bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prospective cohort study of workers diagnosed with COVID-19 and subsequent unemployment
Authors: Nagata Tomohisa , Nagata Masako , Hino Ayako , Tateishi Seiichiro , Ogami Akira , Tsuji Mayumi , Matsuda Shinya , Fujino Yoshihisa , Mori Koji ,   for the CORoNaWork project
Published: July 2021

AbstractObjectivesThe purpose of this study was to investigate the relationships of workers being diagnosed with coronavirus disease 2019 (COVID-19) and being identified as close contacts of infected persons with unemployment in Japan.MethodsThis was a prospective cohort study using questionnaires about COVID-19 administered to Japanese workers. A baseline survey conducted on December 22–25, 2020, was used to determine history of being diagnosed with COVID-19 or being identified as a close contact of an infected person. Unemployment since the baseline survey was ascertained with a follow-up survey on February 18 and 19, 2021. The odds ratios of unemployment were estimated using a multilevel logistic model with adjusted covariates nested in prefecture of residence.ResultsWomen (n = 8,771) accounted for 44% of the total sample (n = 19,941), and the mean age was 48.0 years. In terms of unemployment because of negative reasons, the multivariate analysis showed that the odds ratio of unemployment associated with being diagnosed with COVID-19 was 2.18 (95% CI: 1.05–4.55) and that the odds ratio associated with being identified as a close contact was 0.93 (95% CI: 0.29–2.95).ConclusionsThere is an association between workers being diagnosed with COVID-19 and unemployment. Occupational health professionals should follow up closely with workers diagnosed with COVID-19 after they return to work to prevent them from becoming unemployed against their will.
Diagnostic errors are common in acute pediatric respiratory disease: a prospective, single-blinded multicenter diagnostic accuracy study in Australian Emergency Departments
Authors: Porter Paul , Brisbane Joanna , Tan Jamie , Bear Natasha , Choveaux Jennifer , Della Phillip , Abeyratne Udantha 
Published: July 2021

ABSTRACTBackgroundDiagnostic errors are a global health priority and a common cause of preventable harm. There is limited data available for the prevalence of misdiagnosis in pediatric acute-care settings. Respiratory illnesses, which are particularly challenging to diagnose, are the most frequent reason for presentation to pediatric emergency departments.ObjectiveTo determine the diagnostic error rate of acute childhood respiratory diseases in emergency departments.MethodsProspective, multicenter, single-blinded, diagnostic accuracy study in two well-resourced pediatric emergency departments in a large Australian city. Between September 2016 and August 2018, a convenience sample of children aged 29 days to 12 years who presented with respiratory symptoms was enrolled. The emergency department discharge diagnoses were reported by clinicians based upon standard clinical diagnostic definitions. These diagnoses were compared against consensus diagnoses given by an expert panel of pediatric specialists using standardized disease definitions after they reviewed all medical records.ResultsFor 620 participants, the positive and negative percent agreement (%, [95% CI]) of the emergency department compared with the expert panel diagnoses were generally poor: isolated upper respiratory tract disease (61.4 [51.2, 70.9], 90.9 [88.1, 93.3]), croup (75.6 [64.9, 84.4], 97.9 [96.2, 98.9]), lower respiratory tract disease (86.4 [83.1, 89.6], 92.9 [87.7, 96.4]), bronchiolitis (66.9 [58.6, 74.5], 94.3 [80.8, 99.3]), asthma/reactive airway disease (91.0 [85.8, 94.8], 93.0 [90.1, 95.3]), clinical pneumonia (62·9 [49·7, 74·8], 95·0 [92·8, 96·7]), focal (consolidative) pneumonia (54·8 [38·7, 70·2], 86.2 [79.3, 91.5]). Only 59% of chest x-rays with consolidation were correctly identified. Between 6.9% and 14.5% of children were inappropriately prescribed based on their eventual diagnosis.ConclusionIn well-resourced emergency departments, we have identified a previously unrecognized high diagnostic error rate for acute childhood respiratory disorders, particularly in pneumonia and bronchiolitis. These errors lead to the potential of avoidable harm and the administration of inappropriate treatment.
DACO: Distortion/artefact correction for diffusion MRI data in an integrated framework
Authors: Hsu Yung-Chin , Tseng Wen-Yih Isaac 
Published: July 2021

