bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aspirin Use is Associated with Decreased Mortality in Patients with COVID-19: A Systematic Review and Meta-analysis
Authors: Savarapu Pramod , Baral Nischit , Adhikari Govinda , Akanbi Maxwell , Abdelazeem Basel , Isa Sakiru O. , Khan Ashiya , Ali Maham , Jenumula Sravanthi , Kesari Kavitha , Kunadi Arvind 
Published: July 2021

ABSTRACTBackgroundNovel Corona Virus Disease 2019 (COVID-19) has resulted in more than three and half million deaths worldwide as of June 6, 2021. The role of aspirin in prevention of COVID-19 mortality has not been much studied. We aimed to study the relationship between aspirin use and covid-19 mortality.MethodsWe searched PubMed, MEDLINE, EMBASE, and Cochrane database for studies from January 2019 till June 6, 2021 with inclusion criteria of RCT, Cohort study, studies reporting mortality, and comparison studies on aspirin versus non-aspirin. Statistical analysis was done with Review Manager 5.4 statistical software using the inverse variance method. We assessed the pooled hazard ratio (HR), and 95% confidence interval using the random effect model and I-squared test was used to determine statistical heterogeneity.ResultsWe included five retrospective cohort studies which met our inclusion criteria with total of 14065 participants in both groups. There were 6797 participants in the aspirin group and 7268 participants in the non-aspirin group. Our results show that the use of aspirin was associated with 53% decrease in mortality compared to non-aspirin in patients with COVID-19 (adjusted HR 0.47, 95% CI 0.35-0.63, P< 0.001, I2= 47%). In the analysis restricted to patients hospitalized for COVID-19, the use of aspirin was associated with a 49% reduction in the risk for in-hospital mortality (adjusted HR 0.51, 95% CI 0.33-0.80, P = 0.004, I2= 39%).ConclusionsOur results show that aspirin is associated with decrease in both overall mortality and in-hospital mortality in patients with COVID-19.
Linking rhizosphere processes across scales: Opinion
Authors: Schnepf A. , Carminati A. , Ahmed M. A. , Ani M. , Benard P. , Bentz J. , Bonkowski M. , Brax M. , Diehl D. , Duddek P. , Kröner E. , Javaux M. , Landl M. , Lehndorff E. , Lippold E. , Lieu A. , Mueller C. W. , Oburger E. , Otten W. , Portell X. , Phalempin M. , Prechtel A. , Schulz R. , Vanderborght J. , Vetterlein D. 
Published: July 2021

AbstractPurposeSimultaneously interacting small-scale rhizosphere processes determine emergent plant-scale behaviour, including growth, transpiration, nutrient uptake, soil carbon storage and transformation by microorganisms. Current advances in modelling and experimental methods open the path to unravel and link those processes.MethodsWe present a series of examples of state-of-the art simulations addressing this multi-scale, multi-process problem from a modelling point of view, as well as from the point of view of integrating newly available rhizosphere data and images.ResultsEach example includes a model that links scales and experimental data to set-up simulations that explain and predict spatial and temporal distribution of rhizodeposition as driven by root architecture development, soil structure, presence of root hairs, soil water content and distribution of soil water. Furthermore, two models explicitly simulate the impact of the rhizodeposits on plant nutrient uptake and soil microbial activity, respectively. This exemplifies the currently available state of the art modelling tools in this field: image-based modelling, pore-scale modelling, continuum scale modelling and functional-structural plant modelling. We further show how to link the pore scale to the continuum scale by homogenisation or by deriving effective physical parameters like viscosity from nano-scale chemical properties.ConclusionModelling allows to integrate and make use of new experimental data across different rhizosphere processes (and thus across different disciplines) and scales. Described models are tools to test hypotheses and consequently improve our mechanistic understanding of how rhizosphere processes impact plant-scale behaviour. Linking multiple scales and processes is the logical next step for future research.
Implications of trade network structure and population dynamics for food security and equality
Authors: Fair Kathyrn R , Bauch Chris T , Anand Madhur 
Published: July 2021

