bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genome dashboards: Framework and Examples
Authors: Li Zilong , Sun Ran , Bishop Thomas C. 
Published: October 2019

ABSTRACTGenomics is a sequence-based informatics science and a 3D structure-based material science. Here we describe a framework for developing genome dashboards specifically designed to unify informatics with studies of chromatin structure and dynamics. The framework is based on the mathematical representation of geometrically exact rod models and the generalization of DNA base pair step parameters. A Model-View-Controller software design approach is proposed to implement genome dashboards as finite state machines either as desktop or web based applications. Two examples are demonstrated using our minimal genome dashboard called G-Dash-min. The data unification achieved with a genome dashboard supports the bi-directional exchange of data between informatics and structure. Thus any experimentally or theoretically determined sequence based informatics track can inform DNA, nucleosome or chromatin modeling (e.g. nucleosome positions) and structure features can be analyzed as informatics tracks in a genome browser (e.g. DNA base pair step parameters: Roll, Tilt, Twist). Here the framework is applied to chromatin, but genome dashboards are more broadly applicable. Genome dashboards are a novel means of investigating structure-function relationships for regions of the genome ranging from base pairs to entire chromosomes and for generating, validating, and testing mechanistic hypotheses.
Population-specific causal disease effect sizes in functionally important regions impacted by selection
Authors: Shi Huwenbo , Gazal Steven , Kanai Masahiro , Koch Evan M. , Schoech Armin P. , Siewert Katherine M. , Kim Samuel S. , Luo Yang , Amariuta Tiffany , Huang Hailiang , Okada Yukinori , Raychaudhuri Soumya , Sunyaev Shamil R. , Price Alkes L. 
Published: October 2019

AbstractMany diseases and complex traits exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We developed a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and applied S-LDXR to genome-wide association summary statistics for 31 diseases and complex traits in East Asians (EAS) and Europeans (EUR) (average NEAS=90K, NEUR=267K) with an average trans-ethnic genetic correlation of 0.85 (s.e. 0.01). We determined that squared trans-ethnic genetic correlation was 0.82× (s.e. 0.01) smaller than the genome-wide average at SNPs in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes were more population-specific in functionally important regions, including conserved and regulatory regions. In analyses of regions surrounding specifically expressed genes, causal effect sizes were most population-specific for skin and immune genes and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.
From a deep learning model back to the brain - inferring morphological markers and their relation to aging
Authors: Levakov Gidon , Rosenthal Gideon , Shelef Ilan , Raviv Tammy Riklin , Avidan Galia 
Published: October 2019

AbstractWe present a Deep Learning framework for the prediction of chronological age from structural MRI scans. Previous findings associate an overestimation of brain age with neurodegenerative diseases and higher mortality rates. However, the importance of brain age prediction goes beyond serving as biomarkers for neurological disorders. Specifically, utilizing convolutional neural network (CNN) analysis to identify brain regions contributing to the prediction can shed light on the complex multivariate process of brain aging. Previous work examined methods to attribute pixel/voxel-wise contributions to the prediction in a single image, resulting in ‘explanation maps’ that were found noisy and unreliable. To address this problem, we developed an inference framework for combining these maps across subjects, thus creating a population-based rather than a subject-specific map. We applied this method to a CNN ensemble trained on predicting subjects’ age from raw T1 brain images of 10,176 subjects. Evaluating the model on an untouched test set resulted in mean absolute error of 3.07 years and a correlation between chronological and predicted age of r=0.98. Using the inference method, we revealed that cavities containing CSF, previously found as general atrophy markers, had the highest contribution for age prediction. Comparing maps derived from different models within the ensemble allowed to assess differences and similarities in brain regions utilized by the model. We showed that this method substantially increased the replicability of explanation maps, converged with results from voxel-based morphometry age studies and highlighted brain regions whose volumetric variability contributed the most to the prediction.HighlightsCNNs ensemble is shown to estimate “brain age” from sMRI with an MAE of ∼3.1 yearsA novel framework enables to highlight brain regions contributing to the predictionThis framework results in explanation maps showing consistency with the literatureAs sample size increases, these maps show higher inter-sample replicabilityCSF cavities reflecting general atrophy were found as a prominent aging biomarker
Deep metagenomics examines the oral microbiome during dental caries, revealing novel taxa and co-occurrences with host molecules
Authors: Baker J.L. , Morton J.T. , Dinis M. , Alverez R. , Tran N.C. , Knight R. , Edlund A. 
Published: October 2019