AbstractIn this paper we propose a registration-based algorithm to correct various distortions or artefacts (DACO) commonly observed in diffusion weighted (DW) magnetic resonance images (MRI). The registration in DACO is proceeded on the basis of a pseudo b0 image, which is synthesized from the anatomical images such as T1-weighted image or T2-weighted image, and a pseudo diffusion MRI (dMRI) data, which is derived from the Gaussian model of diffusion tensor imaging (DTI) or the Hermite model of MAP-MRI. DACO corrects (1) the susceptibility-induced distortions, (2) the intensity inhomogeneity, and (3) the misalignment between the dMRI data and anatomical images by registering the real b0 image to the pseudo b0 image, and corrects (4) the eddy current (EC)-induced distortions and (5) the head motions by registering each of the DW images in the real dMRI data to the corresponding image in the pseudo dMRI data. As the above artefacts interact with each other, DACO models each type of artefact in an integrated framework and estimates these models in an interleaved and iterative manner. The mathematical formulation of the models and the comprehensive estimation procedures are detailed in this paper. The evaluation using the human connectome project data shows that DACO could estimate the model parameters accurately. Furthermore, the evaluation conducted on the real human data acquired from clinical MRI scanners reveals that the method could reduce the artefacts effectively. The DACO method leverages the anatomical image, which is routinely acquired in clinical practice, to correct the artefacts, minimizing the additional acquisitions needed to conduct the algorithm. Therefore, our method is beneficial to most dMRI data, particularly to those without acquiring the field map or blip-up and blip-down images.
Nonclinical Safety and Immunogenicity of an rVSV-ΔG-SARS-CoV-2-S vaccine in mice, hamsters, rabbits and pigs
Authors: Madar-Balakirski Noa , Rosner Amir , Melamed Sharon , Politi Boaz , Steiner Michal , Tamir Hadas , Yahalom-Ronen Yfat , David Elad Bar , Ben-Shmuel Amir , Sittner Assa , Glinert Itai , Weiss Shay , Bar-Haim Erez , Cohen Hila , Elia Uri , Achdout Hagit , Erez Noam , Rotem Shahar , Lazar Shlomi , Nyska Abraham , Yitzhaki Shmuel , Beth-Din Adi , Levy Haim , Paran Nir , Israely Tomer , Marcus Hadar 
Published: July 2021

AbstractrVSV-ΔG-SARS-CoV-2-S is a clinical stage (Phase 2) replication competent recombinant vaccine against SARS-CoV-2. Nonclinical safety, immunogenicity and efficacy studies were conducted in 4 animal species, using multiple dose levels (up to 108 PFU/animal) and various dosing regimens. There were no treatment related mortalities in any study, or any noticeable clinical signs. Compared to unvaccinated controls, hematology and biochemistry parameters were unremarkable and no adverse histopathological findings gave cause for safety concern in any of the studies. There was no viral shedding in urine, nor viral RNA detected in whole blood or serum samples 7 days post vaccination. The rVSV-ΔG-SARS-CoV-2-S vaccine immune response gave rise to neutralizing antibodies, cellular immune response, and increased lymphocytic cellularity in the spleen germinal centers and regional lymph node. No evidence for neurovirulence was found in C57BL/6 immune competent mice or in highly sensitive IFNAR KO mice. Vaccine virus replication and distribution in K18 hACE2 transgenic mice showed a gradual clearance from the vaccination site with no vaccine virus recovered from the lungs. The rVSV-ΔG-SARS-CoV-2-S vaccine was well tolerated locally and systemically and elicited an effective immunogenic response up to the highest dose tested, supporting further clinical development.
T-CoV: a comprehensive portal of HLA-peptide interactions affected by SARS-CoV-2 mutations
Authors: Nersisyan Stepan , Zhiyanov Anton , Shkurnikov Maxim , Tonevitsky Alexander 
Published: July 2021

