bluuebunny/crossref_metadata_2025_split huggingface dataset:
A snap shot of space and time dynamics of COVID-19 risk in Malawi. An application of spatial temporal model
Authors: Ngwira Alfred , Kumwenda Felix , Munthali Eddons , Nkolokosa Duncan 
Published: September 2020

AbstractBackgroundCOVID-19 has been the greatest challenge the world has faced since the second world war. The aim of this study was to investigate the distribution of COVID-19 in both space and time in Malawi.MethodsThe study used publicly available data of COVID-19 cases for the period from 24th June to 20th August, 2020. Semiparametric spatial temporal models were fitted to the number of weekly confirmed cases as an outcome data, with time and location as independent variables.ResultsThe study found significant main effect of location and time with the two interacting. The spatial distribution of COVID-19 showed major cities being at greater risk than rural areas. Over time the COVID-19 risk was increasing then decreasing in most districts with the rural districts being consistently at lower risk.ConclusionFuture or present strategies to avert the spread of COVID-19 should target major cities by limiting international exposure. In addition, the focus should be on time points that had shown high risk.
Environment influences SARS-CoV-2 transmission in the absence of non-pharmaceutical interventions
Authors: Smith Thomas P. , Flaxman Seth , Gallinat Amanda S. , Kinosian Sylvia P. , Stemkovski Michael , Unwin H. Juliette T. , Watson Oliver J. , Whittaker Charles , Cattarino Lorenzo , Dorigatti Ilaria , Tristem Michael , Pearse William D. 
Published: September 2020

AbstractAs COVID-19 continues to spread across the world, it is increasingly important to understand the factors that influence its transmission. Seasonal variation driven by responses to changing environment has been shown to affect the transmission intensity of several coronaviruses. However, the impact of the environment on SARS-CoV-2 remains largely unknown, and thus seasonal variation remains a source of uncertainty in forecasts of SARS-CoV-2 transmission. Here we address this issue by assessing the association of temperature, humidity, UV radiation, and population density with estimates of transmission rate (R). Using data from the United States of America, we explore correlates of transmission across USA states using comparative regression and integrative epidemiological modelling. We find that policy intervention (‘lockdown’) and reductions in individuals’ mobility are the major predictors of SARS-CoV-2 transmission rates, but in their absence lower temperatures and higher population densities are correlated with increased SARS-CoV-2 transmission. Our results show that summer weather cannot be considered a substitute for mitigation policies, but that lower autumn and winter temperatures may lead to an increase in transmission intensity in the absence of policy interventions or behavioural changes. We outline how this information may improve the forecasting of COVID-19, its future seasonal dynamics, and inform intervention policies.
Ancestry May Confound Genetic Machine Learning: Candidate-Gene Prediction of Opioid Use Disorder as an Example
Authors: Hatoum Alexander S. , Wendt Frank R. , Galimberti Marco , Polimanti Renato , Neale Benjamin , Kranzler Henry R. , Gelernter Joel , Edenberg Howard J. , Agrawal Arpana 
Published: September 2020

AbstractBackgroundMachine learning (ML) models are beginning to proliferate in psychiatry, however machine learning models in psychiatric genetics have not always accounted for ancestry. Using an empirical example of a proposed genetic test for OUD, and exploring a similar test for tobacco dependence and a simulated binary phenotype, we show that genetic prediction using ML is vulnerable to ancestral confounding.MethodsWe utilize five ML algorithms trained with 16 brain reward-derived “candidate” SNPs proposed for commercial use and examine their ability to predict OUD vs. ancestry in an out-of-sample test set (N=1000, stratified into equal groups of n=250 cases and controls each of European and African ancestry). We rerun analyses with 8 random sets of allele-frequency matched SNPs. We contrast findings with 11 genome-wide significant variants for tobacco smoking. To document generalizability, we generate and test a random phenotype.ResultsNone of the 5 ML algorithms predict OUD better than chance when ancestry was balanced but were confounded with ancestry in an out-of-sample test. In addition, the algorithms preferentially predicted admixed subpopulations. Random sets of variants matched to the candidate SNPs by allele frequency produced similar bias. Genome-wide significant tobacco smoking variants were also confounded by ancestry. Finally, random SNPs predicting a random simulated phenotype show that the bias attributable to ancestral confounding could impact any ML-based genetic prediction.ConclusionsResearchers and clinicians are encouraged to be skeptical of claims of high prediction accuracy from ML-derived genetic algorithms for polygenic traits like addiction, particularly when using candidate variants.
Clinical characteristics and outcomes of patients with COVID-19 and ARDS admitted to a third level health institution in Mexico City
Authors: Cárdenas Carmen M. Hernández , Sotelo Carlos Torruco , Jurado Felipe , Serna-Secundino Héctor , Aguilar Cristina , García-Olazarán José G. , García Diana Hernández , Goytia Gustavo Lugo 
Published: September 2020

