bluuebunny/crossref_metadata_2025_split huggingface dataset:
Coding Long COVID: Characterizing a new disease through an ICD-10 lens
Authors: Pfaff Emily R , Madlock-Brown Charisse , Baratta John M. , Bhatia Abhishek , Davis Hannah , Girvin Andrew , Hill Elaine , Kelly Liz , Kostka Kristin , Loomba Johanna , McMurry Julie A. , Wong Rachel , Bennett Tellen D , Moffitt Richard , Chute Christopher G , Haendel Melissa ,   The N3C Consortium,   The RECOVER Consortium
Published: April 2022

AbstractBackgroundNaming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes Long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of Long COVID are still in flux, and the deployment of an ICD-10-CM code for Long COVID in the US took nearly two years after patients had begun to describe their condition. Here we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for “Post COVID-19 condition, unspecified.”MethodsWe undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 21,072), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan.ResultsWe established the diagnoses most commonly co-occurring with U09.9, and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty, high education, and high access to medical care. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients.ConclusionsThis work offers insight into potential subtypes and current practice patterns around Long COVID, and speaks to the existence of disparities in the diagnosis of patients with Long COVID. This latter finding in particular requires further research and urgent remediation.
Read2Tree: scalable and accurate phylogenetic trees from raw reads
Authors: Dylus David , Altenhoff Adrian , Majidian Sina , Sedlazeck Fritz J , Dessimoz Christophe 
Published: April 2022

AbstractThe inference of phylogenetic trees is foundational to biology. However, state-of-the-art phylogenomics requires running complex pipelines, at significant computational and labour costs, with additional constraints in sequencing coverage, assembly and annotation quality. To overcome these challenges, we present Read2Tree, which directly processes raw sequencing reads into groups of corresponding genes. In a benchmark encompassing a broad variety of datasets, our assembly-free approach was 10-100x faster than conventional approaches, and in most cases more accurate—the exception being when sequencing coverage was high and reference species very distant. To illustrate the broad applicability of the tool, we reconstructed a yeast tree of life of 435 species spanning 590 million years of evolution. Applied toCoronaviridaesamples, Read2Tree accurately classified highly diverse animal samples and near-identical SARS-CoV-2 sequences on a single tree—thereby exhibiting remarkable breadth and depth. The speed, accuracy, and versatility of Read2Tree enables comparative genomics at scale.
A flexible modeling and inference framework for estimating variant effect sizes from GWAS summary statistics
Authors: Spence Jeffrey P. , Sinnott-Armstrong Nasa , Assimes Themistocles L. , Pritchard Jonathan K. 
Published: April 2022

AbstractGenome-wide association studies (GWAS) have highlighted that almost any trait is affected by many variants of relatively small effect. On one hand this presents a challenge for inferring the effect of any single variant as the signal-to-noise ratio is high for variants of small effect. This challenge is compounded when combining information across many variants in polygenic scores for predicting trait values. On the other hand, the large number of contributing variants provides an opportunity to learn about the average behavior of variants encoded in the distribution of variant effect sizes. Many approaches have looked at aspects of this problem, but no method has unified the inference of the effects of individual variants with the inference of the distribution of effect sizes while requiring only GWAS summary statistics and properly accounting for linkage disequilibrium between variants. Here we present a flexible, unifying framework that combines information across variants to infer a distribution of effect sizes and uses this distribution to improve the estimation of the effects of individual variants. We also develop a variational inference (VI) scheme to perform efficient inference under this framework. We show this framework is useful by constructing polygenic scores (PGSs) that outperform the state-of-the-art. Our modeling framework easily extends to jointly inferring effect sizes across multiple cohorts, where we show that building PGSs using additional cohorts of differing ancestries improves predictive accuracy and portability. We also investigate the inferred distributions of effect sizes across many traits and find that these distributions have effect sizes ranging over multiple orders of magnitude, in contrast to the assumptions implicit in many commonly-used statistical genetics methods.
A multi-continental analysis of the responses of freshwater food webs to climate and land use change
Authors: Barbosa Gedimar Pereira , Siqueira Tadeu 
Published: April 2022

