bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modelling the impact of fexinidazole use on human African trypanosomiasis transmission in the Democratic Republic of Congo
Authors: Das Aatreyee M. , Chitnis Nakul , Burri Christian , Paris Daniel H. , Patel Swati , Spencer Simon E.F. , Miaka Erick M. , Castaño M. Soledad 
Published: June 2021

AbstractGambiense human African trypanosomiasis is a deadly disease that has been declining in incidence since the start of the Century, primarily due to increased screening, diagnosis and treatment of infected people. The main treatment regimen currently in use requires a lumbar puncture as part of the diagnostic process to determine disease stage and hospital admission for drug administration. Fexinidazole is a new oral treatment for stage 1 and non-severe stage 2 human African trypanosomiasis. The World Health Organization has recently incorporated fexinidazole into its treatment guidelines for human African trypanosomiasis. The treatment does not require hospital admission or a lumbar puncture for all patients, which is likely to ease access for patients; however, it does require concomitant food intake, which is likely to reduce adherence. Here, we use a mathematical model calibrated to case and screening data from Mushie territory, in the Democratic Republic of the Congo, to explore the potential negative impact of poor compliance to an oral treatment, and potential gains to be made from increases in the rate at which patients seek treatment. We find that reductions in compliance in treatment of stage 1 cases are projected to result in the largest increase in further transmission of the disease, with failing to cure stage 2 cases also posing a smaller concern. Reductions in compliance may be offset by increases in the rate at which cases are passively detected. Efforts should therefore be made to ensure good adherence for stage 1 patients to treatment with fexinidazole and to improve access to passive care.
Comprehensive characterization of COVID-19 patients with repeatedly positive SARS-CoV-2 tests using a large US electronic health record database
Authors: Dong Xiao , Zhou Yujia , Shu Xiao-ou , Bernstam Elmer V. , Stern Rebecca , Aronoff David M. , Xu Hua , Lipworth Loren 
Published: June 2021

AbstractBackgroundIn the absence of genome sequencing, two positive molecular SARS-CoV-2 tests separated by negative tests, prolonged time, and symptom resolution remain the best surrogate measure of possible re-infection.MethodsUsing a large electronic health record database, we characterized clinical and testing data for 23 patients with repeatedly positive SARS-CoV-2 PCR test results ≥60 days apart, separated by ≥2 consecutive negative test results. Prevalence of chronic medical conditions, symptoms and severe outcomes related to COVID-19 illness were ascertained.ResultsMedian age was 64.5 years, 40% were Black, and 39% were female. 83% smoked within the prior year, 61% were overweight/obese, 83% had immune compromising conditions, and 96% had ≥2 comorbidities. Median interval between the two positive tests was 77 days. Among the 19 patients with 60-89 days between positive tests, 17 (89%) exhibited symptoms or clinical manifestations indicative of COVID-19 at the time of the second positive test and 14 (74%) were hospitalized at the second positive test. Of the four patients with ≥90 days between two positive tests, two had mild or no symptoms at the second positive test and one, an immune compromised patient, had a brief hospitalization at the first diagnosis, followed by ICU admission at the second diagnosis three months later.ConclusionsOur study demonstrated a high prevalence of immune compromise, comorbidities, obesity and smoking among patients with repeatedly positive SARS-CoV-2 tests. Despite limitations, including lack of semi-quantitative estimates of viral load, these data may help prioritize suspected cases of reinfection for investigation and continued surveillance.ImportanceComprehensive characterization of clinical and SARS-CoV-2 testing data for patients with repeatedly positive SARS-CoV-2 tests can help prioritize suspected cases of reinfection for investigation in the absence of sequencing data and for continued surveillance for potential long-term health consequences of SARS-CoV-2 infection.
Early evidence for the safety of certain COVID-19 vaccines using empirical Bayesian modeling from VAERS
Authors: von Csefalvay Chris 
Published: June 2021

