bluuebunny/crossref_metadata_2025_split huggingface dataset:
The impact of non-pharmaceutical interventions on the prevention and control of COVID-19 in New York City
Authors: Yang Jiannan , Zhang Qingpeng , Cao Zhidong , Gao Jianxi , Pfeiffer Dirk , Zhong Lu , Zeng Daniel Dajun 
Published: December 2020

AbstractThe emergence of coronavirus disease 2019 (COVID-19) has infected more than 37 million people worldwide. The control responses varied across countries with different outcomes in terms of epidemic size and social disruption. In this study, we presented an age-specific susceptible-exposed-infected-recovery-death model that considers the unique characteristics of COVID-19 to examine the effectiveness of various non-pharmaceutical interventions (NPIs) in New York City (NYC). Numerical experiments from our model show that the control policies implemented in NYC reduced the number of infections by 72% (IQR 53-95), and the number of deceased cases by 76% (IQR 58-96) by the end of 2020, respectively. Among all the NPIs, social distancing for the entire population and the protection for the elderly in the public facilities is the most effective control measure in reducing severe infections and deceased cases. School closure policy may not work as effectively as one might expect in terms of reducing the number of deceased cases. Our simulation results provide novel insights into the city-specific implementation of NPIs with minimal social disruption considering the locations and population characteristics.
CellKb Immune: a manually curated database of mammalian hematopoietic marker gene sets for rapid cell type identification
Authors: Patil Ajay , Patil Ashwini 
Published: December 2020

AbstractUsing single-cell RNA sequencing, it is now possible to get the detailed gene expression pattern in each cell for millions of cells in a single experiment under varying conditions. Annotating cells by assigning a type or state to them is important for correct interpretation of results. In spite of current advances in technology, assigning cell types in single-cell datasets remains a bottleneck due to the lack of a comprehensive reference database and an accurate search method in a single tool. CellKb Immune is a knowledgebase of manually collected, curated and annotated marker gene sets representing cell types in the immune response in mouse. It allows users to find matching cell types in literature given a list of genes. We evaluated the contents and search methodology of CellKb Immune using a leave-one-out approach. We further used CellKb Immune to annotate previously defined marker gene sets from a bulk RNA-seq dataset to confirm its accuracy and coverage. CellKb Immune provides an easy-to-use online tool with a reliable method to find matching cell types and annotate cells in single-cell experiments. It is available athttps://www.cellkb.com/immune.
Spatiotemporal Dynamics of Intra-tumoral Dependence on NEK2-EZH2 Signaling in Glioblastoma Cancer Progression
Authors: Wang Jia , Pavliukov Marat S , Yamashita Daisuke , Cheng Peng , Zhang Zhuo , Kim Sung-Hak , Nakano Mayu A , Xie Wanfu , Chen Dongquan , Frett Brendan , Hu Wen-hao , Shin Yong Jae , Lee Yeri , Goidts Violaine , Nam Do-Hyun , Li Hong-yu , Nakano Ichiro 
Published: December 2020

AbstractThe highly lethal brain cancer glioblastoma undergoes dynamic changes in molecular profile and cellular phenotype throughout tumor core establishment and in primary-to-recurrent tumor progression. These dynamic changes allow glioblastoma tumors to escape from multimodal therapies, resulting in patient lethality. Here, we identified the emergence of dependence on NEK2-mediated EZH2 signaling, specifically in therapy-resistant tumor core-located glioblastoma cells. In patient-derived glioblastoma core models, NEK2 was required for in vivo tumor initiation, propagation, and radio-resistance. Mechanistically, in glioblastoma core cells, NEK2 binds with EZH2 to prevent its proteasome-mediated degradation in a kinase-dependent manner. Clinically, NEK2 expression is elevated in recurrent tumors after therapeutic failure as opposed to their matched primary untreated cases, and its high expression is indicative of worse prognosis. For therapeutic development, we designed a novel NEK2 kinase inhibitor CMP3a, which effectively attenuated growth of murine glioblastoma models and exhibited a synergistic effect with radiation therapy. Collectively, the emerging NEK2-EZH2 signaling axis is critical in glioblastoma, particularly within the tumor core, and the small molecule inhibitor CMP3a for NEK2 is a potential novel therapeutic agent for glioblastoma.
Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data
Authors: Lause Jan , Berens Philipp , Kobak Dmitry 
Published: December 2020

