bluuebunny/crossref_metadata_2025_split huggingface dataset:
The carbon footprint of bioinformatics
Authors: Grealey Jason , Lannelongue Loïc , Saw Woei-Yuh , Marten Jonathan , Meric Guillaume , Ruiz-Carmona Sergio , Inouye Michael 
Published: March 2021

AbstractBioinformatic research relies on large-scale computational infrastructures which have a non-zero carbon footprint. So far, no study has quantified the environmental costs of bioinformatic tools and commonly run analyses. In this study, we estimate the bioinformatic carbon footprint (in kilograms of CO2equivalent units, kgCO2e) using the freely available Green Algorithms calculator (www.green-algorithms.org). We assess (i) bioinformatic approaches in genome-wide association studies (GWAS), RNA sequencing, genome assembly, metagenomics, phylogenetics and molecular simulations, as well as (ii) computation strategies, such as parallelisation, CPU (central processing unit) vs GPU (graphics processing unit), cloud vs. local computing infrastructure and geography. In particular, for GWAS, we found that biobank-scale analyses emitted substantial kgCO2e and simple software upgrades could make GWAS greener, e.g. upgrading from BOLT-LMM v1 to v2.3 reduced carbon footprint by 73%. Switching from the average data centre to a more efficient data centres can reduce carbon footprint by ~34%. Memory over-allocation can be a substantial contributor to an algorithm’s carbon footprint. The use of faster processors or greater parallelisation reduces run time but can lead to, sometimes substantially, greater carbon footprint. Finally, we provide guidance on how researchers can reduce power consumption and minimise kgCO2e. Overall, this work elucidates the carbon footprint of common analyses in bioinformatics and provides solutions which empower a move toward greener research.
Snapshot of the Atlantic Forest canopy: surveying arboreal mammals in a biodiversity hotspot
Authors: Kaizer Mariane C. , Alvim Thiago H.G. , Novaes Claudio L. , Mcdevitt Allan D. , Young Robert J. 
Published: March 2021

AbstractThe Atlantic Forest of South America supports some of the greatest terrestrial biodiversity on our planet but is now reduced to only a small extent of its original forest cover. It hosts a large number of endemic mammalian species but our knowledge on arboreal mammal ecology and conservation has been hindered by the challenges of observing arboreal species from ground level. Camera trapping has proven to be an effective tool in terrestrial mammal monitoring, but the technique has rarely been used for arboreal species. Here we generated data on the arboreal mammal community based on canopy camera trapping for the first time in the Atlantic Forest, focusing on Caparaó National Park, Brazil. We placed 24 infrared camera traps in the forest canopy distributed in seven areas within the Park, operating continuously from January 2017 to June 2019. In this time, they accumulated 4,736 camera-days and generated 2,256 sets of pictures and 30 second videos of vertebrates. Canopy camera traps were able to detect arboreal mammals spanning a wide variety of body sizes. The local mammal assemblage comprised of 15 identifiable species, including the critically endangered northern muriqui (Brachyteles hypoxanthus) and the buffy-headed marmoset (Callithrix flaviceps), and other rare, nocturnal and inconspicuous species. For the first time, we confirmed the occurrence of the thin-spined porcupine (Chaetomys subspinosus) in the Park. Species richness varied across sampling areas and forest types. Our findings demonstrate the potential of canopy camera trapping for future surveying efforts to better inform conservation strategies for mammals.
State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward learning
Authors: Hein Thomas P , Ruiz Maria Herrojo 
Published: March 2021

