bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of heat-not-burn tobacco products, moderate alcohol drinking, and anti-SARS-CoV-2 IgG antibody titers after BNT162b2 vaccination among Japanese healthcare workers
Authors: Yamamoto Shohei , Tanaka Akihito , Ohmagari Norio , Yamaguchi Koushi , Ishitsuka Kazue , Morisaki Naho , Kojima Masayo , Nishikimi Akihiko , Tokuda Haruhiko , Inoue Manami , Tanaka Shiori , Umezawa Jun , Okubo Ryo , Nishimura Kunihiro , Konishi Maki , Miyo Kengo , Mizoue Tetsuya 
Published: December 2021

AbstractBackgroundThe effect of heat-not-burn (HNB) tobacco product use and moderate alcohol drinking on immunogenicity to coronavirus disease (COVID-19) vaccines remain elusive. This study aimed to examine the association of tobacco product use and alcohol consumption with anti-SARS-CoV-2 spike IgG antibody titers after the BNT162b2 vaccine.MethodsParticipants were 3,457 fully vaccinated healthcare workers in the 4 national centers for advanced medical and research in Japan. Smoking status and alcohol consumption were assessed via a questionnaire, and anti-SARS-CoV-2 spike IgG titers were measured by chemiluminescent enzyme immunoassay using serum collected on the median of 64 days after the second vaccination. Multilevel linear regression models were used to estimate the geometric mean titers (GMT) and the ratios of means (RoM) between groups.ResultsOf vaccinated participants, 99.5% (3,440/3,457) were seropositive. Compared with never-smokers (GMT=119), IgG antibody titers were significantly lower among HNB tobacco users (including those who also smoked cigarettes) (GMT=105; RoM=0.88 [95%CI: 0.78–0.99]) and exclusive cigarettes smokers (GMT=96; RoM=0.81 [95%CI: 0.71–0.92]). Compared with non-drinkers of alcohol (GMT=123), alcohol drinkers consuming <1 go/day (GMT=114; RoM=0.93 [95%CI: 0.88–0.98]), 1–1.9 go/day (GMT=105; RoM=0.85 [95%CI: 0.79–0.93]), and ≥2 go/day (GMT=101; RoM=0.82 [95%CI: 0.72–0.94]) had significantly lower antibody titers (P for trend<0.01). Spline analysis showed a large reduction of antibody until around 1 go/day of alcohol consumption, and then they gradually decreased.ConclusionsResults suggest that in addition to conventional cigarette smoking and heavy alcohol drinking, use of HNB tobacco products and moderate alcohol drinking may be predictors of lower immunological response to COVID-19 vaccine.Key MessagesEpidemiological evidence regarding the association of smoking status and alcohol drinking with COVID-19 vaccine-induced antibody levels is scarce.Users of heat-not-burn (HNB) tobacco products, as well as cigarettes smokers, had lower antibody titers than never-smokers.Not only high-dose but moderate-dose alcohol intake was also associated with decreased vaccine-induced antibody levels.HNB tobacco product use and moderate alcohol drinking may be modifiers of COVID-19 vaccine-induced immunogenicity.
A potent human monoclonal antibody with pan-neutralizing activities directly dislocates S trimer of SARS-CoV-2 through binding both up and down forms of RBD
Authors: Wang Xiaofei , Hu Ao , Chen Xiangyu , Zhang Yixin , Yu Fei , Yue Shuai , Li Arong , Zhang Junsong , Pan Zhiwei , Yang Yang , Lin Yao , Gao Leiqiong , Zhou Jing , Zhao Jing , Li Fang , Shi Yaling , Huang Feng , Yang Xiaofan , Peng Yi , Tu Luoyang , Zhang Huan , Zheng Huanying , He Jun , Zhang Hui , Xu Lifan , Huang QizhAo , Zhu Yongqun , Deng Kai , Ye Lilin 
Published: December 2021

AbstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of novel corona virus disease (COVID-19). The neutralizing monoclonal antibodies (mAbs) targeting the receptor binding domain (RBD) of SARS-CoV-2 are among the most promising strategies to prevent and treat COVID-19. However, SARS-CoV-2 variants of concern (VOCs) profoundly reduced the efficacies of most of mAbs and vaccines approved for clinical use. Herein, we demonstrated mAb 35B5 efficiently neutralizes both wild-type (WT) SARS-CoV-2 and VOCs, including B.1.617.2 (delta) variant, in vitro and in vivo. Cryo-electron microscopy (cryo-EM) revealed that 35B5 neutralizes SARS-CoV-2 by targeting a unique epitope that avoids the prevailing mutation sites on RBD identified in circulating VOCs, providing the molecular basis for its pan-neutralizing efficacy. The 35B5-binding epitope could also be exploited for the rational design of a universal SARS-CoV-2 vaccine.
Graph Transformer for drug response prediction
Authors: Chu Thang , Nguyen Tuan 
Published: December 2021

AbstractBackgroundPrevious models have shown that learning drug features from their graph representation is more efficient than learning from their strings or numeric representations. Furthermore, integrating multi-omics data of cell lines increases the performance of drug response prediction. However, these models showed drawbacks in extracting drug features from graph representation and incorporating redundancy information from multi-omics data. This paper proposes a deep learning model, GraTransDRP, to better drug representation and reduce information redundancy. First, the Graph transformer was utilized to extract the drug representation more efficiently. Next, Convolutional neural networks were used to learn the mutation, meth, and transcriptomics features. However, the dimension of transcriptomics features is up to 17737. Therefore, KernelPCA was applied to transcriptomics features to reduce the dimension and transform them into a dense presentation before putting them through the CNN model. Finally, drug and omics features were combined to predict a response value by a fully connected network. Experimental results show that our model outperforms some state-of-the-art methods, including GraphDRP, GraOmicDRP.Availability of data and materialshttps://github.com/chuducthang77/GraTransDRP.
HiC-GNN: A Generalizable Model for 3D Chromosome Reconstruction Using Graph Convolutional Neural Networks
Authors: Van Hovenga  , Oluwadare Oluwatosin , Kalita Jugal 
Published: December 2021

AbstractChromosome conformation capture (3C) is a method of measuring chromosome topology in terms of loci interaction. The Hi-C method is a derivative of 3C that allows for genome wide quantification of chromosome interaction. From such interaction data, it is possible to infer the three-dimensional (3D) structure of the underlying chromosome. In this paper, we use a node embedding algorithm and a graph neural network to predict the 3D coordinates of each genomic loci from the corresponding Hi-C contact data. Unlike other chromosome structure prediction methods, our method can generalize a single model across Hi-C resolutions, multiple restriction enzymes, and multiple cell populations while maintaining reconstruction accuracy. We derive these results using three separate Hi-C data sets from the GM12878, GM06990, and K562 cell lines. We also compare the reconstruction accuracy of our method to four other existing methods and show that our method yields superior performance. Our algorithm outperforms the state-of-the-art methods in the accuracy of prediction and introduces a novel method for 3D structure prediction from Hi-C data.Author SummaryWe developed a novel method, HiC-GNN, for predicting the three-dimensional structures of chromosomes from Hi-C data. HiC-GNN is unique from other methods for chromosome structure prediction in that it learns in an eager setting rather than a lazy setting. Thus, the models learned by HiC-GNN can be generalized to unseen data. To the authors’ knowledge, this generalizing capability is not present in any existing methods. We show that this generalization is robust to input resolution, restriction enzyme, and contact sparsity. We also show that our method outperforms existing methods using both generalized and non-generalized models. Moreover, we also show that our method is more robust to contact variance than the compared methods.AvailabilityAll our source codes and data are available at https://github.com/OluwadareLab/HiC-GNN, and is made available as a containerized application that can be run on any platform.
Integration of Diverse Transcriptomics Datasets using Random Forest to Predict Universal Functional Pathways in Tfr Cells
Authors: Diallo Alos , Cavazzoni Cecilia B , Sun Jiaoyuan E , Sage Peter T 
Published: December 2021