AbstractBackgroundStandard preprocessing of single-cell RNA-seq UMI data includes normalization by sequencing depth to remove this technical variability, and nonlinear transformation to stabilize the variance across genes with different expression levels. Instead, two recent papers propose to use statistical count models for these tasks: Hafemeister & Satija [1] recommend using Pearson residuals from negative binomial regression, while Townes et al. [2] recommend fitting a generalized PCA model. Here, we investigate the connection between these approaches theoretically and empirically, and compare their effects on downstream processing.ResultsWe show that the model of Hafemeister and Satija produces noisy parameter estimates because it is overspecified, which is why the original paper employs post-hoc smoothing. When specified more parsimoniously, it has a simple analytic solution equivalent to the rank-one Poisson GLM-PCA of Townes et al. Further, our analysis indicates that per-gene overdispersion estimates in Hafemeister and Satija are biased, and that the data are in fact consistent with the overdispersion parameter being independent of gene expression. We then use negative control data without biological variability to estimate the technical overdispersion of UMI counts, and find that across several different experimental protocols, the data are close to Poisson and suggest very moderate overdispersion. Finally, we perform a benchmark to compare the performance of Pearson residuals, variance-stabilizing transformations, and GLM-PCA on scRNA-seq datasets with known ground truth.ConclusionsWe demonstrate that analytic Pearson residuals strongly outperform other methods for identifying biologically variable genes, and capture more of the biologically meaningful variation when used for dimensionality reduction.
Cholesterol metabolism is a potential therapeutic target in Duchenne Muscular Dystrophy
Authors: Amor F. , Vu Hong A. , Corre G. , Sanson M. , Suel L. , Blaie S. , Servais L. , Voit T. , Richard I. , Israeli D. 
Published: December 2020

AbstractBackgroundDuchenne Muscular Dystrophy (DMD) is a lethal muscle disease detected in approximately 1:5000 male births. DMD is caused by mutations in the DMD gene, encoding a critical protein that link the cytoskeleton and the extracellular matrix in skeletal and cardiac muscles. The primary consequence of the disrupted link between the extracellular matrix and the myofiber actin cytoskeleton is thought to involve sarcolemma destabilization, perturbation of Ca+2homeostasis, activation of proteases, mitochondrial damage and tissue degeneration. A recently emphasized secondary aspect of the dystrophic process is a progressive metabolic change of the dystrophic tissue; however, the mechanism and nature of the metabolic dysregulation is yet poorly understood. In this study, we characterized a molecular mechanism of metabolic perturbation in DMD.MethodsWe sequenced plasma miRNA in a DMD cohort, comprising of 54 DMD patients treated or not by glucocorticoid, compared to 27 healthy controls, in three age groups. We developed an original approach for the biological interpretation of miRNA dysregulation, and produced a novel hypothesis concerning metabolic perturbation in DMD. We then used the mdx mouse model for DMD for the investigation of this hypothesis.ResultsWe identified 96 dysregulated miRNAs, of which 74 were up- and 22 down-regulated in DMD. We confirmed the dysregulation in DMD of Dystro-miRs, Cardio-miRs and a large number of the DLK1-DIO3 miRNAs. We also identified numerous dysregulated miRNAs, yet unreported in DMD. Bioinformatics analysis of both target and host genes for dysregulated miRNAs predicted that lipid metabolism might be a critical metabolic perturbation in DMD. Investigation of skeletal muscles of the mdx mouse uncovered dysregulation of transcription factors of cholesterol and fatty acid metabolism (SREBP1 and SREBP2), perturbation of the mevalonate pathway, and accumulation of cholesterol. Elevated cholesterol level was also found in muscle biopsies of DMD patients. Treatment of mdx mice with Simvastatin, a cholesterol-reducing agent, normalized these perturbations and partially restored the dystrophic parameters.ConclusionThis investigation supports that cholesterol metabolism and the mevalonate pathway are potential therapeutic targets in DMD.
RUNX1 controls the dynamics of cell cycle entry of naïve resting B cells by regulating expression of cell cycle and immunomodulatory genes in response to BCR stimulation
Authors: Thomsen Inesa , Kunowska Natalia , de Souza Roshni , Moody Anne-Marie , Crawford Greg , Wang Yi-Fang , Khadayate Sanjay , Strid Jessica , Karimi Mohammad M. , Barr Alexis , Dillon Niall , Sabbattini Pierangela 
Published: December 2020

