bluuebunny/crossref_metadata_2025_split huggingface dataset:
POST-ACUTE SEQUELAE AND ADAPTIVE IMMUNE RESPONSES IN PEOPLE LIVING WITH HIV RECOVERING FROM SARS-COV-2 INFECTION
Authors: Peluso Michael J. , Spinelli Matthew A. , Deveau Tyler-Marie , Forman Carrie A. , Munter Sadie E. , Mathur Sujata , Tang Alex F. , Lu Scott , Goldberg Sarah A. , Arreguin Mireya I. , Hoh Rebecca , Tai Viva , Chen Jessica Y. , Martinez Enrique O. , Chenna Ahmed , Winslow John W. , Petropoulos Christos J. , Sette Alessandro , Weiskopf Daniella , Kumar Nitasha , Lynch Kara L. , Hunt Peter W. , Durstenfeld Matthew S. , Hsue Priscilla Y. , Kelly J. Daniel , Martin Jeffrey N. , Glidden David V. , Gandhi Monica , Deeks Steven G. , Rutishauser Rachel L. , Henrich Timothy J. 
Published: February 2022

ABSTRACTBackgroundLimited data are available on the long-term clinical and immunologic consequences of SARS-CoV-2 infection in people with HIV (PWH).MethodsWe measured SARS-CoV-2 specific humoral and cellular responses in people with and without HIV recovering from COVID-19 (n=39 and n=43, respectively) using binding antibody, surrogate virus neutralization, intracellular cytokine staining, and inflammatory marker assays. We identified individuals experiencing post-acute sequelae of SARS-CoV-2 infection (PASC) and evaluated immunologic parameters. We used linear regression and generalized linear models to examine differences by HIV status in the magnitude of inflammatory and virus-specific antibody and T cell responses, as well as differences in the prevalence of PASC.ResultsAmong PWH, we found broadly similar SARS-CoV-2-specific antibody and T cell responses as compared with a well-matched group of HIV-negative individuals. PWH had 70% lower relative levels of SARS-CoV-2 specific memory CD8+ T cells (p=0.007) and 53% higher relative levels of PD-1+ SARS-CoV-2 specific CD4+ T cells (p=0.007). Higher CD4/CD8 ratio was associated with lower PD-1 expression on SARS-CoV-2 specific CD8+ T cells (0.34-fold effect, p=0.02). HIV status was strongly associated with PASC (odds ratio 4.01, p=0.008), and levels of certain inflammatory markers (IL-6, TNF-alpha, and IP-10) were associated with persistent symptoms.ConclusionsWe identified potentially important differences in SARS-CoV-2 specific CD4+ and CD8+ T cells in PWH and HIV-negative participants that might have implications for long-term immunity conferred by natural infection. HIV status strongly predicted the presence of PASC. Larger and more detailed studies of PASC in PWH are urgently needed.
FiNN: A toolbox for neurophysiological network analysis
Authors: Scherer Maximilian , Wang Tianlu , Guggenberger Robert , Milosevic Luka , Gharabaghi Alireza 
Published: February 2022

AbstractRecently, neuroscience has seen a shift from localist approaches to network-wide investigations of brain function. Neurophysiological signals across different spatial and temporal scales provide insight into neural communication. However, additional methodological considerations arise when investigating network-wide brain dynamics rather than local effects. Specifically, larger amounts of data, investigated across a higher dimensional space, are necessary.Here, we present FiNN (Find Neurophysiological Networks), a novel toolbox for the analysis of neurophysiological data with a focus on functional and effective connectivity. FiNN provides a wide range of data processing methods, and statistical and visualization tools to facilitate inspection of connectivity estimates and the resulting metrics of brain dynamics. The Python toolbox (https://github.com/neurophysiological-analysis/FiNN) and its documentation (https://neurophysiological-analysis.github.io/FiNN/) are freely available.We evaluated FiNN against a number of established frameworks on both a conceptual and an implementation level. We found FiNN to require much less processing time and memory than other toolboxes. In addition, FiNN adheres to a design philosophy of easy access and modifiability, while providing efficient data processing implementations. Since the investigation of network-level neural dynamics is experiencing increasing interest, we place FiNN at the disposal of the neuroscientific community as open-source software.
Locomotion-induced gain of visual responses cannot explain visuomotor mismatch responses in layer 2/3 of primary visual cortex
Authors: Vasilevskaya Anna , Widmer Felix C. , Keller Georg B. , Jordan Rebecca 
Published: February 2022

