bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automatic whole cell organelle segmentation in volumetric electron microscopy
Authors: Heinrich Larissa , Bennett Davis , Ackerman David , Park Woohyun , Bogovic John , Eckstein Nils , Petruncio Alyson , Clements Jody , Xu C. Shan , Funke Jan , Korff Wyatt , Hess Harald F. , Lippincott-Schwartz Jennifer , Saalfeld Stephan , Weigel Aubrey V. ,   COSEM Project Team
Published: November 2020

Cells contain hundreds of different organelle and macromolecular assemblies intricately organized relative to each other to meet any cellular demands. Obtaining a complete understanding of their organization is challenging and requires nanometer-level, threedimensional reconstruction of whole cells. Even then, the immense size of datasets and large number of structures to be characterized requires generalizable, automatic methods. To meet this challenge, we developed an analysis pipeline for comprehensively reconstructing and analyzing the cellular organelles in entire cells imaged by focused ion beam scanning electron microscopy (FIB-SEM) at a near-isotropic size of 4 or 8 nm per voxel. The pipeline involved deep learning architectures trained on diverse samples for automatic reconstruction of 35 different cellular organelle classes - ranging from endoplasmic reticulum to microtubules to ribosomes - from multiple cell types.Automatic reconstructions were used to directly quantify various previously inaccessible metrics about these structures, including their spatial interactions. We show that automatic organelle reconstructions can also be used to automatically register light and electron microscopy images for correlative studies. We created an open data and open source web repository, OpenOrganelle, to share the data, computer code, and trained models, enabling scientists everywhere to query and further reconstruct the datasets.
Identification of an endocannabinoid gut-brain vagal mechanism controlling food reward and energy homeostasis
Authors: Berland Chloé , Castel Julien , Terrasi Romano , Montalban Enrica , Foppen Ewout , Martin Claire , Muccioli Giulio G. , Luquet Serge , Gangarossa Giuseppe 
Published: November 2020

AbstractThe regulation of food intake, asine qua nonrequirement for survival, thoroughly shapes feeding and energy balance by integrating both homeostatic and hedonic values of food. Unfortunately, the widespread access to palatable food has led to the development of feeding habits that are independent from metabolic needs. Among these, binge eating (BE) is characterized by uncontrolled voracious eating. While reward deficit seems to be a major contributor of BE, the physiological and molecular underpinnings of BE establishment remain elusive. Here, we combined a physiologically relevant BE mouse model with multiscalein vivoapproaches to explore the functional connection between the gut-brain axis and the reward and homeostatic brain structures.Our results show that BE elicits compensatory adaptations requiring the gut-to-brain axis which, through the vagus nerve, relies on the permissive actions of peripheral endocannabinoids (eCBs) signaling. Selective inhibition of peripheral CB1 receptors resulted in a vagus-dependent increased hypothalamic activity, modified metabolic efficiency, and dampened activity of mesolimbic dopamine circuit, altogether leading to the suppression of palatable eating. We provide compelling evidence for a yet unappreciated physiological integrative mechanism by which variations of peripheral eCBs control the activity of the vagus nerve, thereby in turn gating the additive responses of both homeostatic and hedonic brain circuits which govern homeostatic and reward-driven feeding.In conclusion, we reveal that vagus-mediated eCBs/CB1R functions represent an interesting and innovative target to modulate energy balance and counteract food-reward disorders.
Novel gene-specific translation mechanism of dysregulated, chronic inflammation reveals promising, multifaceted COVID-19 therapeutics
Authors: Wang Li , Muneer Adil , Xie Ling , Zhang Feng , Wu Bing , Mei Liu , Lenarcic Erik M , Feng Emerald Hillary , Song Juan , Xiong Yan , Yu Xufen , Wang Charles , Gheorghe Ciprian , Torralba Karina , Cook Jeanette Gowen , Wan Yisong Y. , Moorman Nathaniel John , Song Hongjun , Jin Jian , Chen Xian 
Published: November 2020

