bluuebunny/crossref_metadata_2025_split huggingface dataset:
From first to second wave: follow-up of the prospective Covid-19 cohort (KoCo19) in Munich (Germany)
Authors: Radon Katja , Bakuli Abhishek , Pütz Peter , Gleut Ronan Le , Guggenbuehl Noller Jessica Michelle , Olbrich Laura , Saathoff Elmar , Garí Mercè , Schälte Yannik , Frahnow Turid , Wölfel Roman , Pritsch Michael , Rothe Camilla , Pletschette Michel , Rubio-Acero Raquel , Beyerl Jessica , Metaxa Dafni , Forster Felix , Thiel Verena , Castelletti Noemi , Rieß Friedrich , Diefenbach Maximilian N. , Fröschl Günter , Bruger Jan , Winter Simon , Frese Jonathan , Puchinger Kerstin , Brand Isabel , Kroidl Inge , Wieser Andreas , Hoelscher Michael , Hasenauer Jan , Fuchs Christiane 
Published: April 2021

AbstractBackgroundIn the 2ndyear of the Covid-19 pandemic, knowledge about the dynamics of the infection in the general population is still limited. Such information is essential for health planners, as many of those infected show no or only mild symptoms and thus, escape the surveillance system. We therefore aimed to describe the course of the pandemic in the Munich general population living in private households from April 2020 to January 2021.MethodsThe KoCo19 baseline study took place from April to June 2020 including 5313 participants (age 14 years and above). From November 2020 to January 2021, we could again measure SARS-CoV-2 antibody status in 4,433 of the baseline participants (response 83%). Participants were offered a self-sampling kit to take a capillary blood sample (dry blood spot; DBS). Blood was analysed using the Elecsys®Anti-SARS-CoV-2 assay (Roche). Questionnaire information on socio-demographics and potential risk factors assessed at baseline was available for all participants. In addition, follow-up information on health-risk taking behaviour and number of personal contacts outside the household (N=2768) as well as leisure time activities (N=1263) were collected in summer 2020.ResultsWeighted and adjusted (for specificity and sensitivity) SARS-CoV-2 sero-prevalence at follow-up was 3.6% (95% CI 2.9-4.3%) as compared to 1.8% (95% CI 1.3-3.4%) at baseline. 91% of those tested positive at baseline were also antibody-positive at follow-up. While sero-prevalence increased from early November 2021 to January 2021, no indication of geospatial clustering across the city of Munich was found, although cases clustered within households. Taking baseline result and time to follow-up into account, men and participants in the age group 20-34 years were at the highest risk of sero-positivity. In the sensitivity analyses, differences in health-risk taking behaviour, number of personal contacts and leisure time activities partly explained these differences.ConclusionThe number of citizens in Munich with SARS-CoV-2 antibodies was still below 5% during the 2ndwave of the pandemic. Antibodies remained present in the majority of baseline participants. Besides age and sex, potentially confounded by differences in behaviour, no major risk factors could be identified. Non-pharmaceutical public health measures are thus still important.
Mapping the plague through natural language processing
Authors: Krauer Fabienne , Schmid Boris V. 
Published: April 2021

