bluuebunny/crossref_metadata_2025_split huggingface dataset:
Longitudinal characterization of phenotypic profile of T cells in chronic hepatitis B identifies immune markers predicting HBsAg loss
Authors: Xiong Shue , Zhu Dan , Liang Boyun , Li Mingyue , Pan Wen , He Junyi , Wang Hua , Sutter Kathrin , Dittmer Ulf , Lu Mengji , Liu Di , Yang Dongliang , Liu Jia , Zheng Xin 
Published: November 2020

ABSTRACTThe current desirable endpoint of treatment against chronic hepatitis B virus infection (cHBV) is to achieve a functional cure, which is defined as HBsAg loss (sAg-L) with or without anti-HBs seroconversion. However, the immunological features that are associated with functional cure have not been studied in detail. Here, a total of 172 cHBV patients including 31 sAg-L patients, and 24 healthy individuals were examined for their T cell phenotypic profile and HBV-specific T cell responses by flow cytometry. sAg-L patients showed distinct CD4 and CD8 T cell phenotype fingerprints compared to those of HBsAg-positive patients, as indicated by the upregulation of CD25, CD40L and CTLA-4 expression on CD4 T cells; HLA-DR, CD95 and PD-1 on both CD4 and CD8 T cells; as well as a potent HBcAg-specific CD8 T cell response. The changes in the T cell phenotype in sAg-L patients began during rapid HBsAg decrease upon treatment onset, were maintained after sAg-L. HLA-DR expression on T cells was positively correlated with the level of HBsAg reduction and the magnitude of the HBcAg-specific T cell responses in cHBV patients. Importantly, increased HLA-DR and CTLA-4 expression on CD4, as well as HLA-DR and TIM-3 expression on CD8 T cells were identified as predictive factors for HBsAg loss within 48 weeks of therapy in cHBV patients. The onset of HBsAg decrease and subsequent loss in cHBV patients on treatment is associated with significant alterations of both CD4 and CD8 T cell phenotypes. Characterization of the T cell phenotype in cHBV patients possessed greater predicative value for sAg-L.
Cellular and molecular mechanisms of frontal bone development in spotted gar (Lepisosteus oculatus)
Authors: Enny Alyssa , Thompson Andrew W. , Racicot Brett , Braasch Ingo , Nakamura Tetsuya 
Published: November 2020

AbstractBackgroundThe molecular mechanisms initiating vertebrate cranial dermal bone formation is a conundrum in evolutionary and developmental biology. Decades of studies have determined the developmental processes of cranial dermal bones in various vertebrate species, finding possible inducers of dermal bone. However, the evolutionarily derived characters of current experimental model organisms hinder investigations of the ancestral and conserved mechanisms of vertebrate cranial dermal bone induction. Thus, investigating such mechanisms with animals diverging at evolutionarily crucial phylogenetic nodes is imperative.ResultsWe investigated the cellular and molecular foundations of skull frontal bone formation in the spotted garLepisosteus oculatus, a basally branching actinopterygian. Whole-mount bone and cartilage stainings and hematoxylin-eosin section stainings revealed that mesenchymal cell condensations in the frontal bone of spotted gar develop in close association with the underlying cartilage. We also identified novel aspects of frontal bone formation: Upregulation of F-actin and plasma membrane in condensing cells, and extension of podia from osteoblasts to the frontal bone, which may be responsible for bone mineral transport.ConclusionThis study highlights the process of frontal bone formation with dynamic architectural changes of mesenchymal cells in spotted gar, illuminating supposedly ancestral and likely conserved developmental mechanisms of skull bone formation among vertebrates.
Antagonistic activities of Fmn2 and ADF regulate axonal F-actin patch dynamics and the initiation of collateral branching
Authors: Kundu Tanushree , Das Sooraj S , Kumar Divya Sthanu , Sewatkar Lisas K , Ghose Aurnab 
Published: November 2020