ABSTRACTRUNX1 is a transcription factor that plays key roles in haematopoietic development and in adult haematopoiesis and lymphopoiesis. Here we report that RUNX1 is also involved in controlling the dynamics of cell cycle entry of naïve resting B cells in response to stimulation of the B cell receptor (BCR). Conditional knockout ofRunx1in mouse resting B cells resulted in accelerated entry of the cells into S-phase following BCR engagement. Our results indicate that Runx1 regulates the cyclin D2 (Ccnd2) gene, the immediate early genes,Fosl2,Atf3andEgr2, and the Notch effectorRbpj, in B cells, reducing the rate at which transcription of these genes increases following BCR stimulation. RUNX1 interacts with the chromatin remodeller SRCAP, recruiting it to promoter and enhancer regions of theCcnd2gene. BCR-mediated activation triggers switching between binding of RUNX1 and its paralog RUNX3 and between SRCAP and the SWI/SNF remodelling complex member BRG1. We also find that RUNX1 regulates expression of a number of immunomodulatory genes in resting B cells. These include the interferon receptor subunit geneIfnar1, which is upregulated in B cells from lupus patients, thePtpn22gene, which has been identified as a major lupus risk allele, and theLrrk2gene, which is mutated in familial Parkinson’s disease. The hyperresponsiveness of theRunx1knockout B cells to antigen stimulation and its role in regulating a suite of genes that are known to be associated with autoimmune disease suggest that RUNX1 is a major regulator of B cell tolerance and autoimmunity.
Brain-wide functional networks associated with anatomically- and functionally-defined hippocampal subfields using ultrahigh-resolution fMRI
Authors: Chang Wei-Tang , Langella Stephanie K. , Tang Yichuan , Zhang Han , Yap Pew-Thian , Giovanello Kelly S. , Lin Weili 
Published: December 2020

ABSTRACTThe hippocampus is critical for learning and memory and may be separated into anatomically-defined hippocampal subfields (aHPSFs). Many studies have shown that aHPSFs, and their respective functional networks, are differentially vulnerable to a variety of disorders. Hippocampal functional networks, particularly during resting state, are generally analyzed using aHPSFs as seed regions, with the underlying assumption that the function within a subfield is homogeneous, yet heterogeneous between subfields. However, several prior studies that have utilized aHPSFs and assessed brain-wide cortical connectivity have observed similar resting-state functional connectivity profiles between aHPSFs. Alternatively, data-driven approaches offer a means to investigate hippocampal functional organization without a priori assumptions. However, insufficient spatial resolution may lead to partial volume effects at the boundaries of hippocampal subfields, resulting in a number of caveats concerning the reliability of the results. Hence, we developed a functional Magnetic Resonance Imaging (fMRI) sequence on a 7T MR scanner achieving 0.94 mm isotropic resolution with a TR of 2s and brain-wide coverage to 1) investigate whether hippocampal functional segmentation with ultrahigh-resolution data demonstrate similar anatomical, lamellar structures in the hippocampus, and 2) define and compare the brain-wide FC associated with fine-grained aHPSFs and functionally-defined hippocampal subfields (fHPSFs). Using a spatially restricted hippocampal Independent Component Analysis (ICA) approach, this study showed that fHPSFs were arranged along the longitudinal axis of the hippocampus that were not comparable to the lamellar structures of aHPSFs. Contrary to the anatomically defined hippocampal subfields which are bilaterally symmetrical, 13 out of 20 fHPSFs were unilateral. For brain-wide FC, the fHPSFs rather than aHPSFs revealed that a number of fHPSFs connected specifically with some of the functional networks. The visual and sensorimotor networks preferentially connected with different portions of CA1, CA3 and CA4/DG. The DMN was also found to connect more extensively with posterior subfields rather than anterior subfields. Finally, the frontoparietal network (FPN) was anticorrelated with the head portion of CA1. The investigation of functional networks associated with the fHPSFs may enhance the sensitivity of biomarkers for a range of neurological disorders, as network-based approaches take into account disease-related alterations in brain-wide interconnections rather than measuring the regional changes of hippocampus.
Single-Cell Manifold Preserving Feature Selection (SCMER)
Authors: Liang Shaoheng , Mohanty Vakul , Dou Jinzhuang , Miao Qi , Huang Yuefan , Müftüoğlu Muharrem , Ding Li , Peng Weiyi , Chen Ken 
Published: December 2020