ABSTRACTFood web responses to environmental change are not straightforward to understand as they occur through an intricate arrangement of direct and indirect effects. Although previous investigations have advanced knowledge on freshwater food web structure, we must better understand the intricate relationships between the main drivers of environmental change and trophic networks in lentic and lotic ecosystems.We compiled multicontinental data to investigate how climate and land use change are related to the structure of freshwater food webs, considering the inherent differences in lentic and lotic ecosystems. We analyzed the direct and indirect relationships between land use intensity, and temperature and precipitation temporal trends, and food webs using multi-group structural equation modeling.The strength and direction of the relationships between climate, land use, and food webs varied considerably among lentic and lotic ecosystems, but most indicated indirect effects through the number of links in the network. While network connectance both increased and decreased with land use and climate change, the number of trophic levels decreased with land use intensity and maximum temperature and increased with increasing precipitation. Omnivory increased with land use intensity in both ecosystems but was negatively related to changes on maximum temperature in lake food webs.Even though food webs are expected to become more connected in face of disturbances, and our work supported this regarding local warming, the negative relationships between network connectance and land use intensification suggests that food webs become more specialized at disturbed sites. On the other hand, reduction in the number of trophic levels indicates the loss of top consumers in face of warming and increasing land use intensity.The response of food webs in both lentic and lotic ecosystems to climate and land use change occurred mostly through changes in species interactions. Our results indicate that the intensification of land use makes food webs more specialized, with less trophic levels. Also, inherent aspects of freshwater ecosystems seemed to play a major role in the way food webs respond to disturbance and must be considered to fully understand and predict the effects of global changes on freshwater biodiversity.
Expanding risk predictions of pesticide resistance evolution in arthropod pests with a proxy for selection pressure
Authors: Thia Joshua A. , Maino James , Kelly Alicia , Hoffmann Ary A. , Umina Paul A. 
Published: April 2022

ABSTRACTChemical resistance in pest organisms threatens global food security and human health, yet resistance issues are mostly dealt with reactively. Predictive models of resistance risk provide an avenue for field practitioners to implement proactive pest management but require knowledge of factors that drive resistance evolution. Despite the importance of chemical selection pressure on resistance evolution, poor availability of chemical usage data has limited the use of a general multi-species measure of selection pressure in predictive models. We demonstrate the use of pesticide product registrations as a predictor of resistance status and potential proxy of chemical selection pressure. Pesticide product registrations were obtained for 427 USA and 209 Australian agricultural arthropod pests, for 42 and 39 chemical Mode of Action (MoA) groups, respectively. We constructed Bayesian logistic regressions of resistance status as a function of the number of pesticide product registrations and two ecological traits, phagy and voltinism. Our models were well-supported with demonstrated power to discriminate between resistant and susceptible observations in both USA and Australian species sets using cross-validation. Importantly, we observed strong support for a positive association between pesticide products and resistance status. Our work expands the horizon for proactive management by quantitatively linking a proxy for selection pressure on pest species to different chemical MoAs. This proxy for selection pressure can be combined with ecological information to predict the resistance risk in agricultural pests. Because pesticide product registrations can typically be derived from publicly available data, we believe there is broad applicability to other agricultural pests such as weeds and fungi, and to other geographical regions beyond the USA and Australia.
Stress fibers orient traction forces on micropatterns: A hybrid cellular Potts model study
Authors: Schakenraad Koen , Martorana Gaia I. , Bakker Bente H. , Giomi Luca , Merks Roeland M.H. 
Published: April 2022

AbstractAdhering cells exert traction forces on the underlying substrate. We numerically investigate the intimate relation between traction forces, the structure of the actin cytoskeleton, and the shape of cells adhering to adhesive micropatterned substrates. By combining the Cellular Potts Model with a model of cytoskeletal contractility, we reproduce prominent anisotropic features in previously published experimental data on fibroblasts, endothelial cells, and epithelial cells on adhesive micropatterned substrates. Our work highlights the role of cytoskeletal anisotropy in the generation of cellular traction forces, and provides a computational strategy for investigating stress fiber anisotropy in dynamical and multicellular settings.Author summaryCells that make up multicellular life perform a variety of mechanical tasks such as pulling on surrounding tissue to close a wound. The mechanisms by which cells perform these tasks are, however, incompletely understood. In order to better understand how they generate forces on their environment, cells are often studiedin vitroon compliant substrates, which deform under the so called “traction forces” exerted by the cells. Mathematical models complement these experimental approaches because they help to interpret the experimental data, but most models for traction forces on adhesive substrates assume that cells contract isotropically, i.e., they do not contract in a specific direction. However, many cell types contain organized structures of stress fibers - strong contracting cables inside the cell - that enable cells to exert forces on their environment in specific directions only. Here we present a computational model that predicts both the orientations of these stress fibers as well as the forces that cells exert on the substrates. Our model reproduces both the orientations and magnitudes of previously reported experimental traction forces, and could serve as a starting point for exploring mechanical interactions in multicellular settings.
The gut microbiome and early-life growth in a population with high prevalence of stunting
Authors: Robertson Ruairi C. , Edens Thaddeus J. , Carr Lynnea , Mutasa Kuda , Evans Ceri , Gough Ethan K. , Geum Hyun Min , Baharmand Iman , Gill Sandeep K. , Ntozini Robert , Smith Laura E , Chasekwa Bernard , Majo Florence D. , Tavengwa Naume V. , Mutasa Batsirai , Francis Freddy , Tome Joice , Stoltzfus Rebecca J. , Humphrey Jean H. , Prendergast Andrew J. , Manges Amee R. ,   the SHINE Trial Team
Published: April 2022