ABSTRACTRapidly appearing SARS-CoV-2 mutations can affect T cell epitopes, which can help the virus to evade either CD8 or CD4 T-cell responses. We developed T-cell COVID-19 Atlas (T-CoV,https://t-cov.hse.ru) – the comprehensive web portal, which allows one to analyze how SARS-CoV-2 mutations alter the presentation of viral peptides by HLA molecules. The data are presented for common virus variants and the most frequent HLA class I and class II alleles. Binding affinities of HLA molecules and viral peptides were assessed with accuratein silicomethods. The obtained results highlight the importance of taking HLA alleles diversity into account: mutation-mediated alterations in HLA-peptide interactions were highly dependent on HLA alleles. For example, we found that the essential number of peptides tightly bound to HLA-B*07:02 in the reference Wuhan variant ceased to be tight binders for the Indian (Delta) and the UK (Alpha) variants. In summary, we believe that T-CoV will help researchers and clinicians to predict the susceptibility of individuals with different HLA genotypes to infection with variants of SARS-CoV-2 and/or forecast its severity.
Selection-Enriched Genomic Loci (SEGL) Reveals Genetic Loci for Environmental Adaptation and Photosynthetic Productivity in Chlamydomonas reinhardtii
Authors: Lucker Ben F. , Panchy Nicolas L. , Temple Joshua A. , Benning Urs F. , Bibik Jacob D. , Neofotis Peter G. , Weissman Joseph C. , Baxter Ivan R. , Shiu Shin-Han , Kramer David M. 
Published: July 2021

AbstractThis work demonstrates an approach to produce and select hybrid algal strains exhibiting increased photosynthetic productivity under multiple environmental conditions. This simultaneously addresses two major impediments to improving algal bioenergy production: 1) generating new genetic variants with improved performance; and 2) disentangling complex interactions between genetic and physiological factors contributing to these improvements. We pooled progeny generated from mating two environmental isolates of the green alga Chlamydomonas reinhardtii and cultured the pools under multiple environmental conditions. Strains from the outcompeting populations showed substantial (in some cases over 3 fold) increases in productivity over the parental lines under certain environments related to biomass production, including laboratory conditions as well as hyperoxia, fluctuating light, high salinity and high temperature. The results indicate that C. reinhardtii has remarkable, untapped, directed evolution capacity that may be harnessed using breeding and competition approaches. The populations were deep sequenced at multiple time points to identify “Selection-Enriched Genomic Loci” (SEGL) that accumulated in the populations, and thus likely confer increased fitness under the respective environmental conditions. With improved resolution, SEGL mapping can identify allelic combinations used for targeted breeding approaches, generating elite algal lines with multiple desirable traits, as well as to further understand the genetic and mechanistic bases of photosynthetic productivity.Significance StatementIncreasing the photosynthetic efficiency of algae during biomass production is perhaps the most critical hurdle for economically sustainable algal based biofuels. This presents unique challenges because modifications designed to increase photosynthesis often result in decreased fitness, due to production of toxic reactive oxygen species when photosynthesis is not adequately regulated. These problems are exacerbated under natural and outdoor production environments because of the complex nature of photosynthesis and the multifaceted interactions between genetic, environmental and physiological factors. Here, we demonstrate a high throughput biotechnological screening approach that simultaneously produces algal strains with highly increased autotrophic productivity and identifies genomic loci contributing to these improvements. Our results demonstrate that Chlamydomonas reinhardtii exhibits high directed evolutionary capacity readily accessed through breeding and selection.
Recovering Escherichia coli plasmids in the absence of long-read sequencing data
Authors: Paganini J. , Plantinga N.L. , Arredondo-Alonso S. , Willems R.J.L. , Schürch A.C. 
Published: July 2021