AbstractThe advent of vaccines against SARS-CoV-2 ushered in an unprecedented global response to COVID-19, with the largest and most ambitious mass vaccination campaign in human history. The scale of this effort means that safety signals suggesting adverse effects may only be detectable using passive reporting. This paper examines reports to the CDC/FDA’s VAERS system in the first six months of 2021, using an empirical Bayesian model with a gamma Poisson shrinker to identify potential safety signals from COVID-19 vaccines currently on the U.S. market. Based on this preliminary data, it is concluded that the COVID-19 vaccine’s safety significantly exceeds that of previously marketed vaccines, and other than a known risk of thrombotic events, no safety signals of concern emerge.
Seasonal variation in SARS-CoV-2 transmission in temperate climates
Authors: Gavenčiak Tomáš , Monrad Joshua Teperowski , Leech Gavin , Sharma Mrinank , Mindermann Sören , Brauner Jan Markus , Bhatt Samir , Kulveit Jan 
Published: June 2021

AbstractWhile seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. As previous analyses have not accounted for the implementation of non-pharmaceutical interventions (NPIs) in the first year of the pandemic, they may yield biased estimates of seasonal effects. Building on two state-of-the-art observational models and datasets, we adapt a fully Bayesian method for estimating the association between seasonality and transmission in 143 temperate European regions. We find strong seasonal patterns, consistent with a reduction in the time-variableRtof 42.1% (95% CI: 24.7% – 53.4%) from the peak of winter to the peak of summer. These results imply that the seasonality of SARS-CoV-2 transmission is comparable in magnitude to the most effective individual NPIs but less than the combined effect of multiple interventions.
α-ketoglutarate augments prolyl hydroxylase-2 mediated inactivation of phosphorylated-Akt to inhibit induced-thrombosis and inflammation
Authors: Shrimali Nishith M , Agarwal Sakshi , Kaur Simrandeep , Bhattacharya Sulagna , Bhattacharyya Sankar , Prchal Josef T , Guchhait Prasenjit 
Published: June 2021

AbstractPhosphorylation of Akt (pAkt) regulates multiple physiological and pathological processes including thrombosis and inflammation. In an approach to inhibit the pathological signalling of pAkt by prolyl-hydroxylase-2 (PHD2) we employed α-ketoglutarate (αKG), a cofactor of PHD2. Octyl-αKG supplementation to platelets promoted PHD2 activity through elevated intracellular αKG:succinate ratio and reduced aggregationin vitroby suppressing pAkt1(Thr308). Augmented PHD2 activity was confirmed by increased hydroxylated-proline alongside enhanced binding of PHD2 to pAkt in αKG-treated platelets. Contrastingly, inhibitors of PHD2 significantly increased pAkt1 in platelets. Octyl-αKG followed similar mechanism in monocytes to inhibit cytokine secretionin vitro. Our data also describe a suppressed pAkt1 and reduced activation of platelet and leukocyte obtained from mice supplemented with dietary-αKG, unaccompanied by alteration in their counts. Dietary-αKG significantly reduced clot formation and leukocyte accumulation in various organs including lung of mice treated with thrombosis-inducing agent carrageenan. Importantly, we observed a significant rescue effect of dietary-αKG on inflamed lung of SARS-CoV-2 infected hamsters. αKG significantly reduced leukocyte accumulation, clot formation and viral load alongside downmodulation of pAkt in lung of the infected animals. Therefore, our study suggests a safe implementation of dietary-αKG in prevention of Akt-driven anomalies including thrombosis and inflammation, highlighting a better pulmonary management in COVID-19.
The origin of asexual brine shrimps
Authors: Rode Nicolas Olivier , Jabbour-Zahab Roula , Boyer Loreleï , Flaven Élodie , Hontoria Francisco , Van Stappen Gilbert , Dufresne France , Haag Christoph , Lenormand Thomas 
Published: June 2021