ABSTRACTInterstitial collateral branching of axons is a critical component in the development of functional neural circuits. Axon collateral branches are established through a series of cellular processes initiated by the development of a specialized, focal F-actin network in axons. The formation, maintenance and remodelling of this F-actin patch is critical for the initiation of axonal protrusions that are subsequently consolidated to form a collateral branch. However, the mechanisms regulating F-actin patch dynamics are poorly understood.Fmn2 is a formin family member implicated in multiple neurodevelopmental disorders. We find that Fmn2 regulates the initiation of axon collateral protrusions. Fmn2 localises to the protrusion-initiating axonal F-actin patches and regulates the lifetime and size of these F-actin networks. The F-actin nucleation activity of Fmn2 is necessary for F-actin patch stability but not for initiating patch formation. We show that Fmn2 insulates the F-actin patches from disassembly by the actin-depolymerizing factor, ADF, and promotes long-lived, larger patches that are competent to initiate axonal protrusions.The regulation of axonal branching can contribute to the neurodevelopmental pathologies associated with Fmn2 and the dynamic antagonism between Fmn2 and ADF may represent a general mechanism of formin-dependent protection of Arp2/3-initiated F-actin networks from disassembly.
Plant PhysioSpace: a robust tool to compare stress response across plant species
Authors: Esfahani Ali Hadizadeh , Maß Janina , Hallab Asis , Schuldt Bernhard M. , Nevarez David , Usadel Björn , Ott Mark-Christoph , Buer Benjamin , Schuppert Andreas 
Published: November 2020

AbstractGeneralization of transcriptomics results can be achieved by comparison across experiments, which is based on integration of interrelated transcriptomics studies into a compendium. Both characterization of the fate of the organism as well as distinguishing between generic and specific responses can be gained in such a broader context. There are numerous methods for analyzing such data sets, most focusing on gene-wise dimension reduction to obtain marker genes and gene sets, e.g. for pathway analysis. Relying only on isolated biological modules might lead to missing of important confounders and relevant context.We have developed a novel method called Plant PhysioSpace, which provides the ability to compute experimental conditions across species and platforms without a priori reducing the reference information to specific gene-sets. It extracts physiologically relevant signatures from a reference data set, a collection of public data sets, by integrating and transforming heterogeneous reference gene expression data into a set of physiology-specific patterns. New experimental data can be mapped to these patterns, resulting in similarity scores which provide quantitative likeness of the new experiment to the a priori compendium.Because of its robustness against noise and platform bias, Plant PhysioSpace can be used as an inter-species or cross-platform similarity measure. We have demonstrated its success in translating stress responses between different species and platforms (including single-cell technologies).We also report the implementation of two R packages, one software and one data package, and a shiny web application, which provides plant biologists convenient ways to access our method and precomputed models from four different species.
Identification of Covalent Modifications Regulating Immune Signaling Complex Composition and Phenotype
Authors: Frauenstein Annika , Ebner Stefan , Sinha Ankit , Phulphagar Kshiti , Swatek Kirby , Hornburg Daniel , Mann Matthias , Meissner Felix 
Published: November 2020

ABSTRACTCells signal through rearrangements of protein communities governed by covalent modifications and reversible interactions of distinct sets of proteins. A method that identifies those post-transcriptional modifications regulating signaling complex composition and functional phenotypes in one experimental setup would facilitate an efficient identification of novel molecular signaling checkpoints. Here we devisedModifications,Interactions andPhenotypesbyAffinityPurificationMassSpectrometry (MIP-APMS), comprising the streamlined cloning and transduction of tagged proteins into functionalized reporter cells as well as affinity chromatography, followed by MS-based quantification. We report the time-resolved interplay of more than 50 previously undescribed modification and hundreds of protein-protein interactions of 19 immune protein complexes in monocytes. Validation of interdependecies between covalent, reversible and functional protein complex regulations by knock-out or site-specific mutation, revealed isgylation and phosphorylation of TRAF2 as well as ARHGEF18 interaction in Toll-like receptor 2 signaling. Moreover, we identify distinct mechanisms of action for small molecule inhibitors of p-38 (MAPK14). Our method provides a fast and cost-effective pipeline for the molecular interrogation of protein communities in diverse biological systems and primary cells.HighlightsExperimental framework to reveal dynamic signaling checkpoints in primary cellsIdentification of crosstalk between protein modifications and interactions in signaling complexesDiscovery of TRAF2 isgylation, phosphorylation and ARHGEF18 interaction in monocytesDifferential drug mode of action for p-38 (MAPK14) kinase inhibitorsGraphical abstract
Effect of cholesterol on permeability of carbon dioxide across lipid membranes
Authors: Blosser M.C. , So J. , Madani M.S. , Malmstadt N. 
Published: November 2020