AbstractPandemic diseases such as plague have produced a vast amount of literature providing information about the spatiotemporal extent of past epidemics, circumstances of transmission, symptoms, or countermeasures. However, the manual extraction of such information from running text is a tedious process, and much of this information has therefore remained locked into a narrative format. Natural Language processing (NLP) is a promising tool for the automated extraction of epidemiological data from texts, and can facilitate the establishment of datasets. In this paper, we explore the utility of NLP to assist in the creation of a plague outbreak dataset. We first produced a gold standard list of toponyms by manual annotation of a German plague treatise published by Sticker in 1908. We then investigated the performance of five pre-trained NLP libraries (Google NLP, Stanford CoreNLP, spaCy, germaNER and Geoparser.io) for the automated extraction of location data from a compared to the gold standard. Of all tested algorithms, spaCy performed best (sensitivity 0.92, F1 score 0.83), followed closely by Stanford CoreNLP (sensitivity 0.81, F1 score 0.87). Google NLP had a slightly lower performance (F1 score 0.72, sensitivity 0.78). Geoparser and germaNER had a poor sensitivity (0.41 and 0.61) From the gold standard list we produced a plague dataset by linking dates and outbreak places with GIS coordinates. We then evaluated how well automated geocoding services such as Google geocoding, Geonames and Geoparser located these outbreaks correctly. All geocoding services performed poorly and returned the correct GIS information only in 60.4%, 52.7% and 33.8% of all cases. The rate of correct matches was particularly low when it came to historical regions and places. Finally, we compared our newly digitized plague dataset to a re-digitized version of the plague treatise by Biraben and provide an update of the spatio-temporal extent of the second pandemic plague outbreaks. We conclude that NLP tools have their limitations, but they are potentially useful to accelerate the collection of data and the generation of a global plague outbreak database.
COVID-19 Outcomes and Genomic characterization of SARS-CoV-2 isolated from Veterans in New England States
Authors: Lee Megan , Sallah Ya Haddy , Petrone Mary E. , Ringer Matthew , Cosentino Danielle , Vogels Chantal B.F. , Fauver Joseph R. , Alpert Tara , Grubaugh Nathan D. , Gupta Shaili 
Published: April 2021

AbstractIn an older cohort of veterans with a high comorbidity burden, age was the largest predictor of hospitalization, peak disease severity, and mortality. Most infections in six New England states until September, 2020, were from SARS-CoV-2 B.1 lineage, dominated by spike protein D614G substitution in 97.3% of samples.
Citrullination of glucokinase linked to autoimmune diabetes
Authors: Yang Mei-Ling , Horstman Sheryl , Gee Renelle , Guyer Perrin , Lam TuKiet T. , Kanyo Jean , Perdigoto Ana L. , Speake Cate , Greenbaum Carla J. , Overbergh Lut , Kibbey Richard G. , Herold Kevan C. , James Eddie A. , Mamula Mark J. 
Published: April 2021

AbstractInflammation, including reactive oxygen species and inflammatory cytokines in tissue microenvironments amplify the appearance of various post-translational modifications (PTMs) of self-proteins. Previously, a number of PTMs have been identified as autoimmune biomarkers in the initiation and progression of Type 1 diabetes (T1D). Herein, we have identified the citrullination of glucokinase (GK) as a result of inflammation, triggering autoimmunity and affecting its biological functions. Glucokinase is predominantly expressed in hepatocytes to regulate glycogen synthesis, and in pancreatic beta cells, where it acts as a glucose sensor to initiate glycolysis and insulin signaling. Herein, we demonstrate that glucokinase is citrullinated in inflamed non-obese diabetic (NOD) islets as well as in human GK. Autoantibodies against both native and citrullinated GK arise in both spontaneous human T1D and murine models. Likewise, autoreactive CD4+T cells to both native and citrullinated glucokinase epitopes are present in the circulation of T1D patients. Finally, citrullination alters GK biologic activity and suppresses glucose-stimulated insulin secretion. Our studies define glucokinase as a T1D biomarker, providing new insights into altering glucose metabolism, creating neoautoantigens, and better define the broad impact of PTMs on the tissue pathology of T1D.
AGENT: the Arabidopsis Gene Regulatory Network Tool for Exploring and Analyzing GRNs
Authors: Lau Vincent , Woo Rachel , Pereira Bruno , Pasha Asher , Esteban Eddi , Provart Nicholas J. 
Published: April 2021