1AbstractA key challenge in studying organisms and diseases is to detect rare molecular programs and rare cell populations (RCPs) that drive development, differentiation, and transformation. Molecular features such as genes and proteins defining RCPs are often unknown and difficult to detect from unenriched single-cell data, using conventional dimensionality reduction and clustering-based approaches.Here, we propose a novel unsupervised approach, named SCMER, which performs UMAP style dimensionality reduction via selecting a compact set of molecular features with definitive meanings.We applied SCMER in the context of hematopoiesis, lymphogenesis, tumorigenesis, and drug resistance and response. We found that SCMER can identify non-redundant features that sensitively delineate both common cell lineages and rare cellular states ignored by current approaches.SCMER can be widely used for discovering novel molecular features in a high dimensional dataset, designing targeted, cost-effective assays for clinical applications, and facilitating multi-modality integration.
Lack of evidence of ACE2 expression and replicative infection by SARS-CoV-2 in human endothelial cells
Authors: McCracken Ian , Saginc Gaye , He Liqun , Huseynov Alik , Daniels Alison , Fletcher Sarah , Peghaire Claire , Kalna Viktoria , Andaloussi-Mäe Maarja , Muhl Lars , Craig Nicky M. , Griffiths Samantha J. , Haas Jürgen G. , Tait-Burkard Christine , Lendahl Urban , Birdsey Graeme M. , Betsholtz Christer , Noseda Michela , Baker Andrew , Randi Anna M. 
Published: December 2020

AbstractA striking feature of severe COVID-19 is thrombosis in large as well as small vessels of multiple organs. This has led to the assumption that SARS-CoV-2 virus directly infects and damages the vascular endothelium. However, endothelial expression of ACE2, the cellular receptor for SARS-CoV-2, has not been convincingly demonstrated. Interrogating human bulk and single-cell transcriptomic data, we foundACE2expression in endothelial cells to be extremely low or absentin vivoand not upregulated by exposure to inflammatory agentsin vitro. Also, the endothelial chromatin landscape at theACE2locus showed presence of repressive and absence of activation marks, suggesting that the gene is inactive in endothelial cells. Finally, we failed to achieve infection and replication of SARS-CoV-2 in cultured human endothelial cells, which were permissive to productive infection by coronavirus 229E that uses CD13 as the receptor. Our data suggest that SARS-Cov-2 is unlikely to infect endothelial cells directly; these findings are consistent with a scenario where endothelial injury is indirectly caused by the infection of neighbouring epithelial cells and/or due to systemic effects mediated by immune cells, platelets, complement activation, and/or proinflammatory cytokines.
Thinking ahead: spontaneous prediction in context as a keystone of language in humans and machines
Authors: Goldstein Ariel , Zada Zaid , Buchnik Eliav , Schain Mariano , Price Amy , Aubrey Bobbi , Nastase Samuel A. , Feder Amir , Emanuel Dotan , Cohen Alon , Jansen Aren , Gazula Harshvardhan , Choe Gina , Rao Aditi , Kim Se Catherine , Casto Colton , Fanda Lora , Doyle Werner , Friedman Daniel , Dugan Patricia , Melloni Lucia , Reichart Roi , Devore Sasha , Flinker Adeen , Hasenfratz Liat , Levy Omer , Hassidim Avinatan , Brenner Michael , Matias Yossi , Norman Kenneth A. , Devinsky Orrin , Hasson Uri 
Published: December 2020

