bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analytical and clinical evaluation of antibody tests for SARS-CoV-2 serosurveillance studies used in Finland in 2020
Authors: Ekström Nina , Virta Camilla , Haveri Anu , Dub Timothée , Hagberg Lotta , Solastie Anna , Österlund Pamela , Vihervaara Terhi , Erlund Iris , Nohynek Hanna , Melin Merit 
Published: January 2021

AbstractBackgroundSensitive and highly specific antibody tests are critical for detection of SARS-CoV-2 antibodies especially in populations where seroprevalence is low.AimTo set up, optimize and evaluate the analytical and clinical performance of a new in-house microsphere immunoassay for measurement of IgG antibodies to SARS-CoV-2 nucleoprotein for assessment of population seroprevalence in Finland.MethodsWe set up a new in-house microsphere immunoassay (FMIA) with SARS-CoV-2 nucleoprotein and optimized its analytical performance. For evaluation of clinical performance, we tested sera collected in a well-characterized cohort of PCR positive-confirmed SARS-CoV-2 patients (n=89) with mostly mild symptoms, and before the COVID-19 pandemic (n=402), for nucleoprotein specific IgG concentrations by FMIA and a commercial chemiluminescent immunoassay and for neutralizing antibodies by the microneutralization test.ResultsThe analytical performance of FMIA was established in terms of sensitivity, linearity and precision. FMIA discriminated between COVID-19 patient and control samples with high specificity (100%) and sensitivity (100%). We generated FMIA seropositivity cut-offs, 0.46 and 1.71 U/ml, for low- and high-seroprevalence settings, respectively. In addition, we obtained high level of agreement between FMIA results and results by the microneutralization test.ConclusionThe fluorescent microsphere immunoassay showed excellent analytical and clinical performance and is well suited for serosurveillance studies of SARS-CoV-2. However, to optimize analytical sensitivity and clinical specificity of the assay, different seropositivity thresholds depending on the intended use of the assay and the target population, may be needed.
Determinants of the incidence and mortality rates of COVID-19 during the first six months of the pandemic; a cross-country study
Authors: Asem Noha , Ramadan Ahmed , Hassany Mohamed , Ghazy Ramy Mohamed , Abdallah Mohamed , Gamal Eman M. , Hassan Shaimaa , Kamal Nehal , Zaid Hala 
Published: January 2021

AbstractCOVID-19 pandemic raises an extraordinary challenge to the healthcare systems globally. The governments are taking key measures to constrain the corresponding health, social, and economic impacts, however, these measures vary depending on the nature of the crisis and country-specific circumstances.ObjectivesConsidering different incidence and mortality rates across different countries, we aimed at explaining variance of these variables by performing accurate and precise multivariate analysis with aid of suitable predictors, accordingly, the model would proactively guide the governmental responses to the crisis.MethodsUsing linear and exponential time series analysis, this research aimed at studying the incidence and mortality rates of COVID-19 in 18 countries during the first six months of the pandemic, and further utilize multivariate techniques to explain the variance in monthly exponential growth rates of cases and deaths with aid of a set of different predictors: the recorded Google mobility trends towards six categories of places, daily average temperature, daily humidity, and key socioeconomic attributes of each country.ResultsThe analysis showed that changes in mobility trends were the most significant predictors of the incidence and mortality rates, temperature and humidity were also significant but to a much lesser extent, on the other hand, the socioeconomic attributes did not contribute significantly to explaining different incidence and mortality rates across countries.ConclusionChanges in mobility trends across countries dramatically affected the incidence and mortality rates across different countries, thus, it might be used as a proxy measure of contact frequency.
Using Non-Pharmaceutical Interventions and High Isolation of Asymptomatic Carriers to Contain the Spread of SARS-CoV-2 in Nursing Homes
Authors: Schmidt Alec J. , García Yury , Pinheiro Diego , Reichert Tom , Nuño Miriam A. 
Published: January 2021