AbstractGene regulatory networks (GRNs) are complex networks that capture multi-level regulatory events between one or more regulatory macromolecules, such as transcription factors (TFs), and their target genes. Advancements in screening technologies such as enhanced yeast-one-hybrid screens have allowed for high throughput determination of GRNs. However, visualization of GRNs in Arabidopsis has been limited toad hocnetworks and are not interactive. Here, we describe the Arabidopsis GEne Network Tool (AGENT) that houses curated GRNs and provides tools to visualize and explore them. AGENT features include expression overlays, subnetwork motif scanning, and network analysis. We show how to use AGENT’s multiple built-in tools to identify key genes that are involved in flowering and seed development along with identifying temporal multi-TF control of a key transporter in nitrate signaling. AGENT can be accessed athttps://bar.utoronto.ca/AGENT.
scvi-tools: a library for deep probabilistic analysis of single-cell omics data
Authors: Gayoso Adam , Lopez Romain , Xing Galen , Boyeau Pierre , Wu Katherine , Jayasuriya Michael , Melhman Edouard , Langevin Maxime , Liu Yining , Samaran Jules , Misrachi Gabriel , Nazaret Achille , Clivio Oscar , Xu Chenling , Ashuach Tal , Lotfollahi Mohammad , Svensson Valentine , Beltrame Eduardo da Veiga , Talavera-López Carlos , Pachter Lior , Theis Fabian J. , Streets Aaron , Jordan Michael I. , Regier Jeffrey , Yosef Nir 
Published: April 2021

AbstractProbabilistic models have provided the underpinnings for state-of-the-art performance in many single-cell omics data analysis tasks, including dimensionality reduction, clustering, differential expression, annotation, removal of unwanted variation, and integration across modalities. Many of the models being deployed are amenable to scalable stochastic inference techniques, and accordingly they are able to process single-cell datasets of realistic and growing sizes. However, the community-wide adoption of probabilistic approaches is hindered by a fractured software ecosystem resulting in an array of packages with distinct, and often complex interfaces. To address this issue, we developed scvi-tools (https://scvi-tools.org), a Python package that implements a variety of leading probabilistic methods. These methods, which cover many fundamental analysis tasks, are accessible through a standardized, easy-to-use interface with direct links to Scanpy, Seurat, and Bioconductor workflows. By standardizing the implementations, we were able to develop and reuse novel functionalities across different models, such as support for complex study designs through nonlinear removal of unwanted variation due to multiple covariates and reference-query integration via scArches. The extensible software building blocks that underlie scvi-tools also enable a developer environment in which new probabilistic models for single cell omics can be efficiently developed, benchmarked, and deployed. We demonstrate this through a code-efficient reimplementation of Stereoscope for deconvolution of spatial transcriptomics profiles. By catering to both the end user and developer audiences, we expect scvi-tools to become an essential software dependency and serve to formulate a community standard for probabilistic modeling of single cell omics.
Temporal single-cell transcriptomes of zebrafish spinal cord pMN progenitors reveal distinct neuronal and glial progenitor populations
Authors: Scott Kayt , O’Rourke Rebecca , Winkler Caitlin C. , Kearns Christina A. , Appel Bruce 
Published: April 2021

AbstractVentral spinal cord progenitor cells, which express the basic helix loop helix transcription factor Olig2, sequentially produce motor neurons and oligodendrocyte precursor cells (OPCs). Following specification some OPCs differentiate as myelinating oligodendrocytes while others persist as OPCs. Though a considerable amount of work has described the molecular profiles that define motor neurons, OPCs, and oligodendrocytes, less is known about the progenitors that produce them. To identify the developmental origins and transcriptional profiles of motor neurons and OPCs, we performed single-cell RNA sequencing on isolated pMN cells from embryonic zebrafish trunk tissue at stages that encompassed motor neurogenesis, OPC specification, and initiation of oligodendrocyte differentiation. Downstream analyses revealed two distinct pMN progenitor populations: one that appears to produce neurons and one that appears to produce OPCs. This latter population, called Pre-OPCs, is marked by expression ofGS Homeobox 2(gsx2), a gene that encodes a homeobox transcription factor. Using fluorescent in situ hybridizations, we identifiedgsx2-expressing Pre-OPCs in the spinal cord prior to expression of canonical OPC marker genes. Our data therefore reveal heterogeneous gene expression profiles among pMN progenitors, supporting prior fate mapping evidence.HighlightsSingle-cell RNA sequencing reveals the developmental trajectories of neurons and glia that arise from spinal cord pMN progenitor cells in zebrafish embryosTranscriptionally distinct subpopulations of pMN progenitors are the apparent sources of neurons or oligodendrocytes, consistent with fate mapping datagsx2expression marks pMN progenitors that produce oligodendrocyte lineage cells
Chromatin-interacting transposon RNAs linking to the core trans-inhibition circuitry for embryonic stem cell identity
Authors: Xu Wenqiu , Ren Likun , Zheng Caihong , Cai Jun 
Published: April 2021