AbstractHyperinflammation and lymphopenia provoked by SARS-CoV-2-activated macrophages contribute to the high mortality of Coronavirus Disease 2019 (COVID-19) patients. Thus, defining host pathways aberrantly activated in patient macrophages is critical for developing effective therapeutics. We discovered that G9a, a histone methyltransferase that is overexpressed in COVID-19 patients with high viral load, activates translation of specific genes that induce hyperinflammation and impairment of T cell function or lymphopenia. This noncanonical, pro-translation activity of G9a contrasts with its canonical epigenetic function. In endotoxin-tolerant (ET) macrophages that mimic conditions which render patients with pre-existing chronic inflammatory diseases vulnerable to severe symptoms, our chemoproteomic approach with a biotinylated inhibitor of G9a identified multiple G9a-associated translation regulatory pathways that were upregulated by SARS-CoV-2 infection. Further, quantitative translatome analysis of ET macrophages treated progressively with the G9a inhibitor profiled G9a-translated proteins that unite the networks associated with viral replication and the SARS-CoV-2-induced host response in severe patients. Accordingly, inhibition of G9a-associated pathways produced multifaceted, systematic effects, namely, restoration of T cell function, mitigation of hyperinflammation, and suppression of viral replication. Importantly, as a host-directed mechanism, this G9a-targeted, combined therapeutics is refractory to emerging antiviral-resistant mutants of SARS-CoV-2, or any virus, that hijacks host responses.
DeepMosaic: Control-independent mosaic single nucleotide variant detection using deep convolutional neural networks
Authors: Yang Xiaoxu , Xu Xin , Breuss Martin W. , Antaki Danny , Ball Laurel L. , Chung Changuk , Li Chen , George Renee D. , Wang Yifan , Bae Taejeoing , Abyzov Alexej , Wei Liping , Sebat Jonathan ,   NIMH Brain Somatic Mosaicism Network, Gleeson Joseph G. 
Published: November 2020

Introductory paragraphMosaic variants (MVs) reflect mutagenic processes during embryonic development1and environmental exposure2, accumulate with aging, and underlie diseases such as cancer and autism3. The detection of MVs has been computationally challenging due to sparse representation in non-clonally expanded tissues. While heuristic filters and tools trained on clonally expanded MVs with high allelic fractions are proposed, they show relatively lower sensitivity and more false discoveries4–9. Here we present DeepMosaic, combining an image-based visualization module for single nucleotide MVs, and a convolutional neural networks-based classification module for control-independent MV detection. DeepMosaic achieved higher accuracy compared with existing methods on biological and simulated sequencing data, with a 96.34% (158/164) experimental validation rate. Of 932 mosaic variants detected by DeepMosaic in 16 whole genome sequenced samples, 21.89-58.58% (204/932-546/932) MVs were overlooked by other methods. Thus, DeepMosaic represents a highly accurate MV classifier that can be implemented as an alternative or complement to existing methods.
Probing the decision-making mechanisms underlying choice between drug and nondrug rewards in rats
Authors: Vandaele Youna , Lenoir Magalie , Vouillac-Mendoza Caroline , Guillem Karine , Ahmed S.H. 
Published: November 2020

AbstractInvestigating the decision-making mechanisms underlying choice between drug and nondrug rewards is essential to understand how their alterations can contribute to substance use disorders. However, despite some recent effort, this investigation remains a challenge in a drug choice setting, notably when it comes to delineate the role of goal-directed versus habitual control mechanisms. The goal of this study was to try probing these different mechanisms by comparing response latencies measured during sampling (i.e., only one option is available) and choice trials. A deliberative goal-directed control mechanism predicts a lengthening of latencies during choice whereas a habitual control mechanism predicts no change in latencies. Alternatively, a race-like response competition mechanism, such as that postulated by the behavioral ecology-inspired Sequential Choice Model (SCM), predicts instead a shortening of response latencies during choice compared to sampling. Here we tested the predictions of these different mechanisms by conducting a systematic retrospective analysis of all cocaine versus saccharin choice experiments conducted in rats in our laboratory over the past 12 years. Overall, we found that rats engage a deliberative goal-directed mechanism after limited training, but shift to a SCM-like response selection mechanism after more extended training. The latter finding suggests that habitual control is engaged in a choice setting via a race-like response competition mechanism, and thus, that the SCM is not a general model of choice, as formulated initially, but a specific model of habitual choice.
Proximity Measures as Graph Convolution Matrices for Link Prediction in Biological Networks
Authors: Coşkun Mustafa , Koyutürk Mehmet 
Published: November 2020

