a resource for mouse model studies.
Heritable variation in resistance to the endonuclear parasiteHolospora undulataacross clades ofParamecium caudatum
Authors: Weiler Jared , Zilio Giacomo , Zeballos Nathalie , Noergaard Louise , Alberto Winiffer D. Conce , Krenek Sascha , Kaltz Oliver , Bright Lydia 
Published: September 2020

AbstractResistance is a key determinant in interactions between hosts and their parasites. Understanding the amount and distribution of genetic variation in this trait can provide insights into (co)evolutionary processes and their potential to shape patterns of diversity in natural populations. Using controlled inoculation in experimental mass cultures, we investigated the quantitative genetic variation in resistance to the bacterial parasiteHolospora undulataacross a worldwide collection of strains of its ciliate hostParamecium caudatum. We combined the observed variation with available information on the phylogeny and biogeography of the strains. We found substantial variation in resistance among strains (with broad-sense heritability > 0.5), repeatable between laboratories and ranging from total resistance to near-complete susceptibility. Early (one week post inoculation) measurements provided higher estimates of resistance heritability than did later measurements (2-3 weeks), possibly due to diverging epidemiological dynamics in replicate cultures of the same strains. Genetic distance (based on a neutral marker) was positively correlated with the difference in resistance phenotype between strains (r = 0.45), essentially reflecting differences between highly divergent clades (haplogroups) within the host species. Haplogroup A strains, mostly European, were less resistant to the parasite (49% infection prevalence) than non-European haplogroup B strains (28%). At a smaller geographical scale (within Europe), strains that are geographically closer to the parasite origin (Southern Germany) were more susceptible to infection than those from further away. These patterns are consistent with a picture of local parasite adaptation. Our study demonstrates ample natural genetic variation in resistance on which selection can act and hints at symbiont adaptation producing signatures in geographic and lineage-specific patterns of resistance in this model system.
Genome-wide Chromatin Accessibility is Restricted by ANP32E
Authors: Murphy Kristin E. , Meng Fanju W. , Makowski Claire E. , Murphy Patrick J. 
Published: September 2020

ABSTRACTGenome-wide chromatin state underlies gene expression potential and cellular function. Epigenetic features and nucleosome positioning contribute to the accessibility of DNA, but widespread regulators of chromatin state are largely unknown. Our study investigates how control of genomic H2A.Z localization by ANP32E contributes to chromatin state in mouse fibroblasts. We define H2A.Z as a universal chromatin accessibility factor, and demonstrate that through antagonism of H2A.Z, ANP32E restricts genome-wide DNA access. In the absence of ANP32E, H2A.Z accumulates at promoters in a hierarchical manner. H2A.Z initially localizes downstream of the transcription start site, and if H2A.Z is already present downstream, additional H2A.Z accumulates upstream. This hierarchical H2A.Z accumulation coincides with improved nucleosome positioning, heightened transcription factor binding, and increased expression of neighboring genes. Thus, ANP32E dramatically influences genome-wide chromatin accessibility through refinement of H2A.Z patterns, providing a means to reprogram chromatin state and to hone gene expression levels.
A Hybrid of Light-Field and Light-Sheet Imaging to Decouple Myocardial Biomechanics from Intracardiac Flow Dynamics
Authors: Wang Zhaoqiang , Ding Yichen , Satta Sandro , Roustaei Mehrdad , Fei Peng , Hsiai Tzung K. 
Published: September 2020

AbstractBiomechanical forces intimately contribute to cardiac morphogenesis. However, 4-D (3-D space + time) imaging is needed to investigate the developmental cardiac mechanics with high temporal and spatial resolution. We hereby integrated light-sheet fluorescence microscopy (LSFM) with light-field microscopy (LFM), to simultaneously visualize myocardial contractility and intracardiac blood flow in three dimensions at 200 volumes per second (vps). LSFM allows for reconstruction of the myocardial contraction in zebrafish embryo; and LFM enables simultaneous tracking of the blood cells entering and leaving the contracting heart. We herein established particle tracking velocimetry to interrogate the trajectories of intracardiac blood cells, and we demonstrated deformable image registration to reveal a decrease in the myocardial contractility from atrioventricular (AV) canal to the outflow tract (OFT). We imaged myocardium undergoing torsional contraction and blood flow undergoing regurgitation. Taken together, the integration of light-field and light-sheet microscopy, followed by an image-based analysis pipeline, provides the biomechanical insights into coupling myocardial kinetics with rotational contraction along with intracardiac flow dynamics during development.
Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity
Authors: Ba-Alawi Wail , Nair Sisira Kadambat , Li Bo , Mammoliti Anthony , Smirnov Petr , Mer Arvind Singh , Penn Linda , Haibe-Kains Benjamin 
Published: September 2020