AbstractDeparting from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models are trained to generate appropriate linguistic responses in a given context. We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process natural language: 1) both are engaged in continuous next-word prediction before word-onset; 2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise (i.e., prediction error signals); 3) both represent words as a function of the previous context. In support of these three principles, our findings indicate that: a) the neural activity before word-onset contains context-dependent predictive information about forthcoming words, even hundreds of milliseconds before the words are perceived; b) the neural activity after word-onset reflects the surprise level and prediction error; and c) autoregressive DLM contextual embeddings capture the neural representation of context-specific word meaning better than arbitrary or static semantic embeddings. Together, our findings suggest that autoregressive DLMs provide a novel and biologically feasible computational framework for studying the neural basis of language.
Newfound coding potential of transcripts unveils missing members of human protein communities
Authors: Leblanc Sebastien , Brunet Marie A , Jacques Jean-François , Lekehal Amina M , Duclos Andréa , Tremblay Alexia , Bruggeman-Gascon Alexis , Samandi Sondos , Brunelle Mylène , Cohen Alan A , Scott Michelle S , Roucou Xavier 
Published: December 2020

AbstractRecent proteogenomic approaches have led to the discovery that regions of the transcriptome previously annotated as non-coding regions (i.e. UTRs, open reading frames overlapping annotated coding sequences in a different reading frame, and non-coding RNAs) frequently encode proteins (termed alternative proteins). This suggests that previously identified protein-protein interaction networks are partially incomplete since alternative proteins are not present in conventional protein databases. Here we used the proteogenomic resource OpenProt and a combined spectrum- and peptide-centric analysis for the re-analysis of a high throughput human network proteomics dataset thereby revealing the presence of 280 alternative proteins in the network. We found 19 genes encoding both an annotated (reference) and an alternative protein interacting with each other. Of the 136 alternative proteins encoded by pseudogenes, 38 are direct interactors of reference proteins encoded by their respective parental gene. Finally, we experimentally validate several interactions involving alternative proteins. These data improve the blueprints of the human protein-protein interaction network and suggest functional roles for hundreds of alternative proteins.
A Robust Genome-Wide Association Study Uncovers Signature Genetic Alterations amongPseudomonas aeruginosaCystic Fibrosis Isolates
Authors: Hwang Wontae , Yong Ji Hyun , Min Kyung Bae , Lee Kang-Mu , Yoon Sang Sun 
Published: December 2020

AbstractPseudomonas aeruginosa(PA) is an opportunistic pathogen that causes diverse human infections such as chronic airway infection in cystic fibrosis (CF) patients. Although many sequenced genomes are available, a comprehensive comparison between genomes of CF versus non-CF PA isolates remains yet to be conducted. In order to gain a deeper understanding into the PA adaptation in the CF airway, we performed a Genome-Wide Association Study (GWAS) using a total of 1,001 PA genomes. Genetic variations uniquely identified among CF isolates were categorized into (i) alterations in protein-coding regions either large- or small-scale and (ii) polymorphic variations in intergenic regions. We introduced each CF-specific genetic alteration into the genome of PAO1, a prototype PA strain and experimentally validated their outcomes. Loci readily mutated among CF isolates include genes encoding a probable sulphatase and a probable TonB-dependent receptor (PA2332~PA2336), L-cysteine transporter (YecS, PA0313) and a probable transcriptional regulator (PA5438). A promoter region of heme/hemoglobin uptake outer membrane receptor (PhuR, PA4710) was similarly identified as meaningfully different between the CF and non-CF isolate groups. Our analysis, the first of its kind, highlights how PA evolves its genome to persist and survive within the context of chronic CF infection.