AbstractDetermining how and how often asexual lineages emerge within sexual species is central to our understanding of sex-asex transitions and the long-term maintenance of sex. Asexuality can arise “by transmission” from an existing asexual lineage to a new one, through different types of crosses. The occurrence of these crosses, cryptic sex, variation in ploidy and recombination within asexuals greatly complicates the study of sex-asex transitions, as they preclude the use of standard phylogenetic methods and genetic distance metrics. In this study we show how to overcome these challenges by developing new approaches to investigate the origin of the various asexual lineages of the brine shrimpArtemia parthenogenetica. We use a large sample of asexuals, including all known polyploids, and their sexual relatives. We combine flow cytometry with mitochondrial and nuclear DNA data. We develop new genetic distance measures and methods to compare various scenarios describing the origin of the different lineages. We find that all diploid and polyploidA. parthenogeneticalikely arose within the last 80,000 years through successive and nested hybridization events that involved backcrosses with different sexual species. AllA. parthenogeneticahave the same common ancestor and therefore likely carry the same asexuality gene(s) and reproduce by automixis. These findings radically change our view of sex-asex transitions in this group, and show the importance of considering asexuality “by transmission” scenarios. The methods developed are applicable to many other asexual taxa.
Enviromic-based Kernels Optimize Resource Allocation with Multi-trait Multi-environment Genomic Prediction for Tropical Maize
Authors: Gevartosky Raysa , Carvalho Humberto Fanelli , Costa-Neto Germano , Montesinos-López Osval A. , Crossa José , Fritsche-Neto Roberto 
Published: June 2021

AbstractGenomic prediction (GP) success is directly dependent on establishing a training population, where incorporating envirotyping data and correlated traits may increase the GP accuracy. Therefore, we aimed to design optimized training sets for multi-trait for multi-environment trials (MTMET). For that, we evaluated the predictive ability of five GP models using the genomic best linear unbiased predictor model (GBLUP) with additive + dominance effects (M1) as the baseline and then adding genotype by environment interaction (G × E) (M2), enviromic data (W) (M3), W+G × E (M4), and finally W+G × W (M5), where G × W denotes the genotype by enviromic interaction. Moreover, we considered single-trait multi-environment trials (STMET) and MTMET for three traits: grain yield (GY), plant height (PH), and ear height (EH), with two datasets and two cross-validation schemes. Afterward, we built two kernels for genotype by environment by trait interaction (GET) and genotype by enviromic by trait interaction (GWT) to apply genetic algorithms to select genotype:environment:trait combinations that represent 98% of the variation of the whole dataset and composed the optimized training set (OTS). Using OTS based on enviromic data, it was possible to increase the response to selection per amount invested by 142%. Consequently, our results suggested that genetic algorithms of optimization associated with genomic and enviromic data efficiently design optimized training sets for genomic prediction and improve the genetic gains per dollar invested.
Cell entry inhibitor with sulfonated colloid gold as new potent broad-spectrum virucides
Authors: Chin Chur 
Published: June 2021

AbstractWe recently developed nontoxic, broad-spectrum virucidal gold nanoparticles, less than 10nm sized, modified with sulfonic acids (mesilate) that mimic heparan sulfates.Camostat, a serine protease inhibitor can introduce gold nanoparticles to the influenza virus via ionic bonds.In this study, we examined the ability of a novel sulfonated colloid gold to inhibit the virus in vivo. Thus, camostat-colloid gold is a promising candidate for the development of antiviral drugs to prevent and treat influenza infection.
Alpha-synuclein induces epigenomic dysregulation of glutamate signaling and locomotor pathways
Authors: Schaffner Samantha L. , Wassouf Zinah , Lazaro Diana F. , Xylaki Mary , Gladish Nicole , Lin David T. S. , MacIsaac Julia , Ramadori Katia , Schulze-Hentrich Julia M. , Outeiro Tiago F. , Kobor Michael S. 
Published: June 2021

