bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multivariate Analysis of Factors Affecting COVID-19 Case and Death Rate in U.S. Counties: The Significant Effects of Black Race and Temperature
Authors: Li Adam Y. , Hannah Theodore C , Durbin John , Dreher Nickolas , McAuley Fiona M , Marayati Naoum Fares , Spiera Zachary , Ali Muhammad , Gometz Alex , Kostman JT , Choudhri Tanvir F. 
Published: April 2020

AbstractObjectivesCoronavirus disease-19 (COVID-19) has spread rapidly around the world, and many risk factors including patient demographics, social determinants of health, environmental variables, underlying health conditions, and adherence to social distancing have been hypothesized to affect case and death rates. However, little has been done to account for the potential confounding effects of these factors. Using a large multivariate analysis, this study illuminates modulators of COVID-19 incidence and mortality in U.S. counties while controlling for risk factors across multiple domains.MethodsData on COVID-19 and various risk factors in all U.S. counties was collected from publicly available data sources through April 14, 2020. Counties with at least 50 COVID-19 cases were included in case analyses and those with at least 10 deaths were included in mortality models. The 661 counties meeting inclusion criteria for number of cases were grouped into quartiles and comparisons of risk factors were made using t-tests between the highest and lowest quartiles. Similar comparisons for 217 counties were made for above average and below average deaths/100,000. Adjusted linear and logistic regression analyses were performed to evaluate the independent effects of factors that significantly impacted cases and deaths.ResultsUnivariate analyses demonstrated numerous significant differences between cohorts for both cases and deaths. Risk factors associated with increased cases and/or deaths per 100,000 included increased GDP per capita, decreased social distancing, increased age, increased percent Black, decreased percent Hispanic, decreased percent Asian, decreased health, increased poverty, increased diabetes, increased coronary heart disease, increased physical inactivity, increased alcohol consumption, increased tobacco use, and decreased access to primary care. Multivariate regression analyses demonstrated Black race is a risk factor for worse COVID-19 outcome independent of comorbidities, poverty, access to health care, and other mitigating factors. Lower daily temperatures was also an independent risk factor in case load but not deaths.ConclusionsU.S. counties with a higher proportion of Black residents are associated with increased COVID-19 cases and deaths. However, the various suggested mechanisms, such as socioeconomic and healthcare predispositions, did not appear to drive the effect of race in our model. Counties with higher average daily temperatures are also associated with decreased COVID-19 cases but not deaths. Several theories are posited to explain these findings, including prevalence of vitamin D deficiency. Additional studies are needed to further understand these effects.
Analysis and Estimation of Length of In-Hospital Stay Using Demographic Data of COVID-19 Recovered Patients in Singapore
Authors: Sreevalsan-Nair Jaya , Vangimalla Reddy Rani , Ghogale Pritesh Rajesh 
Published: April 2020

AbstractSingapore is one of the countries which has taken early, systematic, and rigorous nationwide responses to slowing the COVID-19 contagion down. By February 4, 2020, the government of Singapore restricted the mobility of the vulnerable age-groups. In the current study, we study the influence of the age-based vulnerability of the population with respect to COVID-19 conditions on the recovery of COVID-19 patients. We study 245 patients in Singapore recovered and discharged during January 23–April 01. We first study the descriptive statistics of the length of in-hospital stay (LOS) of the COVID-19 patients based on demographic variables, namely age, and gender. Then, we determine the distribution of LOS, using local and generalized linear regression models. We take the approach of periodization based on critical changes in the disease transmission model. Even though the overall recovery rate has reduced drastically after a sudden spike in daily confirmations, our analysis shows that there is a considerable shift in the COVID-19 confirmations to the population in the non-vulnerable age-groups. We show that the LOS of the non-vulnerable age group is considerably lower at 9 days, as opposed to 15 or 20 days in the existing literature.
COVID-19 Outbreak Prediction with Machine Learning
Authors: Ardabili Sina F. , Mosavi Amir , Ghamisi Pedram , Ferdinand Filip , Varkonyi-Koczy Annamaria R. , Reuter Uwe , Rabczuk Timon , Atkinson Peter M. 
Published: April 2020

AbstractSeveral outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak.
DETECTION AND SEGMENTATION OF ENDOSCOPIC ARTEFACTS AND DISEASES USING DEEP ARCHITECTURES
Authors: Nguyen Nhan T. , Tran Dat Q. , Nguyen Dung B. 
Published: April 2020

