bluuebunny/crossref_metadata_2025_split huggingface dataset:
Can tracking mobility be used as a public health tool against COVID-19 following the expiration of stay-at-home mandates?
Authors: Her Peter , Saeed Sahar , Tram Khai Hoan , Bhatnagar Sahir R 
Published: August 2021

AbstractBackgroundConsidering the emergence of SARS-CoV-2 variants and low vaccine access and uptake, minimizing human interactions remains an effective strategy to mitigate the spread of SARS-CoV-2. The aim of this study was to create a novel multidimensional mobility index to capture the complexity of human interaction and evaluate its utility as an early indicator of surges in COVID-19 cases.MethodsWe used publicly available anonymous cell phone data compiled by SafeGraph, from all counties in Illinois, Ohio, Michigan and Indiana between January 1st to December 8, 2020. Six metrics of mobility were extracted for each county. Changes in mobility were defined as a time-updated 7-day rolling average. We used an unsupervised machine learning method known as functional principal component analysis (fPCA) to construct the latent mobility index (MI) using the six metrics of mobility. Associations between our MI and COVID-19 cases were estimated using a quasi-Poisson hierarchical generalized additive model adjusted for population density and the COVID-19 community vulnerability index.ResultsIndividual mobility metrics varied significantly by counties and by calendar time. More than 50% of the variability in the data was explained by the first principal component by each state, indicating good dimension reduction. Following the expiration of stay-at-home orders, mobility increased across all counties and this was particularly evident on weekends. While an individual metric of mobility was not associated with surges of COVID-19, our MI was independently associated with COVID-19 cases in all four states given varying time-lags.ConclusionFollowing the expiration of stay-at-home orders, a single metric of mobility was not sensitive enough to capture the complexity of human interactions. Monitoring mobility can be an important public health tool, however, it should be modelled as a multidimensional construct.
A machine learning approach for identification of gastrointestinal predictors for the risk of COVID-19 related hospitalization
Authors: Lipták Peter , Banovčin Peter , Rosoľanka Róbert , Prokopič Michal , Kocan Ivan , Žiačikova Ivana , Uhrík Peter , Grendár Marian , Hyrdel Rudolf 
Published: August 2021

AbstractBackground and aimCOVID-19 can be presented with various gastrointestinal symptoms. Shortly after the pandemic outbreak several machine learning algorithms have been implemented to assess new diagnostic and therapeutic methods for this disease. Aim of this study is to assess gastrointestinal and liver related predictive factors for SARS-CoV-2 associated risk of hospitalization.MethodsData collection was based on questionnaire from the COVID-19 outpatient test center and from the emergency department at the University hospital in combination with data from internal hospital information system and from the mobile application used for telemedicine follow-up of patients. For statistical analysis SARS-CoV-2 negative patients were considered as controls to three different SARS-CoV-2 positive patient groups (divided based on severity of the disease).ResultsTotal of 710 patients were enrolled in the study. Presence of diarrhea and nausea was significantly higher in emergency department group than in the COVID-19 outpatient test center. Among liver enzymes only aspartate transaminase (AST) has been significantly elevated in the hospitalized group compared to patients discharged home. Based on random forest algorithm, AST has been identified as the most important predictor followed by age or diabetes mellitus. Diarrhea and bloating have also predictive importance although much lower than AST.ConclusionSARS-CoV-2 positivity is connected with isolated AST elevation and the level is linked with the severity of the disease. Furthermore, using machine learning random forest algorithm, we have identified elevated AST as the most important predictor for COVID-19 related hospitalizations.
Implication of the cellular factor CTCF in the regulation of Bovine Leukemia Virus latency and tridimensional chromatin organization
Authors: Rodari Anthony , Bellefroid Maxime , Galais Mathilde , Krijger Peter H.L. , Nestola Lorena , Plant Estelle , Vos Erica S.M. , Van Driessche Benoit , Vanhulle Caroline , Ammar Amina Ait , Ciuffi Angela , de Laat Wouter , Van Lint Carine 
Published: August 2021

