bluuebunny/crossref_metadata_2025_split huggingface dataset:
MaxHiC: robust estimation of chromatin interaction frequency in Hi-C and capture Hi-C experiments
Authors: Alinejad-Rokny Hamid , Modegh Rassa Ghavami , Rabiee Hamid R. , Rezaie Narges , Tam Kin Tung , Forrest Alistair R. R. 
Published: April 2020

AbstractHi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions, and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than random (background) interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly identifies statistically significant interactions in both Hi-C and capture Hi-C experiments. MaxHiC uses a negative binomial distribution model and a maximum likelihood technique to correct biases in both Hi-C and capture Hi-C libraries. We systematically benchmark MaxHiC against major Hi-C background correction tools and demonstrate using published Hi-C and capture Hi-C datasets that 1) Interacting regions identified by MaxHiC have significantly greater levels of overlap with known regulatory features (e.g. active chromatin histone marks, CTCF binding sites, DNase sensitivity) and also disease-associated genome-wide association SNPs than those identified by currently existing models, and 2) the pairs of interacting regions are more likely to be linked by eQTL pairs and more likely to link known regulatory features than any of the existing methods. We also demonstrate that interactions between different genomic region types have distinct distance distribution only revealed by MaxHiC. MaxHiC is publicly available as a python package for the analysis of Hi-C and capture Hi-C data.Author summaryMaxHiC is a robust machine learning based tool for identifying significant interacting regions from both Hi-C and capture Hi-C data. All the current existing models are designed for either Hi-C or capture Hi-C data, however we developed MaxHiC to be applicable for both Hi-C and capture Hi-C libraries (two different models have been used for Hi-C and capture Hi-C data). MaxHiC is also able to analyse very deep Hi-C libraries (e.g., MicroC) without any computational issues. MaxHiC significantly outperforms current existing tools in terms of enrichment of interactions between known regulatory regions as well as biologically relevant interactions.
Succinct dynamic variation graphs
Authors: Eizenga Jordan M. , Novak Adam M. , Kobayashi Emily , Villani Flavia , Cisar Cecilia , Heumos Simon , Hickey Glenn , Colonna Vincenza , Paten Benedict , Garrison Erik 
Published: April 2020

AbstractMotivationPangenomics is a growing field within computational genomics. Many pangenomic analyses use bidirected sequence graphs as their core data model. However, implementing and correctly using this data model can be difficult, and the scale of pangenomic data sets can be challenging to work at. These challenges have impeded progress in this field.ResultsHere we present a stack of two C++ libraries,libbdsgandlibhandlegraph, which use a simple, field-proven interface, designed to expose elementary features of these graphs while preventing common graph manipulation mistakes. The libraries also provide a Python binding. Using a diverse collection of pangenome graphs, we demonstrate that these tools allow for efficient construction and manipulation of large genome graphs with dense variation. For instance, the speed and memory usage is up to an order of magnitude better than the prior graph implementation in the vg toolkit, which has now transitioned to usinglibbdsg’s implementations.Availabilitylibhandlegraphandlibbdsgare available under an MIT License fromhttps://github.com/vgteam/libhandlegraphandhttps://github.com/vgteam/libbdsg.Contacterik.garrison@ucsc.edu
Shifting Baselines: Physiological legacies contribute to the response of reef coral to frequent heat waves
Authors: Wall Christopher B. , Ricci Contessa A. , Wen Alexandra D. , Ledbetter Bren E. , Klinger Delania E. , Mydlarz Laura D. , Gates Ruth D. , Putnam Hollie M. 
Published: April 2020