SUMMARYThe aim of this work is to provide a comment on a recent paper by Muzzu and Saleem (2021). In brief, our concern is that the authors claim that visuomotor mismatch responses in mouse visual cortex can be explained by a locomotion-induced gain of visual halt responses, without directly comparing these responses to mismatch responses. Without a direct comparison, the claim that one response can explain the other appears difficult to uphold, more so because previous work finds that a uniform locomotion-induced gain cannot explain mismatch responses. To support these arguments, we analyzed a series of layer 2/3 calcium imaging datasets and show that coupling between visual flow and locomotion greatly enhances mismatch responses in an experience dependent manner compared to halts in non-coupled visual flow. This is consistent with mismatch responses representing visuomotor prediction errors. Thus, we conclude that feature selectivity cannot explain mismatch responses in mouse visual cortex.
Learning vs. minding: How subjective costs can mask motor learning
Authors: Healy Chadwick M. , Berniker Max , Ahmed Alaa A. 
Published: February 2022

AbstractWhen learning new movements some people make larger kinematic errors than others, interpreted as a reduction in motor-learning ability. Consider a learning task where error-cancelling strategies incur higher effort costs, specifically where subjects reach to targets in a force field. Concluding that those with greater error have learned less has a critical assumption: everyone uses the same error-canceling strategy. Alternatively, it could be that those with greater error may be choosing to sacrifice error reduction in favor of a lower effort movement. Here, we test this hypothesis in a dataset that includes both younger and older adults, where older adults exhibited greater kinematic errors. Utilizing the framework of optimal control theory, we infer subjective costs (i.e., strategies) and internal model accuracy (i.e., proportion of the novel dynamics learned) by fitting a model to each population’s trajectory data. Our results demonstrate trajectories are defined by a combination of the amount learned and strategic differences represented by relative cost weights. Based on the model fits, younger adults could have learned between 65-90% of the novel dynamics. Critically, older adults could have learned between 60-85%. Each model fit produces trajectories that match the experimentally observed data, where a lower proportion learned in the model is compensated for by increasing costs on kinematic errors relative to effort. This suggests older and younger adults could be learning to the same extent, but older adults have a higher relative cost on effort compared to younger adults. These results call into question the proposition that older adults learn less than younger adults and provide a potential explanation for the equivocal findings in the literature. Importantly, our findings suggest that the metrics commonly used to probe motor learning paint an incomplete picture, and that to accurately quantify the learning process the subjective costs of movements should be considered.Author SummaryHere we show that how a person values effort versus error in their movements has an impact on their overall strategy for performing those movements and adapting to a novel environment. When error alone is considered as a measure of learning, it appears that certain populations such as older adults are significantly worse at learning new motor tasks. However, using an optimal control framework, we are able to parse out differences in how much a population or person has learned, as well as how they subjectively value factors such as effort and error. In the case of older adults, we show that they could be learning as much as younger adults but exhibit larger errors because they care more about expending extra effort to reduce them.
Scalable sequence database search using Partitioned Aggregated Bloom Comb-Trees
Authors: Marchet Camille , Limasset Antoine 
Published: February 2022

