bluuebunny/crossref_metadata_2025_split huggingface dataset:
Broad decline and subsequent differential re-emergence of respiratory viruses during COVID-19 pandemic response measures, Singapore 2020
Authors: Wan Wei Yee , Thoon Koh Cheng , Loo Liat Hui , Chan Kian Sing , Oon Lynette L. E. , Ramasamy Adaikalavan , Maiwald Matthias 
Published: March 2021

AbstractImportanceCOVID-19 pandemic control measures affect the prevalence of other respiratory viruses. Effects on some viruses have been described; however, the broader impact and temporal relationship of control measures on virus decline and subsequent re-emergence have not been thoroughly documented. Understanding these phenomena may influence health policies.ObjectiveTo examine the prevalence of unrelated respiratory viruses in relation to population-wide pandemic response measures and phases in 2020 in Singapore.Design, Setting, and ParticipantsData from respiratory multiplex PCRs from 3 major hospitals (total 3700 beds) in Singapore were collated. The full dataset consisted of 42,558 test results, 19,898 from 2019 and 22,660 from 2020.Main Outcomes and MeasuresWeekly virus prevalence data were mapped onto prevailing pandemic response measures, in order to elucidate temporal relationships and differential virus responses. Pre-pandemic data from 2019 were compared with data from 2020.ResultsEarly response measures, even before national lockdown, were followed by a dramatic reduction of influenza viruses and a more gradual decline of other respiratory viruses, including respiratory syncytial virus, parainfluenza viruses, endemic coronaviruses and metapneumovirus. Marked decline of enterovirus/rhinovirus and adenovirus, however, was only observed during lockdown. About 13 weeks into phased reopening, enterovirus/rhinovirus re-emerged, followed by adenovirus, the latter mainly in the pediatric population. All other viruses remained at low levels until the end of 2020.Conclusions and RelevanceCOVID-19 control measures in Singapore had a significant impact on a broad range of respiratory viruses. Effects of various control measures varied between phases and different viruses. Influenza viruses declined earliest and most dramatically; relaxation of measures was followed by re-emergence of enterovirus/rhinovirus and adenovirus. These patterns are presumably a result of different propensities for contact versus droplet and overall ease of transmission, and different virus reservoirs. Further studies into these phenomena are a matter of public health importance.Key PointsQuestionWhat were the effects of COVID-19 pandemic control measures in Singapore on the prevalence of other respiratory viruses?FindingsViruses responded differently to control measures. Influenza viruses declined rapidly after early control measures and remained near-absent during reopening after lockdown. Enterovirus/rhinovirus and adenovirus declined later and re-emerged earlier than other viruses during phased reopening.MeaningPopulation-wide interventions resulted in a broad decline and subsequent differential re-emergence of non-targeted respiratory viruses, corresponding to different patterns of virus response to control measures.
Overexpression-based detection of translatable circular RNAs is vulnerable to coexistent linear RNA byproducts
Authors: Jiang Yanyi , Chen Xiaofan , Zhang Wei 
Published: March 2021

AbstractIn RNA field, the demarcation between coding and non-coding has been negotiated by the recent discovery of occasionally translated circular RNAs (circRNAs). Although absent of 5’ cap structure, circRNAs can be translated cap-independently. Complementary intron-mediated overexpression is one of the most utilized methodologies for circRNA research but not without bearing echoing skepticism for its poorly defined mechanism and latent coexistent side products. In this study, leveraging such circRNA overexpression system, we have interrogated the protein-coding potential of 30 human circRNAs containing infinite open reading frames in HEK293T cells. Surprisingly, pervasive translation signals are detected by immunoblotting. However, intensive mutagenesis reveals that numerous translation signals are generated independently of circRNA synthesis. We have developed a dual tag strategy to isolate translation noise and directly demonstrate that the fallacious translation signals originate from cryptically spliced linear transcripts. The concomitant linear RNA byproducts, presumably concatemers, can be translated to allow pseudo rolling circle translation signals, and can involve backsplicing junction (BSJ) to disqualify the BSJ-based evidence for circRNA translation. We also find non-AUG start codons may engage in the translation initiation of circRNAs. Taken together, our systematic evaluation sheds light on heterogeneous translational outputs from circRNA overexpression vector and comes with a caveat that ectopic overexpression technique necessitates extremely rigorous control setup in circRNA translation and functional investigation.
On-off transition and ultrafast decay of amino acid luminescence driven by modulation of supramolecular packing
Authors: Arnon Zohar A. , Kreiser Topaz , Yakimov Boris , Brown Noam , Aizen Ruth , Shaham-Niv Shira , Makam Pandeeswar , Qaisrani Muhammad Nawaz , Poli Emiliano , Ruggiero Antonella , Slutsky Inna , Hassanali Ali , Shirshin Evgeny , Levy Davide , Gazit Ehud 
Published: March 2021