AbstractGlobal climate change is altering coral reef ecosystems. Notably, marine heat waves are producing widespread coral bleaching events that are increasing in frequency, with projections for annual bleaching events on reefs worldwide by mid-century.The response of corals to elevated seawater temperatures can be modulated by abiotic factors at site of origin and dominant endosymbiont type, which can result in a shift in multiple coral traits and drive physiological legacy effects that influence the trajectory of reef corals under subsequent thermal stress events. It is critical, therefore, to evaluate the potential for shifting physiological and cellular baselines driven by these factors inin situbleaching (and recovery) events. Here, we use the back-to-back regional bleaching events of 2014 and 2015 in the Hawaiian Islands and subsequent recovery periods to test the hypothesis that coral multivariate trait space (here termed physiotype, sensu (Van Straalen, 2003) shift in multiple bleaching events, modulated by both environmental histories and symbiotic partnerships (Symbiodiniaceae).Despite fewer degree heating weeks in the first-bleaching event relative to the second (7vs. 10), bleaching severity in a dominant reef building coral on Hawaiian reefs,Montipora capitata, was greater (~70%vs. 50% bleached cover) and differences due to environmental history (reef site) were more pronounced. Melanin, an immune cytotoxic response, provided an initial defense during the first event, potentially priming antioxidant activity, which peaked in the second-bleaching event (i.e., a legacy effect). While magnitude of bleaching differed, immune response patterns were shared among corals harboring heat-sensitive and heat-tolerant Symbiodiniaceae. This supports a pattern of increased constitutive immunity in corals resulting from repeat bleaching events, with greater specialized enzymes (catalase, peroxidase, superoxide dismutase) and attenuated melanin synthesis.This study demonstrates bleaching events have implications for reef corals beyond shaping their ecological assemblages. These events can change the magnitude and/or identity of response variables contributing to physiotype, thus generating physiological legacies carried over into the future. Quantifying baseline coral physiotypes and tracking their shifts will be critical to understanding and forecasting the effects of increased bleaching frequency on coral biology and ecology in the Anthropocene.
Cumulative distribution functions: An alternative approach to examine the triggering of prepared motor actions in the StartReact effect
Authors: McInnes Aaron N. , Castellote Juan M. , Kofler Markus , Honeycutt Claire F. , Lipp Ottmar V. , Riek Stephan , Tresilian James R. , Marinovic Welber 
Published: April 2020

AbstractThere has been much debate concerning whether startling sensory stimuli can activate a fast-neural pathway for movement triggering (StartReact) which is different from that of voluntary movements. Activity in sternocleidomastoid (SCM) electromyogram is suggested to indicate activation of this pathway. We evaluated whether SCM activity can accurately identify trials which may differ in their neurophysiological triggering and assessed the use of cumulative distribution functions (CDFs) of reaction time (RT) data to identify trials with the shortest RTs for analysis. Using recent datasets from the StartReact literature, we examined the relationship between RT and SCM activity. We categorised data into short/longer RT bins using CDFs and used linear mixed effects models to compare potential conclusions that can be drawn when categorising data on the basis of RT versus on the basis of SCM activity. The capacity of SCM to predict RT is task-specific, making it an unreliable indicator of distinct neurophysiological mechanisms. Classification of trials using CDFs is capable of capturing potential task- or muscle-related differences in triggering whilst avoiding the pitfalls of the traditional SCM activity based classification method. We conclude that SCM activity is not always evident on trials that show the early triggering of movements seen in the StartReact phenomenon. We further propose that a more comprehensive analysis of data may be achieved through the inclusion of CDF analyses. These findings have implications for future research investigating movement triggering as well as for potential therapeutic applications of StartReact.
A novel score for highly accurate and efficient prediction of native protein structures
Authors: Wu Lu-yun , Xia Xia-yu , Pan Xian-ming 
Published: April 2020

AbstractProtein structure resolution has lagged far behind sequence determination, as it is often laborious and time-consuming to resolve individual protein structure – more often than not even impossible. For computational prediction, due to the lack of detailed knowledge on the folding driving forces, how to design an energy function is still an open question. Furthermore, an effective criterion to evaluate the performance of the energy function is also lacking. Here we present a novel knowledge-based-energy scoring function, simply considering the interactions of peptide bonds, rather than, as conventionally, the residues or atoms as the most important energy contribution. This energy scoring was evaluated by selecting the X-ray structure from a large number of possibilities. It not only outperforms the best of the previously published statistical potentials, but also has very low computational expense. Besides, we suggest an alternative criterion to evaluate the performance of the energy scoring function, measured by the template modeling score of the selected rank-one. We argue that the comparison should allow for some deviation between the x-ray and predicted structures. Collectively, this accurate and simple energy scoring function, together with the optimized criterion, will significantly advance the computational protein structure prediction.
Apparent Reconsolidation Interference Without Generalized Amnesia
Authors: Alfei Joaquín M. , De Gruy Hérnan , De Bundel Dimitri , Luyten Laura , Beckers Tom 
Published: April 2020

