bluuebunny/crossref_metadata_2025_split huggingface dataset:
Religious affiliation and the risk of COVID 19 related mortality; a retrospective analysis of variation in pre and post lockdown risk by religious group in England and Wales
Authors: Gaughan Charlotte Hannah , Ayoubkhani Daniel , Nafilyan Vahe , Goldblatt Peter , White Chris , Tingay Karen , Bannister Neil 
Published: October 2020

AbstractBackgroundCOVID 19 mortality risk is associated with demographic and behavioural factors; furthermore religious gatherings have been linked with the spread of COVID. We sought to understand the variation in the risk of COVID 19 related death across religious groups in the UK both before and after lockdown.MethodsWe conducted a retrospective cohort study of usual residents in England and Wales enumerated at the 2011 Census (n = 48,422,583), for risk of death involving COVID-19 using linked death certificates. Cox regression models were estimated to compare risks between religious groups. Time dependent religion coefficients were added to the model allowing hazard ratios (HRs) pre and post lockdown period to be estimated separately.ResultsCompared to Christians all religious groups had an elevated risk of death involving COVID-19; the largest age adjusted HRs were for Muslim and Jewish males at 2.5 (95% confidence interval 2.3-2.7) and 2.1 (1.9-2.5), respectively. The corresponding HRs for Muslim and Jewish females were 1.9 (1.7-2.1) and 1.5 (1.7-2.1). The difference in risk between groups contracted after lockdown. Those who affiliated with no religion had the lowest risk of COVID 19 related death before and after lockdown.ConclusionThe majority of the variation in COVID 19 mortality risk was explained by controlling for socio demographic and geographic determinants; however, Jews remained at a higher risk of death compared to all other groups. Lockdown measures were associated with reduced differences in COVID 19 mortality rates between religious groups, further research is required to understand the causal mechanisms.
Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model
Authors: Bhattacharyya Arinjita , Chakraborty Tanujit , Rai Shesh N. 
Published: October 2020

ABSTRACTAn unprecedented outbreak of the novel coronavirus (COVID-19) in the form of peculiar pneumonia has spread globally since its first case in Wuhan province, China, in December 2019. Soon after, the infected cases and mortality increased rapidly. The future of the pandemic’s progress was uncertain, and thus, predicting it became crucial for public health researchers. These future predictions help the effective allocation of health care resources, stockpiling, and help in strategic planning for clinicians, government authorities, and public health policymakers after understanding the extent of the effect. The main objective of this paper is to develop a hybrid forecasting model that can generate real-time out-of-sample forecasts of COVID-19 outbreaks for five profoundly affected countries, namely the USA, Brazil, India, UK, and Canada. A novel hybrid approach based on the Theta method and Autoregressive neural network (ARNN) model, named Theta-ARNN (TARNN) model, is developed. Daily new cases of COVID-19 are nonlinear, non-stationary, and volatile; thus a single specific model cannot be ideal for future prediction of the pandemic. However, the newly introduced hybrid forecasting model with an acceptable prediction error rate can help healthcare and government for effective planning and resource allocation. The proposed method outperforms traditional univariate and hybrid forecasting models for the test data sets on an average.
Social support for self-care: patient strategies for managing diabetes and hypertension in rural Uganda
Authors: Tusubira Andrew K. , Nalwadda Christine K , Akiteng Ann R , Hsieh Evelyn , Ngaruiya Christine , Rabin Tracy L , Hawley Nicola , Kalyesubula Robert , Ssinabulya Isaac , Schwartz Jeremy I , Armstrong-Hough Mari 
Published: October 2020

