bluuebunny/crossref_metadata_2025_split huggingface dataset:
Consumers’ active choice behavior promotes coevolutionary units
Authors: de Alvarenga Allan Maurício Sanches Baptista , Borges Marcelo Eduardo , Jorge Leonardo Ré , Varassin Isabela Galarda , Araújo Sabrina Borges Lino 
Published: June 2021

AbstractIndividual behavior and local context are processes that can influence the structure and evolution of ecological interactions. In trophic interactions, consumers can increase their fitness by actively choosing resources that enhance their chances of exploring them successfully. Upon searching for potential resources, they are able to decide which one to choose according to their fitness benefit and maneuverability. Mathematical modeling is often employed in theoretical studies to understand the coevolutionary dynamics between these species. However, they often disregard the individual consumer behavior since the complexity of these systems usually requires simplifying assumptions about interaction details. Using an individual-based model, we model a community of several species that interact antagonistically. The trait of each individual is modeled explicitly and is subjected to the interaction pressure. In addition, consumers can actively choose the resources that guarantee greater fitness. We show that active consumer choice can generate coevolutionary units over time. It means that the traits of both consumers and resources converge into multiple groups with similar traits, exerting reciprocal selective pressure between them. We also observed that network structure has a greater dependence on the parameter that delimits active consumer choice than on the intensity of selective pressure. Consequently, this parameter can closely match empirical networks. Thus, we consider that the inclusion of consumers’ active choice behavior in the models plays an important role in the ecological and evolutionary processes that structure these communities.
Microbiota and stress: a loop that impacts memory
Authors: Kraimi Narjis , Lormant Flore , Calandreau Ludovic , Kempf Florent , Zemb Olivier , Lemarchand Julie , Constantin Paul , Parias Céline , Germain Karine , Dupont Christèle , Rabot Sylvie , Philippe Catherine , Foury Aline , Moisan Marie-Pierre , Carvalho Anaïs Vitorino , Coustham Vincent , Dardente Hugues , Velge Philippe , Cousin Patrice , Chaumeil Thierry , Leterrier Christine 
Published: June 2021

AbstractChronic stress and the gut microbiota appear to comprise a feed-forward loop, which contributes to the development of depressive disorders. Evidence suggests that memory can also be impaired by either chronic stress or microbiota imbalance. However, it remains to be established whether these could be a part of an integrated loop model and be responsible for memory impairments. To shed light on this, we used a two-pronged approach in Japanese quail: first stress-induced alterations in gut microbiota were characterized, then we tested whether this altered microbiota could affect brain and memory function when transferred to a germ-free host. The cecal microbiota of chronically stressed quails was found to be significantly different from that of unstressed individuals with lower α and β diversities and increased Bacteroidetes abundance largely represented by the Alistipes genus, a well-known stress target in rodents and humans. The transfer of this altered microbiota into germ-free quails decreased their spatial and cue-based memory abilities as previously demonstrated in the stressed donors. The recipients also displayed increased anxiety-like behavior, reduced basal plasma corticosterone levels and differential gene expression in the brain. Furthermore, cecal microbiota transfer from a chronically stressed individual was sufficient to mimic the adverse impact of chronic stress on memory in recipient hosts and this action may be related to the Alistipes genus. Our results provide evidence of a feed-forward loop system linking the microbiota-gut-brain axis to stress and memory function and suggest that maintaining a healthy microbiota could help alleviate memory impairments linked to chronic stress.
COBREXA.jl: constraint-based reconstruction and exascale analysis
Authors: Kratochvíl Miroslav , Heirendt Laurent , Wilken St. Elmo , Pusa Taneli , Arreckx Sylvain , Noronha Alberto , van Aalst Marvin , Satagopam Venkata P , Ebenhöh Oliver , Schneider Reinhard , Trefois Christophe , Gu Wei 
Published: June 2021

SummaryCOBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models.Availability and implementationhttps://doi.org/10.17881/ZKCR-BT30.Contactchristophe.trefois@uni.lu,wei.gu@uni.lu
Risk of Bias Assessment in Preclinical Literature using Natural Language Processing
Authors: Wang Qianying , Liao Jing , Lapata Mirella , Macleod Malcolm 
Published: June 2021