ABSTRACTBovine Leukemia Virus (BLV)-induced tumoral development is a multifactorial phenomenon which remains largely unelucidated. Here, we highlighted the critical role of the cellular CCCTC-binding factor (CTCF) both in the regulation of BLV transcriptional activities and in the deregulation of the tridimensional (3D) chromatin architecture surrounding the BLV integration site. We demonstrated the in vivo recruitment of CTCF to three conserved CTCF binding motifs along the BLV provirus. Next, we showed a critical role for CTCF in delimitating the epigenetic landscape along the BLV provirus as well as to repress the 5’Long Terminal Repeat (LTR) promoter activity, thereby contributing to viral latency, while favoring the 3’LTR promoter activity. Finally, we demonstrated that BLV integration deregulated host cellular 3D chromatin organization through the formation of abnormal viral/host chromatin loops. Altogether, our results highlight CTCF as a new critical effector of BLV transcriptional regulation and BLV-induced physiopathology.
Recurrent allopolyploidization events diversify eco-physiological traits in marsh orchids
Authors: Wolfe Thomas M. , Balao Francisco , Trucchi Emiliano , Bachmann Gert , Gu Wenjia , Baar Juliane , Hedrén Mikael , Weckwerth Wolfram , Leitch Andrew R. , Paun Ovidiu 
Published: August 2021

AbstractWhole-genome duplication, in particular allopolyploidy, has shaped the evolution of angiosperms and other organisms. Structural reorganization of chromosomes and repatterning of gene expression is frequently observed in early generation allopolyploids, with potential ecological consequences. The relative contributions of environmental and intrinsic drivers to these changes are unknown. We show here that in marsh orchids (Dactylorhiza, Orchidaceae), recurrently-formed allopolyploids are characterized by distinct eco-physiologies matching their respective environments, providing us with an excellent study system to address this question. Here we integrate eco-physiological and transcriptomic comparative studies to reveal a complex suite of intertwined, pronounced differences between sibling allopolyploids. We show thatDactylorhiza majalisthat is distributed in Central and Southern Europe favors meadows with mesic soils. Its sibling allopolyploidD. traunsteinerioccurs in fens, characterized by soils depleted by macro- and micronutrients, mainly in previously glaciated European areas. We further uncover distinct features in their nutrient transport, leaf elemental chemistry, light-harvesting, photoprotection, and stomata activity, that appear all linked to their distinct ecologies, in particular soil chemistry differences at their native sites. Recurrent polyploidization hence enriches biodiversity through eco-physiological diversification, providing the opportunity for sibling allopolyploids to evolve as distinct evolutionary units, despite pervasive interspecific gene flow.Significance StatementWhole-genome duplication resulting in polyploidy has underpinned the evolution of flowering plants and other organisms, and is important for many crops. However, the ecological implications of polyploidy remain little understood. Here, we demonstrate that two sibling allopolyploid marsh orchid species prefer distinct habitats, and have evolved a suite of distinctive ecophysiological characters (e.g. nutrient transport, energy harvesting and photoprotection). We argue that the divergence of these characters in the nascent polyploids drove adaptation into distinct ecological niches (low nutrient fens versus meadows with mesic soils), generating ecological barriers that maintains distinct, independent lineages, even in the presence of interspecific gene flow.
Barrier gesture relaxation during vaccination campaign in France: modelling impact of waning immunity
Authors: Vignals Carole , Dick David W. , Thiébaut Rodolphe , Wittkop Linda , Prague Mélanie , Heffernan Jane 
Published: August 2021