AbstractBackgroundThe growing burden of non-communicable diseases (NCDs) threatens low-income countries. Self-care practices are crucial for successful management of NCDs to prevent complications. We sought to understand self-care efforts and their facilitators among patients with diabetes and hypertension in rural Uganda.MethodsBetween April and June 2019, we conducted a cross-sectional qualitative study among adult patients from outpatient NCD clinics at three health facilities in Uganda. We conducted 19 in-depth interviews exploring treatment practices and response to symptoms. We used content analysis to identify emergent themes.ResultsThree themes emerged in patients’ descriptions of their self-care practices. First, patients preferred conventional medicines as their first line of resort, but often used traditional medicines. In particular, patients used traditional medicines to mitigate the negative impacts of inconsistent access to conventional medicines and to supplement those medicines. Second, patients adopted a wide range of vernacular practices to supplement treatments and unavailable diagnostic tests, including tasting their urine to gauge blood-sugar level. Finally, patients sought social support for self-care activities, relying on networks of family members and peers for instrumental and emotional support. Patients saw their children as the most reliable source of support, especially money for medicines, transport and home necessities.InterpretationPatients valued conventional medicines but also engaged in varied self-care practices. They depended upon networks of social support from family and peers to maintain self-care. Interventions to improve self-care may be more effective if they improve access to medicines and engage or enhance patients’ social support networks.
The neuronal associations of respiratory-volume variability in the resting state
Authors: Shams Sayedmohammad , LeVan Pierre , Chen J. Jean 
Published: October 2020

AbstractThe desire to enhance the sensitivity and specificity of resting-state (rs-fMRI) measures has prompted substantial recent research into removing noise components. Chief among contributions to noise in rs-fMRI are physiological processes, and the neuronal implications of respiratory-volume variability (RVT), a main rs-fMRI-relevant physiological process, is incompletely understood. The potential implications of RVT in modulating and being modulated by autonomic nervous regulation, has yet to be fully understood by the rs-fMRI community. In this work, we use high-density electroencephalography (EEG) along with simultaneously acquired RVT recordings to help address this question. We hypothesize that (1) there is a significant relationship between EEG and RVT in multiple EEG bands, and (2) that this relationship varies by brain region. Our results confirm our first hypothesis, although all brain regions are shown to be equally implicated in RVT-related EEG-signal fluctuations. The lag between RVT and EEG is consistent with previously reported values. However, an interesting finding is related to the polarity of the correlation between RVT and EEG. Our results reveal potentially two main regimes of EEG-RVT association, one in which EEG leads RVT with a positive association between the two, and one in which RVT leads EEG but with a positive association between the two. We propose that these two patterns can be interpreted differently in terms of the involvement of higher cognition. These results further suggest that treating RVT simply as noise is likely a questionable practice, and that more work is needed to avoid discarding cognitively relevant information when performing physiological correction rs-fMRI.
Association of Pre-COVID-19 Lymphocytopenia with Fatality
Authors: Burack W.R. , Rock P. , Burton D. , Cai X. 
Published: October 2020

AbstractLymphocytopenia during the COVID-19 has been associated with fatality. We tested whether pre-existing lymphocytopenia reported prior to any possible exposure to SARS-COV2 (from 2010 to 2019) was associated with fatality. Using all patients diagnosed on testing in a single regional laboratory, we identified 1137 subjects with PCR positive for SARS-COV2 and at least one available complete blood count from the decade prior to any possible exposure to the virus. Bivariate analysis indicated an association between pre-existing lymphocytopenia (defined as absolute lymphocyte count <0.9×109 /L) and fatality (18% versus 4%). Furthermore, a logistic regression model, accounting for both patient age and number of blood counts obtained, indicated the subjects with pre-existing lymphocytopenia were 1.4 times as likely to die. Because the absolute lymphocyte count is almost universally available and easily interpreted, this biomarker of the risk of fatality could be widely useful.
Treatment nonresponse in children receiving silver diamine fluoride for caries: A pilot study
Authors: Ruff Ryan Richard , Paul Bidisha , Sierra Maria A , Xu Fangxi , Crystal Yasmi , Li Xin , Saxena Deepak 
Published: October 2020