AbstractGiven trade’s importance to maintaining food security, it is crucial to understand the relationship between human population growth, land use, food supply, and trade. We develop a metapopulation model coupling human population dynamics to agricultural land use and food production in “patches” (regions and countries) connected via trade networks. Patches that import sparingly or fail to adjust their demand sharply in response to changes in food per capita experience food insecurity. They fall into a feedback loop between increasing population growth and decreasing food per capita, particularly if they are peripheral to the network. A displacement effect is also evident; patches that are more central and/or import more heavily preserve their natural land states. Their reliance on imports means other patches must expand their agricultural land. These results emphasize that strategies for improving food security and equality must account for the combined effects of network topology and patch-level characteristics.
On the Etiology of Aesthetic Chills: a Behavioral Genetic Study
Authors: Bignardi Giacomo , Chamberlain Rebecca , Kevenaar Sofieke T , Tamimy Zenab , Boomsma Dorret I 
Published: July 2021

AbstractAesthetic chills, broadly defined as a somatic marker of peak emotional-hedonic responses, are experienced by individuals across a variety of human cultures. Yet individuals vary widely in the propensity of feeling them. These individual differences have been studied in relation to demographics, personality, and neurobiological and physiological factors, but no study to date has explored the genetic etiological sources of variation. To partition genetic and environmental sources of variation in the propensity of feeling aesthetic chills, we fitted a biometrical genetic model to data from 14127 twins (from 8995 pairs), collected by the Netherlands Twin Register. Both genetic and unique environmental factors accounted for variance in aesthetic chills, with heritability estimated at .36 ([.33, .39] 95% CI). We found females more prone than males to report feeling aesthetic chills. However, a test for genotype x sex interaction did not show evidence that heritability differs between sexes. We thus show that the propensity of feeling aesthetic chills is not shaped by nurture alone, but it also reflects underlying genetic propensities.
scSGL: Signed Graph Learning for Single-Cell Gene Regulatory Network Inference
Authors: Karaaslanli Abdullah , Saha Satabdi , Aviyente Selin , Maiti Tapabrata 
Published: July 2021

AbstractMotivationElucidating the topology of gene regulatory networks (GRNs) from large single-cell RNA sequencing (scRNAseq) datasets, while effectively capturing its inherent cell-cycle heterogeneity and dropouts, is currently one of the most pressing problems in computational systems biology. Recently, graph learning (GL) approaches based on graph signal processing (GSP) have been developed to infer graph topology from signals defined on graphs. However, existing GL methods are not suitable for learning signed graphs, which represent a characteristic feature of GRNs, as they account for both activating and inhibitory relationships between genes. They are also incapable of handling high proportion of zero values, which represent dropouts in single cell experiments. To this end, we propose a novel signed GL approach, scSGL, that learns GRNs based on the assumption of the smoothness and non-smoothness of gene expressions over activating and inhibitory edges, respectively. scSGL is then extended with kernels to take the nonlinearity of co-expressions into account and handle high proportion of dropouts. From GSP perspective, this extension corresponds to assuming smoothness/non-smoothness of graph signals in a higher dimensional space defined by the kernel. The proposed approach is formulated as a non-convex optimization problem and solved using an efficient ADMM framework.ResultsIn our experiments on simulated and real single cell datasets, scSGL compares favorably with other single cell gene regulatory network reconstruction algorithms.AvailabilityThe scSGL code and analysis scripts are available at (https://github.com/Single-Cell-Graph-Learning/scSGL).
Stochastic comparison of synchronization in activator- and repressor-based coupled gene oscillators
Authors: Shamim Ul Hasan A B M , Dey Supravat , Kurata Hiroyuki , Singh Abhyudai 
Published: July 2021