AbstractThe incidence of infections caused by multidrug-resistant E. coli strains has risen in the past years. Antibiotic resistance in E. coli is often mediated by acquisition and maintenance of plasmids. The study of E. coli plasmid epidemiology and genomics often requires long-read sequencing information, but recently a number of tools that allow plasmid prediction from short-read data have been developed.Here, we reviewed 25 available plasmid prediction tools and categorized them into binary plasmid/chromosome classification tools and plasmid reconstruction tools. We benchmarked six tools that aim to reliably reconstruct distinct plasmids, with a special focus on plasmids carrying antibiotic resistance genes (ARGs) such as extended-spectrum beta-lactamase genes. They use either assembly graph information (plasmidSPAdes, gplas), reference databases (MOB-Suite, FishingForPlasmids) or both (HyAsP and SCAPP) to produce plasmid predictions. The benchmark data set consisted of 240 E. coli strains, harboring 631 plasmids, which were representative for the diversity of E. coli in public databases. Notably, these strains were not used for training any of the tools.We found that two thirds (n=425, 66.3.%) of all plasmids were correctly reconstructed by at least one of the six tools, with a range of 92 (14.58%) to 317 (50.23%) correctly predicted plasmids. However, the majority of plasmids that carried antibiotic resistance genes (n=85, 57.8%) could not be completely recovered as distinct plasmids by any of the tools. MOB-suite was the only tool that was able to correctly reconstruct the majority of plasmids (n=317, 50.23%), and performed best at reconstructing large plasmids (n=166, 46.37%) and ARG-plasmids (n=41, 27.9%), but predictions frequently contained chromosome contamination (40%). In contrast, plasmidSPAdes reconstructed the highest fraction of plasmids smaller than 18 kbp (n=168, 61.54%). Large ARG-plasmids, however, were recovered with small precision values (median=0.47, IQR=0.61), indicating that plasmidSPAdes frequently merged sequences derived from distinct replicons. Additionally, only 63% of all plasmid-borne ARGs were correctly predicted by plasmidSPAdes.The remaining four tools (FishingForPlasmids, HyAsP, SCAPP and gplas) were able to correctly reconstruct a combined total of 18 plasmids that were missed by MOB-suite and plasmidSPAdes.Available bioinformatic tools can provide valuable insight into E. coli plasmids, but also have important limitations. This work will serve as a guideline for selecting the most appropriate plasmid reconstruction tool for studies focusing on E. coli plasmids in the absence of long-read sequencing data.
A Deep Learning Framework for Prediction of Clinical Drug Response of Cancer Patients and Identification of Drug Sensitivity Biomarkers using Preclinical Samples
Authors: Hostallero David Earl , Wei Lixuan , Wang Liewei , Cairns Junmei , Emad Amin 
Published: July 2021

ABSTRACTBackgroundPrediction of the response of cancer patients to different treatments and identification of biomarkers of drug sensitivity are two major goals of individualized medicine. In this study, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines, to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue and cancer type of the tumours and to reduce the statistical discrepancies between cell lines and patient tumours. In addition, this model identifies a small set of genes whose mRNA expression are predictive of drug response in the trained model, enabling identification of biomarkers of drug sensitivity.ResultsUsing data from two large databases of cancer cell lines and cancer tumours, we showed that this model can distinguish between sensitive and resistant tumours for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our siRNA knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that all of these genes significantly influence the drug sensitivity of the MCF7 cell line to this drug. In addition, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways.ConclusionsIn summary, this study provides a powerful deep learning framework for prediction of drug response and for identification of biomarkers of drug sensitivity in cancer.
Structure of mycobacterial CIII2CIV2 respiratory supercomplex bound to the tuberculosis drug candidate telacebec (Q203)
Authors: Yanofsky David J. , Di Trani Justin M. , Krol Sylwia , Abdelaziz Rana , Bueler Stephanie A. , Imming Peter , Brzezinski Peter , Rubinstein John L. 
Published: July 2021