AbstractObjectives: Silver diamine fluoride (SDF) is a nonsurgical therapy for the arrest and prevention of dental caries with demonstrated clinical efficacy. Approximately 20% of children receiving SDF fail to respond to treatment. The objective of this study was to develop a predictive model of treatment nonresponse using machine learning.Methods: An observational pilot study (N=20) consisting of children with and without active decay and who did and did not respond to silver diamine fluoride provided salivary samples and plaque from infected and contralateral sites. 16S rRNA genes from samples were amplified and sequenced on an Illumina Miseq and analyzed using QIIME. The association between operational taxonomic units and treatment nonresponse was assessed using lasso regression and artificial neural networks.Results: Bivariate group comparisons of bacterial abundance indicate a number of genera were significantly different between nonresponders and those who responded to SDF therapy. No differences were found between nonresponders and caries-active subjects. Prevotella pallens and Veillonella denticariosi were retained in full lasso models and combined with clinical variables in a six-input multilayer perceptron.Discussion: The acidogenic and acid-tolerant nature of retained bacterial species may overcome the antimicrobial effects of SDF. Further research to validate the model in larger external samples is needed.
The effect of the definition of ‘pandemic’ on quantitative assessments of infectious disease outbreak risk
Authors: Singer Benjamin J , Thompson Robin N , Bonsall Michael B 
Published: October 2020

AbstractIn the early stages of an outbreak, the term ‘pandemic’ can be used to communicate about infectious disease risk, particularly by those who wish to encourage a large-scale public health response. However, the term lacks a widely accepted quantitative definition. We show that, under alternative quantitative definitions of ‘pandemic’, an epidemiological metapopulation model produces different estimates of the probability of a pandemic. Critically, we show that using different definitions alters the projected effects on the pandemic risk of key parameters such as inter-regional travel rates, degree of pre-existing immunity, and heterogeneity of transmission rates between regions. Our analysis provides a foundation for understanding the scientific importance of precise language when discussing pandemic risk, illustrating how alternate definitions affect the conclusions of modelling studies. This serves to highlight that those working on pandemic preparedness must remain alert to the variability in the use of the term ‘pandemic’, and provide specific quantitative definitions when undertaking one of the types of analysis that we show to be sensitive to the pandemic definition.
Willingness of Nigerian residents to disclose COVID-19 symptoms and take COVID-19 test
Authors: Oladoyin Victoria , Okunlola Oluyemi , Israel Oluwaseyi , Ibirongbe Demilade , Osifo Joy , Obembe Taiwo , Omode Paulinus , Osunmakinwa Olugbenga 
Published: October 2020

AbstractBackgroundAn understanding of willingness of people to disclose coronavirus disease 2019 (COVID-19) symptoms and take the COVID-19 test will help provide important insight for motivators towards the self-surveillance and testing strategies recommended by the World Health Organization to curtail and halt the transmission of COVID-19.ObjectivesThis study assessed willingness to disclose symptoms suggestive of COVID-19 and willingness to take COVID-19 test as well as their predictors.MethodsA cross-sectional online survey of 524 Nigerian adults, aged ≥ 18 years, residing in Nigeria and who had not taken the COVID-19 test was conducted. Information on willingness to disclose COVID-19 symptoms, take COVID-19 test and possible predictors were collected. Data were analysed using descriptive and inferential statistics evaluated at 5% significance level.ResultsMean age of respondents was 35.8 ± 10.7 years and 57.0% were males. Majority (85.8% and 86.2% respectively) were willing to disclose COVID-19 symptoms and take COVID-19 test. Self-risk perception of contracting COVID-19 predicted both willingness to disclose COVID-19 symptoms (aOR=3.236; 95%CI=1.836-5.704) and take COVID-19 test (aOR=3.174; 95%CI=1.570-6.419). Willingness to disclose COVID-19 symptoms (aOR=13.060; 95%CI= 6.253-27.276), knowledge of someone who had taken the test (aOR= 4.106; 95%CI= 1.179-14.299) and thought that it was important for people to know their COVID-19 status (aOR=3.123; 95%CI= 1.516-6.434) also predicted willingness to take COVID-19 test.ConclusionNigerians are willing to disclose symptoms suggestive of COVID-19 and take the COVID-19 test. Investment in interventions developed based on the predicting factors will help speed up the finding and testing of suspected COVID-19 cases.
“Safety and efficacy of pharmacotherapy used for the management of COVID 19: A systematic review and meta-analysis of randomized control trials”
Authors: Kumar Sah Sujit , Undela Krishna , Chand Sharad , Ramesh Madhan , Subramanian R , Gona Oliver Joel , George Sophia M. , Nandakumar UP , Aryal Santosh , Niharika P , Shastry C.S. 
Published: October 2020