AbstractMotivationLink prediction is an important and well-studied problem in computational biology, with a broad range of applications including disease gene prioritization, drug-disease associations, and drug response in cancer. The general principle in link prediction is to use the topological characteristics and the attributes–if available– of the nodes in the network to predict new links that are likely to emerge/disappear. Recently, graph representation learning methods, which aim to learn a low-dimensional representation of topological characteristics and the attributes of the nodes, have drawn increasing attention to solve the link prediction problem via learnt low-dimensional features. Most prominently, Graph Convolution Network (GCN)-based network embedding methods have demonstrated great promise in link prediction due to their ability of capturing non-linear information of the network. To date, GCN-based network embedding algorithms utilize a Laplacian matrix in their convolution layers as the convolution matrix and the effect of the convolution matrix on algorithm performance has not been comprehensively characterized in the context of link prediction in biomedical networks. On the other hand, for a variety of biomedical link prediction tasks, traditional node similarity measures such as Common Neighbor, Ademic-Adar, and other have shown promising results, and hence there is a need to systematically evaluate the node similarity measures as convolution matrices in terms of their usability and potential to further the state-of-the-art.ResultsWe select 8 representative node similarity measures as convolution matrices within the single-layered GCN graph embedding method and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug–drug interaction (DDI) prediction, protein–protein interaction (PPI) prediction. Our experimental results demonstrate that the node similarity-based convolution matrices significantly improves GCN-based embedding algorithms and deserve more attention in the future biomedical link predictionAvailabilityOur method is implemented as a python library and is available at githublinkContactmustafa.coskun@agu.edu.trSupplementary informationSupplementary data are available atBioinformaticsonline.
Life histories decide reserve benefits in transient yields and bycatch persistence
Authors: Chen Renfei , Tu Chengyi , Liu Quan-Xing 
Published: November 2020

AbstractRecent research indicates that marine reserves can both improve fisheries yields of target species and maintain the persistence of bycatch species. However, the prevalent equilibrium analyses prevent our understandings in transient behavior at short-time scales. Here, we develop high dimensional theoretical frameworks by considering age structure to assess the relative advantages between reserve-only and no-reserve fisheries management strategies. Our results show that whether strategies with only reserves can achieve higher fisheries yields (measured by both weight and number) and maintain bycatch persistence depends on the life histories of both target and bycatch species through perspectives of transient oscillations. Our research has important practical applications especially for the West Coast groundfish fishery in the USA, as it suggests that reserves can perform benefits in both fisheries and conservation goals for target species with older ages at maturity and lower adult survivorship.
Discovery of rhodomyrtone as a broad-spectrum antiviral inhibitor with anti-SARS-CoV-2 activity
Authors: Tang Wei , Lu Jing , Song Qiao-Yun , Li Man-Mei , Chen Li-Feng , Hu Li-Jun , Yang Shao-Min , Zhang Dong-Mei , Wang Ying , Li Yao-Lan , Ye Wen-Cai 
Published: November 2020