AbstractBackgroundIn December 2019, the first cases of severe pneumonia associated with a new coronavirus were reported in Wuhan, China. Severe respiratory failure requiring intensive care was reported in up to 5% of cases. There is, however, limited information available in Mexico.ObjectivesThe purpose of this study was to describe the clinical manifestations, and outcomes in a COVID-19 cohort attended to from March to May 2020 in our RICU. In addition, we explored the association of clinical variables with mortality.MethodsThe first consecutive patients admitted to the RICU from March 3, 2020, to Jun 24, 2020, with confirmed COVID-19 were investigated. Clinical and laboratory data were obtained. Odds ratios (ORs) were calculated using a logistic regression model. The survival endpoint was mortality at discharge from the RICU.ResultsData from 68 consecutive patients were analyzed. Thirty-eight patients survived, and 30 died (mortality: 44.1 %). Of the 16 predictive variables analyzed, only 6 remained significant in the multivariate analysis [OR (95% confidence interval)]: no acute kidney injury (AKI)/AKI 1: [.61 (.001;.192)]; delta lymphocyte count: [.061 (.006;.619)]; delta ventilatory ratio: [8.19 (1.40;47.8)]; norepinephrine support at admission: [34.3 (2.1;550)]; body mass index: [1.41 (1.09;1.83)]; and bacterial coinfection: [18.5 (1.4;232)].ConclusionsWe report the characteristics and outcome of patients with ARDS and COVID-19. We found six independent factors associated with the mortality risk: delta lymphocyte count, delta ventilatory ratio, BMI, norepinephrine support, no AKI/AKI 1, and bacterial coinfection.
The impact of natural disasters on the spread of COVID-19: a geospatial, agent based epidemiology model
Authors: Van Wyk de Vries Maximillian , Rambabu Lekaashree 
Published: September 2020

AbstractBackgroundNatural disasters and infectious diseases are global issues, resulting in widespread disruption to human health and livelihood. At the scale of a global pandemic, the cooccurrence of natural disasters is inevitable. However, the impact of natural disasters on the spread of COVID-19 has not been extensively evaluated using agent based epidemiology models.MethodsWe create an agent-based epidemiology model based on both COVID-19 clinical and epidemiological data and geographic data. We first model 35 scenarios with varying natural disaster timing and duration for a COVID-19 outbreak in a theoretical region. We then evaluate the potential effect of an eruption of Vesuvius volcano on the spread of COVID-19 in Campania, Italy. Our objective is to determine if the occurrence of a natural disaster during the COVID-19 pandemic is likely to increase infection cases and disease related fatalities.ResultsIn a majority of cases, the occurrence of a natural disaster increases the number of disease related fatalities. When the natural disaster occurs at the beginning of the outbreak within a given region, there is little to no increase in the progression of disease spread. However, the occurrence of a natural disaster close to the peak of infections may increase the number of fatalities by more than five-fold. In a theoretical test case, for a natural disaster that occurred fifty days after first infection case, the median increase in fatalities is 2%, 59%, and 180% for a 2, 14, and 31-day long natural disaster respectively, when compared to the no natural disaster scenario.ConclusionWe propose that the compound risk from natural disasters is greatest in the case of already widespread disease outbreak. The key risk factors for increase in spread of infection and disease related fatalities are the timing of the natural disaster relative to the peak in infections and the duration of the natural disaster.
Context influences how individuals with misophonia respond to sounds
Authors: Edelstein M. , Monk B. , Ramachandran V.S. , Rouw R. 
Published: September 2020