AbstractBackgroundCOVID-19 is a novel coronavirus, which is highly contagious and a threat to human health, spreading across nearly 235 countries, affecting 33.8 million and causing 1.01 million fatalities as of 22 September 2020. Researchers have invested tremendous efforts to develop vaccines or effective drug therapy but have not yet been fruitful. Hence, we planned to conduct this systematic review and meta-analysis to supplement the readers with comprehensive data and credible information on the safety and efficacyof essential pharmacotherapy during the pharmacological management of COVID-19.MethodsTheprotocol will be designed based on the updated PRISMA-P 2015 guidelines. An elaborate search of electronic databases such as PubMed/Medline, Web of Science, Scopus, The Cochrane Library, ClinicalTrials.gov, Google Scholar, Medrxiv and other potential databases for articles published during January 2020 to 10 October 2020 is planned to be conducted. Following this,randomized control trials published in English language that assess the safety and efficacy of pharmacotherapy versusplacebo or standard of care or usual care will be evaluated for inclusion. The primary outcomes will include time to clinical recovery and the probability for the negative conversion of COVID-19. Secondary outcomes will quantifythe proportion of patients relieved of symptoms, the all-cause mortality, morbidity, detection of viral RNA, time needed to achieve a negative viral load, ordinal scale changes, ventilatorand oxygen requirements,length of hospital stayand the incidence of adverse and serious adverse events.RevMan V.5.3 computer software packages will be utilised to conduct an accurate statistical analysis of the study. Thebinary random-effects model will be used at a 95 % confidence interval to estimate the weighted effect size ofdichotomous data and continuous data studies. The results of statistical analysis will be considered statistically significant whena p-value <0.05 is attained.ResultsSelected studies will be used to evaluate the safety and efficacy of pharmacotherapy used during the management of the novel COVID-19.ConclusionThis study will be a qualitative and quantitative pool of comprehensive evidence regarding the safety and efficacy of pharmacotherapy on COVID-19.PROSPERO registrationCRD42020205433
Evidence and urgency related EEG signals during dynamic decision-making in humans
Authors: Yau Y. , Hinault T. , Taylor M. , Cisek P. , Fellows L.K. , Dagher A. 
Published: October 2020

AbstractA successful class of models link decision-making to brain signals by assuming that evidence accumulates to a decision threshold. These evidence accumulation models have identified neuronal activity that appears to reflect sensory evidence and decision variables that drive behavior. More recently, an additional evidence-independent and time-variant signal, named urgency, has been hypothesized to accelerate decisions in the face of insufficient evidence. However, most decision-making paradigms tested with fMRI or EEG in humans have not been designed to disentangle evidence accumulation from urgency. Here we use a face-morphing decision-making task in combination with EEG and a hierarchical Bayesian model to identify neural signals related to sensory and decision variables, and to test the urgency-gating model. We find that an evoked potential time-locked to the decision, the centroparietal positivity, reflects the decision variable from the computational model. We further show that the unfolding of this signal throughout the decision process best reflects the product of sensory evidence and an evidence-independent urgency signal. Urgency varied across subjects, suggesting that it may represent an individual trait. Our results show that it is possible to use EEG to distinguish neural signals related to sensory evidence accumulation, decision variables, and urgency. These mechanisms expose principles of cognitive function in general and may have applications to the study of pathological decision-making as in impulse control and addictive disorders.Significance StatementPerceptual decisions are often described by a class of models that assumes sensory evidence accumulates gradually over time until a decision threshold is reached. In the present study, we demonstrate that an additional urgency signal impacts how decisions are formed. This endogenous signal encourages one to respond as time elapses. We found that neural decision signals measured by EEG reflect the product of sensory evidence and an evidence-independent urgency signal. A nuanced understanding of human decisions, and the neural mechanisms that support it, can improve decision-making in many situations and potentially ameliorate dysfunction when it has gone awry.
Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech
Authors: Jain Shailee , Mahto Shivangi , Turek Javier S. , Vo Vy A. , LeBel Amanda , Huth Alexander G. 
Published: October 2020