AbstractMemories remain dynamic after consolidation, and when reactivated, they can be rendered vulnerable to various pharmacological agents that disrupt the later expression of memory (i.e., amnesia). Such drug-induced post-reactivation amnesia has traditionally been studied in AAA experimental designs, where a memory is initially created for a stimulus A (be it a singular cue or a context) and later reactivated and tested through exposure to the exact same stimulus. Using a contextual fear conditioning procedure in rats and midazolam as amnestic agent, we recently demonstrated that drug-induced amnesia can also be obtained when memories are reactivated through exposure to a generalization stimulus (GS, context B) and later tested for that same generalization stimulus (ABB design). However, this amnestic intervention leaves fear expression intact when at test animals are instead presented with the original training stimulus (ABA design) or a novel generalization stimulus (ABC design). The underlying mechanisms of post-reactivation memory malleability and of MDZ-induced amnesia for a generalization context remain largely unknown. Here, we evaluated whether, like typical CS-mediated (or AAA) post-reactivation amnesia, GS-mediated (ABB) post-reactivation amnesia displays key features of a destabilization-based phenomenon. We first show that ABB post-reactivation amnesia is critically dependent on prediction error at the time of memory reactivation and provide evidence for its temporally graded nature. In line with the known role of GluN2B-NMDA receptor activation in memory destabilization, we further demonstrate that pre-reactivation administration of ifenprodil, a selective antagonist of GluN2B-NMDA receptors, prevents MDZ-induced ABB amnesia. In sum, our data reveal that ABB MDZ-induced post-reactivation amnesia exhibits the hallmark features of a destabilization-dependent phenomenon. Implication of our findings for a reconsolidation-based account of post-reactivation amnesia are discussed.
Testing different forms of regulation of yolk thyroid hormone transfer in pied flycatchers
Authors: Sarraude Tom , Hsu Bin-Yan , Groothuis Ton G.G. , Ruuskanen Suvi 
Published: April 2020

AbstractHormones transferred from mothers to their offspring are considered a maternal tool to prepare progeny for expected environmental conditions, increasing maternal fitness. To flexibly influence offspring, mothers should be able to transmit the hormonal signals independent of their own hormonal status. However, the ability to regulate hormone transfer to the next generation is under debate. We studied the transfer of thyroid hormones (THs) to eggs in a bird model. We elevated thyroxine (T4,the prohormone for the biological active triiodothyronine, T3) during egg-laying using T4implants on females of a wild population of pied flycatchers (Ficedula hypoleuca), and measured plasma and yolk T4and T3as a response. To our knowledge, studies that manipulated a prohormone and measured the change in its active metabolites have rarely been conducted. We found an increase in plasma and yolk T4and no change in T3concentrations leading to a similar decrease in yolk T3/T4ratio in response to the T4treatment in plasma and yolk. This suggests that mothers are able to regulate the conversion of T4in T3to avoid potential costs of elevated exposure to the active hormone to herself and to her progeny. Finally, contrary to our predictions, we found no evidence of regulatory mechanisms at the follicle level, which is essential for independent regulation of yolk hormone transfer.Summary statementThyroid hormones have been overlooked in the context of hormone-mediated maternal effects. We found that mothers may regulate yolk thyroid hormone transfer by regulating the conversion of the active form of the hormone.
Self-normalizing learning on biomedical ontologies using a deep Siamese neural network
Authors: Smaili Fatima Zohra , Gao Xin , Hoehndorf Robert 
Published: April 2020

