bluuebunny/crossref_metadata_2025_split huggingface dataset:
Allocation of COVID-19 Vaccines Under Limited Supply
Authors: Chen Xin , Li Menglong , Simchi-Levi David , Zhao Tiancheng 
Published: August 2020

Problem definitionThis paper considers how to allocate COVID-19 vaccines to different age groups when limited vaccines are available over time.Academic/practical relevanceVaccine is one of the most effective interventions to contain the ongoing COVID-19 pandemic. However, the initial supply of the COVID-19 vaccine will be limited. An urgent problem for the government is to determine who to get the first dose of the future COVID-19 vaccine.MethodologyWe use epidemic data from New York City to calibrate an age-structured SAPHIRE model that captures the disease dynamics within and across various age groups. The model and data allow us to derive effective static and dynamic vaccine allocation policies minimizing the number of confirmed cases or the numbers of deaths.ResultsThe optimal static policies achieve a much smaller number of confirmed cases and deaths compared to other static benchmark policies including the pro rata policy. Dynamic allocation policies, including various versions of the myopic policy, significantly improve on static policies.Managerial implicationsFor static policies, our numerical study shows that prioritizing the older groups is beneficial to reduce deaths while prioritizing younger groups is beneficial to avert infections. For dynamic policies, the older groups should be vaccinated at early days and then switch to younger groups. Our analysis provides insights on how to allocate vaccines to the various age groups, which is tightly connected to the decision-maker’s objective.
Discretizing clinical information can reduce antibiotic misuse: a game theoretic approach
Authors: Diamant Maya , Baruch Shoham , Kassem Eias , Muhsen Khitam , Samet Dov , Leshno Moshe , Obolski Uri 
Published: August 2020

AbstractThe overuse of antibiotics is exacerbating the antibiotic resistance crisis. Since this problem is a classic common-goods dilemma, it naturally lends itself to a game-theoretic analysis. Hence, we designed a model wherein physicians weigh whether antibiotics should be prescribed, given that antibiotic usage depletes its future effectiveness. The physicians’ decisions rely on the probability of a bacterial infection before definitive laboratory results are available. We show that the physicians’ equilibrium decision-rule of antibiotic prescription is not socially optimal. However, we prove that discretizing the information provided to physicians can mitigate the gap between their equilibrium decisions and the social optimum of antibiotic prescription. Despite this problem’s complexity, the effectiveness of the discretization solely depends on the distribution of available information. This is demonstrated on theoretic distributions and a clinical dataset. Our results provide a game-theory based guide for optimal output of current and future decision support systems of antibiotic prescription.
SARS-CoV-2 infections and antibody responses among health care workers in a Spanish hospital after a month of follow-up
Authors: Moncunill Gemma , Mayor Alfredo , Santano Rebeca , Jiménez Alfons , Vidal Marta , Tortajada Marta , Sanz Sergi , Méndez Susana , Llupià Anna , Aguilar Ruth , Alonso Selena , Barrios Diana , Carolis Carlo , Cisteró Pau , Chóliz Eugenia , Cruz Angeline , Fochs Silvia , Jairoce Chenjerai , Hecht Jochen , Lamoglia Montserrat , Martínez Mikel J. , Moreno Javier , Mitchell Robert A. , Ortega Natalia , Pey Nuria , Puyol Laura , Ribes Marta , Rosell Neus , Sotomayor Patricia , Torres Sara , Williams Sarah , Barroso Sonia , Vilella Anna , Trilla Antoni , Varela Pilar , Dobaño Carlota , Garcia-Basteiro Alberto L 
Published: August 2020