ABSTRACTStunting affects one-in-five children globally and is associated with greater infectious morbidity, mortality and neurodevelopmental deficits. Recent evidence suggests that the early-life gut microbiome affects child growth through immune, metabolic and endocrine pathways, and microbiome perturbations may contribute to undernutrition. We examined early-life fecal microbiome composition and function in 875 stool samples collected longitudinally in 335 children from 1-18 months of age in rural Zimbabwe, from a cluster-randomized trial of improved water, sanitation, and hygiene (WASH), and improved infant and young child feeding (IYCF). Using whole metagenome shotgun sequencing, we examined the effect of the interventions, in addition to environmental or host factors including maternal HIV infection, on the succession of the early-life gut microbiome, and employed extreme gradient boosting machines (XGBoost) to model microbiome maturation and to predict child growth. WASH and IYCF interventions had little impact on the fecal microbiome, however children who were HIV-exposed but uninfected exhibited over-diversification and over-maturity of the early-life gut microbiome in addition to reduced abundance ofBifidobacteriaspecies. Taxonomic microbiome features were poorly predictive of linear and ponderal growth, however functional metagenomic features, particularly B-vitamin and nucleotide biosynthesis pathways, moderately predicted both attained linear and ponderal growth and growth velocity. We find that the succession of the gut microbiome in a population at risk of stunting is unresponsive to WASH and IYCF interventions, but is strongly associated with maternal HIV infection, which may contribute to deficits in growth. New approaches targeting the gut microbiome in early childhood may complement efforts to combat child undernutrition.One sentence summaryThe gut microbiome of rural Zimbabwean infants undergoes programmed maturation that is unresponsive to sanitation and nutrition interventions but is comprehensively modified by maternal HIV infection and can moderately predict linear growth.
eIf2α-regulated translation modulates the early adhesion of mesenchymal-like cells
Authors: Caillier Alexia , Morin Nadine , Lavigne Mathilde , Lambert Jean-Philippe , Mazrouï Rachid , Bergeman Jonathan , Huot Marc-Étienne 
Published: April 2022

ABSTRACTCellular invasion is a complex process that requires several interdependent biological mechanisms, which are initiated by changes in adhesion that establish a morphology favorable for migration. Hence, the regulation of adhesion potential is a rate-limiting step in metastasis. Our previous work revealed that de novo translation is necessary to regulate the adhesion of mesenchymal-like cells; however, the underlying translational regulatory mechanism and the identity of newly synthesized proteins needed for the adhesion process remain unidentified. Here, we describe a translational regulatory mechanism that modulates mesenchymal cell adhesion. We observed a drastic decrease in translation during the initial phase of adhesion, followed by a reprogramming of the translatome, characterized by an orchestrated wave of mRNA translation that increases the expression of specific proteins involved in adhesion. We observed that phosphorylation of eukaryotic translation initiation factor 2 subunit alpha (eIF2α), which inhibits general translation initiation, was drastically increased at the beginning of cell adhesion. As adhesion progressed, the selective increase in the translation of adhesion-related mRNAs intensified as eIF2α phosphorylation gradually decreased over time in mensenchymal cells, but not in epithelial cells. Taken together, we have identified a translational regulatory mechanism specifically affecting the adhesion process of mesenchymal cells, as well as metastatic cells that have undergone epithelial-to-mesenchymal transition.One sentence summaryTranslation regulates mesenchymal cell adhesion
Mechanism of dopamine traveling waves in the striatum: theory and experiment
Authors: Matityahu Lior , Gilin Naomi , Atamna Yara , Tiroshi Lior , Wickens Jeffery R. , Goldberg Joshua A. 
Published: April 2022

SummaryStriatal dopamine (DA) encodes reward, with recent work showing that DA release occurs in spatiotemporal waves. However, the mechanism of DA waves is unknown. Here we report that the striatal cholinergic neuropil also exhibits wave-like activity, and that the spatial scale of striatal DA release is extended by nicotinic receptors. Based on these findings we hypothesized that the local reciprocal interaction between cholinergic interneurons (CIN) and DA axons suffices to drive endogenous traveling waves. We show that the morphological and physiological properties of the CIN-DA interaction can be modeled as a reaction-diffusion system that gives rise to traveling waves. Analytically-tractable versions of the model show that the structure and the nature of propagation of CIN and DA traveling waves depend on their coupling, and that traveling waves can give rise to empirically observed correlations between these signals. Our model provides a biophysical mechanism for wave formation and predicts that the observed DA and CIN waves are strongly coupled phenomena.
CPGL: Prediction of compound-protein interaction by integrating graph attention network with long short-term memory neural network
Authors: Zhao Minghua , Yuan Min , Yang Yaning , Xu Steven X 
Published: April 2022