AbstractDental caries is the most common chronic infectious disease globally. The microbial communities associated with caries have mainly been examined using relatively low-resolution 16S rRNA gene amplicon sequencing and/or using downstream analyses that are unsound for the compositional nature of the data provided by sequencing. Additionally, the relationship between caries, oral microbiome composition, and host immunological markers has not been explored. In this study, the oral microbiome and a panel of 38 host markers was analyzed across the saliva from 23 children with dentin caries and 24 children with healthy dentition. Metagenomic sequencing, followed by investigation using tools designed to be robust for compositional data, illustrated that severalPrevotellaspp. were prevalent in caries, whileRothiaspp. were associated with the health. The contributional diversity (extent to which multiple taxa contribute to each pathway) of functional pathways present in the oral microbiome was decreased in the caries group. This decrease was especially noticeable in several pathways known to impede caries pathogenesis, including arginine and branched-chain amino acid biosynthesis. 10 host immunological markers were found to be significantly elevated in the saliva of the caries group, and microbe-metabolite co-occurrence analysis provided an atlas of relationships contributing to the bi-directional influence between the oral microbiome and the host immune system. Finally, 527 metagenome-assembled genomes were obtained from the metagenomics data, representing 151 species. 23 taxa were novel genera/species and a further 20 taxa were novel species. This study thus serves as a model analysis pipeline that will tremendously expand our knowledge of the oral microbiome and its relationship to dental caries once applied to large populations.
Strand-specific single-cell methylomics reveals distinct modes of DNA demethylation dynamics during early mammalian development
Authors: Sen Maya , Mooijman Dylan , Boisset Jean-Charles , Chialastri Alex , Popovic Mina , Heindryckx Björn , de Sousa Lopes Susana M. Chuva , Dey Siddharth S. , van Oudenaarden Alexander 
Published: October 2019

AbstractDNA methylation (5mC) is central to cellular identity and the global erasure of 5mC from the parental genomes during preimplantation mammalian development is critical to reset the methylome of terminally differentiated gametes to the pluripotent cells in the blastocyst. While active and passive modes of demethylation have both been suggested to play a role in this process, the relative contribution of these two mechanisms to genome-wide 5mC erasure remains unclear. Here, we report a new high-throughput single-cell method (scMspJI-seq) that enables strand-specific quantification of 5mC, thereby allowing us to systematically probe the dynamics of global demethylation. First, when applied to hybrid mouse embryonic stem cells, we identified substantial cell-to-cell strand-specific 5mC heterogeneity, with a small group of cells displaying asymmetric levels of 5mCpG between the two DNA strands of a chromosome suggesting loss of maintenance methylation. Next, using scMspJI-seq in preimplantation mouse embryos, we discovered that methylation maintenance is active till the 16-cell stage followed by passive demethylation in a fraction of cells within the early blastocyst at the 32-cell stage of development. Finally, we found that human preimplantation embryos qualitatively show temporally delayed yet similar demethylation dynamics as mouse preimplantation embryos. Collectively, these results demonstrate that scMspJI-seq is a sensitive and cost-effective method to map the strand-specific genome-wide patterns of 5mC in single cells, thereby enabling quantitative investigation of methylation dynamics in developmental systems.
In pursuit of visual attention: SSVEP frequency-tagging moving targets
Authors: de Lissa Peter , Caldara Roberto , Nicholls Victoria , Miellet Sebastien 
Published: October 2019