AbstractBackgroundMutations and multiplications in the gene encoding for alpha-synuclein are associated with Parkinson’s disease (PD). However, not all individuals with alpha-synuclein variants develop PD, suggesting that additional factors are involved. We hypothesized that increased alpha-synuclein might alter epigenetic regulation of PD pathways.ObjectivesTo identify genome-wide DNA methylation and hydroxymethylation changes induced by overexpression of two alpha-synuclein variants in human dopaminergic neurons, and to relate these to the corresponding transcriptome.MethodsWe assessed DNA methylation and hydroxymethylation at >850,000 CpGs using the EPIC BeadChip in LUHMES cells differentiated to dopaminergic neurons. Control LUHMES neurons, LUHMES neurons overexpressing wild type alpha-synuclein, and LUHMES neurons overexpressing A30P alpha-synuclein were compared. We used SMITE network analysis to identify functionally related genes with altered DNA methylation, DNA hydroxymethylation, and/or gene expression, incorporating LUHMES H3K4me1 ChIP-seq to delineate enhancers in addition to the default promoter and gene body regions.ResultsUsing stringent statistical thresholds, we found that increased expression of wild type or A30P mutant alpha-synuclein induced DNA methylation changes at thousands of CpGs and DNA hydroxymethylation changes at hundreds of CpGs. Differentially methylated sites in both genotypes were enriched for several processes including movement-associated pathways and glutamate signaling. For glutamate and other signaling pathways (i.e. PDGF, insulin), this differential DNA methylation was also associated with transcriptional changes.ConclusionsOur results indicated that alpha-synuclein altered the DNA methylome of dopaminergic neurons, influencing regulation of pathways involved in development, signaling, and metabolism. This supports a role for alpha-synuclein in the epigenetic etiology of PD.
Improved SNV discovery in barcode-stratified scRNA-seq alignments
Authors: Prashant NM , Liu Hongyu , Dillard Christian , Ibeawuchi Helen , Alsaeedy Turkey , Chan Kwan Hang , Horvath Anelia 
Published: June 2021

AbstractSingle cell SNV analysis is an emerging and promising strategy to connect cell-level genetic variation to cell phenotypes. At the present, SNV detection from 10x Genomics scRNA-seq data is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gain of information of SNV assessments from individual cell scRNA-seq data, where the alignments are split by barcode prior to the variant call. For our analyses we use publicly available sequencing data on the human breast cancer cell line MCF7 cell line generated at consequent time-points during anti-cancer treatment. We analyzed SNV calls by three popular variant callers – GATK, Strelka2 and Mu-tect2, in combination with a method for cell-level tabulation of the sequencing read counts bearing SNV alleles – SCReadCounts. Our analysis shows that variant calls on individual cell alignments identify at least two-fold higher number of SNVs as compared to the pooled scRNA-seq. We demonstrate that scSNVs exclusively called in the single cell alignments (scSNVs) are substantially enriched in novel genetic variants and in coding functional annotations, in particular, stop-codon and missense substitutions. Furthermore, we find that the expression of some scSNVs correlates with the expression of their harbouring gene (cis-scReQTLs).Overall, our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes on the need of cell-level variant detection approaches and tools. Given the growing accumulation of scRNA-seq datasets, cell-level variant assessments are likely to significantly contribute to the understanding of the cellular heterogeneity and the relationship between genetics variants and functional phenotypes. In addition, cell-level variant assessments from scRNA-seq can be highly informative in cancer where they can help elucidate somatic mutations evolution and functionality.
Survey of COVID-19 associated symptoms and reported deaths in an urban community in Kano, Nigeria
Authors: Shahani Disha , Farouq Zayyad Sani , Galadima Hadiza , Khare Ashna , Ravi Nirmal 
Published: June 2021