ABSTRACTWe describe in this paper our deep learning-based approach for the EndoCV2020 challenge, which aims to detect and segment either artefacts or diseases in endoscopic images. For the detection task, we propose to train and optimize EfficientDet—a state-of-the-art detector—with different EfficientNet backbones using Focal loss. By ensembling multiple detectors, we obtain a mean average precision (mAP) of 0.2524 on EDD2020 and 0.2202 on EAD2020. For the segmentation task, two different architectures are proposed: UNet with EfficientNet-B3 encoder and Feature Pyramid Network (FPN) with dilated ResNet-50 encoder. Each of them is trained with an auxiliary classification branch. Our model ensemble reports an sscore of 0.5972 on EAD2020 and 0.701 on EDD2020, which were among the top submitters of both challenges.
COVID-19 Epidemic Dynamics and Population Projections from Early Days of Case Reporting in a 40 million population from Southern India
Authors: Pant Rashmi , Choudhury Lincoln P. , Rajesh Jammy G. , Yeldandi Vijay V. 
Published: April 2020

AbstractIndia reported its first COVID19 case on 30 January 2020. Since then the epidemic has taken different trajectories across different geographical locations in the country. This study explores the population aggregated trajectories of COVID19 susceptible, infected and recovered or dead cases in the south Indian state of Telangana with a population of approximately 40 million. Information on cases reported from March 2 to April 4 was collated from government records. The susceptible-infected-removed (SIR) model for the spread of an infectious disease was used. Transmission parameters were extracted from existing literature that has emerged over past weeks from other regions with similar population densities as Telangana. Optimisation algorithms were used to get basic reproduction rate for different phases of nonpharmaceutical interventions rolled by the government. Peak accumulation is projected towards end of July with 36% of the population being infected by August 2020 if the population lockdown or social distancing mechanism is not continued. The number of deaths assuming no intervention is projected to be 488000 (95% CI: (329400, 646600)). A draconian enforcement of population lockdown combined with hand and face hygiene adherence would reduce the transmission by at least 99.7% whereas partial social distancing and hygiene would reduce it by 51.2%. Transmission parameters reported should be interpreted with caution as they are population aggregated and do not consider unique characteristics of susceptibility among micro-clusters and vulnerable individuals. More data will need to be collected to optimize transmission parameters and evaluate the full complexity, to simulate real world scenarios in the models.
Identity-by-descent detection across 487,409 British samples reveals fine-scale population structure, evolutionary history, and trait associations
Authors: Saada Juba Nait , Kalantzis Georgios , Shyr Derek , Robinson Martin , Gusev Alexander , Palamara Pier Francesco 
Published: April 2020

AbstractDetection of Identical-By-Descent (IBD) segments provides a fundamental measure of genetic relatedness and plays a key role in a wide range of genomic analyses. We developed a new method, called FastSMC, that enables accurate biobank-scale detection of IBD segments transmitted by common ancestors living up to several hundreds of generations in the past. FastSMC combines a fast heuristic search for IBD segments with accurate coalescent-based likelihood calculations and enables estimating the age of common ancestors transmitting IBD regions. We applied FastSMC to 487,409 phased samples from the UK Biobank and detected the presence of ∼214 billion IBD segments transmitted by shared ancestors within the past 1,500 years. We quantified time-dependent shared ancestry within and across 120 postcodes, obtaining a fine-grained picture of genetic relatedness within the past two millennia in the UK. Sharing of common ancestors strongly correlates with geographic distance, enabling the localization of a sample’s birth coordinates from genomic data. We sought evidence of recent positive selection by identifying loci with unusually strong shared ancestry within recent millennia and we detected 12 genome-wide significant signals, including 7 novel loci. We found IBD sharing to be highly predictive of the sharing of ultra-rare variants in exome sequencing samples from the UK Biobank. Focusing on loss-of-function variation discovered using exome sequencing, we devised an IBD-based association test and detected 29 associations with 7 blood-related traits, 20 of which were not detected in the exome sequencing study. These results underscore the importance of modelling distant relatedness to reveal subtle population structure, recent evolutionary history, and rare pathogenic variation.
Extracellular levels of glucose in the hippocampus and striatum during maze training for food or water reward in male rats
Authors: Scavuzzo C.J. , Newman L.A. , Gold P.E. , Korol D.L. 
Published: April 2020