AbstractPrevious research has shown that visual attention does not always exactly follow gaze direction, leading to the concepts of overt and covert attention. However, it is not yet clear how such covert shifts of visual attention to peripheral regions impact the processing of the targets we directly foveate as they move in our visual field. The current study utilised the co-registration of eye-position and EEG recordings while participants tracked moving targets that were embedded with a 30 Hz frequency tag in a Steady State Visually Evoked Potentials (SSVEP) paradigm. When the task required attention to be divided between the moving target (overt attention) and a peripheral region where a second target might appear (covert attention), the SSVEPs elicited by the tracked target at the 30 Hz frequency band were significantly lower than when participants did not have to covertly monitor for a second target. Our findings suggest that neural responses of overt attention are reduced when attention is divided between covert and overt areas. This neural evidence is in line with theoretical accounts describing attention as a pool of finite resources, such as the perceptual load theory. Altogether, these results have practical implications for many real-world situations where covert shifts of attention may reduce visual processing of objects even when they are directly being tracked with the eyes.
Realistic fisheries management reforms could mitigate the impacts of climate change in most countries
Authors: Free Christopher M. , Mangin Tracey , Molinos Jorge García , Ojea Elena , Costello Christopher , Gaines Steven D. 
Published: October 2019

AbstractAlthough climate change is altering the productivity and distribution of marine fisheries, climate-adaptive fisheries management could mitigate many of the negative impacts on human society. We forecast global fisheries biomass, catch, and profits to 2100 under three climate scenarios (RCPs 4.5, 6.0, 8.5) and five levels of management reform to (1) determine the impact of climate change on national fisheries and (2) quantify the national-scale benefits of implementing climate-adaptive fisheries reforms. Management reforms accounting for shifting productivity and shifting distributions would yield higher catch and profits in the future relative to today for 60-65% of countries under the two least severe climate scenarios but for only 35% of countries under the most severe scenario. Furthermore, these management reforms would yield higher cumulative catch and profits than business-as-usual management for nearly all countries under the two least severe climate scenarios but would yield lower cumulative catch for 40% of countries under the most severe scenario. Fortunately, perfect fisheries management is not necessary to achieve these benefits: transboundary cooperation with 5-year intervals between adaptive interventions would result in comparable outcomes. However, the ability for realistic management reforms to offset the negative impacts of climate change is bounded by changes in underlying biological productivity. Although realistic reforms could generate higher catch and profits for 23-50% of countries experiencing reductions in productivity, the remaining countries would need to develop, expand, and reform aquaculture and other food production sectors to offset losses in capture fisheries. Still, climate-adaptive management is more profitable than business-as-usual management in all countries and we provide guidance on implementing – and achieving the benefits of – climate-adaptive fisheries reform along a gradient of scientific, management, and enforcement capacities.
Frequency Conservation Score (FCS): the power of conservation and allele frequency for variant pathogenic prediction
Authors: Alarcon Jose Luis Cabrera , Enriquez Jose Antonio , Sánchez-Cabo Fátima 
Published: October 2019