ABSTRACTObjectiveWe sought to apply natural language processing to the task of automatic risk of bias assessment in preclinical literature, which could speed the process of systematic review, provide information to guide research improvement activity, and support translation from preclinical to clinical research.Materials and MethodsWe use 7,840 full-text publications describing animal experiments with yes/no annotations for five risk of bias items. We implement a series of models including baselines (support vector machine, logistic regression, random forest), neural models (convolutional neural network, recurrent neural network with attention, hierarchical neural network) and models using BERT with two strategies (document chunk pooling and sentence extraction). We tune hyperparameters to obtain the highest F1 scores for each risk of bias item on the validation set and compare evaluation results on the test set to our previous regular expression approach.ResultsThe F1 scores of best models on test set are 82.0% for random allocation, 81.6% for blinded assessment of outcome, 82.6% for conflict of interests, 91.4% for compliance with animal welfare regulations and 46.6% for reporting animals excluded from analysis. Our models significantly outperform regular expressions for four risk of bias items.ConclusionFor random allocation, blinded assessment of outcome, conflict of interests and animal exclusions, neural models achieve good performance, and for animal welfare regulations, BERT model with sentence extraction strategy works better.
WeightedLD: The Application of Sequence Weights to Linkage Disequilibrium
Authors: Charles Oscar J. , Roberts Joseph , Breuer Judith , Goldstein Richard A. 
Published: June 2021

AbstractSequence-weighting methods are commonly employed to account for biases in sequence datasets. We use a weighting scheme which considers the observed distinctiveness of sequences and apply it to calculations of linkage disequilibrium. Each sequence now contributes a weighted score to linkage disequilibrium measurements of pairwise loci. We demonstrate that this reduces the effect of uneven sampling, as underrepresented groups of sequences will each contribute more individually than redundant, similar sequences.AvailabilitySource code for a python and rust implementation are freely available at under an MIT license at github.com/ojcharles/WeightedLD.Contactr.goldstein@ucl.ac.uk or oscar.charles.18@ucl.ac.uk
Mapping the sequence specificity of heterotypic amyloid interactions enables the identification of aggregation modifiers
Authors: Louros Nikolaos , Ramakers Meine , Michiels Emiel , Konstantoulea Katerina , Morelli Chiara , Garcia Teresa , D’Haeyer Sam , Goossens Vera , Audenaert Dominique , Rousseau Frederic , Schymkowitz Joost 
Published: June 2021

AbstractHeterotypic amyloid interactions between related protein sequences have been observed in functional and disease amyloids. While sequence homology seems to favour heterotypic amyloid interactions, we have no systematic understanding of the structural rules determining such interactions nor whether they inhibit or facilitate amyloid assembly. Using structure-based thermodynamic calculations and extensive experimental validation, we performed a comprehensive exploration of the defining role of sequence promiscuity in amyloid interactions. Using this knowledge, we demonstrate, using tau as a model system, that predicted cross-interactions driven by sequence homology indeed can modify nucleation, fibril morphology, kinetic assembly and cellular spreading of aggregates. We also find that these heterotypic amyloid interactions can result in the mis-localisation of brain-expressed protein sequences with prevalent activities in neurodegenerative disorders. Our findings suggest a structural mechanism by which the proteomic background can modulate the aggregation propensity of amyloidogenic proteins and discuss how such sequence-specific proteostatic perturbations could contribute to the selective cellular susceptibility of amyloid disease progression.
Experimental depletion of gut microbiota diversity reduces host thermal tolerance and fitness under heat stress in a vertebrate ectotherm
Authors: Fontaine Samantha S. , Mineo Patrick M. , Kohl Kevin D. 
Published: June 2021

AbstractPredicting the responses of ectotherms to climate change is a global conservation priority which requires identifying factors that influence how animals respond physiologically to changing temperature. Host-associated microbial communities impact animal physiology and have been shown to influence host thermal tolerance in invertebrate systems. However, the role of commensal microbiota in thermal tolerance of ectothermic vertebrates is unknown. Here we show that experimentally depleting the diversity of the tadpole gut microbiome through environmental water sterilization reduces the host’s acute thermal tolerance to both heat and cold, alters the thermal sensitivity of locomotor performance, and reduces animal survival under acute heat stress. We show that these tadpoles have reduced activities of mitochondrial enzymes and altered metabolic rates compared to tadpoles colonized with a diverse microbiota, which could underlie differences in thermal phenotypes. Our results demonstrate, for the first time, a link between the gut microbiome of an ectothermic vertebrate and the host’s thermal tolerance, performance, and fitness, thus highlighting the importance of considering host-associated microbial communities when predicting species’ responses to climate change.
Meta-ecosystem dynamics drive the spatial distribution of functional groups in river networks
Authors: Jacquet Claire , Carraro Luca , Altermatt Florian 
Published: June 2021