AbstractMotivationOntologies are widely used in biomedicine for the annotation and standardization of data. One of the main roles of ontologies is to provide structured background knowledge within a domain as well as a set of labels, synonyms, and definitions for the classes within a domain. The two types of information provided by ontologies have been extensively exploited in natural language processing and machine learning applications. However, they are commonly used separately, and thus it is unknown if joining the two sources of information can further benefit data analysis tasks.ResultsWe developed a novel method that applies named entity recognition and normalization methods on texts to connect the structured information in biomedical ontologies with the information contained in natural language. We apply this normalization both to literature and to the natural language information contained within ontologies themselves. The normalized ontologies and text are then used to generate embeddings, and relations between entities are predicted using a deep Siamese neural network model that takes these embeddings as input. We demonstrate that our novel embedding and prediction method using self-normalized biomedical ontologies significantly outperforms the state-of-the-art methods in embedding ontologies on two benchmark tasks: prediction of interactions between proteins and prediction of gene–disease associations. Our method also allows us to apply ontology-based annotations and axioms to the prediction of toxicological effects of chemicals where our method shows superior performance. Our method is generic and can be applied in scenarios where ontologies consisting of both structured information and natural language labels or synonyms are used.Availabilityhttps://github.com/bio-ontology-research-group/Ontology-based-normalizationContactrobert.hoehndorf@kaust.edu.saandxin.gao@kaust.edu.sa
Genetic analysis of the novel SARS-CoV-2 host receptorTMPRSS2in different populations
Authors: Russo Roberta , Andolfo Immacolata , Lasorsa Vito Alessandro , Iolascon Achille , Capasso Mario 
Published: April 2020

AbstractThe infection coronavirus disease 2019 (COVID-19) is caused by a virus classified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). At cellular level, virus infection initiates with binding of viral particles to the host surface cellular receptor angiotensin converting enzyme 2 (ACE2). SARS-CoV-2 engages ACE2 as the entry receptor and employs the cellular serine protease 2 (TMPRSS2) for S protein priming. TMPRSS2 activity is essential for viral spread and pathogenesis in the infected host. Understanding how TMPRSS2 protein expression in the lung varies in the population could reveal important insights into differential susceptibility to influenza and coronavirus infections. Here, we systematically analyzed coding-region variants inTMPRSS2and the eQTL variants, which may affect the gene expression, to compare the genomic characteristics ofTMPRSS2among different populations. Our findings suggest that the lung-specific eQTL variants may confer different susceptibility or response to SARS-CoV-2 infection from different populations under the similar conditions. In particular, we found that the eQTL variant rs35074065 is associated with high expression ofTMPRSS2but with a low expression of the interferon (IFN)-α/β-inducible gene,MX1, splicing isoform. Thus, these subjects could account for a more susceptibility either to viral infection or to a decrease in cellular antiviral response.
Metabolic precision labeling enables selective probing of O-linkedN-acetylgalactosamine glycosylation
Authors: Debets Marjoke F. , Tastan Omur Y. , Wisnovsky Simon P. , Malaker Stacy A. , Angelis Nikolaos , Moeckl Leonhard K. R. , Choi Junwon , Flynn Helen , Wagner Lauren J. S. , Bineva-Todd Ganka , Antononopoulos Aristotelis , Cioce Anna , Browne William M. , Li Zhen , Briggs David C. , Douglas Holly L. , Hess Gaelen T. , Agbay Anthony J. , Roustan Chloe , Kjaer Svend , Haslam Stuart M. , Snijders Ambrosius P. , Bassik Michael C. , Moerner W. E. , Li Vivian S. W. , Bertozzi Carolyn R. , Schumann Benjamin 
Published: April 2020