AbstractNatural language contains information at multiple timescales. To understand how the human brain represents this information, one approach is to build encoding models that predict fMRI responses to natural language using representations extracted from neural network language models (LMs). However, these LM-derived representations do not explicitly separate information at different timescales, making it difficult to interpret the encoding models. In this work we construct interpretable multi-timescale representations by forcing individual units in an LSTM LM to integrate information over specific temporal scales. This allows us to explicitly and directly map the timescale of information encoded by each individual fMRI voxel. Further, the standard fMRI encoding procedure does not account for varying temporal properties in the encoding features. We modify the procedure so that it can capture both short- and long-timescale information. This approach outperforms other encoding models, particularly for voxels that represent long-timescale information. It also provides a finer-grained map of timescale information in the human language pathway. This serves as a framework for future work investigating temporal hierarchies across artificial and biological language systems.
Effect of temperature and precipitation on the daily new cases and daily new death in seven cities around the globe
Authors: Chaudhary Amar Prashad , Nelson K Adna , S Harish , S Mydhily , KJ Chaitanya , Nayak Lovely , Sah Chiranjibi 
Published: October 2020

AbstractBackgroundThis study was done to understand the effect of temperature and precipitation in COVID-19.ObjectiveTo study the effect of temperature and precipitation on transmission of COVID-19.To study the effect of temperature and precipitation on daily death of COVID-19.MethodologyWe collected 3 consecutive month data of seven cities around the world which were effected most by the COVID-19. Data included weather variables i.e temperature (average temperature, maximum temperature and minimum temperature), precipitation, daily new cases and daily new death.ConclusionIncrease in average temperature reduces daily death and increase in maximum temperature reduces transmission.
A Data-Informed Approach for Analysis, Validation, and Identification of COVID-19 Models
Authors: Olmez S. Yagiz , Mori Jameson , Miehling Erik , Başar Tamer , Smith Rebecca L. , West Matthew , Mehta Prashant G. 
Published: October 2020

AbstractThe COVID-19 pandemic has generated an enormous amount of data, providing a unique opportunity for modeling and analysis. In this paper, we present a data-informed approach for building stochastic compartmental models that is grounded in the Markovian processes underlying these models. Our initial data analyses reveal that the SIRD model – susceptiple (S), infected (I), recovered (R), and death (D) – is not consistent with the data. In particular, the transition times expressed in the dataset do not obey exponential distributions, implying that there exist unmodeled (hidden) states. We make use of the available epidemiological data to inform the location of these hidden states, allowing us to develop an augmented compartmental model which includes states for hospitalization (H) and end of infectious viral shedding (V). Using the proposed model, we characterize delay distributions analytically and match model parameters to empirical quantities in the data to obtain a good model fit. Insights from an epidemiological perspective are presented, as well as their implications for mitigation and control strategies.
Automatic Recognition of Auditory Brainstem Response Characteristic Waveform based on BiLSTM
Authors: Chen Cheng , Zhan Li , Pan Xiaoxin , Wang Zhiliang , Guo Xiaoyu , Qin Handai , Xiong Fen , Shi Wei , Shi Min , Ji Fei , Wang Qiuju , Yu Ning , Xiao Ruoxiu 
Published: October 2020

AbstractBackgroundAuditory brainstem response (ABR) test is widely used in newborn hearing screening and hearing disease diagnosis. Identifying and marking are challenging and repetitive tasks because of complex rules of ABR characteristic waveform and interference of background noise.MethodsThis study proposes an automatic method to recognize ABR characteristic waveform. First, binarization is created to mark 1024 sampling points accordingly. The selected characteristic area of ABR data is 0-8ms. The marking area is enlarged to expand feature information and reduce marking error. Second, a bi-directional long short-term memory (BiLSTM) network structure is established to improve relevance of sampling points, and an ABR sampling point classifier is obtained by training. Finally, mark points are obtained through thresholding.ResultsSpecific structure, related parameters, recognition effect, and noise resistance of network were explored in 614 sets of ABR clinical data, and recognition accuracy of waves I, III, and V can reach 92.91%.DiscussionThus, the proposed method can reduce the repetitive work of doctors and meet accuracy effectively. Therefore, this method has clinical potential.