bluuebunny/crossref_metadata_2025_split huggingface dataset:
Statistical Inferences and Analysis based on the COVID-19 Data from the United States
Authors: Rethnakar Nivedita 
Published: December 2021

ABSTRACTThis paper investigates the mortality statistics of the COVID-19 pandemic from the United States perspective. We bring out several exciting and glaring aspects of the pandemic, otherwise hidden, using empirical data analysis and statistical inference tools. First, specific patterns seen in demographics such as race/ethnicity are analyzed qualitatively and quantitatively. We looked at the role of factors such as population density in mortality rates. A detailed study of the connections between COVID-19 and other respiratory diseases is also covered. Finally, we examine the temporal dynamics of the COVID-19 outbreak and vaccines’ stellar impact in controlling the pandemic. Statistical inference such as the ones gathered in this paper would be helpful for better scientific understanding, policy preparation, and thus adequately preparing, should a similar situation arise in the future.
TRAWLING: a Transcriptome Reference Aware of spLIciNG events
Authors: Nanni Noemi Di , Reyes Alejandro , Ho Daniel , Ihry Robert , Kauffmann Audrey , Durand Eric Y. , de Weck Antoine 
Published: December 2021

AbstractAlternative splicing is critical for human gene expression regulation and plays an important role in multiple human diseases. In this context, RNA sequencing has emerged as powerful approach to detect alternative splicing events.In parallel, fast alignment-free methods have emerged as a viable alternative to quantify gene and transcript level abundance from RNAseq data. However, the ability to detect differential splicing events is dependent on the annotation of the transcript reference provided by the user.Here, we introduce a new reference transcriptome aware of splicing events, TRAWLING, which simplifies the detection of aberrant splicing events in a fast and simple way. In addition, we evaluate the performances and the benefits of aligning transcriptome data to TRAWLING using three different RNA sequencing datasets: whole transcriptome sequencing, single cell RNA sequencing and Digital RNA with pertUrbation of Genes.Collectively, our comprehensive evaluation underlines the value of using TRAWLING in transcriptomic data analysis.Availability and implementationOur code is available athttps://github.com/Novartis/TRAWLING
The human brain networks mediating the vestibular sensation of self-motion
Authors: Hadi Zaeem , Pondeca Yuscah , Calzolari Elena , Mahmud Mohammad , Chepisheva Mariya , Smith Rebecca M , Rust Heiko , Sharp David J , Seemungal Barry M 
Published: December 2021

AbstractVestibular Agnosia - where peripheral vestibular activation triggers the usual reflex nystagmus response but with attenuated or no self-motion perception - is found in brain disease with disrupted cortical network functioning, e.g. traumatic brain injury (TBI) or neurodegeneration (Parkinson’s Disease). Patients with acute focal hemispheric lesions (e.g. stroke) do not manifest vestibular agnosia. Thus brain network mapping techniques, e.g. resting state functional MRI (rsfMRI), are needed to interrogate functional brain networks mediating vestibular agnosia. Whole-brain rsfMRI was acquired from 39 prospectively recruited acute TBI patients with preserved peripheral vestibular function, along with self-motion perceptual thresholds during passive yaw rotations in the dark. Following quality-control checks, 25 patient scans were analyzed. TBI patients were classified as having vestibular agnosia (n = 11) or not (n = 14) via laboratory testing of self-motion perception. Using independent component analysis, we found altered functional connectivity in the right superior longitudinal fasciculus and left rostral prefrontal cortex in vestibular agnosia. Moreover, regions of interest analyses showed both inter-hemispheric and intra-hemispheric network disruption in vestibular agnosia. In conclusion, our results show that vestibular agnosia is mediated by bilateral anterior and posterior network dysfunction and reveal the distributed brain mechanisms mediating vestibular self-motion perception.
High School Science Fair: Ethnicity Trends in Student Participation and Experience
Authors: Grinnell Frederick , Dalley Simon , Reisch Joan 
Published: December 2021