ABSTRACTMisophonia is a newly researched condition in which specific sounds cause an intense, aversive response in individuals, characterized by negative emotions and autonomic arousal. Although virtually any sound can become a misophonic “trigger,” the most common sounds appear to be bodily sounds related to chewing and eating as well as other repetitive sounds. An intriguing aspect of misophonia is the fact that many misophonic individuals report that they are triggered more, or even only, by sounds produced by specific individuals, and less, or not at all, by sounds produced by animals (although there are always exceptions).In general, anecdotal evidence suggests that misophonic triggers involve a combination of sound stimuli and contextual cues. The aversive stimulus is more than just a sound and can be thought of as a Gestalt of features which includes sound as a necessary component as well as additional contextual information. In this study, we explore how contextual information influences misophonic responses to human chewing, as well as sonically similar sounds produced by non-human sources. The current study revealed that the exact same sound can be perceived as being much more or less aversive depending on the contextual information presented alongside the auditory information. The results of this study provide a foundation for potential cognitive based therapies.
The chaos in calibrating crop models
Authors: Wallach Daniel , Palosuo Taru , Thorburn Peter , Hochman Zvi , Gourdain Emmanuelle , Andrianasolo Fety , Asseng Senthold , Basso Bruno , Buis Samuel , Crout Neil , Dibari Camilla , Dumont Benjamin , Ferrise Roberto , Gaiser Thomas , Garcia Cecile , Gayler Sebastian , Ghahramani Afshin , Hiremath Santosh , Hoek Steven , Horan Heidi , Hoogenboom Gerrit , Huang Mingxia , Jabloun Mohamed , Jansson Per-Erik , Jing Qi , Justes Eric , Kersebaum Kurt Christian , Klosterhalfen Anne , Launay Marie , Lewan Elisabet , Luo Qunying , Maestrini Bernardo , Mielenz Henrike , Moriondo Marco , Nariman Zadeh Hasti , Padovan Gloria , Olesen Jørgen Eivind , Poyda Arne , Priesack Eckart , Pullens Johannes Wilhelmus Maria , Qian Budong , Schütze Niels , Shelia Vakhtang , Souissi Amir , Specka Xenia , Srivastava Amit Kumar , Stella Tommaso , Streck Thilo , Trombi Giacomo , Wallor Evelyn , Wang Jing , Weber Tobias K.D. , Weihermüller Lutz , de Wit Allard , Wöhling Thomas , Xiao Liujun , Zhao Chuang , Zhu Yan , Seidel Sabine J. 
Published: September 2020

AbstractCalibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.HighlightsWe propose a new approach to deriving calibration recommendations for system modelsApproach is based on analyzing calibration in multi-model simulation exercisesResulting recommendations are holistic and anchored in actual practiceWe apply the approach to calibration of crop models used to simulate phenologyRecommendations concern: objective function, parameters to estimate, software used
Three-dimensional Printing of Customized Bioresorbable Airway Stents
Authors: Paunović Nevena , Bao Yinyin , Coulter Fergal Brian , Masania Kunal , Geks Anna Karoline , Klein Karina , Rafsanjani Ahmad , Cadalbert Jasmin , Kronen Peter W. , Kleger Nicole , Karol Agnieszka , Luo Zhi , Rüber Fabienne , Brambilla Davide , von Rechenberg Brigitte , Franzen Daniel , Studart André R. , Leroux Jean-Christophe 
Published: September 2020

AbstractCentral airway obstruction is a life-threatening disorder causing a high physical and psychological burden to patients due to severe breathlessness and impaired quality of life. Standard-of-care airway stents are silicone tubes, which cause immediate relief, but are prone to migration, especially in growing patients, and require additional surgeries to be removed, which may cause further tissue damage. Customized airway stents with tailorable bioresorbability that can be produced in a reasonable time frame would be highly needed in the management of this disorder. Here, we report poly(D,L-lactide-co-ε-caprolactone) methacrylate blends-based biomedical inks and their use for the rapid fabrication of customized and bioresorbable airway stents. The 3D printed materials are cytocompatible and exhibit silicone-like mechanical properties with suitable biodegradability.In vivostudies in healthy rabbits confirmed biocompatibility and showed that the stents stayed in place for 7 weeks after which they became radiographically invisible. The developed biomedical inks open promising perspectives for the rapid manufacturing of the customized medical devices for which high precision, tuneable elasticity and predictable degradation are sought-after.
The impact of digital contact tracing on the SARS-CoV-2 pandemic - a comprehensive modelling study
Authors: Pollmann Tina R , Pollmann Julia , Wiesinger Christoph , Haack Christian , Shtembari Lolian , Turcati Andrea , Neumair Birgit , Meighen-Berger Stephan , Zattera Giovanni , Neumair Matthias , Apel Uljana , Okolie Augustine , Müller Johannes , Schönert Stefan , Resconi Elisa 
Published: September 2020