AbstractThe Sequence Read Archive public database has reached 45 Peta-bytes of raw sequences and doubles its nucleotide content every two years. Although BLAST-like methods can routinely search for a sequence in a small collection of genomes, making searchable immense public resources accessible is beyond the reach of alignment-based strategies. In recent years, abundant literature tackled the task of finding a sequence in extensive sequence collections usingk-mer-based strategies. At present, the most scalable methods are approximate membership query data structures that combine the ability to query small signatures or variants while being scalable to collections up to 10,000 eukaryotic samples. Here, we present PAC, a novel approximate membership query data structure for querying collections of sequence datasets. PAC index construction works in a streaming fashion without any disk footprint besides the index itself. It shows a 3 to 6 fold improvement in construction time compared to other compressed methods for comparable index size. A PAC query can need single random access and be performed in constant time in favorable instances. Using limited computation resources, we built PAC for very large collections. They include 32,000 human RNA-seq samples in five days, the entire Genbank bacterial genome collection in a single day for an index size of 3.5TB. The latter is, to our knowledge, the largest sequence collection ever indexed using an approximate membership query structure. We also showed that PAC’s ability to query 500,000 transcript sequences in less than an hour. PAC’s open-source software is available athttps://github.com/Malfoy/PAC.
Positional Context of Myonuclear Transcription during Injury-induced Muscle Regeneration
Authors: Buckley Kole H. , Nestor-Kalinoski Andrea L. , Pizza Francis X. 
Published: February 2022

AbstractFundamental aspects underlying skeletal muscle regeneration after injury are poorly understood. This investigation begins to address deficiencies in knowledge by examining the kinetics of myonuclear accretion, positioning, and global transcription during injury-induced muscle regeneration in mice. We demonstrate that myonuclear accretion plateaus within 7 d of an injury and that the majority (~70%) of myonuclei are centrally aligned in linear arrays (nuclear chains) throughout the course of regeneration. Relatively few myonuclei were found in a peripheral position (~20%) or clustered (~10%) together during regeneration. Importantly, transcriptional activity of individual myonuclei in nuclear chains was high, and greater than that of peripheral or clustered myonuclei. Transcription occurring primarily in nuclear chains elevated the collective transcriptional activity of regenerating myofibers during the later stage of regeneration. Importantly, the number of myonuclei in chains and their transcriptional activity were statistically correlated with an increase in myofiber size during regeneration. Our findings demonstrate the positional context of transcription during regeneration and highlight the importance of centralized nuclear chains in facilitating hypertrophy of regenerating myofibers after injury.
Shisa7-dependent regulation of GABAA receptor single-channel gating kinetics
Authors: Castellano David , Wu Kunwei , Keramidas Angelo , Lu Wei 
Published: February 2022

AbstractGABAA receptors (GABAARs) mediate the majority of fast inhibitory transmission throughout the brain. Although it is widely known that pore-forming subunits critically determine receptor function, it is unclear whether their single-channel properties are modulated by GABAAR-associated transmembrane proteins. We previously identified Shisa7 as a GABAAR auxiliary subunit that modulates the trafficking, pharmacology, and kinetic properties of these receptors. In particular, Shisa7 accelerates receptor deactivation; however, the underlying mechanisms by which Shisa7 controls GABAAR kinetics have yet to be determined. Here we have performed single-channel recordings and find that while Shisa7 does not change channel slope conductance, it reduces the frequency of openings. Importantly, Shisa7 modulates GABAAR gating by decreasing the duration and open probability (Po) within bursts. Kinetic analysis of dwell times, activation modeling, and macroscopic simulations indicate that Shisa7 accelerates GABAAR deactivation by governing the time spent between close and open states during gating. Together, our data provide a mechanistic basis for how Shisa7 controls GABAAR gating kinetics and reveal for the first time that GABAAR single-channel properties can be modulated by an auxiliary subunit. These findings shed light on processes that shape the temporal dynamics of GABAergic transmission.
Cross-modality Synthesis of EM Time Series and Live Fluorescence Imaging
Authors: Santella Anthony , Kolotuev Irina , Kizilyaprak Caroline , Bao Zhirong 
Published: February 2022

