bluuebunny/crossref_metadata_2025_split huggingface dataset:
Review of Forecasting Models for Coronavirus (COVID-19) Pandemic in India during Country-wise Lockdowns
Authors: Gola Abhinav , Arya Ravi Kumar , Animesh  , Dugh Ravi 
Published: August 2020

AbstractBackgroundCOVID-19 is widely spreading across the globe right now. While some countries have flattened the curve, others are struggling to control the spread of the infection. Precise risk prediction modeling is key to accurate prevention and containment of COVID-19 infection, as well as for the preparation of resources needed to deal with the pandemic in different regions.MethodsGiven the vast differences in approaches and scenarios used by these models to predict future infection rates, in this study, we compared the accuracy among different models such as regression models, ARIMA model, multilayer perceptron, vector autoregression, susceptible exposed infected recovered (SEIR), susceptible infected recovered (SIR), recurrent neural networks (RNNs), long short term memory networks (LSTM) and exponential growth model in prediction of the total COVID-19 confirmed cases. We did so by comparing the predicted rates of these models with actual rates of COVID-19 in India during the nationwide lockdowns.ResultsFew of these models accurately predicted COVID-19 incidence and mortality rates in six weeks, though some provided close results. While advanced warning can help mitigate and prepare for an impending or ongoing epidemic, using poorly fitting models for prediction could lead to substantial adverse outcomes.ImplicationsAs the COVID-19 pandemic continues, accurate risk prediction is key to effective public health interventions. Caution should be taken when choosing different risk prediction models based on specific scenarios and needs. To improve risk prediction of infectious disease such as COVID-19 for policy guidance and recommendations on best practices, both internal (e.g., specific virus characteristics in transmission and mutation) and external factors (e.g., large-scale human behaviors such as school opening, parties, and breaks) should be considered and appropriately weighed.
Leverage Real-world Longitudinal Data in Large Clinical Research Networks for Alzheimer’s Disease and Related Dementia (ADRD)
Authors: Duan Rui , Chen Zhaoyi , Tong Jiayi , Luo Chongliang , Lyu Tianchen , Tao Cui , Maraganore Demetrius , Bian Jiang , Chen Yong 
Published: August 2020

AbstractWith vast amounts of patients’ medical information, electronic health records (EHRs) are becoming one of the most important data sources in biomedical and health care research. Effectively integrating data from multiple clinical sites can help provide more generalized real-world evidence that is clinically meaningful. To analyze the clinical data from multiple sites, distributed algorithms are developed to protect patient privacy without sharing individual-level medical information. In this paper, we applied the One-shot Distributed Algorithm for Cox proportional hazard model (ODAC) to the longitudinal data from the OneFlorida Clinical Research Consortium to demonstrate the feasibility of implementing the distributed algorithms in large research networks. We studied the associations between the clinical risk factors and Alzheimer’s disease and related dementia (ADRD) onsets to advance clinical research on our understanding of the complex risk factors of ADRD and ultimately improve the care of ADRD patients.
COVID-19 pandemic in Djibouti: epidemiology and the response strategy followed to contain the virus during the first two months, 17 March to 16 May 2020
Authors: Elhakim Mohamed , Tourab Saleh Banoita , Zouiten Ahmed 
Published: August 2020