AbstractIt has been experimentally observed that various biomolecules exhibit clear luminescence in the visible upon aggregation, contrary their monomeric state. However, the physical basis for this phenomenon is still elusive. Here, we systematically examine all coded amino acids to provide non-biased insights into this phenomenon. Several amino acids, including non-aromatic, show intense visible luminescence. While lysine crystals display the highest signal, the very chemically similar non-coded ornithine does not, implying a role for molecular packing rather than the chemical characteristics of the molecule. Furthermore, cysteine show luminescence that is indeed crystal-packing-dependent as repeated rearrangements between two crystal structures result in a reversible on-off optical transition. In addition, ultrafast lifetime decay is experimentally validated, corroborating a recently raised hypothesis regarding the governing role of nπ* states in the emission formation. Collectively, our study supports the hypothesis that electronic interactions between molecules that are non-fluorescent and non-absorbing at the monomeric state may result in reversible optically-active states by the formation of supramolecular fluorophores.
Measuring splash-dispersal of a major wheat pathogen in the field
Authors: Karisto Petteri , Suffert Frédéric , Mikaberidze Alexey 
Published: March 2021

AbstractCapacity for dispersal is a fundamental fitness component of plant pathogens. Empirical characterization of plant pathogen dispersal is of prime importance for understanding how plant pathogen populations change in time and space. We measured dispersal ofZymoseptoria triticiin natural environment. Primary disease gradients were produced by rain-splash driven dispersal and subsequent transmission via asexual pycnidiospores from infected source. To achieve this, we inoculated field plots of wheat (Triticum aestivum) with two distinctZ. triticistrains and a 50/50 mixture of the two strains. We measured effective dispersal of theZ. triticipopulation based on pycnidia counts using automated image analysis. The data were analyzed using a spatially-explicit mathematical model that takes into account the spatial extent of the source. We employed robust bootstrapping methods for statistical testing and adopted a two-dimensional hypotheses test based on the kernel density estimation of the bootstrap distribution of parameter values. Genotyping of re-isolated pathogen strains with strain-specific PCR-reaction further confirmed the conclusions drawn from the phenotypic data. The methodology presented here can be applied to other plant pathosystems.We achieved the first estimates of the dispersal kernel of the pathogen in field conditions. The characteristic spatial scale of dispersal is tens of centimeters – consistent with previous studies in controlled conditions. Our estimation of the dispersal kernel can be used to parameterize epidemiological models that describe spatial-temporal disease dynamics within individual wheat fields. The results have the potential to inform spatially targeted control of crop diseases in the context of precision agriculture.
Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep
Authors: Rué-Queralt J. , Stevner A. , Tagliazucchi E. , Laufs H. , Kringelbach M. L. , Deco G. , Atasoy S. 
Published: March 2021

AbstractCurrent state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable imaging quality and resolution, yet, theintrinsicdimensionality of brain dynamics in different states (wakefulness, light and deep sleep) remains unknown. Here we present a novel method to reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this novelintrinsic manifoldframework to fMRI data acquired in wakefulness and sleep, we reveal the nonlinear differences between wakefulness and three different sleep stages, and successfully decode these different brain states with an average accuracy of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds of all participants share a common topology. Overall, our results reveal the intrinsic manifold underlying the spatiotemporal dynamics of brain activity and demonstrate how this manifold enables the decoding of different brain states such as wakefulness and various sleep stages.
The unraveling of balanced complexes in metabolic networks
Authors: Langary Damoun , Küken Anika , Nikoloski Zoran 
Published: March 2021

AbstractBalanced complexes in biochemical networks are at core of several theoretical and computational approaches that make statements about the properties of the steady states supported by the network. Recent computational approaches have employed balanced complexes to reduce metabolic networks, while ensuring preservation of particular steady-state properties; however, the underlying factors leading to the formation of balanced complexes have not been studied, yet. Here, we present a number of factorizations providing insights in mechanisms that lead to the origins of the corresponding balanced complexes. The proposed factorizations enable us to categorize balanced complexes into four distinct classes, each with specific origins and characteristics. They also provide the means to efficiently determine if a balanced complex in large-scale networks belongs to a particular class from the categorization. The results are obtained under very general conditions and irrespective of the network kinetics, rendering them broadly applicable across variety of network models. Application of the categorization shows that all classes of balanced complexes are present in large-scale metabolic models across all kingdoms of life, therefore paving the way to study their relevance with respect to different properties of steady states supported by these networks.HighlightsBalanced complexes are ubiquitous in real-world networks, and facilitate insights in steady state flux phenotypes in large-scale metabolic networks.Novel factorizations are proposed that explain the formation of balanced complexes and enable their categorization.The results also provide a computationally-efficient tool for the identification of balanced complexes in large-scale networks.Examination of metabolic network models across all kingdoms of life shows that all categories naturally arise in this type of networks.
Physical mechanisms of ESCRT-III-driven cell division in archaea
Authors: Harker-Kirschneck L. , Hafner A. E. , Yao T. , Pulschen A. , Hurtig F. , Vanhille-Campos C. , Hryniuk D. , Culley S. , Henriques R. , Baum B. , Šarić A. 
Published: March 2021