ABSTRACTObjectiveUsing a pandemic influenza model modified for COVID-19, this study investigated the degree of control over pre-symptomatic transmission that common non-pharmaceutical interventions (NPIs) would require to reduce the spread in long-term care facilities.MethodsWe created a stochastic compartmental SEIR model with Poisson-distributed transition states that compared the effect of R0, common NPIs, and isolation rates of pre-symptomatic carriers primarily on attack rate, peak cases, and timing in a 200-resident nursing home. Model sensitivity was assessed with 1st order Sobol’ indices.ResultsThe most rigorous NPIs decreased the peak number of infections by 4.3 and delayed the peak by 9.7 days in the absence of pre-symptomatic controls. Reductions in attack rate were not likely, even with rigorous application of all defined NPIs, unless pre-symptomatic carriers were identified and isolated at rates exceeding 76%. Attack rate was most sensitive to the pre-symptomatic isolation rate (Sobol’ index > 0.7) and secondarily to R0.ConclusionsCommon NPIs delayed and reduced epidemic peaks. Reducing attack rates ultimately required efficient isolation of pre-symptomatic cases, including rapid antigen tests on a nearly daily basis. This must be accounted for in testing and contact tracing plans for group living settings.
The ferroxidase HEPHaestin in Lung cancer: Pathological Significance and Prognostic Value
Authors: Zacchi Paola , Belmonte Beatrice , Mangogna Alessandro , Morello Gaia , Scola Letizia , Martorana Anna , Borelli Violetta 
Published: January 2021

AbstractIron is a fundamental nutrient utilized by living cells to support several key cellular processes. Despite its paramount role to sustain cell survival, excess of labile iron availability can inflict severe cell damage via reactive oxygen species generation which, in turn, can promote neoplastic transformation. The lung is particularly sensitive to iron-induced oxidative stress, given the high oxygen tensions herein present. Moreover, cigarette smoke as well as air pollution particulate can function as vehicles of iron supply, leading to an iron dysregulation condition shown to be crucial in the pathogenesis of several respiratory diseases including lung cancer. Hephaestin (HEPH) belongs to a group of exocytoplasmic ferroxidases emerged to contribute to cellular iron homeostasis by favouring its export. Although HEPH can affect the concentration of intracellular iron labile pool, its expression in lung cancer and its influence on prognosis have not been investigated.In this study we explored the expression pattern and prognostic value of HEPH in the most prevalent histotypes of lung cancers including lung adenocarcinoma and lung squamous cell carcinoma across in silico analyses using UALCAN, Gepia and Kaplan-Meier plotter bioninformatics. We took advantage of TIMER to assess the correlation between HEPH and tumour infiltrating immune and non-immune cells. Then we performed immunohistochemical analysis to dissect the presence of HEPH either in “healthy” and tumor lung tissues. Overall, our data suggest a positive correlation between higher level of HEPH expression with a favorable prognosis in both cancer histotypes.
Rates of serious clinical outcomes in survivors of hospitalisation with COVID-19: a descriptive cohort study within the OpenSAFELY platform
Authors:   The OpenSAFELY Collaborative, Tazare John , Walker Alex J , Tomlinson Laurie , Hickman George , Rentsch Christopher T , Williamson Elizabeth J , Bhaskaran Krishnan , Evans David , Wing Kevin , Mathur Rohini , Wong Angel YS , Schultze Anna , Bacon Seb , Bates Chris , Morton Caroline E , Curtis Helen J , Nightingale Emily , McDonald Helen I , Mehrkar Amir , Inglesby Peter , Davy Simon , MacKenna Brian , Cockburn Jonathan , Hulme William J , Warren-Gash Charlotte , Bhate Ketaki , Nitsch Dorothea , Powell Emma , Mulick Amy , Forbes Harriet , Minassian Caroline , Croker Richard , Parry John , Hester Frank , Harper Sam , Eggo Rosalind M , Evans Stephen JW , Smeeth Liam , Douglas Ian J , Goldacre Ben 
Published: January 2021