AbstractDetermining the permeability of lipid membranes to gases is important for understanding the biological mechanisms of gas transport. Experiments on model membranes have been used to determine the permeability of lipid bilayers in the absence of proteins. Previous measurements have used a number of different methods and obtained widely varying results. We have developed a microfluidic based microscopy assay that measures the rate of CO2permeation in Giant Unilamellar Vesicles (GUVs), and we report permeability data for the POPC-cholesterol system. We find that cholesterol has a strong effect on permeability; bilayers containing high levels of cholesterol are an order of magnitude less permeable than bilayers without cholesterol, 9.9 ± 1.0 x 10−4cm/s vs. 9.6 ± 1.4 x 10−3cm/s.Statement of SignificanceDiffusion of dissolved gasses such as carbon dioxide through cell membranes is an important step in physiological processes. Key to understanding the behavior in cells is the measurement of gas diffusion through model lipid membranes, which isolates the effect of the lipids from other membrane components and allows for control of the composition. Previous measurements have yielded different results for the magnitude of gas transport, and have disagreed on the amount that cholesterol affects transport. The present study presents new data on gas transport across lipid mixtures containing cholesterol, and develops a microfluidic assay for gas transport that will enable further work.
Adding software to package management systems can increase their citation by 280%
Authors: Jalili Vahid , Clements Dave , Grüning Björn , Blankenberg Daniel , Goecks Jeremy 
Published: November 2020

AbstractA growing number of biomedical methods and protocols are being disseminated as open-source software packages. When put in concert with other packages, they can execute in-depth and comprehensive computational pipelines. Therefore, their integration with other software packages plays a prominent role in their adoption in addition to their availability. Accordingly, package management systems are developed to standardize the discovery and integration of software packages. Here we study the impact of package management systems on software dissemination and their scholarly recognition. We study the citation pattern of more than 18,000 scholarly papers referenced by more than 23,000 software packages hosted by Bioconda, Bioconductor, BioTools, and ToolShed—the package management systems primarily used by the Bioinformatics community. Our results suggest that there is significant evidence that the scholarly papers’ citation count increases after their respective software was published to package management systems. Additionally, our results show that the impact of different package management systems on the scholarly papers’ recognition is of the same magnitude. These results may motivate scientists to distribute their software via package management systems, facilitating the composition of computational pipelines and helping reduce redundancy in package development.Significance StatementSoftware packages are the building blocks of computational pipelines. A myriad of packages are developed; however, the lack of integration and discovery standards hinders their adoption, leaving most scientists’ scholarly contributions unrecognized. Package management systems are developed to facilitate software dissemination and integration. However, developing software to meet their code and packaging standards is an involved process. Therefore, our study results on the significant impact of the package management systems on scholarly paper’s recognition can motivate scientists to invest in disseminating their software via package management systems. Dissemination of more software via package management systems will lead to a more straightforward composition of computational pipelines and less redundancy in software packages.
Track-To-Learn: A general framework for tractography with deep reinforcement learning
Authors: Théberge Antoine , Desrosiers Christian , Descoteaux Maxime , Jodoin Pierre-Marc 
Published: November 2020