ABSTRACTIdentifying biomarkers predictive of cancer cells’ response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have boosted the research for finding predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common challenge in these methods is the lack of interpretability as to how they make the predictions and which features were the most associated with response, hindering the clinical translation of these models. To alleviate this issue, we develop a new machine learning pipeline based on the recent LOBICO approach that explores the space of bimodally expressed genes in multiple largein vitropharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. Using our method, we used a compendium of three of the largest pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rate in independent datasets.
Comparison between cultivation and sequencing based approaches for microbiota analysis in swabs and biopsies of chronic wounds
Authors: Mahnic Aleksander , Breznik Vesna , Ihan Maja Bombek , Rupnik Maja 
Published: September 2020

AbstractChronic wounds are a prominent health concern affecting 0.2% of individuals in the Western population. Microbial colonization and the consequent infection contribute significantly to the healing process of chronic wounds. We have compared cultivation and 16S amplicon sequencing (16S-AS) for the characterization of bacterial populations in swabs and biopsy tissues obtained from 45 chronic wounds and analysed metadata for wound-specific and clinical-outcome-associated correlations with bacterial community structure.Using cultivation approach, we detected a total of 39 bacterial species, on average 2.89 per sample (SD=1.93). Comparison of cultivation results between swabs and biopsy samples showed no significant advantage of one sampling method over the other. 16S-AS was advantageous in comparison to the cultivation approach in case of highly diverse communities, where we could additionally detect numerous obligate and facultative anaerobic bacteria from generaAnaerococcus, Finegoldia, Porphyromonas, MorganellaandProvidencia. Based on the community diversity, chronic wound microbiota could be distributed into three groups, however, no correlation between groups and clinical outcome was observed. Clinically estimated presence of biofilm and a larger surface area at the initial visit were most significantly associated with unfavourable clinical outcomes after one-year follow-up visit.Corynebacteriumwas the single most predictive bacterial genus associated with unfavourable clinical outcomes in our study.
Discovery of divided RdRp sequences and a hitherto unknown genomic complexity in fungal viruses
Authors: Chiba Yuto , Yaguchi Takashi , Urayama Syun-ichi , Hagiwara Daisuke 
Published: September 2020

AbstractBy identifying variations in viral RNA genomes, cutting-edge metagenome technology has potential to reshape current concepts about the evolution of RNA viruses. This technology, however, cannot process low-homology genomic regions properly, leaving the true diversity of RNA viruses unappreciated. To overcome this technological limitation we applied an advanced method, Fragmented and Primer-Ligated Double-stranded (ds) RNA Sequencing (FLDS), to screen RNA viruses from 155 fungal isolates, which allowed us to obtain complete viral genomes in a homology-independent manner. We created a high-quality catalog of 19 RNA viruses (12 viral species) that infectAspergillusisolates. Among them, nine viruses were not detectable by the conventional methodology involving agarose gel electrophoresis of dsRNA, a hallmark of RNA virus infections. Segmented genome structures were determined in 42% of the viruses. Some RNA viruses had novel genome architectures; one contained a dual methyltransferase domain and another had a separated RNA-dependent RNA polymerase (RdRp) gene. A virus from a different fungal taxon (Pyricularia) had an RdRp sequence that was separated on different segments, suggesting that a divided RdRp is widely present among fungal viruses, despite the belief that all RNA viruses encode RdRp as a single gene. These findings illustrate the previously hidden diversity and evolution of RNA viruses, and prompt reconsideration of the structural plasticity of RdRp. By highlighting the limitations of conventional surveillance methods for RNA viruses, we showcase the potential of FLDS technology to broaden current knowledge about these viruses.Author SummaryThe development of RNA-seq technology has facilitated the discovery of RNA viruses in all types of biological samples. However, it is technically difficult to detect highly novel viruses using RNA-seq. We successfully reconstructed the genomes of multiple novel fungal RNA viruses by screening host fungi using a new technology, FLDS. Surprisingly, we identified two viral species whose RNA-dependent RNA polymerase (RdRp) proteins were separately encoded on different genome segments, overturning the commonly accepted view of the positional unity of RdRp proteins in viral genomes. This new perspective on divided RdRp proteins should hasten the discovery of viruses with unique RdRp structures that have been overlooked, and further advance current knowledge and understanding of the diversity and evolution of RNA viruses.
Epidemiology and clinical outcome of COVID-19: A multi-centre cross sectional study from Bangladesh
Authors: Mannan Adnan , Hamidullah Mehedi H. M. , Chy Naim Uddin Hasan A , Qayum Md. Omar , Akter Farhana , Rob Abdur , Biswas Prasun , Hossain Sanjida , Ayub Mustak Ibn 
Published: September 2020