AbstractBackgroundFirst cases of COVID-19 were reported from Wuhan, China, in December 2019, and it progressed rapidly. On 30 January, WHO declared the new disease as a PHEIC, then as a Pandemic on 11 March. By mid-March, the virus spread widely; Djibouti was not spared and was hit by the pandemic with the first case detected on 17 March. Djibouti worked with WHO and other partners to develop a preparedness and response plan, and implemented a series of intervention measures. MoH together with its civilian and military partners, closely followed WHO recommended strategy based on four pillars: testing, isolating, early case management, and contact tracing. From 17 March to 16 May, Djibouti performed the highest per capita tests in Africa and isolated, treated and traced the contacts of each positive case, which allowed for a rapid control of the epidemic.MethodsCOVID-19 data included in this study was collected through MoH Djibouti during the period from 17 March to 16 May 2020.ResultsA total of 1,401 confirmed cases of COVID-19 were included in the study with 4 related deaths (CFR: 0.3%) and an attack rate of 0.15%. Males represented (68.4%) of the cases, with the age group 31-45 years old (34.2%) as the most affected. Djibouti conducted 17,532 tests, and was considered as a champion for COVID-19 testing in Africa with 18.2 tests per 1000 habitant. All positive cases were isolated, treated and had their contacts traced, which led to early and proactive diagnosis of cases and in turn yielded up to 95-98% asymptomatic cases. Recoveries reached 69% of the infected cases with R0 (0.91). The virus was detected in 4 regions in the country, with the highest percentage in the capital (83%).ConclusionDjibouti responded to COVID-19 pandemic following an efficient and effective strategy, using a strong collaboration between civilian and military health assets that increased the response capacities of the country. Partnership, coordination, solidarity, proactivity and commitment were the pillars to confront COVID-19 pandemic.
SalivaDirect: A simplified and flexible platform to enhance SARS-CoV-2 testing capacity
Authors: Vogels Chantal B.F. , Watkins Anne E. , Harden Christina A. , Brackney Doug E. , Shafer Jared , Wang Jianhui , Caraballo César , Kalinich Chaney C. , Ott Isabel M. , Fauver Joseph R. , Kudo Eriko , Lu Peiwen , Venkataraman Arvind , Tokuyama Maria , Moore Adam J. , Muenker M. Catherine , Casanovas-Massana Arnau , Fournier John , Bermejo Santos , Campbell Melissa , Datta Rupak , Nelson Allison ,   Yale IMPACT Research Team, Dela Cruz Charles S. , Ko Albert I. , Iwasaki Akiko , Krumholz Harlan M. , Matheus JD , Hui Pei , Liu Chen , Farhadian Shelli F. , Sikka Robby , Wyllie Anne L. , Grubaugh Nathan D. 
Published: August 2020

AbstractCurrent bottlenecks for improving accessibility and scalability of SARS-CoV-2 testing include diagnostic assay costs, complexity, and supply chain shortages. To resolve these issues, we developed SalivaDirect, which received Emergency Use Authorization (EUA) from the U.S. Food and Drug Administration on August 15th, 2020. The critical component of our approach is to use saliva instead of respiratory swabs, which enables non-invasive frequent sampling and reduces the need for trained healthcare professionals during collection. Furthermore, we simplified our diagnostic test by(1)not requiring nucleic acid preservatives at sample collection,(2)replacing nucleic acid extraction with a simple proteinase K and heat treatment step, and(3)testing specimens with a dualplex quantitative reverse transcription PCR (RT-qPCR) assay. We validated SalivaDirect with reagents and instruments from multiple vendors to minimize the risk for supply chain issues. Regardless of our tested combination of reagents and instruments from different vendors, we found that SalivaDirect is highly sensitive with a limit of detection of 6-12 SARS-CoV-2 copies/μL. When comparing SalivaDirect to paired nasopharyngeal swabs using the authorized ThermoFisher Scientific TaqPath COVID-19 combo kit, we found high agreement in testing outcomes (>94%). In partnership with the National Basketball Association (NBA) and Players Association, we conducted a large-scale(n= 3,779) SalivaDirect usability study and comparison to standard nasal/oral tests for asymptomatic and presymptomatic SARS-CoV-2 detection. From this cohort of healthy NBA players, staff, and contractors, we found that 99.7% of samples were valid using our saliva collection techniques and a 89.5% positive and >99.9% negative test agreement to swabs, demonstrating that saliva is a valid and noninvasive alternative to swabs for large-scale SARS-CoV-2 testing. SalivaDirect is a flexible and inexpensive ($1.21-$4.39/sample in reagent costs) option to help improve SARS-CoV-2 testing capacity. Register to become a designated laboratory to use SalivaDirect under our FDA EUA on our website: publichealth.yale.edu/salivadirect/.
A nonsense mutation of bone morphogenetic protein-15 (BMP15) causes both infertility and increased litter size in pigs
Authors: Flossmann Gabriele , Wurmser Christine , Pausch Hubert , Tenghe Amabel , Dodenhoff Jörg , Dahinten Günther , Götz Kay-Uwe , Russ Ingolf , Fries Ruedi 
Published: August 2020