AbstractThe outbreak of new viruses, such as serve acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as well as the emerging of drug-resistance viruses highlight the urgent need for the development of broad-spectrum antiviral drugs. Herein, we report the discovery of a plant-derived small molecule, 6,8-dihydroxy-9-isobutyl-2,2,4,4-tetramethyl-7-(3-methylbutanoyl)-4,9-dihydro-1H-xanthene-1,3(2H)-dione (rhodomyrtone, RDT), which exhibited potent broad-spectrum antiviral activities against several RNA and DNA viruses, including SARS-CoV-2, respiratory syncytial virus (RSV), herpes simplex virus type 1 (HSV-1), herpes simplex virus type 2 (HSV-2), varicella-zoster virus (VZV), human cytomegalovirus (HCMV), and Kaposi’s sarcoma-associated herpesvirus (KSHV). RDT can significantly suppress viral gene expression and show the low possibility to elicit drug-resistant variants. Mechanistic study implied that RDT inhibited viral infection by disturbing the cellular factors that essential for viral gene expression. Our results suggested that RDT might be a promising lead compound for the development of broad-spectrum antiviral drugs.
Hydrophilic Shell Matrix Proteins ofNautilus pompiliusand The Identification of a Core Set of Conchiferan Domains
Authors: Setiamarga Davin H. E. , Hirota Kazuki , Yoshida Masa-aki , Takeda Yusuke , Kito Keiji , Shimizu Keisuke , Isowa Yukinobu , Ikeo Kazuho , Sasaki Takenori , Endo Kazuyoshi 
Published: November 2020

AbstractDespite being a member of the shelled mollusks (Conchiferans), most members of extant cephalopods have lost their external biomineralized shells, except for the Nautiloids. Here, we report the result of our study to identify major Shell Matrix Proteins and their domains in the NautiloidNautilus pompilius, in order to gain a general insight into the evolution of Conchiferan Shell Matrix Proteins. In order to do so, we conducted transcriptomics of the mantle, and proteomics of the shell ofN. pompiliussimultaneously. Analyses of obtained data identified 61 distinct shell-specific sequences. Of the successfully annotated 27 sequences, protein domains were predicted in 19. Comparative analysis ofNautilussequences with four Conchiferans for which Shell Matrix Protein data were available (the pacific oyster, the pearl oyster, the limpet, and theEuhadrasnail) revealed that three proteins and six domains of the shell proteins are conserved in all Conchiferans. Interestingly, when the terrestrialEuhadrasnail was excluded, another five proteins and six domains were found to be shared among the four marine Conchiferans. Phylogenetic analyses indicated that most of these proteins and domains were present in the ancestral Conchiferan, but employed in shell formation later and independently in most clades. Although further studies utilizing deeper sequencing techniques to obtain genome and full-length sequences, and functional analyses, must be done in the future, our results here provide important pieces of information for the elucidation of the evolution of Conchiferan shells at the molecular level.
Quaternary climate change explains global patterns of tree beta-diversity
Authors: Xu Wu-Bing , Guo Wen-Yong , Serra-Diaz Josep M. , Schrodt Franziska , Eiserhardt Wolf L. , Enquist Brian J. , Maitner Brian S. , Merow Cory , Violle Cyrille , Anand Madhur , Belluau Michaël , Bruun Hans Henrik , Byun Chaeho , Catford Jane A. , Cerabolini Bruno E. L. , Chacón-Madrigal Eduardo , Ciccarelli Daniela , Cornelissen Johannes H. C. , Dang-Le Anh Tuan , Frutos Angel de , Dias Arildo S. , Giroldo Aelton B. , Gutiérrez Alvaro G. , Hattingh Wesley , He Tianhua , Hietz Peter , Hough-Snee Nate , Jansen Steven , Kattge Jens , Komac Benjamin , Kraft Nathan , Kramer Koen , Lavorel Sandra , Lusk Christopher H. , Martin Adam R. , Ma Ke-Ping , Mencuccini Maurizio , Michaletz Sean T. , Minden Vanessa , Mori Akira S. , Niinemets Ülo , Onoda Yusuke , Onstein Renske E. , Peñuelas Josep , Pillar Valério D. , Pisek Jan , Pound Matthew J. , Robroek Bjorn J.M. , Schamp Brandon , Slot Martijn , Sosinski Ênio , Soudzilovskaia Nadejda A. , Thiffault Nelson , van Bodegom Peter , van der Plas Fons , Zheng Jingming , Svenning Jens-Christian , Ordonez Alejandro 
Published: November 2020