AbstractObjectivesTo investigate SARS-CoV-2 associated epidemiology and clinical outcomes in Bangladesh to understand the course of COVID-19 pandemic and suggest prevention measures.MethodsA cross-sectional retrospective study was conducted among 1,021 RT-PCR confirmed but recovered COVID-19 cases from six participating hospitals in Bangladesh.ResultsOf the total sample, 111 (10.9%) cases were asymptomatic while the number of symptomatic cases were 910 (89.1%). Higher prevalence of COVID-19 persisted in the male population (75%) and for the 31-40 age group. More than 85% of the samples reported BCG vaccination mark. Common symptoms observed in our study samples were fever (72.4%), cough (55.9%), loss of taste (40.7%) and body ache (40%); whereas for the biochemical parameters, Neutrophil (46.4%), D-dimer (46.1%), Ferritin (37.9%) and SGPT (36.8%) levels were found elevated. Post-COVID complications including pain (31.8%), loss of concentration (24.4%) and anxiety or depression (23.1%) were found significantly prevalent.ConclusionOur study has shown that adult males aged between 31-40 in Bangladesh are more vulnerable to being infected with COVID-19. With an indication for the rising trend of the asymptomatic cases, deployment of interventions to curb further community spread is necessary to avoid the grave outcomes of COVID-19 in Bangladesh.
Robust SARS-COV-2 serological population screens via multi-antigen rules-based approach
Authors: Fotis Christos , Meimetis Nikolaos , Tsolakos Nikos , Politou Marianna , Akinosoglou Karolina , Pliaka Vicky , Minia Angeliki , Terpos Evangelos , Trougakos Ioannis P. , Mentis Andreas , Marangos Markos , Panayiotakopoulos George , Dimopoulos Meletios A. , Gogos Charalampos , Spyridonidis Alexandros , Alexopoulos Leonidas G. 
Published: September 2020

ABSTRACTMore than 300 SARS-COV-2 serological tests have recently been developed using either the nucleocapsid phosphoprotein (N), the spike glycoprotein subunit (S1), and more recently the receptor binding domain (RBD). Most of the assays report very good clinical performance characteristics in well-controlled clinical settings. However, there is a growing belief that good performance characteristics that are obtained during clinical performance trials might not be sufficient to deliver good diagnostic results in population-wide screens that are usually characterized with low seroprevalence. In this paper, we developed a serological assay against N, S1 and RBD using a bead-based multiplex platform and a rules-based computational approach to assess the performance of single and multi-antigen readouts in well-defined clinical samples and in a population-wide serosurvey from blood donors. Even though assays based on single antigen readouts performed similarly well in the clinical samples, there was a striking difference between the antigens on the population-wide screen. Asymptomatic individuals with low antibody titers and sub-optimal assay specificity might contribute to the large discrepancies in population studies with low seroprevalence. A multi-antigen assay requiring partial agreement between RBD, N and S1 readouts exhibited enhanced specificity, less dependency on assay cut-off values and an overall more robust performance in both sample settings. Our data suggest that assays based on multiple antigen readouts combined with a rules-based computational consensus can provide a more robust platform for routine antibody screening.One Sentence SummaryClinical and Population-level performance of single and multiplex SARS-CoV-2 serological assays.
Live cell PNA labelling enables erasable fluorescence imaging of membrane proteins
Authors: Gavins Georgina C. , Gröger Katharina , Bartoschek Michael D. , Wolf Philipp , Beck-Sickinger Annette G. , Bultmann Sebastian , Seitz Oliver 
Published: September 2020

AbstractDNA nanotechnology is an emerging field, which promises fascinating opportunities for the manipulation and imaging of proteins on a cell surface. The key to progress in the area is the ability to create the nucleic acid-protein junction in the context of living cells. Here we report a covalent labelling reaction, which installs a biostable peptide nucleic acid (PNA) tag. The reaction proceeds within minutes and is specific for proteins carrying a 2 kDa coiled coil peptide tag. Once installed the PNA label serves as a generic landing platform that enables the recruitment of fluorescent dyes via nucleic acid hybridization. We demonstrate the versatility of this approach by recruiting different fluorophores, assembling multiple fluorophores for increased brightness, and achieving reversible labelling by way of toehold mediated strand displacement. Additionally, we show that labelling can be carried out using two different coiled coil systems, with EGFR and ETBR, on both HEK293 and CHO cells. Finally, we apply the method to monitor internalization of EGFR on CHO cells.
Small molecules inhibit SARS-COV-2 induced aberrant inflammation and viral replication in mice by targeting S100A8/A9-TLR4 axis
Authors: Guo Qirui , Zhao Yingchi , Li Junhong , Liu Jiangning , Guo Xuefei , Zhang Zeming , Cao Lili , Luo Yujie , Bao Linlin , Wang Xiao , Wei Xuemei , Deng Wei , Chen Luoying , Zhu Hua , Gao Ran , Qin Chuan , Wang Xiangxi , You Fuping 
Published: September 2020