AbstractLiving systems propagate by undergoing rounds of cell growth and division. Cell division is at heart a physical process that requires mechanical forces, usually exerted by protein assemblies. Here we developed the first physical model for the division of archaeal cells, which despite their structural simplicity share machinery and evolutionary origins with eukaryotes. We show how active geometry changes of elastic ESCRT-III filaments, coupled to filament disassembly, are sufficient to efficiently split the cell. We explore how the non-equilibrium processes that govern the filament behaviour impact the resulting cell division. We show how a quantitative comparison between our simulations and dynamic data for ESCRTIII-mediated division inSulfolobus acidocaldarius, the closest archaeal relative to eukaryotic cells that can currently be cultured in the lab, and reveal the most likely physical mechanism behind its division.
Comparative genome analysis using sample-specific string detection in accurate long reads
Authors: Khorsand Parsoa , Denti Luca ,   Human Genome Structural Variant Consortium, Bonizzoni Paola , Chikhi Rayan , Hormozdiari Fereydoun 
Published: March 2021

AbstractMotivationComparative genome analysis of two or more whole-genome sequenced (WGS) samples is at the core of most applications in genomics. These include discovery of genomic differences segregating in population, case-control analysis in common diseases, and rare disorders. With the current progress of accurate long-read sequencing technologies (e.g., circular consensus sequencing from PacBio sequencers) we can dive into studying repeat regions of genome (e.g., segmental duplications) and hard-to-detect variants (e.g., complex structural variants).ResultsWe propose a novel framework for addressing the comparative genome analysis by discovery of strings that are specific to one genome (“samples-specific” strings). We have developed an accurate and efficient novel method for discovery of samples-specific strings between two groups of WGS samples. The proposed approach will give us the ability to perform comparative genome analysis without the need to map the reads and is not hindered by shortcomings of the reference genome. We show that the proposed approach is capable of accurately finding samples-specific strings representing nearly all variation (>98%) reported across pairs or trios of WGS samples using accurate long reads (e.g., PacBio HiFi data).AvailabilityThe proposed tool is publicly available athttps://github.com/Parsoa/PingPong.
Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd
Authors: Gabor Attila , Tognetti Marco , Driessen Alice , Tanevski Jovan , Guo Baosen , Cao Wencai , Shen He , Yu Thomas , Chung Verena ,   Single Cell Signaling in Breast Cancer DREAM Consortium members, Bodenmiller Bernd , Saez-Rodriguez Julio 
Published: March 2021

AbstractRecent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell-types, with important implications to understand and treat diseases such as cancer. These technologies are however limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organised the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry data set, covering 36 markers in over 4,000 conditions totalling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.Graphical AbstractKey pointsOver 80 million single-cell multiplexed measurements across 67 cell lines, 54 conditions and 10 time points to benchmark predictive models of single cell signaling73 approaches from 27 teams for predicting response to kinase inhibitors on single cell level, and dynamic response from unperturbed basal omics dataPredictions of single marker models correlate with measurements with a correlation coefficient of 0.76Top models of whole signaling response models perform almost as well as a biological replicateCell-line specific variation in dynamics can be predicted from basal omics
Pan-genomic Matching Statistics for Targeted Nanopore Sequencing
Authors: Ahmed Omar , Rossi Massimiliano , Kovaka Sam , Schatz Michael C. , Gagie Travis , Boucher Christina , Langmead Ben 
Published: March 2021

AbstractNanopore sequencing is an increasingly powerful tool for genomics. Recently, computational advances have allowed nanopores to sequence in a targeted fashion; as the sequencer emits data, software can analyze the data in real time and signal the sequencer to eject “non-target” DNA molecules. We present a novel method called SPUMONI, which enables rapid and accurate targeted sequencing with the help of efficient pangenome indexes. SPUMONI uses a compressed index to rapidly generate exact or approximate matching statistics (half-maximal exact matches) in a streaming fashion. When used to target a specific strain in a mock community, SPUMONI has similar accuracy as minimap2 when both are run against an index containing many strains per species. However SPUMONI is 12 times faster than minimap2. SPUMONI’s index and peak memory footprint are also 15 to 4 times smaller than minimap2, respectively. These improvements become even more pronounced with even larger reference databases; SPUMONI’s index size scales sublinearly with the number of reference genomes included. This could enable accurate targeted sequencing even in the case where the targeted strains have not necessarily been sequenced or assembled previously. SPUMONI is open source software available fromhttps://github.com/oma219/spumoni.
In vivotissue-specific chromatin profiling inDrosophila melanogasterusing GFP-tagged nuclei
Authors: Jauregui-Lozano Juan , Bakhle Kimaya , Weake Vikki M. 
Published: March 2021