AbstractAnxiety influences how the brain estimates and responds to uncertainty. These behavioural effects have been described within predictive coding and Bayesian inference frameworks, yet the associated neural correlates remain unclear. Recent work suggests that predictions in generative models of perception are represented in alpha (8–12 Hz) and beta oscillations (13–30 Hz). Updates to predictions are driven by prediction errors weighted by precision (inverse variance; pwPE), and are encoded in gamma oscillations (>30 Hz) and associated with suppression of beta activity. We tested whether state anxiety alters the neural oscillatory activity associated with predictions and pwPE during learning. Healthy human participants performed a probabilistic reward-learning task in a volatile environment. In our previous work, we described learning behaviour in this task using a hierarchical Bayesian model, revealing more precise (biased) beliefs about the reward tendency in state anxiety, consistent with reduced learning in this group. The model provided trajectories of predictions and pwPEs for the current study, allowing us to assess their parametric effects on the time-frequency representations of EEG data. Using convolution modelling for oscillatory responses, we found that, relative to a control group, state anxiety increased beta activity in frontal and sensorimotor regions during processing of pwPE, and in fronto-parietal regions during encoding of predictions. No effects of state anxiety on gamma modulation were found. Our findings expand prior evidence on the oscillatory representations of predictions and pwPEs into the reward-learning domain. The results suggest that state anxiety modulates beta-band oscillatory correlates of pwPE and predictions in generative models, providing insights into the neural processes associated with biased belief updating and poorer reward learning.
Variability of EEG electrode positions and their underlying brain regions: visualising gel artifacts from a simultaneous EEG-fMRI dataset
Authors: Scrivener C. L. , Reader A. T. 
Published: March 2021

AbstractWe investigated the between-subject variability of EEG electrode placement from a simultaneously recorded EEG-fMRI dataset. Neuro-navigation software was used to localise electrode positions inxyzand MNI space, made possible by the gel artifacts present in the structural MRI images. To assess variation in the brain regions directly underneath each electrode, we used both raw MNI coordinates and labels from the Harvard-Oxford Cortical atlas. In a sample of 20 participants, the mean standard deviation of electrode placement was 3.94 mm inx, 5.55 mm iny, and 7.17 mm inz, with the largest variation in parietal and occipital electrodes. In addition, the brain regions covered by electrode pairs was not always consistent; for example, the mean location of electrode P07 was mapped to BA18, whereas P08 was closer to BA19. Further, electrode C1 was mapped to the left primary motor cortex, whereas C2 was closer to right pre-motor cortex. Overall, the results emphasise the variation in electrode positioning that can be found even in a fixed cap, potentially caused by between-subject differences in brain morphology. We present a relatively simple method for approximating the location of electrodes in a simultaneous EEG-fMRI data set with accompanying analysis code, and suggest that researchers check the regions underlying their EEG ROIs to improve the generalisability and reliability of their neuroimaging results.
Multimodal image fusion via deep generative models
Authors: Dimitri Giovanna Maria , Spasov Simeon , Duggento Andrea , Passamonti Luca , Lio’ Pietro , Toschi Nicola 
Published: March 2021

AbstractRecently, it has become progressively more evident that classic diagnostic labels are unable to accurately and reliably describe the complexity and variability of several clinical phenotypes. This is particularly true for a broad range of neuropsychiatric illnesses such as depression and anxiety disorders or behavioural phenotypes such as aggression and antisocial personality. Patient heterogeneity can be better described and conceptualized by grouping individuals into novel categories, which are based on empirically-derived sections of intersectingcontinuathat span both across and beyond traditional categorical borders. In this context, neuroimaging data carry a wealth of spatiotemporally resolved information about each patient’s brain. However, they are usually heavily collapseda priorithrough procedures which are not learned as part of model training, and consequently not optimized for the downstream prediction task. This is due to the fact that every individual participant usually comes with multiple whole-brain 3D imaging modalities often accompanied by a deep genotypic and phenotypic characterization, hence posing formidable computational challenges.In this paper we design and validate a deep learning architecture based on generative models rooted in a modular approach and separable convolutional blocks (which result in a 20-fold decrease in parameter utilization) in order to a) fuse multiple 3D neuroimaging modalities on a voxel-wise level, b) efficiently convert them into informative latent embeddings through heavy dimensionality reduction, c) maintain excellent generalizability and minimal information loss. As proof of concept, we test our architecture on the well characterized Human Connectome Project database (n=974 healthy subjects), demonstrating that our latent embeddings can be clustered into easily separable subject strata which, in turn, map to extremely different phenotypical information (including organic, neuropsychological, personality variables) which was not included in the embedding creation process.The ability to extract meaningful and separable phenotypic information from brain images alone can aid in creating multi-dimensional biomarkers able to chart spatio-temporal trajectories which may correspond to different pathophysiological mechanisms unidentifiable to traditional data analysis approaches. In turn, this may be of aid in predicting disease evolution as well as drug response, hence supporting mechanistic disease understanding and also empowering clinical trials.
Vinculin controls endothelial cell junction dynamics during vascular lumen formation
Authors: Kotini Maria P. , van der Stoel Miesje M. , Han Mitchell K. , Kirchmaier Bettina , de Rooij Johan , Affolter Markus , Huveneers Stephan , Belting Heinz-Georg 
Published: March 2021