AbstractDiffusion MRI tractography is currently the only non-invasive tool able to assess the white-matter structural connectivity of a brain. Since its inception, it has been widely documented that tractography is prone to producing erroneous tracks while missing true positive connections. Anatomical priors have been conceived and implemented in classical algorithms to try and tackle these issues, yet problems still remain and the conception and validation of these priors is very challenging. Recently, supervised learning algorithms have been proposed to learn the tracking procedure implicitly from data, without relying on anatomical priors. However, these methods rely on labelled data that is very hard to obtain. To remove the need for such data but still leverage the expressiveness of neural networks, we introduceTrack-To-Learn: A general framework to pose tractography as a deep reinforcement learning problem. Deep reinforcement learning is a type of machine learning that does not depend on ground-truth data but rather on the concept of “reward”. We implement and train algorithms to maximize returns from a reward function based on the alignment of streamlines with principal directions extracted from diffusion data. We show that competitive results can be obtained on known data and that the algorithms are able to generalize far better to new, unseen data, than prior machine learning-based tractography algorithms. To the best of our knowledge, this is the first successful use of deep reinforcement learning for tractography.
Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data
Authors: Hong Rui , Koga Yusuke , Bandyadka Shruthi , Leshchyk Anastasia , Wang Yichen , Akavoor Vidya , Cao Xinyun , Sarfraz Irzam , Wang Zhe , Alabdullatif Salam , Jansen Frederick , Yajima Masanao , Johnson W. Evan , Campbell Joshua D. 
Published: November 2020

AbstractSingle-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular heterogeneity within complex tissues. However, a variety of technical artifacts can be present in scRNA-seq data and need to be assessed before downstream analyses can be performed. While several algorithms and tools have been developed to perform individual quality control (QC) tasks, they are scattered in different packages across several programming environments. Comprehensive pipelines to streamline the process of generating and visualizing QC metrics are lacking. To address this need, we built the SCTK-QC pipeline within thesingleCellTKR package (https://github.com/compbiomed/singleCellTK). Features in this pipeline include the ability to import data from 11 different preprocessing tools or file formats, perform empty droplet detection with 2 different algorithms, generate standard quality control metrics such as number of UMIs per cell or the percentage of mitochondrial counts, predict doublets using 6 different algorithms, and estimate ambient RNA. QC data can be exported to R and/or Python objects used in popular down-stream workflows. Results are visualized in an easy-to-read HTML report. This pipeline can also be used by non-computational users with an interactive graphical user interface developed with R/Shiny. Overall, the SCTK-QC pipeline will streamline and standardize QC analysis for scRNA-seq data across a variety of different single-cell transcriptomic platforms and preprocessing tools.
Characterization of the microbiome of Down syndrome mouse model (Ts65Dn) in standard and high-fat diet
Authors: Grabowicz Ilona E. , Fructuoso Marta , DeToma Ilario , Dierssen Mara , Wilczyński Bartek 
Published: November 2020

AbstractThe intestinal microbiota is known to affect its host in numerous ways and can be altered by many factors including the host genotype and high-calorie diets. Down syndrome (DS) is a genetic neurodevelopmental disorder caused by the total or partial triplication of chromosome 21. Recently, a human study reported microbiota differences between DS and euploid humans. To further explore the differences due to the genotype, we here investigated the microbiome of trisomic mice (Ts65Dn). In trisomic mice we found a significant enrichment in abundances of bacteria:Bacteroides ovatus, B. thetaiotaomicron, andAkkermansia muciniphila- the mucus-degrading and gut-health promoting species. Since diet composition has an effect on microbiota species, we studied the effect of a high-fat diet on the observed genotypic differences. Our study provides evidence that microbiomes of trisomic mice on the control diet present more inter-individual differences than WT mice. Moreover, we observed that the high-fat diet led to increased differences between individuals and this effect was even more pronounced in the trisomic than in WT mice. We validated the results obtained with widely used 16rRNA gene sequencing with the sequencing of the total RNA.HighlightsDown syndrome (DS) model mice faecal microbiomes are characterized by an overrepresentation ofBacteroides ovatus, Bacteroides thetaiotaomicron, andAkkermansia muciniphilaspecies.DS mice are characterized by higher heterogeneity of their microbiome communities than WT mice.High-fat diet leads to more diverse microbiome communities between mice, especially in trisomic genotype.
Secondary transmission of COVID-19 in preschool and school settings after their reopening in northern Italy: a population-based study
Authors: Larosa Elisabetta , Djuric Olivera , Cassinadri Mariateresa , Cilloni Silvia , Bisaccia Eufemia , Vicentini Massimo , Venturelli Francesco , Rossi Paolo Giorgi , Pezzotti Patrizio , Bedeschi Emanuela ,   the Reggio Emilia Covid-19 Working Group
Published: November 2020