SummaryThe SARS-CoV-2 pandemic poses an unprecedented public health crisis. Accumulating evidences suggest that SARS-CoV-2 infection causes dysregulation of immune system. However, the unique signature of early immune responses remains elusive. We characterized the transcriptome of rhesus macaques and mice infected with SARS-CoV-2. Alarmin S100A8 was robustly induced by SARS-CoV-2 in animal models as well as in COVID-19 patients. Paquinimod, a specific inhibitor of S100A8/A9, could reduce inflammatory response and rescue the pneumonia with substantial reduction of viral titers in SASR-CoV-2 infected animals. Remarkably, Paquinimod treatment resulted in 100% survival of mice in a lethal model of mouse coronavirus (MHV) infection. A novel group of neutrophils that contributed to the uncontrolled inflammation and onset of COVID-19 were dramatically induced by coronavirus infections. Paquinimod treatment could reduce these neutrophils and regain antiviral responses, unveiling key roles of S100A8/A9 and noncanonical neutrophils in the pathogenesis of COVID-19, highlighting new opportunities for therapeutic intervention.
Geospatial HIV-1 subtype C gp120 sequence diversity and its predicted impact on broadly neutralizing antibody sensitivity
Authors: Sutar Jyoti , Deshpande Suprit , Mullick Ranajoy , Hingankar Nitin , Patel Vainav , Bhattacharya Jayanta 
Published: September 2020

AbstractEvolving diversity in globally circulating HIV-1 subtypes presents formidable challenge in defining and developing neutralizing antibodies for prevention and treatment. HIV-1 subtype C is responsible for majority of global HIV-1 infections. Broadly neutralizing antibodies (bnAbs) capable of neutralizing distinct HIV-1 subtypes by targeting conserved vulnerable epitopes on viral envelope protein (Env) are being considered as promising antiviral agents for prevention and treatment. In the present study, we examined the diversity in genetic signatures and attributes that differentiate region-specific global HIV-1 subtype Cgp120sequences associated with virus neutralization outcomes to key bnAbs having distinct epitope specificities. A total of 1814 full length HIV-1 subtype C gp120 sequence from 37 countries were retrieved from Los Alamos National Laboratory HIV database (www.hiv.lanl.gov). The amino acid sequences were assessed for their phylogenetic association, variable loop lengths and prevalence of potential N-linked glycosylation sites (pNLGS). Responses of these sequences to bnAbs were predicted with a machine learning algorithm ‘bNAb-ReP’ and compared with those reported in the CATNAP database. Phylogenetically, sequences from Asian countries including India clustered together however differed significantly when compared with pan African subtype C sequences. Variable loop lengths within Indian and African clusters were distinct from each other, specifically V1, V2 and V4 loops. Furthermore, V1V2 and V2 alone sequences were also found to vary significantly in their charges. Pairwise analyses at each of the 25 pNLG sites indicated distinct country specific profiles. Highly significant differences (p<0.001***) were observed in prevalence of four pNLGS (N130, N295, N392 and N448) between South Africa and India, having most disease burden associated with subtype C. Our findings highlight that the distinctly evolving clusters within global intra-subtype Cgp120sequences are likely to influence the disparate region-specific sensitivity of circulating HIV-1 subtype C to bnAbs.
Benefits of Deep Learning Classification of Continuous Noninvasive Brain-Computer Interface Control
Authors: Stieger James R. , Engel Stephen A. , Suma Daniel , He Bin 
Published: September 2020

AbstractNoninvasive brain-computer interfaces (BCIs) assist paralyzed patients by providing access to the world without requiring surgical intervention. While the performance of noninvasive BCI is hindered by long training times and variable user proficiency, it may be improved by deep learning methods, such as convolutional neural networks (CovNets). Prior work has suggested that the application of deep learning to EEG signals collected over the motor cortex during motor imagery based BCI increases classification accuracy in standard sensorimotor rhythm (SMR) BCI datasets. It remains to be seen whether these improvements can generalize to practical scenarios such as continuous control tasks (as opposed to prior work reporting one classification per trial), or whether valuable information remains latent outside of the motor cortex (as no prior work has compared full scalp coverage to motor only electrode montages). Here we report that deep learning methods significantly increase offline classification accuracy on an independent, large, and longitudinal online motor imagery BCI dataset with up to 4-classes and continuous 2-dimensional feedback. Improvements in classification accuracy were found to negatively correlate with initial online BCI performance, suggesting deep learning methods preferentially benefit BCI participants who need it most. The CovNets also significantly increased the information transfer rate (ITR) of the BCI system: They produced a two-fold increase in ITR without loss in classification accuracy when comparing CovNet models trained with full scalp EEG coverage to the traditional motor cortex specific decoding. Our results suggest that a variety of neural biomarkers useful for BCI, including those outside the motor cortex, can be detected through deep learning methods.