AbstractBlood vessel morphogenesis is driven by coordinated endothelial cell behaviors, which depend on dynamic cell-cell interactions. Remodeling of endothelial cell-cell junctions promote morphogenetic cellular events while preserving vascular integrity. Here, we have analyzed the dynamics of endothelial cell-cell junctions during lumen formation in angiogenic sprouts. By live-imaging of the formation of intersegmental blood vessels in zebrafish, we demonstrate that lumen expansion is accompanied by the formation of transient finger-shaped junctions. Formation and maintenance of these junctional fingers are positively regulated by blood pressure whereas inhibition of blood flow prevents their formation. Using fluorescent reporters, we show that the tension-sensor Vinculin localizes to junctional fingers. Furthermore, loss of vinculin function, invinculin aand -bdouble knockouts, prevents junctional finger formation in angiogenic sprouts, whereas endothelial expression of avinculintransgene is sufficient to restore junctional fingers. Taken together, our findings suggest a mechanism in which lumen expansion during angiogenesis leads to an increase in junctional tension, which triggers recruitment of vinculin and formation of junctional fingers. We propose that endothelial cells may employ force-dependent junctional remodeling to react to changes in external forces to protect cell-cell contacts and to maintain vascular integrity during sprouting angiogenesis.
Latent representation of the human pan-celltype epigenome through a deep recurrent neural network
Authors: Dsouza Kevin B. , Li Adam Y. , Bhargava Vijay K. , Libbrecht Maxwell W. 
Published: March 2021

AbstractThe availability of thousands of assays of epigenetic activity necessitates compressed representations of these data sets that summarize the epigenetic landscape of the genome. Until recently, most such representations were celltype specific, applying to a single tissue or cell state. Recently, neural networks have made it possible to summarize data across tissues to produce a pan-celltype representation. In this work, we propose Epi-LSTM, a deep long short-term memory (LSTM) recurrent neural network autoencoder to capture the long-term dependencies in the epigenomic data. The latent representations from Epi-LSTM capture a variety of genomic phenomena, including gene-expression, promoter-enhancer interactions, replication timing, frequently interacting regions and evolutionary conservation. These representations outperform existing methods in a majority of cell-types, while yielding smoother representations along the genomic axis due to their sequential nature.
Rapid Identification of Methylase Specificity (RIMS-seq) jointly identifies methylated motifs and generates shotgun sequencing of bacterial genomes
Authors: Baum Chloé , Yu-Cheng-Lin  , Fomenkov Alexey , Anton Brian P. , Chen Lixin , Evans Thomas C. , Roberts Richard J , Tolonen Andrew C , Ettwiller Laurence 
Published: March 2021

AbstractDNA methylation is widespread amongst eukaryotes and prokaryotes to modulate gene expression and confer viral resistance. 5-methylcytosine (m5C) methylation has been described in genomes of a large fraction of bacterial species as part of restriction-modification systems, each composed of a methyltransferase and cognate restriction enzyme. Methylases are site-specific and target sequences vary across organisms. High-throughput methods, such as bisulfite-sequencing can identify m5C at base resolution but require specialized library preparations and Single Molecule, Real-Time (SMRT) Sequencing usually misses m5C. Here, we present a new method called RIMS-seq (Rapid Identification of Methylase Specificity) to simultaneously sequence bacterial genomes and determine m5C methylase specificities using a simple experimental protocol that closely resembles the DNA-seq protocol for Illumina. Importantly, the resulting sequencing quality is identical to DNA-seq, enabling RIMS-seq to substitute standard sequencing of bacterial genomes. Applied to bacteria and synthetic mixed communities, RIMS-seq reveals new methylase specificities, supporting routine study of m5C methylation while sequencing new genomes.
Identification of putative causal loci in whole-genome sequencing data via knockoff statistics
Authors: He Zihuai , Liu Linxi , Wang Chen , Guen Yann Le , Lee Justin , Gogarten Stephanie , Lu Fred , Montgomery Stephen , Tang Hua , Silverman Edwin K. , Cho Michael H. , Greicius Michael , Ionita-Laza Iuliana 
Published: March 2021