AbstractContact tracing is one of several strategies employed in many countries to curb the spread of SARS-CoV-2. Digital contact tracing (DCT) uses tools such as cell-phone applications to improve tracing speed and reach. We model the impact of DCT on the spread of the virus for a large epidemiological parameter space consistent with current literature on SARS-CoV-2. We also model DCT in combination with random testing (RT) and social distancing (SD).Modelling is done with two independently developed individual-based (stochastic) models that use the Monte Carlo technique, benchmarked against each other and against two types of deterministic models.For current best estimates of the number of asymptomatic SARS-CoV-2 carriers (approximately 40%), their contagiousness (similar to that of symptomatic carriers), the reproductive number before interventions (R0 at least 3) we find that DCT must be combined with other interventions such as SD and/or RT to push the reproductive number below one. At least 60% of the population would have to use the DCT system for its effect to become significant. On its own, DCT cannot bring the reproductive number below 1 unless nearly the entire population uses the DCT system and follows quarantining and testing protocols strictly. For lower uptake of the DCT system, DCT still reduces the number of people that become infected.When DCT is deployed in a population with an ongoing outbreak where of the population have already been infected, the gains of the DCT intervention come at the cost of requiring up to 15% of the population to be quarantined (in response to being traced) on average each day for the duration of the epidemic, even when there is sufficient testing capability to test every traced person.
Alternative or Complementary Role of Serological Rapid Antibody Test in the Management of Possible COVID-19 Cases
Authors: Yildirim Fatma , Gulhan Pınar Yildiz , Diken Ozlem Ercen , Capraz Aylin , Simsek Meltem , Yildirim Berna Botan , Taysi Muhammet Ridvan , Ozturk Sakine Yilmaz , Demirtas Nurcan , Ergil Julide , Dirican Adem , Uzar Tugce , Karaman Irem , Ozkaya Sevket 
Published: September 2020

AbstractBackgroundAlthough the gold diagnostic method for COVID-19 is accepted as the detection of viral particles by reverse transcription polymerase chain reaction (RT-PCR), serology testing for SARS-CoV-2 is at increased demand. A primary aim for utilization of serological tests are to better quantify the number of COVID-19 cases including those RT-PCR samples were negative but showing clinical and radiological signs of COVID-19. In this study, we aimed to report the features of the patients that were diagnosed and treated as possible COVID-19 cases whose multiple nasopharyngeal swab samples were negative by RTPCR but serological IgM/IgG antibody against SARS-CoV-2 were detected by rapid antibody test.MethodWe retrospectively analyzed eighty suspected COVID-19 cases that have at least two negative consecutive COVID-19 PCR test and were subjected to serological rapid antibody test.ResultThe specific antibodies against SARS-CoV-2 were detected as positive in twenty-two patients. The mean age of patient group was 63.2 ± 13.1 years old with male /female ratio 11/11. Cough was the most common symptom with 90.9%. Most common presenting chest CT findings were bilateral ground glass opacities (77.2%) and alveolar consolidations (50.09%). The mean duration from symptom initiation to hospital admission, to hospitalization, to treatment initiation and to detection of antibody positivity were 8.6 ± 7.2, 11.2 ± 5.4, 7.9 ± 3.2 and 24 ± 17 days, respectively.ConclusionOur study demonstrated the feasibility of COVID-19 diagnosis based on rapid antibody test in the cases of patients whose RT-PCR samples were negative. We suggest that the detection of antibodies against SARS-CoV-2 with rapid antibody test should be included in the diagnostic algorithm in suspected COVID-19 patients.
Heritability of ethanol consumption and pharmacokinetics in a genetically diverse panel of Collaborative Cross mouse strains and their inbred founders
Authors: Bagley Jared R. , Chesler Elissa J. , Philip Vivek M. ,   Center for the Systems Genetics of Addiction, Jentsch James D. 
Published: September 2020