AbstractThe meta-ecosystem concept provides a theoretical framework to study the effect of local and regional flows of resources on ecosystem dynamics. Meta-ecosystem theory has hitherto been applied to highly abstract landscapes, and meta-ecosystem dynamics in real-world landscapes remain largely unexplored. River networks constitute a prime example of meta-ecosystems, being characterized by directional resource flows from upstream to downstream communities and from the terrestrial to the aquatic realm. These flows have been thoroughly described by the River Continuum Concept (RCC), a seminal concept in freshwater ecology, stating that observed spatial variations in the relative abundances of invertebrate functional groups reflect systematic shifts in types and locations of food resources, which are in turn determined by the physical attributes of river reaches. Hence, the RCC represents a solid conceptual basis for determining how changes in landscape structure and resource flows will translate into local and regional changes in community composition. Here, we develop and analyse a riverine meta-ecosystem model inspired by the RCC, which builds upon a physically-based landscape model of dendritic river networks. We show that the spatial distributions and regional biomass of invertebrate functional groups observed in stream communities are determined by the spatial structure and scaling attributes of dendritic river networks, as well as by specific rates of resource flows. Neglecting any of these aspects in modelling river meta-ecosystems would result in unrealistic community patterns. Moreover, we observed that high rates of resource flow, for example due to river anthropization, have a negative effect on the regional biomass of all functional groups studied, and can lead to cascading extinctions at the meta-ecosystem scale. Our work paves the way for the development of physically-based meta-ecosystem models to understand the structure and functioning of real-world ecosystems.
STENCIL: A web templating engine for visualizing and sharing life science datasets
Authors: Sun Qi , Nematbakhsh Ali , Kuntala Prashant K , Kellogg Gretta , Pugh B. Franklin , Lai William KM 
Published: June 2021

AbstractThe ability to aggregate experimental data analysis and results into a concise and interpretable format is a key step in evaluating the success of an experiment. This critical step determines baselines for reproducibility and is a key requirement for data dissemination. However, in practice it can be difficult to consolidate data analyses that encapsulates the broad range of datatypes available in the life sciences. We present STENCIL, a web templating engine designed to organize, visualize, and enable the sharing of interactive data visualizations. STENCIL leverages a flexible web framework for creating templates to render highly customizable visual front ends. This flexibility enables researchers to render small or large sets of experimental outcomes, producing high-quality downloadable and editable figures that retain their original relationship to the source data. REST API based back ends provide programmatic data access and supports easy data sharing. STENCIL is a lightweight tool that can stream data from Galaxy, a popular bioinformatic analysis web platform. STENCIL has been used to support the analysis and dissemination of two large scale genomic projects containing the complete data analysis for over 2,400 distinct datasets. Code and implementation details are available on GitHub: https://github.com/CEGRcode/stencil
A Novel Organoid Model of In Vitro Spermatogenesis Using Human Induced Pluripotent Stem Cells
Authors: Robinson Meghan , Witherspoon Luke , Willerth Stephanie , Flannigan Ryan 
Published: June 2021

AbstractInfertility is thought to be caused by genetic mutations and dysfunction in the cellular niche where spermatogenesis takes place. An understanding of the specialized cellular processes which drive spermatogenesis is needed to develop treatments; however, the development ofin vitrosystems to study these cells has been hindered by our reliance on rarely available human testicular tissues for research. Human induced pluripotent stem cells (hiPSCs) can be used to derive human testicular-like cells, and thus provide an avenue for the development ofin vitrotesticular model systems. Therefore, this study set out to engineer a human testicular tissue model using hiPSCs for the first time. We demonstrate the ability of hiPSC-derived testicular cells to self-organize and mature into testicular-like tissues using organoid culture. Moreover, we show that hiPSC-derived testicular organoids promote testicular somatic cell maturation and spermatogenesis up to the post-meiotic spermatid stage. These hiPSC-derived testicular organoids have the potential to replace rarely available primary testicular tissues to further infertility research in anin vitrosetting.
Microfluidic bioprinting for the in vitro generation of novel biomimetic human testicular tissues
Authors: Robinson Meghan , Bedford Erin , Witherspoon Luke , Willerth Stephanie M. , Flannigan Ryan 
Published: June 2021