AbstractThe analysis of whole-genome sequencing studies is challenging due to the large number of rare variants in noncoding regions and the lack of natural units for testing. We propose a statistical method to detect and localize rare and common risk variants in whole-genome sequencing studies based on a recently developed knockoff framework. It can (1) prioritize causal variants over associations due to linkage disequilibrium thereby improving interpretability; (2) help distinguish the signal due to rare variants from shadow effects of significant common variants nearby; (3) integrate multiple knockoffs for improved power, stability and reproducibility; and (4) flexibly incorporate state-of-the-art and future association tests to achieve the benefits proposed here. In applications to whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project (ADSP) and COPDGene samples from NHLBI Trans-Omics for Precision Medicine (TOPMed) Program we show that our method compared with conventional association tests can lead to substantially more discoveries.
Exposure to sevoflurane results in changes of transcription factor occupancy in sperm and inheritance of autism
Authors: Wang Hsiao-Lin V. , Forestier Samantha , Corces Victor G. 
Published: March 2021

ABSTRACTOne in 54 children in the U.S. is diagnosed with Autism Spectrum Disorder (ASD). De novo germline and somatic mutations cannot account for all cases of ASD, suggesting that epigenetic alterations triggered by environmental exposures may be responsible for a subset of ASD cases. Human and animal studies have shown that exposure of the developing brain to general anesthetic (GA) agents can trigger neurodegeneration and neurobehavioral abnormalities but the effects of general anesthetics on the germ line have not been explored in detail. We exposed pregnant mice to sevoflurane during the time of embryonic development when the germ cells undergo epigenetic reprogramming and found that more than 38% of the directly exposed F1 animals exhibit impairments in anxiety and social interactions. Strikingly, 44-47% of the F2 and F3 animals, which were not directly exposed to sevoflurane, show the same behavioral problems. We performed ATAC-seq and identified more than 1,200 differentially accessible sites in the sperm of F1 animals, 69 of which are also present in the sperm of F2 animals. These sites are located in regulatory regions of genes strongly associated with ASD, includingArid1b, Ntrk2, andStmn2. These findings suggest that epimutations caused by exposing germ cells to sevoflurane can lead to ASD in the offspring, and this effect can be transmitted through the male germline inter and trans-generationally.Summary sentencePregnant mouse F0 females exposed to sevoflurane give rise to F1 males with sociability and anxiety defects. These behaviors are transmitted to F2 and F3 males. Their sperm show changes in transcription factor occupancy in genes implicated in autism.
Phage Display Broadly Identifies Inhibitor-reactive Regions in von Willebrand Factor
Authors: Yee Andrew , Dai Manhong , Croteau Stacy E. , Shavit Jordan A. , Pipe Steven W. , Siemieniak David , Meng Fan , Ginsburg David 
Published: March 2021