ABSTRACTBackgroundPrediction of pathogenic variants is one of the biggest challenges for researchers and clinicians in the time of next-generation sequencing technologies. Stratification of individuals based on truly pathogenic variants might lead to improved, personalized treatments.ResultsWe present Frequency Conservation Score (FCS) and Frequency Conservation Score for Mitochondrial DNA (FCSMt) two methods for the detection of pathogenic single nucleotide variants in nuclear and mitochondrial DNA, respectively. These scores are based in a random forest model trained over a set of potentially relevant predictors: (i) conservation scores (PhastCons and phyloP); (ii) locus variability at each genomic position built from gnomAD database and (iii) physicochemical distance for amino acids substitutions and the impact/consequence over the canonical transcript. FCS showed an AUC of 98% for deleteriousness in an independent validation dataset, outperforming other scores such as metaLR, metaSVM, REVEL, DANN, CADD, SIFT, PROVEAN or FATHMM-MKL. Moreover, FCSMt presented an AUC=0.92 for pathogenic mitochondrial SNVs detection. The tool is available at http://bioinfo.cnic.es/FCSConclusionsFCS and FCS-Mt improve pathogenic mutation detection, allowing the prioritization of relevant variants in Whole Exome and Whole Genome Sequencing Analysis.
Feasibility of spiral fMRI based on an LTI gradient model
Authors: Graedel Nadine N. , Kasper Lars , Engel Maria , Nussbaum Jennifer , Wilm Bertram J. , Pruessmann Klaas P. , Vannesjo S. Johanna 
Published: October 2019

AbstractSpiral imaging is very well suited for functional MRI, however its use has been limited by the fact that artifacts caused by gradient imperfections and B0 inhomogeneity are more difficult to correct compared to EPI. Effective correction requires accurate knowledge of the traversed k-space trajectory. With the goal of making spiral fMRI more accessible, we have evaluated image reconstruction using trajectories predicted by the gradient impulse response function (GIRF), which can be determined in a one-time calibration step.GIRF-predicted reconstruction was tested for high-resolution (0.8 mm) fMRI at 7T. Image quality and functional results of the reconstructions using GIRF-prediction were compared to reconstructions using the delay-corrected nominal trajectory and concurrent field monitoring.The reconstructions using nominal spiral trajectories contain substantial artifacts and the activation maps contain misplaced activation. Image artifacts are substantially reduced when using the GIRF-predicted reconstruction, and the activation maps for the GIRF-predicted and monitored reconstructions largely overlap. The GIRF reconstruction provides a large increase in the spatial specificity of the activation compared to the nominal reconstruction.The GIRF-reconstruction generates image quality and fMRI results similar to using a concurrently monitored trajectory. The presented approach does not prolong or complicate the fMRI acquisition. Using GIRF-predicted trajectories has the potential to enable high-quality spiral fMRI in situations where concurrent trajectory monitoring is not available.HighlightsThis work investigates the feasibility of using a one-time system calibration to account for k-space trajectory deviations in spiral fMRI.This versatile calibration is based on a linear time-invariant gradient model, the gradient impulse response function (GIRF).We show that the image quality and the spatial specificity of the fMRI activation are substantially improved when using the GIRF-predicted trajectories.Basing reconstructions on nominal gradient inputs, on the other hand, induces image artifacts and misplaced fMRI activation.We demonstrate that system characterization via the GIRF can enable spiral fMRI in situations where concurrent trajectory monitoring is unavailable.
Mutational signatures of replication timing and epigenetic modification persist through the global divergence of mutation spectra across the great ape phylogeny
Authors: Goldberg Michael E. , Harris Kelley 
Published: October 2019