AbstractBackgroundPatients with COVID-19 are thought to be at higher risk of cardiometabolic and pulmonary complications, but quantification of that risk is limited. We aimed to describe the overall burden of these complications in survivors of severe COVID-19.MethodsWorking on behalf of NHS England, we used linked primary care records, death certificate and hospital data from the OpenSAFELY platform. We constructed three cohorts: patients discharged following hospitalisation with COVID-19, patients discharged following hospitalisation with pneumonia in 2019, and a frequency-matched cohort from the general population in 2019. We studied eight cardiometabolic and pulmonary outcomes. Absolute rates were measured in each cohort and Cox regression models were fitted to estimate age/sex adjusted hazard ratios comparing outcome rates between discharged COVID-19 patients and the two comparator cohorts.ResultsAmongst the population of 31,716 patients discharged following hospitalisation with COVID-19, rates for majority of outcomes peaked in the first month post-discharge, then declined over the following four months. Patients in the COVID-19 population had markedly increased risk of all outcomes compared to matched controls from the 2019 general population, especially for pulmonary embolism (HR 12.86; 95% CI: 11.23 - 14.74). Outcome rates were more similar when comparing patients discharged with COVID-19 to those discharged with pneumonia in 2019, although COVID-19 patients had increased risk of type 2 diabetes (HR 1.23; 95% CI: 1.05 - 1.44).InterpretationCardiometabolic and pulmonary adverse outcomes are markedly raised following hospitalisation for COVID-19 compared to the general population. However, the excess risks were more comparable to those seen following hospitalisation with pneumonia. Identifying patients at particularly high risk of outcomes would inform targeted preventive measures.FundingWellcome, Royal Society, National Institute for Health Research, National Institute for Health Research Oxford Biomedical Research Centre, UK Medical Research Council, UK Research and Innovation, Health and Safety Executive.
Integrated evaluation of telomerase activation and telomere maintenance across cancer cell lines
Authors: Hu Kevin , Ghandi Mahmoud , Huang Franklin W. 
Published: January 2021

AbstractIn cancer, telomere maintenance is critical for the development of replicative immortality. Using genome sequences from the Cancer Cell Line Encyclopedia and Genomics of Drug Sensitivity in Cancer Project, we calculated telomere content across 1,299 cancer cell lines. We find that telomerase reverse transcriptase (TERT) expression correlates with telomere content in lung, central nervous system, and leukemia cell lines. Using CRISPR/Cas9 screening data, we show that lower telomeric content is associated with dependency of CST telomere maintenance components. Increased dependencies of shelterin members are associated with wild-typeTP53status. Investigating the epigenetic regulation ofTERT, we find widespread allele-specific expression in promoter-wildtype contexts.TERTpromoter-mutant cell lines exhibit hypomethylation at PRC2-repressed regions, suggesting a cooperative global epigenetic state in the reactivation of telomerase. By incorporating telomere content with genomic features across comprehensively characterized cell lines, we provide further insights into the role of telomere regulation in cancer immortality.
Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars
Authors: Dyrka Witold , Gąsior-Głogowska Marlena , Szefczyk Monika 
Published: January 2021

AbstractBackgroundAmyloid signaling motifs are a class of protein motifs which share basic structural and functional features despite lack of apparent sequence homology. They are hard to detect in large sequence databases either with the alignment-based profile methods (due to short length and diversity) or with generic amyloid- and prion-finding tools (due to insufficient discriminative power). We propose to address the challenge with a machine learning grammatical model capable of generalizing over diverse collections of unaligned yet related motifs.ResultsFirst, we introduce and test improvements to our probabilistic context-free grammar framework for protein sequences that allow for inferring more sophisticated models achieving high sensitivity at low false positive rates. Then, we infer universal grammars for a collection of recently identified bacterial amyloid signaling motifs and demonstrate that the method is capable of generalizing by successfully searching for related motifs in fungi. The results are compared to available alternative methods. Finally, we conduct spectroscopy analyses of selected peptides to verify their structural and functional relationship.ConclusionsWhile the profile HMMs remain the method of choice for modeling homologous sets of sequences, PCFGs seem more suitable for building meta-family descriptors and extrapolating beyond the seed sample.
FIVEx: an interactive multi-tissue eQTL browser
Authors: Kwong Alan , Boughton Andrew P. , Wang Mukai , VandeHaar Peter , Boehnke Michael , Abecasis Gonçalo , Kang Hyun Min 
Published: January 2021