AbstractPeripheral and central administration of glucose potently enhance cognitive functions. The present experiments examined changes in brain extracellular glucose levels while rats were trained to solve hippocampus-sensitive place or striatum-sensitive response learning tasks for food or water reward. During the first minutes of either place or response training, extracellular glucose levels declined in both the hippocampus and striatum, an effect not seen in untrained, rewarded rats. Subsequently, glucose increased in both brain areas under all training conditions, approaching asymptotic levels ∼15-25 min into training. Compared to untrained-food controls, training with food reward resulted in significant glucose increases in the hippocampus but not striatum; striatal glucose levels exhibited large increases to food intake in both trained and untrained groups. In rats trained to find water, glucose levels increased significantly above the values seen in untrained rats in both hippocampus and striatum. In contrast to results seen with lactate measurements, the magnitude of training-related increases in hippocampus and striatum glucose levels did not differ by task under either reward condition. The decreases in glucose early in training might reflect an increase in brain glucose consumption, perhaps triggering increased brain uptake of glucose from blood, as evident in the increases in glucose later in training. Together with past findings measuring lactate levels under the same conditions, the initial decreases in glucose may also stimulate increased production of lactate from astrocytes to support neural metabolism directly and/or to act as a signal to increase blood flow and glucose uptake into the brain.HighlightsGlucose levels in hippocampus and striatum decrease at the start of training.Glucose levels increase in both brain areas later in training.Glucose changes in both brain areas were similar for place and response tasks.Glucose levels responded similarly to training for either food and water rewards.Early decreases in glucose may trigger increased production astrocytic lactate.
Cross-reactive neutralization of SARS-CoV-2 by serum antibodies from recovered SARS patients and immunized animals
Authors: Zhu Yuanmei , Yu Danwei , Han Yang , Yan Hongxia , Chong Huihui , Ren Lili , Wang Jianwei , Li Taisheng , He Yuxian 
Published: April 2020

AbstractThe current COVID-19 pandemic, caused by a novel coronavirus SARS-CoV-2, poses serious threats to public health and social stability, calling for urgent need for vaccines and therapeutics. SARS-CoV-2 is genetically close to SARS-CoV, thus it is important to define the between antigenic cross-reactivity and neutralization. In this study, we firstly analyzed 20 convalescent serum samples collected from SARS-CoV infected individuals during the 2003 SARS outbreak. All patient sera reacted strongly with the S1 subunit and receptor-binding domain (RBD) of SARS-CoV, cross-reacted with the S ectodomain, S1, RBD, and S2 proteins of SARS-CoV-2, and neutralized both SARS-CoV and SARS-CoV-2 S protein-driven infections. Multiple panels of antisera from mice and rabbits immunized with a full-length S and RBD immunogens of SARS-CoV were also characterized, verifying the cross-reactive neutralization against SARS-CoV-2. Interestingly, we found that a palm civet SARS-CoV-derived RBD elicited more potent cross-neutralizing responses in immunized animals than the RBD from a human SARS-CoV strain, informing a strategy to develop a universe vaccine against emerging CoVs.SummarySerum antibodies from SARS-CoV infected patients and immunized animals cross-neutralize SARS-CoV-2 suggests strategies for universe vaccines against emerging CoVs.
A Scalable, Easy-to-Deploy, Protocol for Cas13-Based Detection of SARS-CoV-2 Genetic Material
Authors: Rauch Jennifer N. , Valois Eric , Solley Sabrina C. , Braig Friederike , Lach Ryan S. , Audouard Morgane , Ponce-Rojas Jose Carlos , Costello Michael S. , Baxter Naomi J. , Kosik Kenneth S. , Arias Carolina , Acosta-Alvear Diego , Wilson Maxwell Z. 
Published: April 2020

AbstractThe COVID-19 pandemic has created massive demand for widespread, distributed tools for detecting SARS-CoV-2 genetic material. The hurdles to scalable testing include reagent and instrument accessibility, availability of highly-trained personnel, and large upfront investment. Here we showcase an orthogonal pipeline we call CREST (Cas13-based, Rugged, Equitable, Scalable Testing) that addresses some of these hurdles. Specifically, CREST pairs commonplace and reliable biochemical methods (PCR) with low-cost instrumentation, without sacrificing detection sensitivity. By taking advantage of simple fluorescence visualizers, CREST allows for a binary interpretation of results. CREST may provide a point- of-care solution to increase the distribution of COVID-19 surveillance.
Lies, Gosh Darn Lies, and Not Enough Good Statistics: Why Epidemic Model Parameter Estimation Fails
Authors: Platt Daniel E. , Parida Laxmi , Zalloua Pierre 
Published: April 2020

AbstractAn opportunity exists in exploring epidemic modeling as a novel way to determine physiological and demic parameters for genetic association studies on a population/environmental (quasi) epidemiological study level. First, the spread of SARS-COV-2 has produced population specific lineages; second, epidemic spread model parameters are tied directly to these physiological and demic rates (e. g. incubation time, recovery time, transmission rate); and third, these parameters may serve as novel phenotypes to associate with region-specific genetic mutations as well as demic characteristics (e. g. age structure, cultural observance of personal space, crowdedness). Therefore, we sought to understand whether the parameters of epidemic models could be determined from the trajectory of infections, recovery, and hospitalizations prior to peak, and also to evaluate the quality and comparability of data between jurisdictions reporting their statistics necessary for the analysis of model parameters across populations. We found that, analytically, the pre-peak growth of an epidemic is limited by a subset of the model variates, and that the rate limiting variables are dominated by the expanding eigenmode of their equations. The variates quickly converge to the ratio of eigenvector components of the positive growth rate, which determines the doubling time. There are 9 parameters and 4 independent components in the eigenmode, leaving 5 undetermined parameters. Those parameters can be strikingly population dependent, and can have significant impact on estimates of hospital loads downstream. Without a sound framework, measurements of infection rates and other parameters are highly corrupted by uneven testing rates to uneven counting and reporting of relevant values. From the standpoint of phenotype parameters, this means that structured experiments must be performed to estimate these parameters in order to perform genetic association studies, or to construct viable models that accurately predict critical quantities such as hospitalization loads.
Sex, androgens and regulation of pulmonary AR, TMPRSS2 and ACE2
Authors: Baratchian Mehdi , McManus Jeffrey M. , Berk Mike , Nakamura Fumihiko , Mukhopadhyay Sanjay , Xu Weiling , Erzurum Serpil , Drazba Judy , Peterson John , Klein Eric A. , Gaston Ben , Sharifi Nima 
Published: April 2020