AbstractThe chromatin landscape defines cellular identity in multicellular organisms with unique patterns of DNA accessibility and histone marks decorating the genome of each cell type. Thus, profiling the chromatin state of different cell types in an intact organism under disease or physiological conditions can provide insight into how chromatin regulates cell homeostasisin vivo. To overcome the many challenges associated with characterizing chromatin state in specific cell types, we developed an improved approach to isolateDrosophilanuclei tagged with GFP expressed under Gal4/UAS control. Using this protocol, we profiled chromatin accessibility using Omni-ATAC, and examined the distribution of histone marks using ChIP-seq and CUT&Tag in adult photoreceptor neurons. We show that the chromatin landscape of photoreceptors reflects the transcriptional state of these cells, demonstrating the quality and reproducibility of our approach for profiling the transcriptome and epigenome of specific cell types inDrosophila.
Reward boosts reinforcement-based motor learning
Authors: Vassiliadis Pierre , Derosiere Gerard , Dubuc Cecile , Lete Aegryan , Crevecoeur Frederic , Hummel Friedhelm C , Duque Julie 
Published: March 2021

AbstractBesides relying heavily on sensory and reinforcement feedback, motor skill learning may also depend on the level of motivation experienced during training. Yet, how motivation by reward modulates motor learning remains unclear. In 90 healthy subjects, we investigated the net effect of motivation by reward on motor learning while controlling for the sensory and reinforcement feedback received by the participants. Reward improved motor skill learning beyond performance-based reinforcement feedback. Importantly, the beneficial effect of reward involved a specific potentiation of reinforcement-related adjustments in motor commands, which concerned primarily the most relevant motor component for task success and persisted on the following day in the absence of reward. We propose that the long-lasting effects of motivation on motor learning may entail a form of associative learning resulting from the repetitive pairing of the reinforcement feedback and reward during training, a mechanism that may be exploited in future rehabilitation protocols.
BubbleGun: Enumerating Bubbles and Superbubbles in Genome Graphs
Authors: Dabbaghie Fawaz , Ebler Jana , Marschall Tobias 
Published: March 2021

AbstractMotivationWith the fast development of third generation sequencing machines,de novogenome assembly is becoming a routine even for larger genomes. Graph-based representations of genomes arise both as part of the assembly process, but also in the context ofpangenomesrepresenting a population. In both cases, polymorphic loci lead tobubblestructures in such graphs. Detecting bubbles is hence an important task when working with genomic variants in the context of genome graphs.ResultsHere, we present a fast general-purpose tool, calledBubbleGun, for detecting bubbles and superbubbles in genome graphs. Furthermore,BubbleGundetects and outputs runs of linearly connected bubbles and superbubbles, which we callbubble chains. We showcase its utility on de Bruijn graphs and compare our results tovg’s snarl detection. We show thatBubbleGunis considerably faster thanvgespecially in bigger graphs, where it reports all bubbles in less than 30 minutes on a human sample de Bruijn graph of around 2 million nodes.AvailabilityBubbleGunis available and documented athttps://github.com/fawaz-dabbaghieh/bubble_gununder MIT license.Contactfawaz@hhu.deortobias.marschall@hhu.deSupplementary informationSupplementary data are available atBioinformaticsonline.
PIMBA: a PIpeline for MetaBarcoding Analysis
Authors: Oliveira Renato R. M. , Silva Raíssa , Nunes Gisele L. , Oliveira Guilherme 
Published: March 2021

ABSTRACTSummaryDNA metabarcoding is an emerging monitoring method capable of assessing biodiversity from environmental samples (eDNA). Advances in computational tools have been required due to the increase of Next-Generation Sequencing data. Tools for DNA metabarcoding analysis, such as MOTHUR, QIIME, Obitools, and mBRAVE have been widely used in ecological studies. However, some difficulties are encountered when there is a need to use custom databases. Here we present PIMBA, a PIpeline for MetaBarcoding Analysis, which allows the use of customized databases, as well as other reference databases used by the softwares mentioned here. PIMBA is an open-source and user-friendly pipeline that consolidates all analyses in just three command lines.Availability and Implementationhttps://github.com/reinator/pimbaContactrenato.renison@gmail.comandguilherme.oliveira@pq.itv.org.