AbstractBackgroundNigeria reported the first case of COVID-19 on February 27, 2020. By June of 2020, many people reported experiencing mild COVID-19 associated symptoms, yet did not get tested due to inaccessible testing and insufficient knowledge of the disease. There were media stories quoting grave diggers in Kano who reported high burial rates during this time.MethodsIn order to draw more data on COVID-19 cases during this time period, we conducted a cross-sectional symptom survey in Kano, surveying 291 adults. Participants were asked to report demographic characteristics, past COVID-19 testing and symptoms, and community deaths. To assess associations between COVID-19 associated symptoms and socio-demographic characteristics, bivariate analyses using Chi-square tests were performed. A logistic regression assessing the association between any reported symptoms and the kind of work (indoor/outdoor) was done while adjusting for age, gender and education level.ResultsHalf of the respondents reported at least one symptom associated with COVID-19; the three most common symptoms were loss of appetite, cough, and fever. There was a statistically significant relationship between age group of the respondent and presence of COVID-19 associated symptoms. Gender or level of education did not have statistically significant association with COVID-19 associated symptoms among the respondents. People with outdoor occupations such as trading and hawking were more than twice as likely to report COVID-19 associated symptoms compared to those who were unemployed. Just under half of the respondents reported knowing someone who died in their community, with unexplained causes attributed to two-thirds of these cases. Our study found evidence of COVID-19 associated symptoms especially among the older population and unexplained deaths in Kano. Lack of confirmatory laboratory tests and absence of baseline vital statistics precluded us from finding definitive evidence for or against COVID-19 infection and associated mortality.
Convergence of case-specific epigenetic alterations identify a confluence of genetic vulnerabilities tied to opioid dependence
Authors: Corradin Olivia , Sallari Richard , Hoang An T. , Kassim Bibi S , Hutta Gabriella Ben , Cuoto Lizette , Quach Bryan C. , Lovrenert Katreya , Hays Cameron , Gryder Berkley E. , Iskhakova Marina , Cates Hannah , Song Yanwei , Bartels Cynthia F. , Hancock Dana B. , Mash Deborah C. , Johnson Eric O. , Akbarian Schahram , Scacheri Peter C. 
Published: June 2021

ABSTRACTOpioid dependence is a highly heterogeneous disease driven by a variety of genetic and environmental risk factors which have yet to be fully elucidated. We interrogated the effects of opioid dependence on the brain using ChIP-seq to quantify patterns of H3K27 acetylation in dorsolateral prefrontal cortical neurons isolated from 51 opioid-overdose cases and 51 accidental death controls. Among opioid cases, we observed global hypoacetylation and identified 388 putative enhancers consistently depleted for H3K27ac. Machine learning on H3K27ac patterns predicts case-control status with high accuracy. We focus on case-specific regulatory alterations, revealing 81,399 hypoacetylation events, uncovering vast inter-patient heterogeneity. We developed a strategy to decode this heterogeneity based on convergence analysis, which leveraged promoter-capture Hi-C to identify five genes over-burdened by alterations in their regulatory network or “plexus”:ASTN2, KCNMA1, DUSP4, GABBR2, ENOX1. These convergent loci are enriched for opioid use disorder risk genes and heritability for generalized anxiety, number of sexual partners, and years of education. Overall, our multi-pronged approach uncovers neurobiological aspects of opioid dependence and captures genetic and environmental factors perpetuating the opioid epidemic.
Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data
Authors: Chen Xiaoqiao , Chen Sisi , Thomson Matt 
Published: June 2021

AbstractSequencing costs currently prohibit the application of single-cell mRNA-seq to many biological and clinical analyses. Targeted single-cell mRNA-sequencing reduces sequencing costs by profiling reduced gene sets that capture biological information with a minimal number of genes. Here, we introduce an active learning method (ActiveSVM) that identifies minimal but highly-informative gene sets that enable the identification of cell-types, physiological states, and genetic perturbations in single-cell data using a small number of genes. Our active feature selection procedure generates minimal gene sets from single-cell data through an iterative cell-type classification task where misclassified cells are examined at each round of analysis to identify maximally informative genes through an ‘active’ support vector machine (ActiveSVM) classifier. By focusing computational resources on misclassified cells, ActiveSVM scales to analyze data sets with over a million single cells. We demonstrate that ActiveSVM feature selection identifies gene sets that enable 90% cell-type classification accuracy across a variety of data sets including cell atlas and disease characterization data sets. The method generalizes to reveal genes that respond to genetic perturbations and to identify region specific gene expression patterns in spatial transcriptomics data. The discovery of small but highly informative gene sets should enable substantial reductions in the number of measurements necessary for application of single-cell mRNA-seq to clinical tests, therapeutic discovery, and genetic screens.