AbstractBackgroundSchool closures was one of the main measures undertaken to reduce the number of social contacts during the first wave of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. We aimed to describe the data on secondary transmission of SARS-CoV-2 among students and teachers/personnel after the reopening of preschools and schools in Reggio Emilia, Italy.MethodsThis prospective population-based study included all consecutive cases leading to an investigation in 41 classes of 36 educational institutions (8 infant-toddler centres and preschools, 10 primary and 18 secondary schools) in the period September 1 – October 15, 2020, in Reggio Emilia province, Italy. We report the characteristics of the school, of the index case, including the possible source of infection, the number of contacts (students and teachers/personnel) that were identified and tested and the characteristics of secondary cases.ResultsIn the study period, 994 students and 204 teachers were tested during related investigations due to notification of 43 primary cases (38 among students and 5 among teachers). Of these, 10 students and two teachers created 39 secondary cases, resulting in an attack rate of 3.9%. There were no secondary cases among teachers/stuff. Secondary transmission occurred in one primary school and 8 secondary schools. Except for two students and one teacher, the possible source of infection for all index cases was identified as they had all had previous contact with a positive case; the majority of secondary cases did not report any previous close contact with a positive case. The clusters ranged from one to 22 secondary cases.ConclusionsTransmission at school occurred in a non-negligible number of cases, particularly in secondary schools. Prompt testing and isolation of classmates could probably reduce the risk of transmission in school settings.
SARS-CoV-2 epidemic after social and economic reopening in three US states reveals shifts in age structure and clinical characteristics
Authors: Wikle Nathan , Tran Thu Nguyen-Anh , Gentilesco Bethany , Leighow Scott M , Albert Joseph , Strong Emily R , Břinda Karel , Inam Haider , Yang Fuhan , Hossain Sajid , Chan Philip , Hanage William P , Messick Maria , Pritchard Justin R , Hanks Ephraim M , Boni Maciej F 
Published: November 2020

AbstractIn the United States, state-level re-openings in spring 2020 presented an opportunity for the resurgence of SARS-CoV-2 transmission. One important question during this time was whether human contact and mixing patterns could increase gradually without increasing viral transmission, the rationale being that new mixing patterns would likely be associated with improved distancing, masking, and hygiene practices. A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases. Here, we analyze age-structured case, hospitalization, and death time series from three states – Rhode Island, Massachusetts, and Pennsylvania – that had successful re-openings in May 2020 without summer waves of infection. Using a Bayesian inference framework on eleven daily data streams and flexible daily population contact parameters, we show that population-average mixing rates dropped by >50% during the lockdown period in March/April, and that the correlation between overall population mobility and transmission-capable mobility was broken in May as these states partially re-opened. We estimate the reporting rates (fraction of symptomatic cases reporting to health system) at 96.0% (RI), 72.1% (MA), and 75.5% (PA); in Rhode Island, when accounting for cases caught through general-population screening programs, the reporting rate estimate is 94.5%. We show that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Attack rate estimates through August 31 2020 are 6.4% (95% CI: 5.8% – 7.3%) of the total population infected for Rhode Island, 5.7% (95% CI: 5.0% – 6.8%) in Massachusetts, and 3.7% (95% CI: 3.1% – 4.5%) in Pennsylvania, with some validation available through published seroprevalence studies. Infection fatality rates (IFR) estimates for the spring epidemic are higher in our analysis (>2%) than previously reported values, likely resulting from the epidemics in these three states affecting the most vulnerable sub-populations, especially the most vulnerable of the ≥80 age group.