AbstractBackgroundAt the peak of the COVID-19 pandemic in Spain, cumulative prevalence of SARS-CoV-2 infection in a cohort of 578 randomly selected health care workers (HCW) from Hospital Clínic de Barcelona was 11.2%.MethodsA follow-up survey one month after the baseline (April-May 2020) measured SARS-CoV-2 infection by real time reverse-transcriptase polymerase chain reaction (rRT-PCR) and IgM, IgA, IgG and subclasses to the receptor-binding domain of the SARS-CoV-2 spike protein by Luminex. Prevalence of infection was defined by a positive SARS-CoV-2 rRT-PCR and/or antibody seropositivity.ResultsThe cumulative prevalence of infection at month 1 was 14.9% (84/565) and the seroprevalence 14.5% (82/565) for IgM and/or IgG and/or IgA. We found 25 (5%) new infections in participants without previous evidence of infection at baseline (501) and two participants seroreverted for IgM and/or IgG and/or IgA. Among seropositive participants at baseline, IgM and IgA levels generally declined at month 1 (antibody decay rates of 0.49 (95% CI, 0.40-0.60) and 0.34 (95% CI, 0.26-0.44)), respectively. Eight percent of the participants seroreverted for IgM and 11% for IgA. Subjects reporting COVID-19-like symptoms and laboratory and other technicians had higher risk of infection. The most frequent subclass responses were IgG1 and IgG2, followed by IgG3, with higher levels of IgG1, and only IgA1 but no IgA2 was detected.ConclusionsOur findings highlight the importance of a continuous and improved surveillance of SARS-CoV-2 infections in HCW, particularly in high risk groups. The decay of IgA and IgM levels have implications for seroprevalence studies using these isotypes.
The Role of Machine Learning Techniques to Tackle COVID-19 Crisis: A Systematic Review
Authors: Syeda Hafsa Bareen , Syed Mahanazuddin , Sexton Kevin , Syed Shorabuddin , Begum Salma , Syed Farhan , Yu Feliciano 
Published: August 2020

AbstractBackgroundThe novel coronavirus responsible for COVID-19 has caused havoc with patients presenting a spectrum of complications forcing the healthcare experts around the globe to explore new technological solutions, and treatment plans. Machine learning (ML) based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize them in response to the challenges posed by the COVID-19 pandemic.ObjectiveThe objective of this study is to conduct a systematic literature review on the role of ML as a comprehensive and decisive technology to fight the COVID-19 crisis in the arena of epidemiology, diagnosis, and disease progression.MethodsA systematic search in PubMed, Web of Science, and CINAHL databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to identify all potentially relevant studies published and made available between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and ML. A total of 128 qualified articles were reviewed and analyzed based on the study objectives.ResultsThe 128 publications selected were classified into three themes based on ML applications employed to combat the COVID-19 crisis: Computational Epidemiology (CE), Early Detection and Diagnosis (EDD), and Disease Progression (DP). Of the 128 studies, 70 focused on predicting the outbreak, the impact of containment policies, and potential drug discoveries, which were grouped into the CE theme. For the EDD, we grouped forty studies that applied ML techniques to detect the presence of COVID-19 using the patients’ radiological images or lab results. Eighteen publications that focused on predicting the disease progression, outcomes (recovery and mortality), Length of Stay (LOS), and number of Intensive Care Unit (ICU) days for COVID-19 positive patients were classified under the DP theme.ConclusionsIn this systematic review, we assembled the current COVID-19 literature that utilized ML methods to provide insights into the COVID-19 themes, highlighting the important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.
Genetic Variation in Blood Pressure and Lifetime Risk of Peripheral Artery Disease: A Mendelian Randomization Study
Authors: Levin Michael G. , Klarin Derek , Walker Venexia M. , Gill Dipender , Lynch Julie , Lee Kyung M. , Assimes Themistocles L. , Natarajan Pradeep , Hung Adriana M. , Edwards Todd , Rader Daniel J. , Gaziano J. Michael , Davies Neil M. , Tsao Philip S. , Chang Kyong-Mi , Voight Benjamin F. , Damrauer Scott M. 
Published: August 2020