AbstractInside living cells, proteins or mRNA can show oscillations even without a periodic driving force. Such genetic oscillations are precise timekeepers for cell-cycle regulations, pattern formation during embryonic development in higher animals, and daily cycle maintenance in most organisms. The synchronization between oscillations in adjacent cells happens via intercellular coupling, which is essential for periodic segmentation formation in vertebrates and other biological processes. While molecular mechanisms of generating sustained oscillations are quite well understood, how do simple intercellular coupling produces robust synchronizations are still poorly understood? To address this question, we investigate two models of coupled gene oscillators - activator-based coupled oscillators (ACO) and repressor-based coupled oscillators (RCO) models. In our study, a single autonomous oscillator (that operates in a single cell) is based on a negative feedback, which is delayed by intracellular dynamics of an intermediate species. For the ACO model (RCO), the repressor protein of one cell activates (represses) the production of another protein in the neighbouring cell after a intercellular time delay. We investigate the coupled models in the presence of intrinsic noise due to the inherent stochasticity of the biochemical reactions. We analyze the collective oscillations from stochastic trajectories in the presence and absence of explicit coupling delay and make careful comparison between two models. Our results show no clear synchronizations in the ACO model when the coupling time delay is zero. However, a non-zero coupling delay can lead to anti-phase synchronizations in ACO. Interestingly, the RCO model shows robust in-phase synchronizations in the presence and absence of the coupling time delay. Our results suggest that the naturally occurring intercellular couplings might be based on repression rather than activation where in-phase synchronization is crucial.
Identifying patients at high risk of inappropriate drug dosing in periods with renal dysfunction
Authors: Kaas-Hansen Benjamin Skov , Rodríguez Cristina Leal , Placido Davide , Thorsen-Meyer Hans-Christian , Nielsen Anna Pors , Dérian Nicolas , Brunak Søren , Andersen Stig Ejdrup 
Published: July 2021

AbstractIntroductionDosing of renally cleared drugs in patients with kidney failure often deviates from clinical guidelines but little is known about what is predictive of receiving inappropriate doses.Methods and materialsWe combined data from the Danish National Patient Register and in-hospital data on drug administrations and estimated glomerular filtration rates for admissions between 1 October 2009 and 1 June 2016, from a pool of about 2.9 million persons. We trained artificial neural network and linear logistic ridge regression models to predict the risk of five outcomes (>0, ≥1, ≥2, ≥3 and ≥5 inappropriate doses daily) with index set 24 hours after admission. We used time-series validation for evaluating discrimination, calibration, clinical utility and explanations.ResultsOf 52,451 admissions included, 42,250 (81%) were used for model development. The median age was 77 years; 50% of admissions were of women. ≥5 drugs were used between admission start and index in 23,124 admissions (44%); the most common drug classes were analgesics, systemic antibacterials, diuretics, antithrombotics, and antacids. The neural network models had better discriminative power (all AUROCs between 0.77 and 0.81) and were better calibrated than their linear counterparts. The main prediction drivers were use of anti-inflammatory, antidiabetic and anti-Parkison’s drugs as well as having a diagnosis of chronic kidney failure. Sex and age affected predictions but slightly.ConclusionOur models can flag patients at high risk of receiving at least one inappropriate dose daily in a controlled in-silico setting. A prospective clinical study may confirm this holds in real-life settings and translates into benefits in hard endpoints.
Genetic regulation ofOAS1nonsense-mediated decay underlies association with risk of severe COVID-19
Authors: Banday A Rouf , Stanifer Megan L , Florez-Vargas Oscar , Onabajo Olusegun O , Zahoor Muhammad A , Papenberg Brenen W , Ring Timothy J , Lee Chia-Han , Andreakos Evangelos , Arons Evgeny , Barsh Greg , Biesecker Leslie G , Boyle David L , Burnett-Hartman Andrea , Carrington Mary , Chang Euijin , Choe Pyoeng Gyun , Chrisholm Rex L , Dalgard Clifton , Edberg Jeff , Erdmann Nathan , Feigelson Heather S , Firestein Gary S , Gehring Adam J , Ho Michelle , Holland Steven , Hutchinson Amy A , Im Hogune , Ison Michael G , Kim Hong Bin , Kreitman Robert J , Korf Bruce R , Mirabello Lisa , Pacheco Jennifer A , Peluso Michael J , Rader Daniel J , Redden David T , Ritchie Marylyn D , Rosenbloom Brooke , Sant Anna Hanaisa P , Savage Sharon , Siouti Eleni , Triantafyllia Vasiliki , Vargas Joselin M , Verma Anurag , Vij Vibha , Wesemann Duane R , Yeager Meredith , Yu Xu , Zhang Yu , Boulant Steeve , Chanock Stephen J , Feld Jordan J , Prokunina-Olsson Ludmila 
Published: July 2021