AbstractIn this paper, we report ethnicity trends in student participation and experience in high school science and engineering fair (SEFs). SEF participation showed significant ethnic diversity. For survey students, the approximate distribution was Asian-32%; Black-11%; Hispanic-20%; White-33%; Other-3%. Comparing the SEF level at which students competed from school to district to region to state levels, we observed that black students made up only 4.5% of the students who participated in SEF beyond the school level, whereas students from other ethnic groups were more equally represented at all levels. The lower percentage of Black students resulted from a combination of lower overall participation in SEF and lower percentage of those students who did participate to advance to SEFs beyond the school level. Students who advanced to SEFs beyond the school level frequently received help from scientists, coaching for the interview, and were not required to participate in SEF. Black students received the least help from scientists, were least likely to receive coaching for the interview, and were most likely to be required to participate in SEF. They also were most likely to receive no help from parents, teachers, or scientists. Asian and Hispanic students (63.8% and 56.8%) indicated a greater interest in careers in science and engineering (S&E) compared to Black and White students (43.7% & 50.7%). In addition to career interest, the most important experiences that correlated with students who indicated that SEF increased their interests in S&E were getting help from the internet, books and magazines; getting help fine tuning the report; and overcoming obstacles by doing more background research, making a timeline, and perseverance. Black students did not report a positive effect of any of these strategies but experienced time pressure as more of an obstacle than did other students. Our findings identify a wide range of student experiences associated with positive SEF outcomes that could be enhanced for all students but especially Black students. More involvement of scientists in helping students who participate in SEFs would be particularly valuable.
MitoVisualize: A resource for analysis of variants in human mitochondrial RNAs and DNA
Authors: Lake Nicole J. , Zhou Lily , Xu Jenny , Lek Monkol 
Published: December 2021

ABSTRACTWe present MitoVisualize, a new tool for analysis of the human mitochondrial DNA (mtDNA). MitoVisualize enables visualization of: (1) the position and effect of variants in mitochondrial transfer RNA (tRNA) and ribosomal RNA (rRNA) secondary structures alongside curated variant annotations, (2) data across RNA structures, such as to show all positions with disease-associated variants or with post-transcriptional modifications, and (3) the position of a base, gene or region in the circular mtDNA map, such as to show the location of a large deletion. All visualizations can be easily downloaded as figures for reuse. MitoVisualize can be useful for anyone interested in exploring mtDNA variation, though is designed to facilitate mtDNA variant interpretation in particular. MitoVisualize can be accessed via https://www.mitovisualize.org/. The source code is available at https://github.com/leklab/mito_visualize/.
Elucidating design principles for engineering cell-derived vesicles to inhibit SARS-CoV-2 infection
Authors: Gunnels Taylor F. , Stranford Devin M. , Mitrut Roxana E. , Kamat Neha P. , Leonard Joshua N. 
Published: December 2021

AbstractThe ability of pathogens to develop drug resistance is a global health challenge. The SARS-CoV-2 virus presents an urgent need wherein several variants of concern resist neutralization by monoclonal antibody therapies and vaccine-induced sera. Decoy nanoparticles—cell-mimicking particles that bind and inhibit virions—are an emerging class of therapeutics that may overcome such drug resistance challenges. To date, we lack quantitative understanding as to how design features impact performance of these therapeutics. To address this gap, here we perform a systematic, comparative evaluation of various biologically-derived nanoscale vesicles, which may be particularly well-suited to sustained or repeated administration in the clinic due to low toxicity, and investigate their potential to inhibit multiple classes of model SARS-CoV-2 virions. A key finding is that such particles exhibit potent antiviral efficacy across multiple manufacturing methods, vesicle subclasses, and virus-decoy binding affinities. In addition, these cell-mimicking vesicles effectively inhibit model SARS-CoV-2 variants that evade monoclonal antibodies and recombinant protein-based decoy inhibitors. This study provides a foundation of knowledge that may guide the design of decoy nanoparticle inhibitors for SARS-CoV-2 and other viral infections.
SARS-CoV-2 infection of olfactory epithelial cells and neurons drives acute lung injury and lethal COVID-19 in mice
Authors: Tang Alan T. , Buchholz David W. , Szigety Katherine M. , Imbhiaka Brian , Gao Siqi , Frankfurter Maxwell , Wang Min , Yang Jisheng , Hewins Peter , Mericko-Ishizuka Patricia , Adrian Leu N , Sterling Stephanie , Monreal Isaac A. , Sahler Julie , August Avery , Zhu Xuming , Jurado Kellie A. , Xu Mingang , Morrisey Edward E. , Millar Sarah E. , Aguilar Hector C. , Kahn Mark L. 
Published: December 2021