ABSTRACTGreat ape clades exhibit variation in the relative mutation rates of different three-base-pair genomic motifs, with closely related species having more similar mutation spectra than distantly related species. This pattern cannot be explained by classical demographic or selective forces, but imply that DNA replication fidelity has been perturbed in different ways on each branch of the great ape phylogeny. Here, we use whole-genome variation from 88 great apes to investigate whether these species’ mutation spectra are broadly differentiated across the entire genome, or whether mutation spectrum differences are driven by DNA compartments that have particular functional features or chromatin states. We perform principal component analysis and mutational signature deconvolution on mutation spectra ascertained from compartments defined by features including replication timing and ancient repeat content, finding evidence for consistent species-specific mutational signatures that do not depend on which functional compartments the spectra are ascertained from. At the same time, we find that many compartments have their own characteristic mutational signatures that appear stable across the great ape phylogeny. For example, in a mutation spectrum PCA compartmentalized by replication timing, the second PC explaining 21.2% of variation separates all species’ late-replicating regions from their early-replicating regions. Our results suggest that great ape mutation spectrum evolution is not driven by epigenetic changes that modify mutation rates in specific genomic regions, but instead by trans-acting mutational modifiers that affect mutagenesis across the whole genome fairly uniformly.SIGNIFICANCE STATEMENTAll heritable variation begins with damage or copying mistakes affecting the DNA of sperm, eggs, or embryos. Different DNA motifs can have different mutation rates, and these rates can evolve over time: the spectrum of mutability of three-base-pair motifs has evolved rapidly during great ape diversification. Here, we show that even as ape mutation spectra diverged from each other, ape genomes preserved a landscape of spatial mutation spectrum variation. We can thus deconvolute the mutational process into a mixture of fast-evolving signatures with uniform spatial distributions and conserved signatures that target specific regions. Our findings may ultimately help determine the factors, either genetic or environmental, that contribute to temporal and spatial variation in germline mutagenesis.
Prediction errors indexed by the P3 track the updating of complex long-term memory schemas
Authors: Richter Franziska R. 
Published: October 2019

AbstractMemory schemas are higher-level knowledge structures that store an abstraction of multiple previous experiences. They allow us to retain a multitude of information without the cost of storing every detail. Schemas are believed to be relatively stable, but occasionally have to be updated to remain useful in the face of changing environmental conditions. Once a schema is consolidated, schema updating has been proposed to be the result of a prediction-error (PE) based learning mechanism, similar to the updating of less complex knowledge. However, for schema memory this hypothesis has been difficult to test because no sufficiently sensitive tools to track modifications to complex memory schemas existed so far. Current research on the updating of less complex beliefs and at much shorter time scales has identified the P3 as an electrophysiological correlate of PE-induced updating of beliefs. In this study, I recorded electroencephalography and continuous memory measures during the encoding of schema consistent vs. inconsistent material to test the behavioural and neural correlates of schema updating. I observed that PEs predicted the updating of a schema after a 24-hour delay, especially when participants were faced with inconsistent compared to consistent material. Moreover, the P3 amplitude tracked both the PE at the time of learning as well as the updating of the memory schema in the inconsistent condition. These results demonstrate that schema updating in the face of inconsistent information is driven by PE-based learning, and that similar neural mechanisms underlie the updating of consolidated long-term memory schemas and short-term belief structures.
The Rho/Rac guanine nucleotide exchange factor Vav1 regulates HIF-1α and GLUT-1 expression and glucose uptake in the brain
Authors: Hong Jaewoo , Kim Yurim , Yanpallewar Sudhirkumar , Lin P. Charles 
Published: October 2019

AbstractVav1 is a Rho/Rac guanine nucleotide exchange factor expressed in hematopoietic and endothelial cells that are involved in a wide range of cellular functions. It is also stabilized under hypoxic conditions when it regulates the accumulation of the transcription factor HIF-1α, which activates the transcription of target genes to orchestrate a cellular response to low oxygen. One of the genes induced by HIF-1α is GLUT-1, which is the major glucose transporter expressed in vessels that supply energy to the brain. Here, we identify a role for Vav1 in providing glucose to the brain. We found that Vav1 deficiency downregulates HIF-1α and GLUT-1 levels in endothelial cells, including blood-brain barrier cells. This downregulation of GLUT-1, in turn, reduced glucose uptake to endothelial cells both in vitro and in vivo, and reduced glucose levels in the brain.Furthermore, endothelial cell-specific Vav1 knock-out in mice, which caused glucose uptake deficiency, also led to a learning delay in fear conditioning experiments. Our results suggest that Vav1 promotes learning by activating HIF-1α and GLUT-1 and thereby distributing glucose to the brain. They further demonstrate the importance of glucose transport by endothelial cells in brain functioning and reveal a potential new axis for targeting GLUT-1 deficiency syndromes and other related brain diseases.