AbstractBackgroundAtypical external genitalia are often a sign of reproductive organ pathologies and infertility with both environmental or genetic causes, including karyotypic abnormalities. Genome-wide association studies (GWAS) provide a means for identifying chromosomal regions harboring deleterious DNA-variants causing such phenotypes. We performed a GWAS to unravel the causes of incidental cases of atypically small vulvae in German Landrace gilts.ResultsA case-control GWAS involving Illumina porcine SNP60 BeadChip-called genotypes of 17 gilts with atypically small vulvae and 1,818 control animals (fertile German Landrace sows) identified a significantly associated region on the X-chromosome (P = 8.81 × 10-43). Inspection of whole-genome sequencing data in the critical area allowed us to pinpoint a likely causal variant in the form of a nonsense mutation of bone morphogenetic protein-15 (Sscrofa11.1_X:g.44618787C>T, BMP15:p.R212X). The mutant allele occurs at a frequency of 6.2% in the German Landrace breeding population. Homozygous gilts exhibit underdeveloped, most likely not functional ovaries and are not fertile. Male carriers do not seem to manifest defects. Heterozygous sows produce 0.41±0.02 (P=4.5 × 10-83) piglets more than wildtype animals. However, the mutant allele’s positive effect on litter size accompanies a negative impact on lean meat growth.ConclusionOur results provide an example for the power of GWAS in identifying the genetic causes of a fuzzy phenotype and add to the list of natural deleterious BMP15 mutations that affect fertility in a dosage-dependent manner, the first time in a poly-ovulatory species. We advise eradicating the mutant allele from the German Landrace breeding population since the adverse effects on the lean meat growth outweigh the larger litter size in heterozygous sows.
Twisting of the heart tube during cardiac looping is atbx5-dependent and tissue-intrinsic process
Authors: Tessadori Federico , Kruse Fabian , van den Brink Susanne C. , van den Boogaard Malou , Christoffels Vincent M. , Bakkers Jeroen 
Published: August 2020

AbstractOrgan laterality refers to the Left-Right (LR) asymmetry in disposition and conformation of internal organs, established in the developing embryo. The heart is the first organ to display visible LR asymmetries as it is positioned to the left side of the midline and undergoes rightward looping morphogenesis. Cardiac looping morphogenesis is tightly controlled by a combination of heart-intrinsic and -extrinsic mechanisms. As the mechanisms that drive cardiac looping are not well understood, we performed a forward genetic screen for zebrafish mutants with defective heart looping. We describe a new loss-of-function allele fortbx5a, which displays normal leftward positioning but defective rightward looping morphogenesis. By using live two-photon confocal imaging to map cardiomyocyte behavior during cardiac looping at a single-cell level we establish that during looping, ventricular and atrial cardiomyocytes rearrange in opposite directions towards the outer curvatures of the chambers. As a consequence, the cardiac chambers twist around the atrioventricular canal resulting in torsion of the heart tube, which is compromised intbx5amutants. Manipulations of cardiac looping by chemical treatment andex vivoculture establishes that the twisting of the heart tube depends on intrinsic mechanisms and is independent from tissue growth by cell addition. Furthermore, the cardiac looping defect intbx5amutants is rescued intbx5a/tbx2bdouble mutants, indicating that it requires proper tissue patterning. Together, our results establish that cardiac looping in zebrafish involves twisting of the chambers around the AV canal, which requires correct tissue patterning by Tbx5a.
Adaptive learning through temporal dynamics of state representation
Authors: Razmi Niloufar , Nassar Matthew R. 
Published: August 2020