AbstractThe imidazopyridine telacebec, also known as Q203, is one of only a few new classes of compounds in more than fifty years with demonstrated antituberculosis activity in humans. Telacebec inhibits the mycobacterial respiratory supercomplex composed of complexes III and IV (CIII2CIV2). In mycobacterial electron transport chains, CIII2CIV2 replaces canonical CIII and CIV, transferring electrons from the intermediate carrier menaquinol to the final acceptor, molecular oxygen, while simultaneously transferring protons across the inner membrane to power ATP synthesis. We show that telacebec inhibits the menaquinol:oxygen oxidoreductase activity of purified Mycobacterium smegmatis CIII2CIV2 at concentrations similar to those needed to inhibit electron transfer in mycobacterial membranes and Mycobacterium tuberculosis growth in culture. We then used electron cryomicroscopy (cryoEM) to determine structures of CIII2CIV2 both in the presence and absence of telacebec. The structures suggest that telacebec prevents menaquinol oxidation by blocking two different menaquinol binding modes to prevent CIII2CIV2 activity.
A Newcastle disease virus-vector expressing a prefusion-stabilized spike protein of SARS-CoV-2 induces protective immune responses against prototype virus and variants of concern in mice and hamsters
Authors: Sun Weina , Liu Yonghong , Amanat Fatima , González-Domínguez Irene , McCroskery Stephen , Slamanig Stefan , Coughlan Lynda , Rosado Victoria , Lemus Nicholas , Jangra Sonia , Rathnasinghe Raveen , Schotsaert Michael , Martinez Jose , Sano Kaori , Mena Ignacio , Innis Bruce L , Wirachwong Ponthip , Thai Duong Huu , Oliveira Ricardo Das Neves , Scharf Rami , Hjorth Richard , Raghunandan Rama , Krammer Florian , García-Sastre Adolfo , Palese Peter 
Published: July 2021

AbstractRapid development of coronavirus disease 2019 (COVID-19) vaccines and expedited authorization for use and approval has been proven beneficial to mitigate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread and given hope in this desperate situation. It is believed that sufficient supplies and equitable allocations of vaccines are necessary to limit the global impact of the COVID-19 pandemic and the emergence of additional variants of concern. We have developed a COVID-19 vaccine based on Newcastle disease virus (NDV) that can be manufactured at high yields in embryonated eggs. Here we provide evidence that the NDV vector expressing an optimized spike antigen (NDV-HXP-S), upgraded from our previous construct, is a versatile vaccine that can be used live or inactivated to induce strong antibody responses and to also cross-neutralize variants of concern. The immunity conferred by NDV-HXP-S effectively counteracts SARS-CoV-2 infection in mice and hamsters. It is noteworthy that vaccine lots produced by existing egg-based influenza virus vaccine manufacturers in Vietnam, Thailand and Brazil exhibited excellent immunogenicity and efficacy in hamsters, demonstrating that NDV-HXP-S vaccines can be quickly produced at large-scale to meet global demands.
Selective sorting of microRNAs into exosomes by phase-separated YBX1 condensates
Authors: Liu Xiao-Man , Ma Liang , Schekman Randy 
Published: July 2021

AbstractExosomes may mediate cell-to-cell communication by transporting various proteins and nucleic acids to neighboring cells. Some protein and RNA cargoes are significantly enriched in exosomes. How cells efficiently and selectively sort them into exosomes remains incompletely explored. Previously we reported that YBX1 is required in sorting of miR-223 into exosomes. Here we show that YBX1 undergoes liquid-liquid phase separation (LLPS) in vitro and in cells. YBX1 condensates selectively recruit miR-223 in vitro and into exosomes secreted by cultured cells. Point mutations that inhibit YBX1 phase separation impair the incorporation of YBX1 protein into biomolecular condensates formed in cells, and perturb miR-233 sorting into exosomes. We propose that phase separation-mediated local enrichment of cytosolic RNA binding proteins and their cognate RNAs enables their targeting and packaging by vesicles that bud into multivesicular bodies. This provides a possible mechanism for efficient and selective engulfment of cytosolic proteins and RNAs into intraluminal vesicles which are then secreted as exosomes from cells.
Bayesian estimation of population size and overlap from random subsamples
Authors: Johnson Erik K. , Larremore Daniel B. 
Published: July 2021

AbstractCounting the number of species, items, or genes that are shared between two sets is a simple calculation when sampling is complete. However, when only partial samples are available, quantifying the overlap between two sets becomes an estimation problem. Furthermore, to calculate normalized measures of β-diversity, such as the Jaccard and Sorenson-Dice indices, one must also estimate the total sizes of the sets being compared. Previous efforts to address these problems have assumed knowledge of total population sizes and then used Bayesian methods to produce unbiased estimates with quantified uncertainty. Here, we address populations of unknown size and show that this produces systematically better estimates—both in terms of central estimates and quantification of uncertainty in those estimates. We further show how to use species count data to refine estimates of population size in a Bayesian joint model of populations and overlap.