AbstractLethal COVID-19 is associated with respiratory failure that is thought to be caused by acute respiratory distress syndrome (ARDS) secondary to pulmonary infection. To date, the cellular pathogenesis has been inferred from studies describing the expression of ACE2, a transmembrane protein required for SARS-CoV-2 infection, and detection of viral RNA or protein in infected humans, model animals, and cultured cells. To functionally test the cellular mechanisms of COVID-19, we generated hACE2fl animals in which human ACE2 (hACE2) is expressed from the mouse Ace2 locus in a manner that permits cell-specific, Cre-mediated loss of function. hACE2fl animals developed lethal weight loss and hypoxemia within 7 days of exposure to SARS-CoV-2 that was associated with pulmonary infiltrates, intravascular thrombosis and patchy viral infection of lung epithelial cells. Deletion of hACE2 in lung epithelial cells prevented viral infection of the lung, but not weight loss, hypoxemia or death. Inhalation of SARS-CoV-2 by hACE2fl animals resulted in early infection of sustentacular cells with subsequent infection of neurons in the neighboring olfactory bulb and cerebral cortex— events that did not require lung epithelial cell infection. Pharmacologic ablation of the olfactory epithelium or Foxg1Cre mediated deletion of hACE2 in olfactory epithelial cells and neurons prevented lethality and neuronal infection following SARS-CoV-2 infection. Conversely, transgenic expression of hACE2 specifically in olfactory epithelial cells and neurons in Foxg1Cre; LSL-hACE2 mice was sufficient to confer neuronal infection associated with respiratory failure and death. These studies establish mouse loss and gain of function genetic models with which to genetically dissect viral-host interactions and demonstrate that lethal disease due to respiratory failure may arise from extrapulmonary infection of the olfactory epithelium and brain. Future therapeutic efforts focused on preventing olfactory epithelial infection may be an effective means of protecting against severe COVID-19.
A deep learning-based multisite neuroimage harmonization framework established with traveling-subject dataset
Authors: Tian Dezheng , Zeng Zilong , Sun Xiaoyi , Tong Qiqi , Li Huanjie , He Hongjian , Gao Jiahong , He Yong , Xia Mingrui 
Published: December 2021

AbstractThe accumulation of multisite large-sample MRI datasets collected by large brain research projects in the last decade has provided a critical resource for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects, observed in the imaging data and their derived structural and functional features, has prevented the derivation of consistent findings across different studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we proposed a deep learning-based framework to harmonize imaging data from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling-subject dataset from SRPBS and harmonized the gray matter volume maps from eight source sites to a target site. The proposed framework significantly eliminated inter-site differences in gray matter volume. The embedded encoders successfully captured both the abstract texture of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in site effect removal, data distribution homogenization, and intra-subject similarity improvement. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that could enhance reliability and reproducibility in multisite studies for brain development and brain disorders.
Met/HGFR triggers detrimental reactive microglia in TBI
Authors: Rehman Rida , Miller Michael , Krishnamurthy Sruthi Sankari , Kjell Jacob , Elsayed Lobna , olde Heuvel Florian , Conquest Alison , Chandrasekar Akila , Ludolph Albert , Boeckers Tobias , Mulaw Medhanie A , Goetz Magdalena , Morganti-Kossmann Maria Cristina , Takeoka Aya , Roselli Francesco 
Published: December 2021