AbstractMotivationT follicular regulatory (Tfr) cells are a specialized cell subset that controls humoral immunity. Despite a number of individual transcriptomic studies on these cells, core functional pathways have been difficult to uncover due to the substantial transcriptional overlap of these cells with other effector cell types, as well as transcriptional changes occurring due to disease settings. Developing a core transcriptional module for Tfr cells that integrates multiple cell type comparisons as well as diverse disease settings will allow a more accurate prediction of functional pathways. Researchers studying allergic reactions, immune responses to vaccines, autoimmunity and cancer could use this gene set to better understand the roles of Tfr cells in controlling disease progression.Additional cell types beyond Tfr cells that have similar features of transcriptomic complexity within diverse disease settings may also be studied using similar approaches. High-throughput sequencing technologies allow the generation of large datasets that require specific tools to best interpret the data. The development of a core transcriptional module for Tfr cells will allow investigators to determine if Tfr cells may have functional roles within their biological systems with little knowledge of Tfr biology. With this work, we have addressed the need of core gene modules to define specific subsets of immune cells.ResultsWe introduce an integrated “core Tfr cell gene module” that can be incorporated into GSEA analysis using various input sizes. The integrated core Tfr gene module was built using transcriptomic studies in Tfr cells from several different tissues, disease settings, and cell type comparisons. Random forest was used to integrate the transcriptomic studies to generate the core gene module. A GSEA gene set was formulated from the integrated core Tfr gene module for incorporation into end-user friendly GSEA. The gene sets are presented along with random genes taken from the GTEX data set and are presented as GMT files. The user can upload the gene set to the GSEA website or any gene set tool which takes GMT files. We also present the full results of the model including p-values calculated by random forest. This provides users with more flexibility in choosing a p-value cutoff that is most appropriate for the experimental setting.AvailabilityThe core Tfr gene sets are freely available at:https://github.com/alosdiallo/TFR_Model. We have also included all of the code and data used in developing these gene sets. The code and results are released under an MIT license.Supplementary informationSupplementary data are available at Bioinformatics online.
Density-by-diet interactions during larval development shape adult life-history trait expression and fitness in a polyphagous fly
Authors: Morimoto Juliano , Than Anh The , Nguyen Binh , Lundbäck Ida , Dinh Hue , Ponton Fleur 
Published: December 2021

AbstractHabitat quality early in life determines individual fitness, with possible long-term evolutionary effects on groups and populations. In holometabolous insects, larval ecology plays a major role in determining the expression of traits in adulthood, but how ecological conditions during larval stage interact to shape adult life-history and fitness, particularly in non-model organisms, remains subject to scrutiny. Consequently, our knowledge of the interactive effects of ecological factors on insect development is limited. Here, using the polyphagous fly Bactrocera tryoni, we conducted a fully-factorial design where we manipulated larval density and larval diet (protein-rich, standard, and sugar-rich) to gain insights into how these ecological factors interact to modulate adult fitness. As expected, a protein-rich diet resulted in faster larval development, heavier and leaner adults that were more fecund compared with standard and sugar-rich diets, irrespective of larval density. Females from the protein-rich larval diet had overall higher reproductive rate (i.e., eggs per day) than females from other diets, and reproductive rate decreased linearly with density for females from the protein-rich but non-linearly for females from the standard and sugar-rich diets over time. Surprisingly, adult lipid reserve increased with larval density for adults from the sugar-rich diet (as opposed to decreasing, as in other diets), possibly due to a stress-response to an extremely adverse condition during development (i.e., high intraspecific competition and poor nutrition). Together, our results provide insights into how ecological factors early in life interact and shape the fate of individuals through life-stages in holometabolous insects.
Fractional Laser Treatment Boosts Systemic Anti-Tumor Immunity in Combination with OX40 Agonist and PD-1 Blockade
Authors: Kawakubo Masayoshi , Glahn Joshua , Tao Zhipeng , Demehri Shadmehr , Manstein Dieter 
Published: December 2021