AbstractBoth historical and contemporary environmental conditions determine present biodiversity patterns, but their relative importance is not well understood. One way to disentangle their relative effects is to assess how different dimensions of beta-diversity relate to past climatic changes, i.e., taxonomic, phylogenetic and functional compositional dissimilarity, and their components generated by replacement of species, lineages and traits (turnover) and richness changes (nestedness). Here, we quantify global patterns of each of these aspects of beta-diversity among neighboring sites for angiosperm trees using the most extensive global database of tree species-distributions (43,635 species). We found that temperature change since the Last Glacial Maximum (LGM) was the major influence on both turnover and nestedness components of beta-diversity, with a negative correlation to turnover and a positive correlation to nestedness. Moreover, phylogenetic and functional nestedness was higher than expected from taxonomic beta-diversity in regions that experienced large temperature changes since the LGM. This pattern reflects relatively greater losses of phylogenetic and functional diversity in species-poor assemblages, possibly caused by phylogenetically and functionally selective species extinction and recolonization during glacial-interglacial oscillations. Our results send a strong warning that rapid anthropogenic climate change is likely to result in a long-lasting phylogenetic and functional compositional simplification, potentially impairing forest ecosystem functioning.
Measuring adaptation dynamics to hydrogen peroxide in single human cells using fluorescent reporters
Authors: Simiuc Dana , Dahmani Fatima , Pruvost Alexandra , Guilbert Marie , Brulé Mathilde , Lagadec Chann , Thommen Quentin , Pfeuty Benjamin , Courtade Emmanuel , Anquez François 
Published: November 2020

ABSTRACTWe developed an experimental methodology to monitor response dynamics of single human cells to hydrogen peroxide. Our approach is based on fluidic control of both magnitude and time-evolution of the external perturbation, and on high-throughput imaging of intracellular fluorescent redox reporters. We applied step stimulus to MCF7 cells with hydrogen peroxide concentration in the range of 10 to 500μM. First, our data highlights dynamic adaptation of Reactive Oxygen Species (ROS) scavenging system at several time scales. Nicotinamide Adenine Dinucleotide Phosphate (NADPH) level is rapidly restored within 10 min after a transient decrease, while glutathione (GSH) redox potential is slowly driven back toward pre-stimulus level (within one hour). Extra-cellular glucose is necessary for adaptation of both NADPH level and GSH system. Second, our results also reveal large cell-to-cell variability in the dynamic response to external ROS. Our experimental approach is readily usable to monitor other cellular redox systems such as thioredoxins. As response-heterogeneity can lead to fractional killing, we finally discuss how our methodology can be an opportunity to link dynamics of ROS scavenging and cellular fate such as cell death.
Inherent population structure determines the importance of filtering parameters for reduced representation sequencing analyses
Authors: Selechnik D. , Richardson M.F. , Hess M.K. , Hess A.S. , Dodds K.G. , Martin M. , Chan T.C. , Cardilini A.P.A. , Sherman C.D.H. , Shine R. , Rollins L.A. 
Published: November 2020

AbstractAs technological advancements enhance our ability to study population genetics, we must understand how the intrinsic properties of our datasets influence the decisions we make when designing experiments. Filtering parameter thresholds, such as call rate and minimum minor allele frequency (MAF), are known to affect inferences of population structure in reduced representation sequencing (RRS) studies. However, it is unclear to what extent the impacts of these parameter choices vary across datasets. Here, we reviewed literature on filtering choices and levels of genetic differentiation across RRS studies on wild populations to highlight the diverse approaches that have been used. Next, we hypothesized that choices in filtering thresholds would have the greatest impact when analyzing datasets with low levels of genetic differentiation between populations. To test this hypothesis, we produced seven simulated RRS datasets with varying levels of population structure, and analyzed them using four different combinations of call rate and MAF. We performed the same analysis on two empirical RRS datasets (low or high population structure). Our simulated and empirical results suggest that the effects of filtering choices indeed vary based on inherent levels of differentiation: specifically, choosing stringent filtering choices was important to detect distinct populations that were slightly differentiated, but not those that were highly differentiated. As a result, experimental design and analysis choices need to consider attributes of each specific dataset. Based on our literature review and analyses, we recommend testing a range of filtering parameter choices, and presenting all results with clear justification for ultimate filtering decisions used in downstream analyses.