ABSTRACT:AimsWe aimed to estimate the effect of blood pressure and blood pressure lowering medications (via genetic proxies) on peripheral artery disease.Methods and ResultsGWAS summary statistics were obtained for BP (International Consortium for Blood Pressure + UK Biobank GWAS; N = up to 757,601 individuals), peripheral artery disease (PAD; VA Million Veteran Program; N = 24,009 cases, 150,983 controls), and coronary artery disease (CAD; CARDIoGRAMplusC4D 1000 Genomes; N = 60,801 cases, 123,504 controls). Genetic correlations between systolic BP (SBP), diastolic BP (DBP), pulse pressure (PP) and CAD and PAD were estimated using LD score regression. The strongest correlation was between SBP and CAD (rg= 0.36; p = 3.9 × 10−18). Causal effects were estimated by two-sample MR using a range of pleiotropy-robust methods. Increased SBP, DBP, and PP increased risk of both PAD (SBP OR 1.25 [1.19–1.31] per 10mmHg increase, p = 3 × 10−18; DBP OR 1.27 [1.17–1.39], p = 4 × 10−8; PP OR 1.51 [1.38–1.64], p = 1 × 10−20) and CAD (SBP OR 1.37 [1.29–1.45], p = 2 × 10−24; DBP OR 1.6 [1.45–1.76], p = 7 × 10−22; PP OR 1.56 [1.4–1.75], p = 1 × 10−15). The effects of SBP and DBP were greater for CAD than PAD (pdiff= 0.024 for SBP, pdiff= 4.9 × 10−4for DBP). Increased liability to PAD increased PP (beta = 1.04 [0.62–1.45] mmHg per 1 unit increase in log-odds in liability to PAD, p = 1 × 10−6). MR was also used to estimate the effect of BP lowering through different classes of antihypertensive medications using genetic instruments containing BP-trait associated variants located within genes encoding protein targets of each medication. SBP lowering via calcium channel blocker-associated variants was protective of CAD (OR 0.38 per 10mmHg decrease in SBP; 95% CI 0.19–0.77; p = 0.007).ConclusionsHigher BP is likely to cause both PAD and CAD but may have a larger effect on CAD risk. BP-lowering through calcium-channel blockers (as proxied by genetic variants) decreased risk of CAD.
A deterministic linear infection model to inform Risk-Cost-Benefit Analysis of activities during the SARS-CoV-2 pandemic
Authors: McCarthy John E. , Dumas Bob A. , McCarthy Myles T. , Dewitt Barry D. 
Published: August 2020

AbstractAugust 16, 2020Risk-cost-benefit analysis requires the enumeration of decision alternatives, their associated outcomes, and the quantification of uncertainty. Public and private decision-making surrounding the COVID-19 pandemic must contend with uncertainty about the probability of infection during activities involving groups of people, in order to decide whether that activity is worth undertaking. We propose a deterministic linear model of SARS-CoV-2 infection probability that can produce estimates of relative risk for diverse activities, so long as those activities meet a list of assumptions, including that they do not last longer than one day. We show how the model can be used to inform decisions facing governments and industry, such as opening stadiums or flying on airplanes. We prove that the model is a good approximation of a more refined model in which we assume infections come from a series of independent risks. The linearity assumption makes interpreting and using the model straightforward, and we argue that it does so without significantly diminishing the reliability of the model.
Bromodomain and Extraterminal Inhibition Blocks Inflammation-Induced Cardiac Dysfunction and SARS-CoV-2 Infection (Pre-Clinical)
Authors: Mills Richard J , Humphrey Sean J , Fortuna Patrick RJ , Lor Mary , Foster Simon R , Quaife-Ryan Gregory A , Johnston Rebecca L. , Dumenil Troy , Bishop Cameron , Ruraraju Rajeev , Rawle Daniel J , Le Thuy , Zhao Wei , Lee Leo , Mackenzie-Kludas Charley , Mehdiabadi Neda R , Halliday Christopher , Gilham Dean , Fu Li , Nicholls Stephen J. , Johansson Jan , Sweeney Michael , Wong Norman C.W. , Kulikowski Ewelina , Sokolowski Kamil A. , Tse Brian W. C. , Devilée Lynn , Voges Holly K , Reynolds Liam T , Krumeich Sophie , Mathieson Ellen , Abu-Bonsrah Dad , Karavendzas Kathy , Griffen Brendan , Titmarsh Drew , Elliott David A , McMahon James , Suhrbier Andreas , Subbarao Kanta , Porrello Enzo R , Smyth Mark J , Engwerda Christian R , MacDonald Kelli PA , Bald Tobias , James David E , Hudson James E 
Published: August 2020