AbstractWhile ablative fractional photothermolysis (aFP) with a 10,600 nm CO2laser is employed for a wide variety of dermatologic conditions, its applications in oncology are relatively unexplored. Building off our previous work, we investigated the effect of unilateral aFP treatment in combination with anti-PD-1 blocking antibody and OX40 agonist on bilateral tumor growth and remission. A CT26 wild type (CT26WT) colon carcinoma cell line was established bilaterally on the hind flanks of a standardized mouse model and tumor characteristics were investigated on aFP treated and untreated sides. Remarkably, triple therapy with fractional CO2laser in combination with anti-PD-1 antibodies and OX40 agonists resulted in significantly slower tumor growth and complete remissions on bilateral tumors. Flow cytometric analysis showed the triple treatments elicited an increase of granzyme B+ CD8+T cells due to synergistic effect of aFP treatment and the checkpoint molecules, including the induction of CD103+ CCR7+ dendritic cells (DCs) in aFP-treated tumor by aFP treatment, XCR1+ DCs in drainage lymph node by anti-PD-1 inhibitor and OX40+ Ki67+ CD8+ T cells in the lymph node by OX40 agonist. Triple therapy-mediated tumor regression and survival was abrogated upon CD8+ T cell depletion. Importantly, when two mismatched cancer cells were implanted into mice, the effect of the triple therapy on distant tumor was abrogated, showing antigen specificity of the T cell immunity induced by triple therapy. This study highlights the efficacy of aFP a novel adjuvant for current cancer immunotherapeutics.
Site-specificity of streptococci in the oral microbiome
Authors: McLean Anthony R. , Torres-Morales Julian , Borisy Gary G. , Mark Welch Jessica L. 
Published: December 2021

AbstractPatterns of microbial distribution are determined by as-yet poorly understood rules governing where microbes can grow and thrive. Therefore, a detailed understanding of where bacteria localize is necessary to advance microbial ecology and microbiome-based therapeutics. The site-specialist hypothesis predicts that most microbes in the human oral cavity have a primary habitat within the mouth where they are most abundant. We asked whether this hypothesis accurately describes the distribution of the members of the genus Streptococcus, a clinically relevant taxon that dominates most oral sites. Prior analysis of 16S rRNA gene sequencing data indicated that some oral Streptococcus clades are site-specialists while others may be generalists. However, within complex microbial populations composed of numerous closely-related species and strains, such as the oral streptococci, genome-scale analysis is necessary to provide the resolution to discriminate closely related taxa with distinct functional roles. Here we assess whether individual species within this genus are generalists using publicly available genomic sequence data that provides species-level resolution. We chose a set of high-quality representative genomes for Streptococcus species from the human oral microbiome. Onto these genomes, we mapped short-read metagenomic sequences from supragingival plaque, tongue dorsum, and other sites in the oral cavity. We found that every reliably detectable Streptococcus species in the human oral cavity was a site-specialist and that even closely related species such as S. mitis, S. oralis, and S. infantis specialized in different sites. These findings indicate that closely related bacteria can have distinct habitat distributions in the absence of dispersal limitation and under similar environmental conditions and immune regimes. These three species also share substantially the same species-specific core genes indicating that neither taxonomy nor gene content are clear predictors of site-specialization. Site-specificity may instead be influenced by subtle characteristics such as nucleotide-level divergences within conserved genes.
How do spike collisions affect spike sorting performance?
Authors: Garcia Samuel , Buccino Alessio P. , Yger Pierre 
Published: December 2021

AbstractRecently, a new generation of devices have been developed to record neural activity simultaneously from hundreds of electrodes with a very high spatial density, both for in vitro and in vivo applications. While these advances enable to record from many more cells, they also dramatically increase the amount overlapping “synchronous” spikes (colliding in space and/or in time), challenging the already complicated process of spike sorting (i.e. extracting isolated single-neuron activity from extracellular signals). In this work, we used synthetic ground-truth recordings to quantitatively benchmark the performance of state-of-the-art spike sorters focusing specifically on spike collisions. Our results show that while modern template-matching based algorithms are more accurate than density-based approaches, all methods, to some extent, failed to detect synchronous spike events of neurons with similar extracellular signals. Interestingly, the performance of the sorters is not largely affected by the the spiking activity in the recordings, with respect to average firing rates and spike-train correlation levels.
Can we use anti-predator behavior theory to predict wildlife responses to high-speed vehicles?
Authors: Lunn Ryan B. , Blackwel Bradley F. , DeVault Travis L. , Fernández-Juricic Esteban 
Published: December 2021