AbstractSummaryExpression quantitative trait loci (eQTLs) characterize the associations between genetic variation and gene expression to provide insights into tissue-specific gene regulation. Interactive visualization of tissue-specific eQTLs can facilitate our understanding of functional variants relevant to disease-related traits. However, combining the multi-dimensional nature of eQTLs into a concise and informative visualization is challenging. Existing eQTL visualization tools provide useful ways to summarize the unprecedented scale of transcriptomic data but are not necessarily tailored to answer questions about the functional interpretations of trait-associated variants or other variants of interest. We developed FIVEx, an interactive eQTL browser with an intuitive interface tailored to the functional interpretation of associated variants. It features the ability to navigate seamlessly between different data views while providing relevant tissue- and locus-specific information to offer users a better understanding of population-scale multi-tissue transcriptomic profiles. Our implementation of the FIVEx browser on the Gene-Tissue Expression (GTEx) dataset provides important insights for understanding potential tissue-specific regulatory mechanisms underlying trait-associated signals.Availability and implementationA FIVEx instance visualizing GTEx v8 data can be found athttps://eqtl.pheweb.org. The FIVEx source code is open source under an MIT license athttps://github.com/statgen/fivex.
MAESTRO affords ‘breadth and depth’ for mutation testing
Authors: Gydush Gregory , Nguyen Erica , Bae Jin H. , Rhoades Justin , Reed Sarah C. , Shea Douglas , Xiong Kan , Liu Ruolin , Blewett Timothy , Yu Fangyan , Leong Ka Wai , Choudhury Atish D. , Stover Daniel G. , Tolaney Sara M. , Krop Ian E. , Love J. Christopher , Parsons Heather A. , Makrigiorgos G. Mike , Golub Todd R. , Adalsteinsson Viktor A. 
Published: January 2021

AbstractThe ability to assay large numbers of low-abundance mutations is crucial in biomedicine. Yet, the technical hurdles of sequencing multiple mutations at extremely high depth and accuracy remain daunting. For sequencing low-level mutations, it’s either ‘depth or breadth’ but not both. Here, we report a simple and powerful approach to accurately track thousands of distinct mutations with minimal reads. Our technique called MAESTRO (minoralleleenrichedsequencingthroughrecognitionoligonucleotides) employs massively-parallel mutation enrichment to empower duplex sequencing—one of the most accurate methods—to track up to 10,000 low-frequency mutations with up to 100-fold less sequencing. In example use cases, we show that MAESTRO could enable mutation validation from cancer genome sequencing studies. We also show that it could track thousands of mutations from a patient’s tumor in cell-free DNA, which may improve detection of minimal residual disease from liquid biopsies. In all, MAESTRO improves the breadth, depth, accuracy, and efficiency of mutation testing.
Hi-C Analyses with GENOVA: a case study with cohesin variants
Authors: van der Weide Robin H. , van den Brand Teun , Haarhuis Judith H.I. , Teunissen Hans , Rowland Benjamin D. , de Wit Elzo 
Published: January 2021

AbstractConformation capture-approaches like Hi-C can elucidate chromosome structure at a genome-wide scale. Hi-C datasets are large and require specialised software. Here, we present GENOVA: a user-friendly software package to analyse and visualise conformation capture data. GENOVA is an R-package that includes the most common Hi-C analyses, such as compartment and insulation score analysis. It can create annotated heatmaps to visualise the contact frequency at a specific locus and aggregate Hi-C signal over user-specified genomic regions such as ChIP-seq data. Finally, our package supports output from the major mapping-pipelines. We demonstrate the capabilities of GENOVA by analysing Hi-C data from HAP1 cell lines in which the cohesin-subunits SA1 and SA2 were knocked out. We find that ΔSA1 cells gain intra-TAD interactions and increase compartmentalisation. ΔSA2 cells have longer loops and a less compartmentalised genome. These results suggest that cohesinSA1 forms longer loops, while cohesinSA2 plays a role in forming and maintaining intra-TAD interactions. Our data supports the model that the genome is provided structure in 3D by the counter-balancing of loop formation on one hand, and compartmentalization on the other hand. By differentially controlling loops, cohesinSA1 and cohesinSA2 therefore also affect nuclear compartmentalization. We show that GENOVA is an easy to use R-package, that allows researchers to explore Hi-C data in great detail.
Phototrophy and carbon fixation in Chlorobi postdate the rise of oxygen
Authors: Ward LM , Shih PM 
Published: January 2021