AbstractThe complexity of the signaling events, cellular responses unfolding in neuronal, glial and immune cells upon Traumatic brain injury (TBI) constitutes an obstacle in elucidating pathophysiological links and targets for intervention. We used array phosphoproteomics in a murine mild blunt TBI to reconstruct the temporal dynamics of tyrosine-kinase signaling in TBI and then to scrutinize the large-scale effects of the perturbation of cMet/HGFR, VEGFR1 and Btk signaling by small molecules. cMet/HGFR emerged as a selective modifier of the early microglial response, and cMet/HGFR blockade prevented the induction of microglial inflammatory mediators, of reactive microglia morphology and of TBI-associated responses in neurons, vessels and brain extracellular matrix. Acute or prolonged cMet/HGFR inhibition ameliorated neuronal survival and motor recovery. Early elevation of HGF itself in the CSF of TBI patients suggest that this mechanism has translational value in human subjects. Our findings identify cMet/HGFR as a modulator of early neuroinflammation in TBI with translational potential and indicate several RTK families as possible additional targets for TBI treatment.SummaryControlling neuroinflammation in neurotrauma is an important but unachieved goal. This study exploits a moderate TBI model and array-based proteomics to identify cMet as a new inducer of reactive microglia. A small-molecule inhibitor of cMet contains microglial reactivity, reduces neuronal and vascular alterations, limits behavioural disturbances and accelerates recovery.HighlightsMet is activated in microglia upon TBI and drives microglial reactivity.A Met inhibitor reduces motor dysfunction upon TBI and promotes recovery.Blockade of MET prevents the appearance of a reactive microglia.The cMET inhibitor reduces the sub-acute neuronal loss after TBI.
scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data
Authors: Yang Fan , Wang Wenchuan , Wang Fang , Fang Yuan , Tang Duyu , Huang Junzhou , Lu Hui , Yao Jianhua 
Published: December 2021

AbstractAnnotating cell types based on the single-cell RNA-seq data is a prerequisite for researches on disease progress and tumor microenvironment. Here we show existing annotation methods typically suffer from lack of curated marker gene lists, improper handling of batch effect, and difficulty in leveraging the latent gene-gene interaction information, impairing their generalization and robustness. We developed a pre-trained deep neural network-based model scBERT (single-cell Bidirectional Encoder Representations from Transformers) to overcome the challenges. Following BERT’s approach of pre-train and fine-tune, scBERT obtains a general understanding of gene-gene interaction by being pre-trained on huge amounts of unlabeled scRNA-seq data and is transferred to the cell type annotation task of unseen and user-specific scRNA-seq data for supervised fine-tuning. Extensive and rigorous benchmark studies validated the superior performance of scBERT on cell type annotation, novel cell type discovery, robustness to batch effect, and model interpretability.
Effects of chronic exposure to fluoxetine, eicosapentaenoic acid, and lipopolysaccharide on behavior and hippocampal transcriptome in the rat model of prolonged chronic unpredictable stress
Authors: Demin Konstantin A. , Kolesnikova Tatiana O. , Galstyan David S. , Krotova Nataliya A. , Ilyin Nikita P. , Derzhavina Ksenia A. , Seredinskaya Maria , Kositsyn Yuriy M. , Sorokin Dmitry V. , Nerush Maria O. , Khaybaev Abubakar-Askhab S. , Pushkareva Sofia A. , Petersen Elena V. , de Abreu Murilo S. , Masharsky Alexey , Kalueff Allan V. 
Published: December 2021