AbstractAnimals seem to rely on antipredator behavior to avoid vehicle collisions. There is an extensive body of antipredator behavior theory that have been used to predict the distance/time animals should escape from predators. These models have also been used to guide empirical research on escape behavior from vehicles. However, little is known as to whether antipredator behavior models are appropriate to apply to an approaching high-speed vehicle. We addressed this gap by (a) providing an overview of the main hypothesis and predictions of different antipredator behavior models via a literature review, (b) exploring whether these models can generatequantitativepredictions on escape distance when parameterized with empirical data from the literature, and (c) evaluating their sensitivity to vehicle approach speed via a simulation approach where we assessed model performance based on changes in effect size with variations in the slope of the flight initiation distance (FID) vs. approach speed relationship. We used literature on birds for goals (b) and (c). We considered the following eight models: the economic escape model, Blumstein’s economic escape model, the optimal escape model, the perceptual limit hypothesis, the visual cue model, the flush early and avoid the rush (FEAR) hypothesis, the looming stimulus hypothesis, and the Bayesian model of escape behavior. We were able to generatequantitativepredictions about escape distances with the last five models. However, we were only able to assess sensitivity to vehicle approach speed for the last three models. The FEAR hypothesis is most sensitive to high-speed vehicles when the species follows the spatial (FID remains constant as speed increases) and the temporal margin of safety (FID increases with an increase in speed) rules of escape. The looming stimulus effect hypothesis reached small to intermediate levels of sensitivity to high-speed vehicles when a species follows the delayed margin of safety (FID decreases with an increase in speed). The Bayesian optimal escape model reached intermediate levels of sensitivity to approach speed across all escape rules (spatial, temporal, delayed margins of safety) but only for larger (> 1 kg) species, but was not sensitive to speed for smaller species. Overall, no single antipredator behavior model could characterize all different types of escape responses relative to vehicle approach speed but some models showed some levels of sensitivity for certain rules of escape. We derive some applied applications of our finding by suggesting the estimation of critical vehicle approach speeds for managing populations that are especially susceptible to road mortality. Overall, we recommend that new escape behavior models specifically tailored to high- speeds vehicles should be developed to better predict quantitatively the responses of animals to an increase in the frequency of cars, airplanes, drones, etc. they will be facing in the next decade.
Panzootic chytrid fungus exploits diverse amphibian host environments through plastic infection strategies
Authors: Torres-Sánchez María , Villate Jennifer , McGrath-Blaser Sarah , Longo Ana V. 
Published: December 2021

AbstractWhile many pathogens are limited to a single host, others can jump from host to host, which likely contributes to the emergence of infectious diseases. Despite this threat to biodiversity, traits associated with overcoming eco-evolutionary barriers to achieve host niche expansions are not well understood. Here, we examined the case of Batrachochytrium dendrobatidis (Bd), a multi-host pathogen that infects the skin of hundreds of amphibian species worldwide. To uncover functional machinery driving multi-host invasion, we analyzed Bd transcriptomic landscapes across 14 amphibian hosts and inferred the origin and evolutionary history of pathogenic genes under a phylogenetic framework comprising 12 other early-divergent zoosporic fungi. Our results not only revealed a conserved basal genetic machinery, but also highlighted the ability of Bd to display plastic infection strategies when challenged under suboptimal host environments. We found that genes related to amphibian skin exploitation have arisen mainly via gene duplications. We argue that plastic gene expression can drive variation in Bd lifecycles with different mode and tempo of development. Our findings support the idea that host skin environments exert contrasting selective pressures, such that gene expression plasticity constitutes one of the evolutionary keys leading to the success of this panzootic multi-host pathogen.