AbstractWhile most productivity on the surface of the Earth today is fueled by oxygenic photosynthesis, during the early parts of Earth history it is thought that anoxygenic photosynthesis—using compounds like ferrous iron or sulfide as electron donors—drove most global carbon fixation. Anoxygenic photosynthesis is still performed by diverse bacteria in niche environments today. Of these, the Chlorobi (formerly green sulfur bacteria) are often interpreted as being particularly ancient and are frequently proposed to have fueled the biosphere early in Earth history before the rise of oxygenic photosynthesis. Here, we perform comparative genomic, phylogenetic, and molecular clock analyses to determine the antiquity of the Chlorobi and their characteristic phenotypes. We show that contrary to common assumptions, the Chlorobi clade is relatively young, with anoxygenic phototrophy, carbon fixation via the rTCA pathway, and iron oxidation all significantly postdating the rise of oxygen ~2.3 billion years ago. The Chlorobi therefore could not have fueled the Archean biosphere, but instead represent a relatively young radiation of organisms which likely acquired the capacity for anoxygenic photosynthesis and other traits via horizontal gene transfer sometime after the evolution of oxygenic Cyanobacteria.
DEPP: Deep Learning Enables Extending Species Trees using Single Genes
Authors: Jiang Yueyu , Balaban Metin , Zhu Qiyun , Mirarab Siavash 
Published: January 2021

AbstractPlacing new sequences onto reference phylogenies is increasingly used for analyzing environmental samples, especially microbiomes. However, existing placement methods have a fundamental limitation: they assume that query sequences have evolved using specific models directly on the reference phylogeny. Thus, they can place single-gene data (e.g., 16S rRNA amplicons) onto their own gene tree. This practice is a proxy for a more ambitious goal: extending a (genome-wide) species tree given data from individual genes. No algorithm currently addresses this challenging problem. Here, we introduce Deep-learning Enabled Phylogenetic Placement (DEPP), an algorithm that learns to extend species trees using single genes without pre-specified models. We show that DEPP updates the multi-locus microbial tree-of-life with single genes with high accuracy. We further demonstrate that DEPP can achieve the long-standing goal of combining 16S and metagenomic data onto a single tree, enabling community structure analyses that were previously impossible and producing robust patterns.
Automatic classification of ICA components from infant EEG using MARA
Authors: Marriot Haresign I. , Phillips E. , Whitehorn M. , Noreika V. , Jones E.J.H. , Leong V. , Wass S.V. 
Published: January 2021

AbstractAutomated systems for identifying and removing non-neural ICA components are growing in popularity among adult EEG researchers. Infant EEG data differs in many ways from adult EEG data, but there exists almost no specific system for automated classification of source components from paediatric populations. Here, we adapt one of the most popular systems for adult ICA component classification for use with infant EEG data. Our adapted classifier significantly outperformed the original adult classifier on samples of naturalistic free play EEG data recorded from 10 to 12-month-old infants, achieving agreement rates with the manual classification of over 75% across two validation studies (n=44, n=25). Additionally, we examined both classifiers ability to remove stereotyped ocular artifact from a basic visual processing ERP dataset, compared to manual ICA data cleaning. Here the new classifier performed on level with expert manual cleaning and was again significantly better than the adult classifier at removing artifact whilst retaining a greater amount of genuine neural signal, operationalised through comparing ERP activations in time and space. Our new system (iMARA) offers developmental EEG researchers a flexible tool for automatic identification and removal of artifactual ICA components.