AbstractNon-pharmaceutical interventions have been implemented intermittently for more than a year in most countries of the world to mitigate COVID-19 epidemic. In France, while the vaccination campaign is progressing, the French government has decided to remove many public health restrictions such as business closure, lockdowns and curfews. Nonetheless, social distancing, mask wearing, and hand washing (also called barrier gestures) are still recommended. We utilize an age-structured compartmental SEIR model that takes into account SARS-CoV-2 waning immunity, vaccination, and increased transmissibility from variants of concern, to estimate if barrier gestures can be relaxed without causing a resurgence of severe infections. This model assumes that susceptibility to infection is a function of immunity status, which depends on initial infection severity and vaccination status. It is calibrated on confirmed COVID-19 cases from the French surveillance database, and accounts for changes in contact behaviors due to implementation of nation-wide public health policies. We study partial and full relaxation of barrier gestures occurring from August to December 2021 under various immunity duration assumptions. Maintaining application of barrier gestures appears essential to avoid a resurgence of severe infections that would exceed health care capacities, while surmounting vaccine hesitancy represents the key to consider their relaxation. Immunity duration assumptions significantly influence the short-term dynamic of the epidemic which should be considered for further modelling.
One-step Isolation of Protein C-terminal Peptides from V8 Protease-digested Proteins by Metal Oxide-based Ligand Exchange Chromatography
Authors: Nishida Hiroshi , Ishihama Yasushi 
Published: August 2021

ABSTRACTWe have developed a one-step method to isolate protein C-terminal peptides from V8 protease-digested proteins by metal oxide-based ligand-exchange (MOLEX) chromatography. V8 protease cleaves the C-terminal side of Asp and Glu, affording a digested peptide with two carboxy groups at the C-terminus, whereas the protein C-terminal peptide has only one α-carboxy group. In MOLEX chromatography, a stable chelate is formed between dicarboxylates and metal atoms, so that the non-terminal (i.e., internal) peptide is retained, whereas the protein C-terminal peptide flows through the MOLEX column. After optimization of the MOLEX chromatographic conditions, 1619 protein C-termini were identified from 30 μg of peptides (10 μg each, in triplicate) derived from human HeLa cells by means of nanoLC/MS/MS. When the MOLEX-isolated sample from 200 µg of HeLa peptides was further divided into six fractions by high-pH reversed-phase LC prior to nanoLC/MS/MS, 2202 protein C-termini were identified with less than 3% contamination with internal peptides. We believe this is the largest coverage with the highest purity reported to date in human protein C-terminomics. This fast, simple, sensitive and selective method to isolate protein C-terminal peptides should be useful for profiling protein C-termini on a proteome-wide scale.
Differential Binding Kinetics for Evaluating Immune Checkpoint Inhibitors in Serum
Authors: Yao Danfeng , Yu Heng , Kantor Aaron B. , Osterfeld Sebastian J. , Saito Toshiro , Carbonell M. Luis , Choudhury Kalidip , Wang Shan X. 
Published: August 2021

AbstractThe ability to characterize the binding kinetics of drug-target interactions in a biologically relevant matrix, such as serum or plasma, remains a fundamental challenge in drug discovery. We apply a novel label-based giant magnetoresistance (GMR) biosensor platform to measure protein binding kinetics and affinities of drug-target pairs in buffer and different levels of serum. Specifically, we evaluate three well-established immune checkpoint inhibitors, pembrolizumab, nivolumab and atezolizumab and compare the results with label-free kinetic platforms: surface plasmon resonance (SPR) and bio-layer interferometry (BLI). Labeling of analytes does not affect their association and dissociation rates (on and off rates) from GMR biosensors which enables kinetic measurements in biologically relevant matrices. Only the GMR biossensors is consistently suitable for measuring binding kinetics in up to 80% serum. The faster and different off-rates of the three immune checkpoint inhibitors in the presence of serum should be considered when modeling their pharmacological performance.TeaserWe reveal the effects of serum on binding kinetics of antibody drugs, relevant to the pharmacological performance of immunotherapeutic.
Degradation intermediates as an indicator of mRNA and cell metabolism
Authors: Liu Yusheng , Zhang Yiwei , Nie Hu , Lu Falong , Wang Jiaqiang 
Published: August 2021