AbstractRecent advancements of artificial intelligence based on deep learning algorithms have made it possible to computationally predict compound-protein interaction (CPI) without conducting laboratory experiments. In this manuscript, we integrated a graph attention network (GAT) for compounds and a long short-term memory neural network (LSTM) for proteins, used end-to-end representation learning for both compounds and proteins, and proposed a deep learning algorithm, CPGL (CPI with GAT and LSTM) to optimize the feature extraction from compounds and proteins and to improve the model robustness and generalizability. CPGL demonstrated an excellent predictive performance and outperforms recently reported deep learning models. Based on 3 public CPI datasets, C.elegans, Human and BindingDB, CPGL represented 1 - 5% improvement compared to existing deep-learning models. Our method also achieves excellent results on datasets with imbalanced positive and negative proportions constructed based on the above two datasets. More importantly, using 2 label reversal datasets, GPCR and Kinase, CPGL showed superior performance compared to other existing deep learning models. The AUC were substantially improved by 15% to 50% on the Kinase dataset, indicative of the robustness and generalizability of CPGL.
Functional characterization of bacterial isolates from dye decolorizing consortia and a step-up metabolic engineering based on NADH-regeneration
Authors: Rathod Jagat , Archana G. 
Published: April 2022

AbstractAzo dye decolorizing acclimatized decolorizing consortia are enriched microbial sources of potential azoreductase-efficient bioremediation strains. Here, we characterized eight selected consortial members for their azo decolorization and azoreductase profiling. These efficient dye decolorizing bacterial isolates were affiliated to two major phyla viz. Firmicute (genus-Enterococcus) and Proteobacteria (γ-group). Redox-mediators such as AQDS and AQS were found to significantly increase decolorization except for menadione, and IR functional group signatures highlighted the azo bond reduction and degraded metabolites profiles of each strain. Among isolates, Enterococcus sp. L2 was found to be the most effective strain as it could reduce >90mg/L Reactive violet 5R (RV5R) dye in 3h of incubation. Furthermore, strain L2 possesses profound high NADH and NADPH-dependent azoreductase activity which also corroborated with its superior azo decolorization. As per physicochemical parameters, strain L2 showed an optimum decolorization at pH 8, 40 °C and up to 2% w/v salinity. To channelize reducing equivalence (NADH) to further enhance the dye decolorization in NADH-azoreductase efficient Enterococcus sp. L2, we augmented an NADH co-factor regeneration system. Using pMGS100, a Gram-positive expression vector a constitutive heterologous expression of Mycobacterium vaccae encoded NAD+-dependent formate dehydrogenase enhanced NADH pool which led to a significant 3.2 fold increased dye decolorization in Enterococcus sp. L2 harboring pMGS100 fdh along with a positive effect on growth. Ultimately, an augmentation of formate utilization step could further accelerate azo dye decolorization by fulfilling the co-factor (NADH) requirement of azoreductase along with a growth advantage in the non-model azoreductase-efficient environmentally important strain L2.
3D printed SAXS chamber for controlled in-situ dialysis and optical characterization
Authors: Ehm Tamara , Philipp Julian , Barkey Martin , Ober Martina , Brinkop Achim Theo , Simml David , Westphalen Miriam von , Nickel Bert , Beck Roy , Rädler Joachim O. 
Published: April 2022

Abstract3D printing changes the scope of how samples can be mounted for small angle X-ray scattering (SAXS). In this paper we present a 3D printed X-ray chamber, which allows for in-situ exchange of buffer and in-situ optical transmission spectroscopy. The chamber is made of cyclic olefin copolymers (COC), including COC X-ray windows providing ultra low SAXS background. The design integrates a membrane insert for in-situ dialysis of the 100 µl sample volume against a reservoir, which enables measurements of the same sample under multiple conditions using an in-house X-ray setup equipped with a 17.4 keV molybdenum source. We demonstrate the design’s capabilities by measuring reversible structural changes in lipid and polymer systems as a function of salt concentration and pH. In the same chambers optical light transmission spectroscopy was carried out measuring optical turbidity of the mesophases and local pH values using pH-responsive dyes. Microfluidic exchange and optical spectroscopy combined with in-situ X-ray scattering enables vast applications for the study of responsive materials.
Why resource dynamics matter in the optimization of microbial communities
Authors: Letten Andrew D. , Ludington William B. 
Published: April 2022

AbstractThe impact of resource supply on microbial community dynamics is rarely treated explicitly in research into the control and optimization of natural and synthetic microbial communities. Using simulations, we show that compositional overlap between microbial communities decays rapidly with a change in the frequency of resource pulsing, particularly in the presence of a metabolic trade-off in resource affinity and maximum growth rate. We conclude that resource supply dynamics should be considered both a constraint and a tuning mechanism in microbial community optimization.