AbstractAdvances in cancer treatments have greatly improved pediatric cancer survival rates, leading to quality of life considerations and in particular fertility restoration. Accordingly, pre-pubertal patients have the option to cryopreserve testicular tissue for experimental restorative therapies, including in vitro spermatogenesis, wherein testicular tissue is engineered in vitro and spermatozoa are collected for in vitro fertilization (IVF). Current in vitro systems have been unable to reliably support the generation of spermatozoa from human testicular tissues, likely due to the inability for the dissociated testicular cells to recreate the native architecture of testicular tissue found in vivo. Recent advances in 3-D bioprinting can place cells into geometries at fine resolutions comparable to microarchitectures found in native tissues, and therefore hold promise as a tool for the development of a biomimetic in vitro system for human spermatogenesis. This study assessed the utility of bioprinting technology to recreate the precise architecture of testicular tissue and corresponding spermatogenesis for the first time. We printed testicular cell-laden hollow microtubules at similar resolutions to seminiferous tubules, and compared the results to testicular organoids. We show that the human testicular cells retain their viability and functionality post-printing, and illustrate an intrinsic ability to reorganize into their native cytoarchitecture. This study provides a proof of concept for the use of 3-D bioprinting technology as a tool to create biomimetic human testicular tissues.
Aging-related cerebral microvascular changes visualized using Ultrasound Localization Microscopy in the living mouse
Authors: Lowerison Matthew R. , Sekaran Nathiya Chandra , Zhang Wei , Dong Zhijie , Chen Xi , Llano Daniel A. , Song Pengfei 
Published: June 2021

AbstractAging-related cognitive decline is an emerging health crisis; however, no established unifying mechanism has been identified for the cognitive impairments seen in an aging population. A vascular hypothesis of cognitive decline has been proposed but is difficult to test given the contradictory radiologic needs of high-fidelity microvascular imaging resolution and a broad and deep brain imaging field of view. Super-resolution ultrasound localization microscopy (ULM) offers a potential solution by exploiting circulating microbubbles to achieve a vascular resolution approaching the capillary scale without sacrificing imaging depth. In this report, we apply ULM imaging to a mouse model of aging and quantify differences in cerebral vascularity, blood velocity, and vessel tortuosity across several brain regions. We found significant decreases in blood velocity, and significant increases in vascular tortuosity, across all brain regions in the aged cohort, and significant decreases in blood volume in the cortex. These data provide the first-ever measurements of subcortical microvascular dynamics in vivo and reveal that aging has a major impact on these measurements.
Utilizing proteomics and phosphoproteomics to predict ex vivo drug sensitivity across genetically diverse AML patients
Authors: Gosline Sara JC , Tognon Cristina , Nestor Michael , Joshi Sunil , Modak Rucha , Damnernsawad Alisa , Moon Jamie , Hansen Joshua R. , Hutchinson-Bunch Chelsea , Gritsenko Marina A. , Weitz Karl K. , Traer Elie , Drucker Brian , Agarwal Anupriya , Piehowski Paul , McDermott Jason E. , Rodland Karin 
Published: June 2021

AbstractAcute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemia. This genetic heterogeneity is difficult to treat using conventional therapies that are generally based on the detection of a single driving mutation. Thus, the use of molecular signatures, consisting of multiple functionally related transcripts or proteins, in making treatment decisions may overcome this hurdle and provide a more effective way to inform drug treatment protocols. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify signatures that could predict patient-specific drug responses. The Clinical Proteomic Tumor Analysis Consortium is in the process of extending this cohort to collect proteomic and phosphoproteomic measurements from a subset of these patient samples to evaluate the hypothesis that proteomic signatures can robustly predict drug response in AML patients. We sought to examine this hypothesis on a sub-cohort of 38 patient samples from Beat AML with proteomic and drug response data and evaluate our ability to identify proteomic signatures that predict drug response with high accuracy. For this initial analysis we built predictive models of patient drug responses across 26 drugs of interest using the proteomics and phosphproteomics data. We found that proteomics-derived signatures provide an accurate and robust signature of drug response in the AML ex vivo samples, as well as related cell lines, with better performance than those signatures derived from mutations or mRNA expression. Furthermore, we found that in specific drug-resistant cell lines, the proteins in our prognostic signatures represented dysregulated signaling pathways compared to parental cell lines, confirming the role of the proteins in the signatures in drug resistance. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to predict drug sensitivity in AML.