ABSTRACTGenomic regions have been associated with COVID-19 susceptibility and outcomes, including the chr12q24.13 locus encoding antiviral proteins OAS1-3. Here, we report genetic, functional, and clinical insights into genetic associations within this locus. In Europeans, the risk of hospitalized vs. non-hospitalized COVID-19 was associated with a single 19Kb-haplotype comprised of 76OAS1variants included in a 95% credible set within a large genomic fragment introgressed from Neandertals. The risk haplotype was also associated with impaired spontaneous but not treatment-induced SARS-CoV-2 clearance in a clinical trial with pegIFN-λ1. We demonstrate that two exonic variants, rs10774671 and rs1131454, affect splicing and nonsense-mediated decay ofOAS1. We suggest that genetically-regulated loss ofOAS1expression contributes to impaired spontaneous clearance of SARS-CoV-2 and elevated risk of hospitalization for COVID-19. Our results provide the rationale for further clinical studies using interferons to compensate for impaired spontaneous SARS-CoV-2 clearance, particularly in carriers of theOAS1risk haplotypes.
Using big data analytics to explore the relationship between government stringency and preventative social behaviour during the COVID-19 pandemic in the United Kingdom
Authors: Al-Zubaidy Noor , Crespo Roberto , Jones Sarah , Drikvandi Reza , Gould Lisa , Leis Melanie , Maheswaran Hendramoorty , Neves Ana Luisa , Darzi Ara 
Published: July 2021

AbstractWe evaluated the association between preventative social behaviour and government stringency. Additionally, we sought to evaluate the influence of additional factors including time, need to protect others (using the reported number of COVID-19 deaths as a surrogate measure) and reported confidence in government handling of the COVID-19 pandemic. We used repeated national cross-sectional surveys the UK over the course of 41 weeks from 1st April 2020 to January 28th, 2021, including a total of 38,092 participants. Preventative social behaviour and government stringency index scores were significantly associated on linear regression analyses (R2 =0.6468, p<0.001, and remained significant after controlling for the effect of reported COVID-19 deaths, confidence in government handling of the pandemic, and time (R2=0.898, p<0.001). Longitudinal data suggest that government stringency is an effective tool in promoting preventative social behaviour in the fight against COVID-19.
Single-cell transcriptomics for the 99.9% of species without reference genomes
Authors: Botvinnik Olga Borisovna , Vemuri Venkata Naga Pranathi , Pierce N. Tessa , Logan Phoenix Aja , Nafees Saba , Karanam Lekha , Travaglini Kyle Joseph , Ezran Camille Sophie , Ren Lili , Juang Yanyi , Wang Jianwei , Wang Jianbin , Brown C. Titus 
Published: July 2021

AbstractSingle-cell RNA-seq (scRNA-seq) is a powerful tool for cell type identification but is not readily applicable to organisms without well-annotated reference genomes. Of the approximately 10 million animal species predicted to exist on Earth, >99.9% do not have any submitted genome assembly. To enable scRNA-seq for the vast majority of animals on the planet, here we introduce the concept of “k-mer homology,” combining biochemical synonyms in degenerate protein alphabets with uniform data subsampling via MinHash into a pipeline called Kmermaid. Implementing this pipeline enables direct detection of similar cell types across species from transcriptomic data without the need for a reference genome. Underpinning Kmermaid is the tool Orpheum, a memory-efficient method for extracting high-confidence protein-coding sequences from RNA-seq data. After validating Kmermaid using datasets from human and mouse lung, we applied Kmermaid to the Chinese horseshoe bat (Rhinolophus sinicus), where we propagated cellular compartment labels at high fidelity. Our pipeline provides a high-throughput tool that enables analyses of transcriptomic data across divergent species’ transcriptomes in a genome- and gene annotation-agnostic manner. Thus, the combination of Kmermaid and Orpheum identifies cell type-specific sequences that may be missing from genome annotations and empowers molecular cellular phenotyping for novel model organisms and species.
A tail of plumage colouration: disentangling geographic, seasonal, and dietary effects on plumage colour in a migratory songbird
Authors: Mahoney Sean M. , Reudink Matthew W. , Contina Andrea , Roberts Kelly A. , Schabert Veronica T. , Gunther Emily G. , Covino Kristen M. 
Published: July 2021