SUMMARYCardiac injury and dysfunction occur in COVID-19 patients and increase the risk of mortality. Causes are ill defined, but could be direct cardiac infection and/or inflammation-induced dysfunction. To identify mechanisms and cardio-protective drugs, we use a state-of-the-art pipeline combining human cardiac organoids with phosphoproteomics and single nuclei RNA sequencing. We identify an inflammatory ‘cytokine-storm’, a cocktail of interferon gamma, interleukin 1β and poly(I:C), induced diastolic dysfunction. Bromodomain-containing protein 4 is activated along with a viral response that is consistent in both human cardiac organoids and hearts of SARS-CoV-2 infected K18-hACE2 mice. Bromodomain and extraterminal family inhibitors (BETi) recover dysfunction in hCO and completely prevent cardiac dysfunction and death in a mouse cytokine-storm model. Additionally, BETi decreases transcription of genes in the viral response, decreases ACE2 expression and reduces SARS-CoV-2 infection of cardiomyocytes. Together, BETi, including the FDA breakthrough designated drug apabetalone, are promising candidates to prevent COVID-19 mediated cardiac damage.
Dynamic Expression of Brain Functional Systems Disclosed by Fine-Scale Analysis of Edge Time Series
Authors: Sporns Olaf , Faskowitz Joshua , Teixera Andreia Sofia , Betzel Richard F. 
Published: August 2020

AbstractFunctional connectivity (FC) describes the statistical dependence between brain regions in resting-state fMRI studies and is usually estimated as the Pearson correlation of time courses. Clustering reveals densely coupled sets of regions constituting a set of resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs lasting many minutes but appear to fluctuate on shorter time scales. Here, we propose a new approach to track these temporal fluctuations. Un-wrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair/edge, and reveals fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging sessions and disclose fine-scale profiles of the time-varying levels of expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.
Structural basis for the allosteric regulation of Human Topoisomerase 2α
Authors: Broeck Arnaud Vanden , Lotz Christophe , Drillien Robert , Bedez Claire , Lamour Valérie 
Published: August 2020

AbstractThe human type IIA topoisomerases (Top2) are essential enzymes that regulate DNA topology and chromosome organization. The Top2α isoform is a prime target for antineoplastic compounds used in cancer therapy that form ternary cleavage complexes with the DNA. Despite extensive studies, structural information on this large dimeric assembly is limited to the catalytic domains, hindering the exploration of allosteric mechanism governing the enzyme activities and the contribution of its non-conserved C-terminal domain (CTD). Herein we present cryo-EM structures of the entire human Top2α nucleoprotein complex in different conformations solved at subnanometer resolutions. Our data unveils the molecular determinants that fine tune the allosteric connections between the ATPase domain and the DNA binding/cleavage domain. Strikingly, the reconstruction of the DNA-binding/cleavage domain uncovers a linker leading to the CTD, which plays a critical role in modulating the enzyme’s activities and opens perspective for the analysis of post-translational modifications.
Estimating Heritability of Glycaemic Response to Metformin using Nationwide Electronic Health Records and Population-Sized Pedigree
Authors: Kalka Iris N. , Gavrieli Amir , Shilo Smadar , Rossman Hagai , Artzi Nitzan Shalom , Segal Eran 
Published: August 2020

AbstractVariability of response to medication is a well known phenomenon, determined by both environmental and genetic factors. Understanding the heritable component of the response to medication is of great interest but challenging due to several reasons, including small study cohorts and computational limitations. Here, we studied the heritability of variation in the glycaemic response to metformin, first-line therapeutic agent for type 2 diabetes (T2D), by leveraging 17 years of electronic health records (EHR) data from Israel’s largest healthcare service provider, consisting of over five million patients of diverse ethnicities and socio-economic background. Our cohort consisted of 74,871 T2D patients treated with metformin, with an accumulated number of 1,358,776 HbA1C measurements and 323,260 metformin prescriptions. We estimated the explained variance of glycated hemoglobin (HbA1c%) reduction due to heritability by constructing a six-generation population-size pedigree from pedigree information linked to medical health records. Using a Linear Mixed Model-based framework, a common-practice method for heritability estimation, we calculated a heritability measure of h2 = 10.5% (95% CI, 3.5%-17.5%) for absolute reduction of HbA1c% after metformin treatment, which remained unchanged after adjusting for pre-treatment HbA1c%, and h2 = 12.2% (95% CI, 5.2%-19.3%) for proportional reduction in HbA1c%. To the best of our knowledge, our work is the first to estimate heritability of drug response using EHR data. We demonstrated that while response to metformin treatment has a heritable component, most of the variation is likely due to other factors, further motivating non-genetic analyses aimed at unraveling metformin’s mechanism of action.
Using temperature to analyse the neural basis of a time-based decision
Authors: Monteiro Tiago , Rodrigues Filipe S. , Pexirra Margarida , Cruz Bruno F. , Gonçalves Ana I. , Rueda-Orozco Pavel E. , Paton Joseph J. 
Published: August 2020