AbstractAnimal models are widely used to study stress-induced affective disorders and associated with them neuroinflammation and other neuroimmune processes. Here, we examined rat behavioral and hippocampal transcriptomic responses to prolonged chronic unpredictable stress (PCUS), as well as following a 4-week treatment with a classical antidepressant fluoxetine, an anti-inflammatory agent eicosapentaenoic acid (EPA), a pro-inflammatory agent lipopolysaccharide (LPS) and their combinations. Overall, PCUS evoked an anxiety-like behavioral phenotype in rats (corrected by chronic fluoxetine alone or combined with other drugs), EPA was anxiolytic and LPS promoted anxiety in this model. PCUS evoked pronounced transcriptomic responses in rat hippocampi, including >200 differentially expressed genes. While pharmacological manipulations did not affect hippocampal gene expression markedly,Gpr6,Drd2andAdora2awere downregulated in stressed rats treated with fluoxetine+EPA, suggesting G protein-coupled receptor 6, dopamine D2 receptor and adenosine A2A receptor as potential evolutionarily conserved targets in chronic stress. Overall, these findings support the validity of rat PCUS paradigm as a useful tool to study stress-related affective pathologies and calls for further research probing how various conventional and novel drugs modulate behavioral and neurotranscriptomic biomarkers of chronic stress.
Prdm16 amplifies Notch signaling and suppresses venous lineage specification to prevent arteriovenous malformations during vascular development
Authors: Van Wauwe Jore , Craps Sander , Ashmita KC , Asuncion Lisabel , Vrancaert Pieter , Daems Margo , Finck Chris , Wright Shane , Müller Christian , Leigh Nicholas D. , Frye Maike , MacRae Calum A. , Luttun Aernout , Beerens Manu 
Published: December 2021

AbstractProper arterial versus venous endothelial lineage identity is required to establish a hierarchical network of arteries and veins and prevent the occurrence of life-threatening arteriovenous malformations. The molecular mechanisms that govern arteriovenous lineage specification remain however incompletely understood. Here, we demonstrate that the transcription factor Prdm16 is expressed in arterial but not venous endothelial cells (ECs) from the earliest stages of development, where it actively promotes arterial EC identity by enhancing canonical Notch activity, while simultaneously suppressing the alternative venous cell fate. Concordantly, our results in zebrafish show that Prdm16 coordinates proper arterial development and arteriovenous lineage specification together with canonical Notch signaling, as combined loss of prdm16 and notch in zebrafish invariably leads to arteriovenous malformations (AVMs). Although the arterializing effect of Prdm16 in human ECs is dependent on the absolute levels of the cleaved intracellular domain of the Notch receptors (NICD), Prdm16 does not increase NICD levels per se. Rather, Prdm16 physically and functionally interacts with NICD to potentiate its effect. Prdm16 further finetunes Notch signaling and arterial development by complexing with the Notch downstream effector Hey2, which regulates arterial lineage specification and development across species. Together, our data demonstrate that Prdm16 act as a rheostat for endothelial Notch activity and suggest that Prdm16 signaling may constitute a novel therapeutic target for AVMs.
Constructing a multiple-layer interactome for SARS-CoV-2 in the context of lung disease: Linking the virus with human genes and co-infecting microbes
Authors: Lou Shaoke , Li Tianxiao , Gerstein Mark 
Published: December 2021

AbstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused millions of deaths worldwide. Many efforts have focused on unraveling the mechanism of the viral infection to develop effective strategies for treatment and prevention. Previous studies have provided some clarity on the protein-protein interaction linkages occurring during the life cycle of viral infection; however, we lack a complete understanding of the full interactome, comprising human miRNAs and protein-coding genes and co-infecting microbes. To comprehensively determine this, we developed a statistical modeling method using latent Dirichlet allocation (called MLCrosstalk, for multiple-layer crosstalk) to fuse many types of data to construct the full interactome of SARS-CoV-2. Specifically, MLCrosstalk is able to integrate samples with multiple layers of information (e.g., miRNA and microbes), enforce a consistent topic distribution on all data types, and infer individual-level linkages (i.e., differing between patients). We also implement a secondary refinement with network propagation to allow our microbe-gene linkages to address larger network structures (e.g., pathways). Using MLCrosstalk, we generated a list of genes and microbes linked to SARS-CoV-2. Interestingly, we found that two of the identified microbes, Rothia mucilaginosa and Prevotella melaninogenica, show distinct patterns representing synergistic and antagonistic relationships with the virus, respectively. We also identified several SARS-COV-2-associated pathways, including the VEGFA-VEGFR2 and immune response pathways, which may provide potential targets for drug design.