AbstractPeople adjust their learning rate rationally according to local environmental statistics and calibrate such adjustments based on the broader statistical context. To date, no theory has captured the observed range of adaptive learning behaviors or the complexity of its neural correlates. Here, we attempt to do so using a neural network model that learns to map an internal context representation onto a behavioral response via supervised learning. The network shifts its internal context upon receiving supervised signals that are mismatched to its output, thereby changing the “state” to which feedback is associated. A key feature of the model is that such state transitions can either increase learning or decrease learning depending on the duration over which the new state is maintained. Sustained state transitions that occur after changepoints facilitate faster learning and mimic network reset phenomena observed in the brain during rapid learning. In contrast, state transitions after one-off outlier events are short-lived, thereby limiting the impact of outlying observations on future behavior. State transitions in our model provide the first mechanistic interpretation for bidirectional learning signals, such the p300, that relate to learning differentially according to the source of surprising events and may also shed light on discrepant observations regarding the relationship between transient pupil dilations and learning. Taken together, our results demonstrate that dynamic latent state representations can afford normative inference and provide a coherent framework for understanding neural signatures of adaptive learning across different statistical environments.Significance StatementHow humans adjust their sensitivity to new information in a changing world has remained largely an open question. Bridging insights from normative accounts of adaptive learning and theories of latent state representation, here we propose a feed-forward neural network model that adjusts its learning rate online by controlling the speed of transitioning its internal state representations. Our model proposes a mechanistic framework for explaining learning under different statistical contexts, explains previously observed behavior and brain signals, and makes testable predictions for future experimental studies.
Time-course transcriptome landscape of achene development in lettuce
Authors: Luo Chen , Wang Shenglin , Ning Kang , Chen Zijing , Yang Jingjing , Wang Yixin , Qi Meixia , Wang Qian 
Published: August 2020

AbstractLettuce (Lactuca sativaL.), which belongs to the large Asteraceae (Compositae) family, breeds by sexual reproduction and produce seeds. Actually, lettuce seeds are achenes, which are defined as fruits. However, few studies have described the morphological characteristics of the lettuce achenes, and genes essential for achene development are largely unknown in lettuce. To investigate the gene activity during achene development and determine the possible mechanisms that influence achene development in lettuce, we performed a time-course transcriptome analysis of lettuce achenes. A total of 23,790 expressed genes were detected at the five achene development stages. We investigated the gene expression patterns during achene development and identified the enriched biological processes at the corresponding stages. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses revealed a variety of transcriptomic similarities and differentiation at different achene development stages. Further, transcription factors and phytohormones were found to play important roles during achene development. Finally, we proposed a working model to illustrate the gene expression modules and possible molecular mechanism underlying achene development. Our time-course transcriptome data also provides a foundation for future functional studies to reveal the genetic control of achene development in lettuce.
Single-cell identity definition using random forests and recursive feature elimination
Authors: Park Madeline , Vorperian Sevahn , Wang Sheng , Pisco Angela Oliveira 
Published: August 2020

AbstractSingle-cell RNA sequencing (scRNA-seq) enables the detailed examination of a cell’s underlying regulatory networks and the molecular factors contributing to its identity. We developed scRFE with the goal of generating interpretable gene lists that can accurately distinguish observations (single-cells) by their features (genes) given a metadata category of the dataset. scRFE is an algorithm that combines the classical random forest classifier with recursive feature elimination and cross validation to find the features necessary and sufficient to classify cells in a single-cell RNA-seq dataset by ranking feature importance. It is implemented as a Python package compatible with Scanpy, enabling its seamless integration into any single-cell data analysis workflow that aims at identifying minimal transcriptional programs relevant to describing metadata features of the dataset. We applied scRFE to the Tabula Muris Senis and reproduced established aging patterns and transcription factor reprogramming protocols, highlighting the biological value of scRFE’s learned features.Author summaryscRFE is a Python package that combines a random forest classifier with recursive feature elimination and cross validation to find the features necessary and sufficient to classify cells in a single-cell RNA-seq dataset by ranking feature importance. scRFE was designed to enable straightforward integration as part of any single-cell data analysis workflow that aims at identifying minimal transcriptional programs relevant to describing metadata features of the dataset.
Data-driven causal analysis of observational time series in ecology
Authors: Yuan Alex E , Shou Wenying 
Published: August 2020