AbstractBackgroundInter-individual variation in voluntary ethanol consumption and ethanol response is partially influenced by genetic variation. Discovery of the genes and allelic variants that affect these phenotypes may clarify the etiology and pathophysiology of problematic alcohol use, including alcohol use disorder. Genetically diverse mouse populations also demonstrate heritable variation in ethanol consumption and can be utilized to discover the genes and gene networks that influence this trait. The Collaborative Cross (CC) recombinant inbred strains, Diversity Outbred (DO) population and their eight founder strains are complementary mouse resources that capture substantial genetic diversity and can demonstrate expansive phenotypic variation in heritable traits. These populations may be utilized to discover candidate genes and gene networks that moderate ethanol consumption and other ethanol-related traits.MethodsWe characterized ethanol consumption, preference and pharmacokinetics in the eight founder strains and ten CC strains in 12-hour drinking sessions during the dark phase of the circadian cycle.ResultsEthanol consumption was found to be substantially heritable, both early in ethanol access and over a chronic intermittent access schedule. Ethanol pharmacokinetics were also found to be heritable; however, no association between strain-level ethanol consumption and pharmacokinetics was detected. The PWK/PhJ strain was found to be the highest drinking strain, with consumption substantially exceeding C57BL/6J, a strain commonly used as a model of “high” or “binge” drinking. Notably, we found strong evidence that sex moderated genetic effects on voluntary ethanol drinking.ConclusionsCollectively, this research may serve as a foundation for expanded genetic study of ethanol consumption in the CC/DO and related populations; moreover, we have identified reference strains with extreme consumption phenotypes that effectively represent polygenic models of hazardous ethanol use.
nanoDoc: RNA modification detection using Nanopore raw reads with Deep One-Class Classification
Authors: Ueda Hiroki 
Published: September 2020

AbstractAdvances in Nanopore single-molecule direct RNA sequencing (DRS) have presented the possibility of detecting comprehensive post-transcriptional modifications (PTMs) as an alternative to experimental approaches combined with high-throughput sequencing. It has been shown that the DRS method can detect the change in the raw electric current signal of a PTM; however, the accuracy and reliability still require improvement. Here, I present a new software program, named as nanoDoc, for detecting PTMs from DRS data using a deep neural network. Current signal deviations caused by PTMs are analyzed via Deep One-Class Classification with a convolutional neural network. Using a ribosomal RNA dataset, the software archive displayed an area under the curve (AUC) accuracy of 0.96 for detecting 23 different types of modifications inEscherichia coliandSaccharomyces cerevisiae. Furthermore, I demonstrated a tentative classification of PTMs using unsupervised clustering. Finally, I applied this software to severe acute respiratory syndrome coronavirus 2 data and identified commonly modified sites among three groups. nanoDoc is an open source software (GPLv3) available athttps://github.com/uedaLabR/nanoDocAuthor SummaryRNA post-transcriptional modifications (PTMs) is regulate multiple aspects of RNA function, including alternative splicing, export, stability, and translation, and the method to identify multiple types of PTMs is required for further advancement of this fields called ‘epitranscriptomics’. Nanopore singlemolecule direct RNA sequencing (DRS) can detect such PTMs, however the accuracy of the method needs to be improved. Detecting PTMs can be solved as a One-Class Classification problem, which is widely used in machine learning fields. Thus, a novel software named ‘nanoDoc’ for detecting PTMs was developed. The nanoDoc use convolutional neural network to extract the feature signal from nanopore sequencer and Deep One-Class Classification to detect PTMs as an anomaly. The software archive displayed an area under the curve (AUC) accuracy of 0.96 for detecting 23 different types of modifications inEscherichia coliandSaccharomyces cerevisiae.This software is applicable to different samples, and tested on severe acute respiratory syndrome coronavirus 2, and human transcript data as well.