AbstractThe sex discordance in COVID-19 outcomes has been widely recognized, with males generally faring worse than females and a potential link to sex steroids. A plausible mechanism is androgen-induced expression of TMPRSS2 and/or ACE2 in pulmonary tissues that may increase susceptibility or severity in males. This hypothesis is the subject of several clinical trials of anti-androgen therapies around the world. Here, we investigated the sex-associated TMPRSS2 and ACE2 expression in human and mouse lungs and interrogated the possibility of pharmacologic modification of their expression with anti-androgens. We found no evidence for increasedTMPRSS2expression in the lungs of males compared to females in humans or mice. Furthermore, in male mice, treatment with the androgen receptor antagonist enzalutamide did not decrease pulmonary TMPRSS2. On the other hand, ACE2 and AR expression was sexually dimorphic and higher in males than females. ACE2 was moderately suppressible with enzalutamide therapy. Our work suggests that sex differences in COVID-19 outcomes attributable to viral entry are independent of TMPRSS2. Modest changes in ACE2 could account for some of the sex discordance.
Human iPSC-Derived Cardiomyocytes are Susceptible to SARS-CoV-2 Infection
Authors: Sharma Arun , Garcia Gustavo , Arumugaswami Vaithilingaraja , Svendsen Clive N. 
Published: April 2020

SUMMARYCoronavirus disease 2019 (COVID-19) is a viral pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is predominantly defined by respiratory symptoms, but cardiac complications including arrhythmias, heart failure, and viral myocarditis are also prevalent. Although the systemic ischemic and inflammatory responses caused by COVID-19 can detrimentally affect cardiac function, the direct impact of SARS-CoV-2 infection on human cardiomyocytes is not well-understood. We used human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) as a model system to examine the mechanisms of cardiomyocyte-specific infection by SARS-CoV-2. Microscopy and immunofluorescence demonstrated that SARS-CoV-2 can enter and replicate within hiPSC-CMs, localizing at perinuclear locations within the cytoplasm. Viral cytopathic effect induced hiPSC-CM apoptosis and cessation of beating after 72 hours of infection. These studies show that SARS-CoV-2 can infect hiPSC-CMsin vitro, establishing a model for elucidating the mechanisms of infection and potentially a cardiac-specific antiviral drug screening platform.
Sequential CRISPR gene editing in human iPSCs charts the clonal evolution of leukemia
Authors: Wang Tiansu , Pine Allison R , Wesely Josephine , Yuan Han , Zamparo Lee , Kotini Andriana G , Leslie Christina , Papapetrou Eirini P 
Published: April 2020

AbstractHuman cancers arise through an evolutionary process whereby cells acquire somatic mutations that drive them to outgrow normal cells and create successive clonal populations. “Bottom-up” human cancer evolution models could help illuminate this process, but their creation has faced significant challenges. Here we combined human induced pluripotent stem cell (iPSC) and CRISPR/Cas9 technologies to develop a model of the clonal evolution of acute myeloid leukemia (AML). Through the sequential introduction of 3 disease-causing mutations (ASXL1 C-terminus truncation, SRSF2P95L and NRASG12D), we obtained single, double and triple mutant iPSC lines that, upon hematopoietic differentiation, exhibit progressive dysplasia with increasing number of mutations, capturing distinct premalignant stages, including clonal hematopoiesis, myelodysplastic syndrome, and culminating in a transplantable leukemia. iPSC-derived clonal hematopoietic stem/progenitor cells recapitulate transcriptional and chromatin accessibility signatures of normal and malignant hematopoiesis found in primary human cells. By mapping dynamic changes in transcriptomes and chromatin landscapes, we characterize transcriptional programs driving specific stage transitions and identify vulnerabilities for early therapeutic targeting. Such synthetic “de novo oncogenesis” models can empower the investigation of multiple facets of the malignant transformation of human cells.