AbstractTransposable DNA sequences constitute more than half of the human and mouse genomes. A large number of non-coding RNAs, named as transposon RNAs, are derived from these transposable elements. The cis-regulatory function of transposable DNA elements, such as LINE-1 and Alu has been largely explored. But the biological roles of transposon RNAs aren’t well understood. Here, investigations of RNA-chromatin interactions provide us with comprehensive evidence that specific families of transposon RNAs play roles in trans-regulation linking to the core inhibition circuitry for embryonic stem cell identity. Alternative modes of the RNA-DNA hybrid duplex and protein-recruited RNA scaffold are required for the regulatory activities of transposon RNAs. LINE-1 RNAs co-locating with KAP1 form a negative feedback loop stabilizing the transcription of LINE-1 DNA elements via RNA-DNA hybrids. In another way LINE-1 RNAs, together with the reprogramming three factors and Polycomb repressive complexes, participate in the inhibition on dozens of differentiation-relative genes.
A snapshot of a pandemic: the interplay between social isolation and COVID-19 dynamics in Brazil
Authors: P. Ferreira Cláudia , Marcondes Diego , Melo Mariana P. , Oliva Sérgio M. , M. Peixoto Cláudia , Peixoto Pedro S. 
Published: April 2021

SummaryIn response to the COVID-19 pandemic, most governments around the world implemented some kind of social distancing policy in an attempt to block the spreading of the virus within a territory. In Brazil, this mitigation strategy was first implemented in March 2020 and mainly monitored by social isolation indicators built from mobile geolocation data. While it is well known that social isolation has been playing a crucial role in epidemic control, the precise connections between mobility data indicators and epidemic dynamic parameters have a complex interdependence. In this work, we investigate this dependence for several Brazilian cities, looking also at socioeconomic and demographic factors that influence it. As expected, the increase in the social isolation indicator was shown to be related to the decrease in the speed of transmission of the disease, but the relation was shown to depend on the urban hierarchy level of the city, the human development index and also the epidemic curve stage. Moreover, a high social isolation at the beginning of the epidemic relates to a strong positive impact on flattening the epidemic curve, while less efficacy of this mitigation strategy was observed when it has been implemented later. Mobility data plays an important role in epidemiological modeling and decision-making, however, we discuss in this work how a direct relationship between social isolation data and COVID-19 data is hard to be established. Understanding this interplay is a key factor to better modeling, for which we hope this study contributes.
spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data
Authors: Pardo Brenda , Spangler Abby , Weber Lukas M. , Hicks Stephanie C. , Jaffe Andrew E. , Martinowich Keri , Maynard Kristen R. , Collado-Torres Leonardo 
Published: April 2021

AbstractMotivationSpatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissuespecific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary.ResultsWe describespatialLIBD, an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the user’s computer or through a web application.AvailabilityspatialLIBDis available at bioconductor.org/packages/spatialLIBD.Supplementary informationSupplementary data are available atBioinformaticsonline.
Tractography-based targeting of ventral intermediate nucleus: A comparison between conventional stereotactic targeting and diffusion tensor imaging-based targeting
Authors: Vijayakumari Anupa A. , Parker Drew , Yang Andrew I , Ramayya Ashwin G. , Wolf Ronald L. , Aunapu Darien , Baltuch Gordon H. , Verma Ragini 
Published: April 2021