ABSTRACTThe basal ganglia (BG) are thought to contribute to decision-making and motor control by influencing action selection based on consequences. These functions are critically dependent on timing information that can be extracted from the evolving state of neural populations in the striatum, the major input area of the BG. However, it is debated whether striatal activity underlies latent, dynamic decision processes or kinematics of overt movement. Here, we measured the impact of temperature on striatal population activity and the behavior of rats and compared the observed effects to neural activity and behavior collected in multiple versions of a temporal categorization task. Cooler temperatures caused dilation, and warmer temperatures contraction, of both neural activity and patterns of judgment in time, mimicking endogenous decision-related variability in striatal activity. However, temperature did not similarly affect movement kinematics. These data provide compelling evidence that the time course of evolving striatal population activity dictates the speed of a latent process that is used to guide choices, but not moment by moment kinematics. More broadly, they establish temporal scaling of population activity as a likely cause and not simply a correlate of timing behavior in the brain.
The spatial landscape of gene expression isoforms in tissue sections
Authors: Lebrigand Kevin , Bergenstråhle Joseph , Thrane Kim , Mollbrink Annelie , Meletis Konstantinos , Barbry Pascal , Waldmann Rainer , Lundeberg Joakim 
Published: August 2020

ABSTRACTIn situcapturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and fulllength sequence heterogeneity cannot be characterized at spatial resolution with current transcriptome profiling methods. To that end, we introduce Spatial Isoform Transcriptomics (SiT), an explorative method for characterizing spatial isoform variation and sequence heterogeneity. We show in mouse brain how SIT can be used to profile isoform expression and sequence heterogeneity in different areas of the tissue. SiT reveals regional isoform switching ofPlp1gene between different layers of the olfactory bulb, and use of external single cell data allowed to nominate cell types expressing each isoform. Furthermore, SiT identifies differential isoform usage for several major genes implicated in brain function (Snap25, Bin1, Gnas) that we independently validated byin situsequencing. SiT also provides for the first time an in-depth A-to-I RNA editing map of the adult mouse brain. Data exploration can be performed through an online resource (https://www.isomics.eu), where isoform expression and RNA editing can be visualized in a spatial context.
Ecology of inorganic sulfur auxiliary metabolism in widespread bacteriophages
Authors: Kieft Kristopher , Zhou Zhichao , Anderson Rika E. , Buchan Alison , Campbell Barbara J. , Hallam Steven J. , Hess Matthias , Sullivan Matthew B. , Walsh David A. , Roux Simon , Anantharaman Karthik 
Published: August 2020

ABSTRACTMicrobial sulfur metabolism contributes to biogeochemical cycling on global scales. Sulfur metabolizing microbes are infected by phages that can encode auxiliary metabolic genes (AMGs) to alter sulfur metabolism within host cells but remain poorly characterized. Here we identified 191 phages derived from twelve environments that encoded 227 AMGs for oxidation of sulfur and thiosulfate (dsrA, dsrC/tusE, soxC, soxD and soxYZ). Evidence for retention of AMGs during niche-differentiation of diverse phage populations provided evidence that auxiliary metabolism imparts measurable fitness benefits to phages with ramifications for ecosystem biogeochemistry. Gene abundance and expression profiles of AMGs suggested significant contributions by phages to sulfur and thiosulfate oxidation in freshwater lakes and oceans, and a sensitive response to changing sulfur concentrations in hydrothermal environments. Overall, our study provides novel insights on the distribution, diversity and ecology of phage auxiliary metabolism associated with sulfur and reinforces the necessity of incorporating viral contributions into biogeochemical configurations.