AbstractProtein glycosylation events that happen early in the secretory pathway are often dysregulated during tumorigenesis. These events can be probed, in principle, by monosaccharides with bioorthogonal tags that would ideally be specific for distinct glycan subtypes. However, metabolic interconversion into other monosaccharides drastically reduces such specificity in the living cell. Here, we use a structure-based design process to develop the monosaccharide probe GalNAzMe that is specific for cancer-relevant Ser/Thr-N-acetylgalactosamine (O-GalNAc) glycosylation. By virtue of a branched N-acylamide side chain, GalNAzMe is not interconverted by epimerization to the corresponding N-acetylglucosamine analog like conventional GalNAc-based probes. GalNAzMe enters O-GalNAc glycosylation but does not enter other major cell surface glycan types including Asn (N)-linked glycans. We equip cells with the capacity to biosynthesize the nucleotide-sugar donor UDP-GalNAzMe from a caged precursor. Tagged with a bioorthogonal azide group, GalNAzMe serves as an O-glycan specific reporter in superresolution microscopy, chemical glycoproteomics, a genome-wide CRISPR knock-out (KO) screen, and imaging of intestinal organoids. GalNAzMe is a precision tool that allows a detailed view into the biology of a major type of cancer-relevant protein glycosylation.Significance statementA large portion of all secreted and cell surface proteins in humans are modified by Ser/Thr(O)-linked glycosylation withN-acetylgalactosamine (GalNAc). While of fundamental importance in health and disease, O-GalNAc glycosylation is technically challenging to study because of a lack of specific tools to be used in biological assays. Here, we design an O-GalNAc specific reporter molecule termed GalNAzMe to selectively label O-GalNAc glycoproteins in living human cells. GalNAzMe is compatible with a range of experiments in quantitative biology to broaden our understanding of glycosylation. We further demonstrate that labeling is genetically programmable by expression of a mutant glycosyltransferase, allowing application even to experiments with low inherent sensitivity.
Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity
Authors: Pinotsis D. A. , Miller E. K. 
Published: April 2020

AbstractNeural activity is organized at multiple scales, ranging from the cellular to the whole brain level. Connecting neural dynamics at different scales is important for understanding brain pathology. Neurological diseases and disorders arise from interactions between factors that are expressed in multiple scales. Here, we suggest a new way to link microscopic and macroscopic dynamics through combinations of computational models. This exploits results from statistical decision theory and Bayesian inference. To validate our approach, we used two independent MEG datasets. In both, we found that variability in visually induced oscillations recorded from different people in simple visual perception tasks resulted from differences in the level of inhibition specific to deep cortical layers. This suggests differences in feedback to sensory areas and each subject’s hypotheses about sensations due to differences in their prior experience. Our approach provides a new link between non-invasive brain imaging data, laminar dynamics and top-down control.
Diversity and gene expression patterns of functional groups in sidestream and mainstream wastewater partial-nitritation anammox biofilms
Authors: Suarez Carolina , Gustavsson David , Hermansson Malte , Persson Frank 
Published: April 2020

AbstractPartial nitritation-anammox (PNA) is today used for nitrogen removal from highly concentrated wastewater after anaerobic sludge digestion (sidestream). However, implementation of PNA for treatment of municipal wastewater (mainstream), with its lower ammonium concentration and lower temperature is challenging, which might be due to differences in microbial community composition and/or activity. To investigate this, we compared side-by-side sidestream and mainstream PNA biofilms using amplicon sequencing of 16S rDNA and rRNA,hzsBDNA and mRNA, and the genesnxrB, andamoA. The two communities were different to each other with relatively more heterotrophic denitrifying bacteria and less anammox bacteria in the mainstream. WithhzsBandnxrBwe found microdiversity amongBrocadiaandNitrospira, and turnover (taxa replacement) between sidestream and mainstream. However, in both environmentsBrocadia sapporoensisrepresented most of thehzsBDNA and mRNA reads, despite the different environmental conditions and nitrogen removal rates. All of those populations present in both sidestream and mainstream had no differences in their 16S rRNA:rDNA ratios, supporting recent findings that rRNA:rDNA ratios are poor indicators of bacterial activity. The observed diversity within functional groups and composition differences between sidestream and mainstream add complexity to our view of PNA communities with possible implication for reactor function.