AbstractComplex ecosystems are challenging to understand as they often defy manipulative experiments for practical or ethical reasons. In response, several fields have developed parallel approaches to infer causal relations from observational time series. Yet these methods are easy to misunderstand and often controversial. Here, we provide an accessible and critical review of three statistical causal inference approaches popular in ecological time series analysis: pairwise correlation, Granger causality, and state space reconstruction. For each, we ask what a method tests for, what causal statement it might imply, and when it could lead us astray. We devise new ways of visualizing key concepts, describe some novel pathologies of causal inference methods, and point out how so-called “model-free” causality tests are not assumption-free. We hope that our synthesis will facilitate thoughtful application of causal inference approaches and encourage explicit statements of assumptions.
An extension of Shannon’s entropy to explain taxa diversity and human diseases
Authors: Kamari Farzin , Dadmand Sina 
Published: August 2020

AbstractIn this study, with the use of the information theory, we have proposed and proved a mathematical theorem by which we argue the reason for the existence of human diseases. To introduce our theoretical frame of reference, first, we put forward a modification of Shannon’s entropy, computed for all available proteomes, as a tool to compare systems complexity and distinguish between the several levels of biological organizations. We establish a new approach to differentiate between several taxa and corroborate our findings through the latest tree of life. Furthermore, we found that human proteins with higher mutual information, derived from our theorem, are more prone to be involved in human diseases. We further discuss the dynamics of protein network stability and offer probable scenarios for the existence of human diseases and their varying occurrence rates. Moreover, we account for the reasoning behind our mathematical theorem and its biological inferences.
A Bayesian analysis of the association betweenLeukotriene A4 Hydrolasegenotype and survival probability of tuberculous meningitis patients treated with adjunctive dexamethasone
Authors: Whitworth Laura , Coxon Jacob , van Laarhoven Arjan , Thuong Nguyen Thuy Thuong , Dian Sofiati , Alisjahbana Bachti , Ganiem Ahmad Rizal , van Crevel Reinout , Thwaites Guy , Troll Mark , Edelstein Paul , Sewell Roger , Ramakrishnan Lalita 
Published: August 2020

AbstractTuberculous meningitis (TBM) remains the most devastating form of tuberculosis (TB) with high mortality despite effective antimicrobial treatment. As mortality has been linked to excessive inflammation, anti-inflammatory glucocorticoids are now routinely used as adjunctive treatment with antimicrobial therapy. However, they reduce mortality by only ~ 30%, raising the possibility that only a subset of TBM deaths are caused by inflammatory pathophysiology. Studies in Vietnam found that the survival benefit of adjunctive glucocorticoids was limited to individuals with a common promoter variant in theleukotriene A4 hydrolase(LTA4H) gene encoding an enzyme that regulates inflammatory eicosanoid expression. The variant constitutes a C/T transition with TT homozygotes having increased expression over CT heterozygotes and CC homozygotes. In Vietnam, theLTA4HTT genotype predicted survival, consistent with dexamethasone benefiting only those individuals with a dysregulated hyper-inflammatory response. However, a study of TBM patients in Indonesia did not find theLTA4HTT genotype to confer a significant survival benefit. Given the potential of personalized life-saving anti-inflammatory therapies guided byLTA4Hgenotype, we have used Bayesian methods to analyze the data from both studies. Bayesian analysis reveals that theLTA4HTT genotype confers survival benefit in both the Vietnam and Indonesia cohorts that begins within days and continues long-term. However, its benefit is nullified in the most severe cases where other factors cause early mortality.LTA4HTT genotype is associated with increased survival in HIV-positive patients also. Thus, our analysis extends the association ofLTA4Hgenotype with TBM survival to populations outside of Vietnam and to HIV-positive patients. PatientLTA4Hgenotyping used in conjunction with disease severity assessment may help to target glucocorticoids to patients most likely to benefit from this broadly-acting immunosuppressive regimen despite its significant adverse effects.