AbstractAnalyses across imaging modalities allow the integration of complementary spatiotemporal information about brain development, structure and function. However, systematic atlasing across modalities is limited by challenges to effective image alignment. We combine highly spatially resolved electron microscopy (EM) and highly temporally resolved time-lapse fluorescence microscopy (FM) to examine the emergence of a complex nervous system in C. elegans embryogenesis. We generate an EM pseudo time series at four classic developmental stages and create a landmark-based co-optimization algorithm for cross-modality image alignment, which handles developmental heterochrony among datasets to achieve accurate single-cell level alignment. Synthesis based on the EM series and time-lapse FM series carrying different cell-specific markers reveals critical dynamic behaviors across scales of identifiable individual cells in the emergence of the primary neuropil, the nerve ring, as well as a major sensory organ, the amphid. Our study paves the way for systematic cross-modality analysis in C. elegans and demonstrates a powerful approach that may be applied broadly.HighlightsAn EM time series to examine the emergence of an entire nervous systemA landmark-based co-optimization algorithm for cross modality image alignment in the presence of developmental heterochronyIntegration of EM and fluorescence series reveals cell behaviors at high spatial and temporal resolutionSystematic single-cell annotation of EM data enables efficient navigation and targeted re-imaging
Differential adaptive potential and vulnerability to climate-driven habitat loss in Brazilian mangroves
Authors: de Deus Vidal João , Mori Gustavo Maruyama , Cruz Mariana Vargas , da Silva Michele Fernandes , de Moura Yohans Alves , de Souza Anete Pereira 
Published: February 2022

AbstractGeographic and environmental differences have been identified as factors influencing Brazilian mangrove trees’ genetic diversity. Geographically, distinct species have convergent spatial genetic structures, indicating a limited gene flow between northern and southern populations. Environmentally, genomic studies and common garden experiments have found evidence of local adaptations along the latitudinal gradient of the Brazilian coast. However, little is known about how such adaptive heterogeneity could be affected by a rapidly changing climate in the coming decades, and the combination of deforestation and climate-induced habitat loss may affect these forests and their genetic diversity. Here, we applied two genomic-environmental association methods to model the turnover of potentially adaptive alleles for two dominant mangrove trees: Avicennia germinans and A. schaueriana. We analyzed a total of 134 individuals from six populations of A. germinans and ten populations of A. schaueriana spanning the Brazilian coast from 1 °S to 28 °S. Gradient forest models identified temperature-related variables as the most important predictors for A. germinans outlier loci, whereas both temperature and precipitation were important for A. schaueriana. We modeled allele frequencies and projected them for future climatic scenarios to estimate adaptively driven vulnerability. We assessed climate-driven habitat loss through climate-only distribution models and calculated annual deforestation rates for each sampled region. Finally, to assess the vulnerability of individual populations, we combined the environmental suitability, deforestation data, and adaptive vulnerability projections. For both species, subtropical populations presented a higher vulnerability than equatorial populations to climate-driven habitat loss. We also identified deforestation rates at the sampled sites that were alarmingly higher than the global average mangrove deforestation rate. Our results provide improved estimates of the impacts of ongoing climate change and human-caused habitat loss on the distribution of mangroves and highlight the importance of site-based conservation strategies that consider individual subtropical and equatorial mangrove forests.
Unpacking COVID-19 and Conspiracy Theories in the UK Black Community
Authors: Vandrevala Tushna , Hendy Jane , Hanson Kristin , Alidu Lailah , Ala Aftab 
Published: February 2022