AbstractPlumage ornamentation in birds serves critical inter- and intra-sexual signaling functions. While carotenoid-based plumage colouration is often viewed as a classic condition-dependent sexually selected trait, plumage colouration can be influenced by a wide array of both intrinsic and extrinsic factors. Understanding the mechanisms underlying variation in colouration is especially important for species where the signaling function of ornamental traits is complex or the literature conflicting. Here, we examined variation in the yellow/orange tail feathers of American redstarts (Setophaga ruticilla) passing through two migratory stopover sites in eastern North America during both spring and fall migration to assess the role of geographic variation and seasonality in influencing feather colouration. In addition, we investigated whether diet during moult (inferred via stable isotope analysis of feather δ15N and δ13C) influenced plumage colouration. Our findings indicate that geographic variation, season, and diet all influence individual differences in American redstart colouration, represented by both traditional and tetrahedral colour variables. The extent to which these factors influence colour expression however is largely dependent on the colour metric under study, likely because different colour metrics reflect different attributes of the feather (e.g., structural components vs. pigment deposition). The effects of diet (δ15N) and season were pronounced for brightness, suggesting a strong effect of diet and feather wear/degradation on feather structure. Though hue, a metric that should strongly reflect pigment deposition, also changed from spring to fall, that effect was dependent on age, with only adults experiencing a reduction in ornamentation. Taken together, our results highlight the numerous sources of variation behind plumage coloration and underscores the difficulty of unraveling complex visual signaling systems, such as those in American redstarts.
α-Synuclein polymorphism determines oligodendroglial dysfunction
Authors: Frieg Benedikt , Geraets James A. , Strohäker Timo , Dienemann Christian , Mavroeidi Panagiota , Jung Byung Chul , Kim Woojin S. , Lee Seung-Jae , Xilouri Maria , Zweckstetter Markus , Schröder Gunnar F. 
Published: July 2021

AbstractSynucleinopathies, such as Parkinson’s disease (PD) and Multiple System Atrophy (MSA) are progressive and unremitting neurological diseases. For both PD and MSA, α-synuclein fibril inclusions inside brain cells are neuropathological hallmarks. In addition, amplification of α-synuclein fibrils from body fluids is a potential biomarker distinguishing PD from MSA. However, little is known about the structure of α-synuclein fibrils amplified from human samples and its connection to α-synuclein fibril structure in the human brain. Here we amplified α-synuclein fibrils from PD and MSA brain tissue, characterized its seeding potential in oligodendroglia, and determined the 3D structures by cryo-electron microscopy. We show that the α-synuclein fibrils from a MSA patient are more potent in recruiting the endogenous α-synuclein and evoking a redistribution of TPPP/p25α protein in mouse primary oligoden-droglial cultures compared to those amplified from a PD patient. Cryo-electron microscopy shows that the PD- and MSA-amplified α-synuclein fibrils share a similar protofilament fold but differ in their inter-protofilament interface. The structures of the brain-tissue amplified α-synuclein fibrils are also similar to other in vitro and ex vivo α-synuclein fibrils. Together with published data, our results suggest that αSyn fibrils differ between PD and MSA in their quaternary arrangement and could further vary between different forms of PD and MSA.
Successful microbial colonization of space using an anti-aggregation strategy
Authors: Liu Xiaonan , Wang Miaoxiao , Nie Yong , Wu Xiao-Lei 
Published: July 2021

AbstractMany organisms live in habitats with limited nutrients or space, competition for these resources is ubiquitous. Although spatial factors related to population’s manner of colonizing space influences its success in spatial competition, what these factors are and to what extent they influence the outcome remains under-explored. Here, we applied a simulated competitive model to explore the spatial factors affecting outcomes of competition for space. By quantifying spatial factors using ‘Space Accessibility’, we show that colonizing space in an anti-aggregation manner contributes to microbial competitive success. We also find that the competitive edge derived from being anti-aggregation in colonizing space, which results in a higher ‘Space Accessibility’, neutralizes the disadvantage arising from either lower growth rate or lower initial abundance. These findings shed light on the role of space colonization manners on maintaining biodiversity within ecosystems and provide novel insights critical for understanding how competition for space drives evolutionary innovation.