SummaryBackgroundCorrection of von Willebrand factor (VWF) deficiency with replacement products containing VWF can lead to the development of anti-VWF alloantibodies (i.e., VWF inhibitors) in patients with severe von Willebrand disease (VWD).ObjectiveLocate inhibitor-reactive regions within VWF using phage display.MethodsWe screened a phage library displaying random, overlapping fragments covering the full length VWF protein sequence for binding to a commercial anti-VWF antibody or to immunoglobulins from three type 3 VWD patients who developed VWF inhibitors in response to treatment with plasma-derived VWF. Immunoreactive phage clones were identified and quantified by next generation DNA sequencing (NGS).ResultsNGS markedly increased the number of phage analyzed for locating immunoreactive regions within VWF following a single round of selection and identified regions not recognized in previous reports using standard phage display methods. Extending this approach to characterize VWF inhibitors from three type 3 VWD patients (including two siblings homozygous for the sameVWFgene deletion) revealed patterns of immunoreactivity distinct from the commercial antibody and between unrelated patients, though with notable areas of overlap. Alloantibody reactivity against the VWF propeptide is consistent with incomplete removal of the propeptide from plasma-derived VWF replacement products.ConclusionThese results demonstrate the utility of phage display and NGS to characterize diverse anti-VWF antibody reactivities.
A novel nitrogen concentrating mechanism in the coral-algae symbiosome
Authors: Thies Angus B. , Quijada-Rodriquez Alex R. , Zhouyao Haonan , Weihrauch Dirk , Tresguerres Martin 
Published: March 2021

AbstractCoral algal symbionts are hosted inside the symbiosome of gastrodermal cells, an intracellular compartment that isolates algae from the external environment and allows host cells to control the delivery of metabolites to their symbionts. However, the underlying molecular mechanisms are largely unknown. Here, we report the diel trafficking of NH3-transporting Rhesus (Rh) channels between the cytoplasm and the symbiosome membrane in the coralAcropora yongei, which matches established patterns of nitrogen delivery to endosymbionts. Heterologous expression inXenopusoocytes established thatA. yongeiRh (ayRhp1) is a channel that facilitates NH3diffusion across membranes following its partial pressure gradient. Immunostaining revealed ayRhp1 is widely distributed throughout coral tissues and most abundantly present in oral ectodermal cells, desmocytes, and gastrodermal cells. In the latter, ayRhp1 was observed in the symbiosome membrane of alga-containing cells. Together with V-type H+-ATPases that make the symbiosome highly acidic (pH~4), ayRhp1 constitutes an NH4+-trapping mechanism analogous to that in mammalian renal tubule. Remarkably, ayRhp1 presence in the symbiosome membrane was higher during the day than the night. This indicates a regulatory mechanism that facilitates NH4+delivery to alga during the day, likely to sustain high turnover rates of photosynthetic proteins, while restricting NH4+delivery at night to maintain the endosymbiotic algae in a nitrogen-limited stage that stagnates their growth. The dynamic trafficking of proteins to and away from the symbiosome membrane is a previously unknown mechanism that contributes to metabolic regulation between symbiotic partners.Significance StatementThe endosymbiotic relationship between corals and algae relies on the coordinated exchange of metabolites. Disruption of these metabolic exchanges can result in interruption of the symbiosis; however, the underlying molecular mechanisms are poorly understood. Here we report thatAcropora yongeicoral host cells express ammonia-transporting channel proteins (ayRhp1), which traffic to and away from the symbiosome membrane surrounding the endosymbiotic algae. In conjunction with the acidic symbiosome microenvironment, this mechanism allows host cells to regulate nitrogen delivery to endosymbionts sustaining essential functions while restricting growth. This work provides novel mechanistic information about metabolic regulation of animal-algae symbioses, and advances our understanding of physiological mechanisms that might determine coral local adaptation, resilience, and vulnerability to environmental stress including climate change.
Distributed brain co-processor for tracking electrophysiology and behavior during electrical brain stimulation
Authors: Sladky Vladimir , Nejedly Petr , Mivalt Filip , Brinkmann Benjamin H. , Kim Inyong , St. Louis Erik K. , Gregg Nicholas M. , Lundstrom Brian N. , Crowe Chelsea M. , Pal Attia Tal , Crepeau Daniel , Balzekas Irena , Marks Victoria , Wheeler Lydia P. , Cimbalnik Jan , Cook Mark , Janca Radek , Sturges Beverly K. , Leyde Kent , Miller Kai J. , Van Gompel Jamie J. , Denison Timothy , Worrell Gregory A. , Kremen Vaclav 
Published: March 2021

AbstractEarly implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioral inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behavior and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a hand-held device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes, and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from 9 humans and 8 canines with epilepsy, and then implemented prospectively in out-of-sample testing in 2 pet canines and 4 humans with drug resistant epilepsy living in their natural environments.