AbstractTraditional mRNA degradation rate measurements involves complex experimental design with RNA labeling or transcription blocking together with sampling at multiple timepoints1,2. These experimental requirements limit the application of transcriptome-wide mRNA degradation rate analysis mainly in cultured cells, but rarely in in vivo samples. Therefore, a direct and simple strategy needs to be developed to study mRNA degradation rate. Here, we defined mRNA degradation intermediates as transcripts where decay is about to occur or has partially occurred in the 3’-untranslated regions after poly(A) tail deadenylation, and found that the proportion of mRNA degradation intermediates is a very simple and convenient indicator for evaluating the degradation rate of mRNA in mouse and human cell lines. In addition, we showed that a higher proportion of mRNA degradation intermediates is correlated with faster cell cycle and higher turnover rate of mouse tissues. Further, we validated that in mouse maturing oocytes where transcription is silent3,4, the proportion of mRNA degradation intermediates is positively correlated with the mRNA degradation rate. Together, these results demonstrate that degradation intermediates can function as a good indicator of mRNA, cell, and tissue metabolism, and can be easily assayed by total RNA 3’-end sequencing from a single bulk cell sample without the need for drug treatment or multi-timepoint sampling. This finding is of great potential for studies on mRNA degradation rate at the molecular, cellular, or organic level, including samples or systems that cannot be assayed with previous methods. In addition, further application of the findings into single cells will likely greatly aid the identification and study of rare cells with unique cellular metabolism dynamics such as tissue stem cells and tumor cells.
Local biodiversity change reflects interactions among changing abundance, evenness and richness
Authors: Blowes Shane A. , Daskalova Gergana N. , Dornelas Maria , Engel Thore , Gotelli Nicholas J. , Magurran Anne E. , Martins Inês S. , McGill Brian , McGlinn Daniel J. , Sagouis Alban , Shimadzu Hideyasu , Supp Sarah R. , Chase Jonathan M. 
Published: August 2021

AbstractBiodiversity metrics often integrate data on the presence and abundance of multiple species. Yet understanding covariation of changes to the numbers of individuals, the evenness of species’ relative abundances, and the total number of species remains limited. Using individual-based rarefaction curves, we introduce a conceptual framework to understand how expected positive relationships among changes in abundance, evenness and richness arise, and how they can break down. We then examined interdependencies between changes in abundance, evenness and richness in more than 1100 assemblages sampled either through time or across space. As predicted, richness changes were greatest when abundance and evenness changed in the same direction, and countervailing changes in abundance and evenness acted to constrain the magnitude of changes in species richness. Site-to-site changes in abundance, evenness, and richness were often decoupled, and pairwise relationships between changes in these components across assemblages were weak. In contrast, changes in species richness and relative abundance were strongly correlated for assemblages varying through time. Temporal changes in local biodiversity showed greater inertia and stronger relationships between the component changes when compared to site-to-site variation. Local variation in assemblage diversity was rarely due to a passive sample from a more or less static species abundance distribution. Instead, changing species relative abundances often dominated local variation in diversity. Moreover, how changing relative abundances combined with changes to total abundance frequently determined the magnitude of richness changes. Embracing the interdependencies between changing abundance, evenness and richness can provide new information for better understanding biodiversity change in the Anthropocene.
Harnessing Matrix Completion to Unify and Extend Viral Serology Studies
Authors: Einav Tal , Cleary Brian 
Published: August 2021

SummaryThe development of new vaccine strategies, as well as our understanding of key processes that shape viral evolution and host antibody repertoires, rely on measuring multiple antibody responses against large panels of viruses. Given the enormous diversity of circulating virus strains and antibody responses, exhaustive testing of all antibody-virus interactions is infeasible. Even within individual studies with limited panels, exhaustive testing is not always done, and there is no common framework for combining information across studies with partially overlapping panels, especially when the assay type or host species differ. Results from prior studies have demonstrated that virus-antibody interactions can be characterized in a vastly simpler and lower-dimensional space, suggesting that relatively few measurements could accurately predict unmeasured antibody-virus interactions. Here, we apply matrix completion to several of the largest-scale studies for both influenza and HIV-1. We explore how prediction accuracy evolves as the number of available measurements changes and approximate the number of additional measurements necessary in several highly incomplete datasets (suggesting ∼250,000 measurements could be saved). In addition, we show how the method can combine disparate datasets, even when the number of available measurements is below the theoretical limit that guarantees successful prediction. This approach can be readily generalized to other viruses or more broadly to other low-dimensional biological datasets.SignificanceOne of the central problems in immunology is to characterize how our vast array of antibodies inhibits the diverse pathogens we encounter in our lives. In this work, we apply a well-studied mathematical technique called matrix completion that leverages patterns in partially-observed antibody-virus inhibition data to infer unmeasured interactions. We predict the results of tens of thousands of missing experiments in influenza and HIV-1 studies and quantify the expected error of our estimates. By harnessing matrix completion, future experiments could be designed that only collect a fraction of measurements, saving time and resources while maximizing the information gained.
Proteomic phenotyping of stimulated Müller cells uncovers profound pro-inflammatory signaling and antigen-presenting capacity
Authors: Schmalen Adrian , Lorenz Lea , Grosche Antje , Pauly Diana , Deeg Cornelia A. , Hauck Stefanie M. 
Published: August 2021