AbstractBackgroundThe ventral intermediate (VIM) nucleus of the thalamus is the main target for lesioning using magnetic resonance imaging (MRI) guided focused ultrasound (MRgFUS) or deep brain stimulation (DBS). Targeting of VIM still depends on standard stereotactic coordinates, which do not account for inter-individual variability. Several approaches have been proposed including visualization of dentato-rubro-thalamic tract (DRTT) using diffusion tensor imaging tractography.ObjectiveTo compare probabilistic tracking of DRTT with deterministic tracking of DRTT and stereotactic coordinates to identify the most appropriate approach to target VIM.MethodsIn this retrospective study, we assessed the VIM targeted using stereotactic coordinates, deterministic and probabilistic tracking of DRTT in 19 patients with essential tremor who underwent DBS with VIM targeted using microelectrode recordings. We subsequently determined the positions of VIM derived from these three approaches and compared with that of DBS lead position using paired samplet-tests.ResultsThe probabilistic tracking of DRTT was significantly anterior to the lead (1.45 ± 1.61 mm (P< 0.0001)), but not in the medial/lateral position (−0.29±2.42 mm (P=0.50)). Deterministic tracking of DRTT was significantly lateral (2.16 ± 1.94 mm (P< 0.0001)) and anterior to the lead (1.66 ± 2.1 mm (P< 0.0001)). The stereotactic coordinates were significantly lateral (2.41 ± 1.41 mm (P< 0.0001)) and anterior (1.23 ± 0.89 mm (P< 0.0001)) to the lead.ConclusionProbabilistic tracking of DRTT was found to be superior in targeting VIM compared to deterministic tracking and stereotactic coordinates.
Allelic variation in Class I HLA determines pre-existing memory responses to SARS-CoV-2 that shape the CD8+ T cell repertoire upon viral exposure
Authors: Francis Joshua M. , Leistritz-Edwards Del , Dunn Augustine , Tarr Christina , Lehman Jesse , Dempsey Conor , Hamel Andrew , Rayon Violeta , Liu Gang , Wang Yuntong , Wille Marcos , Durkin Melissa , Hadley Kane , Sheena Aswathy , Roscoe Benjamin , Ng Mark , Rockwell Graham , Manto Margaret , Gienger Elizabeth , Nickerson Joshua ,   MGH COVID-19 Collection and Processing Team, Moarefi Amir , Noble Michael , Malia Thomas , Bardwell Philip D. , Gordon William , Swain Joanna , Skoberne Mojca , Sauer Karsten , Harris Tim , Goldrath Ananda W. , Shalek Alex K. , Coyle Anthony J. , Benoist Christophe , Pregibon Daniel C. 
Published: April 2021

AbstractEffective presentation of antigens by HLA class I molecules to CD8+ T cells is required for viral elimination and generation of long-term immunological memory. In this study, we applied a single-cell, multi-omic technology to generate the first unified ex vivo characterization of the CD8+ T cell response to SARS-CoV-2 across 4 major HLA class I alleles. We found that HLA genotype conditions key features of epitope specificity, TCR α/β sequence diversity, and the utilization of pre-existing SARS-CoV-2 reactive memory T cell pools. Single-cell transcriptomics revealed functionally diverse T cell phenotypes of SARS-CoV-2-reactive T cells, associated with both disease stage and epitope specificity. Our results show that HLA variations influence pre-existing immunity to SARS-CoV-2 and shape the immune repertoire upon subsequent viral exposure.One-Sentence SummaryWe perform a unified, multi-omic characterization of the CD8+ T cell response to SARS-CoV-2, revealing pre-existing immunity conditioned by HLA genotype.
New Segmentation and Feature Extraction Algorithm for Classification of White Blood Cells in Peripheral Smear Images
Authors: Tavakoli Sajad , Ghaffari Ali , Kouzehkanan Zahra Mousavi , Hosseini Reshad 
Published: April 2021

AbstractThis article addresses a new method for the classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shapes and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.65 %, 92.21 %, and 94.20 %, respectively. It is worth mentioning that the hyperparameters of the classifier are fixed only with the Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets. The obtained results demonstrate that the proposed method is robust, fast, and accurate.