AbstractObjectivesConspiracy theories are associated with significant COVID-19 health consequences including lower engagement with protective behaviours. This study uses sensemaking theory, a process of constructing meanings through interpersonal exchanges that enable people to interpret their world to explain the theoretical process underlying the development of conspiratorial beliefs around COVID-19 within Black African and Caribbean communities in the UK.DesignQualitative, in-depth interviews were used.MethodsTwenty-eight members of the communities were recruited; semi-structured interviews were analysed using grounded theory.ResultsOur findings provide an explanation of how an environment of crisis combined with current and historical mistrust, perceived injustice and inequality provided a context in which alternative conspiracy narratives could thrive. The nature of these conspiratorial beliefs made more sense to many of our respondent’s than institutional sources (such as the UK Government). Critically, these alternative beliefs helped respondents shape their decision-making, leading to non-engagement with COVID protective behaviours.ConclusionsWe conclude that the uncertainty of the pandemic, combined with historical and contemporary perceived injustice and mistrust, and a lack of specific identity-aligned messaging, created a perfect environment for conspiratorial sense-making to thrive. This alternative sensemaking was inconsistent with the health-protection messaging espoused by Government. To ensure all groups in society are protected, and for health promotion messages to take purchase, the experiences of different target audiences must be taken into account, with sensemaking anchored in lived experience.
Why FIT and bHLH Ib interdependently regulate Fe-uptake
Authors: Cai Yuerong , Yang Yujie , Ping Huaqian , Lu Chengkai , Lei Rihua , Li Yang , Liang Gang 
Published: February 2022

AbstractFIT (FER-LIKE IRON DEFICIENCY- INDUCED TRANSCRIPTION FACTOR) and four bHLH Ib transcription factors (TFs) bHLH38, bHLH39, bHLH100 and bHLH101, are the master regulators of Fe uptake genes, and they interact with each other to activate the Fe uptake systems. However, it remains unclear why FIT and bHLH Ib depend on each other to regulate the Fe deficiency response. By analyzing Fe deficiency phenotypes and Fe uptake genes, we found that the quadruplebhlh4xmutants (bhlh38 bhlh39 bhlh100 bhlh101) mimic thefitmutant. Subcellular localization analyses indicate that bHLH38 and bHLH39 are preferentially expressed in the cytoplasm whereas bHLH100 and bHLH101 in the nucleus. Transcriptome data show that the genes involved in Fe signaling pathway show the same expression trends inbhlh4xandfit. Genetic analyses suggest that FIT and bHLH Ib depend each other to regulate the Fe deficiency response. Further biochemical assays indicate that bHLH Ib TFs possess the DNA binding ability and FIT has the transcription activation ability. This work concludes that FIT and bHLH Ib form a functional transcription complex in which bHLH Ib is responsible for target recognition and FIT for transcription activation, explaining why FIT and bHLH Ib interdependently regulate Fe uptake.
DILIC: An AI based classifier to search for Drug-Induced Liver Injury literature
Authors: Rathee Sanjay , MacMahon Meabh , Liu Anika , Katritsis Nicholas , Youssef Gehad , Hwang Woochang , Wollman Lilly , Han Namshik 
Published: February 2022

ABSTRACTDrug-Induced Liver Injury (DILI) is a class of Adverse Drug Reactions (ADR) which causes problems in both clinical and research settings. It is the most frequent cause of acute liver failure in the majority of western countries and is a major cause of attrition of novel drug candidates. Manual trawling of literature for is the main route of deriving information on DILI from research studies. This makes it an inefficient process prone to human error. Therefore, an automatized AI model capable of retrieving DILI-related papers from the huge ocean of literature could be invaluable for the drug discovery community. In this project, we built an artificial intelligence (AI) model combining the power of Natural Language Processing (NLP) and Machine Learning (ML) to address this problem. This model uses NLP to filter out meaningless text (e.g. stopwords) and uses customized functions to extract relevant keywords as singleton, pair, triplet and so on. These keywords are processed by apriori pattern mining algorithm to extract relevant patterns which are used to estimate initial weightings for a ML classifier. Along with pattern importance and frequency, an FDA-approved drug list mentioning DILI adds extra confidence in classification. The combined power of these methods build a DILI classifier (DILIC) with 94.91% cross-validation and 94.14% external validation accuracy. To make DILIC as accessible as possible, including to researchers without coding experience, an R Shiny App capable of classifing single or multiple entries for DILI is developed to enhance ease of user experience and made available at https://researchmind.co.uk/diliclassifier/).