AbstractMüller cells are the main macroglial cells of the retina exerting a wealth of functions to maintain retinal homoeostasis. Upon pathological changes in the retina, they become gliotic with both protective and detrimental consequences. Accumulating data also provide evidence for a pivotal role of Müller cells in the pathogenesis of diabetic retinopathy (DR). While microglial cells, the resident immune cells of the retina are considered as main players in inflammatory processes associated with DR, the implication of activated Müller cells in chronic retinal inflammation remains to be elucidated. In order to assess the signaling capacity of Müller cells and their role in retinal inflammation, we performed in-depth proteomic analysis of Müller cell proteomes and secretomes after stimulation with INFγ, TNFα, IL-4, IL-6, IL-10, VEGF, TGFβ1, TGFβ2 and TGFβ3. We used both, primary porcine Müller cells and the human Müller cell line MIO-M1 for our hypothesis generating approach. Our results point towards an intense signaling capacity of Müller cells, which reacted in a highly discriminating manner upon treatment with different cytokines. Stimulation of Müller cells resulted in a primarily pro-inflammatory phenotype with secretion of cytokines and components of the complement system. Furthermore, we observed evidence for mitochondrial dysfunction, implying oxidative stress after treatment with the various cytokines. Finally, both MIO-M1 cells and primary porcine Müller cells showed several characteristics of atypical antigen-presenting cells, as they are capable of inducing MHC class I and MHC class II with co-stimulatory molecules. In line with this, they express proteins associated with formation and maturation of phagosomes. Thus, our findings underline the importance of Müller cell signaling in the inflamed retina, indicating an active role in chronic retinal inflammation underlying the pathogenesis of diabetic retinopathy.
Structure modeling hints at a granular organization of the Vertebrate Golgi ribbon
Authors: Page Karen M. , McCormack Jessica J. , Lopes-da-Silva Mafalda , Patella Francesca , Harrison-Lavoie Kimberly , Burden Jemima J. , Quah Ying-Yi Bernadette , Scaglioni Dominic , Ferraro Francesco , Cutler Daniel F. 
Published: August 2021

AbstractVertebrate cells display a specific Golgi apparatus architecture, known as the “ribbon”, where the functional subunits, the mini-stacks, are linked into a tridimensional network. The importance of the ribbon architecture is underscored by evidence of its disruption in a host of diseases, but just how it relates to the biological Golgi functions remains unclear. Are all the connections between mini-stacks functionally equal? Is the local structure of the ribbon of functional importance? These are difficult questions to address, due to the lack of a secretory cargo providing a quantifiable readout of the functional output of ribbon-embedded mini-stacks. Endothelial cells produce rod-shaped secretory granules, the Weibel-Palade bodies (WPB), whose von Willebrand Factor (VWF) cargo is central to hemostasis. In these cells, the Golgi apparatus exerts a dual control on WPB size at both mini-stack and ribbon levels. Mini-stack dimensions delimit the size of VWF ‘boluses” while the ribbon architecture allows their linear co-packaging at the trans-Golgi network generating WPBs of different lengths. This Golgi/WPB size relationship lends itself to mathematical analysis. Here, different ribbon structures were modeled and their predicted effects on WPB size distribution compared to the ground truth of experimental data. Strikingly, the best-fitting model describes a Golgi ribbon made by linked subunits corresponding to differentially functioning monomer and dimer ministacks. These results raise the intriguing possibility that the fine-grained structure of the Golgi ribbon is more complex than previously thought.