bluuebunny/crossref_metadata_2025_split huggingface dataset:
Facilitating open-science with realistic fMRI simulation: validation and application
Authors: Ellis Cameron T. , Baldassano Christopher , Schapiro Anna C. , Cai Ming Bo , Cohen Jonathan D. 
Published: January 2019

AbstractBackgroundWith advances in methods for collecting and analyzing fMRI data, there is a concurrent need to understand how to reliably evaluate and optimally use these methods. Simulations of fMRI data can aid in both the evaluation of complex designs and the analysis of data.New MethodWe present fmrisim, a new Python package for standardized, realistic simulation of fMRI data. This package is part of BrainIAK: a recently released open-source Python toolbox for advanced neuroimaging analyses. We describe how to use fmrisim to extract noise properties from real fMRI data and then create a synthetic dataset with matched noise properties and a user-specified signal.ResultsWe validate the noise generated by fmrisim to show that it can approximate the noise properties of real data. We further show how fmrisim can help researchers find the optimal design in terms of power.Comparison with other methodsfmrisim ports the functionality of other packages to the Python platform while extending what is available in order to make it seamless to simulate realistic fMRI data.ConclusionsThe fmrisim package holds promise for improving the design of fMRI experiments, which may facilitate both the pre-registration of such experiments as well as the analysis of fMRI data.Highlightsfmrisim can simulate fMRI data matched to the noise properties of real fMRI.This can help researchers investigate the power of their fMRI designs.This also facilitates open science by making it easy to pre-register analysis pipelines.
Financial implications of preterm birth during initial hospitalization: The extent and predictors of catastrophic health expenditure
Authors: Zainal Hadzri , Dahlui Maznah , Su Tin Tin 
Published: January 2019

ABSTRACTPreterm birth incidence has risen globally and the high cost of initial hospitalization poses financial burden to the family. This study assessed family cost at neonatal intensive care units of two hospitals in the state of Kedah, Malaysia. Family’s expenditure was obtained using a structured questionnaire. 126 families who were government employed spent a mean total cost of MYR 549 (MYR 0 - MYR 4,700) compared to MYR 650 (MYR 40 – MYR 9,300) for 244 families who were not government employed. Mean income loss was MYR 310 (MYR 0 – MYR 15,000) and MYR 348 (MYR 0 – MYR 5,500) respectively. Travel expenses was the cost driver for all families. 15% of families in this study were already living below the income poverty line and majority were not government employed. For the rest of the families, 21% became impoverished when one month household income was used for hospitalization cost but this lowered to 9% with cumulative household income by length of hospital stay. Overall incidence of catastrophic health expenditure among families was 38%. Using multivariable logistic regression household income and residential location were predictive factors for catastrophic health expenditure. Despite universal health coverage through subsidy of direct medical (hospital) cost, the high incidence of catastrophic health expenditure and impoverishment among families of preterm infants was attributable to out of pocket payment for direct non-medical cost (such as travel and food) and indirect cost from income loss. Government employed families with an array of employment benefits appear better protected against financial hardship compared to those in private sector or self-employed. Remedial measures include improving neonatal intensive care unit rooming-in service for mothers, complementary financial assistance for families and enhancing universal health coverage through affordable social health insurance for infant healthcare.
Plant growth regulators interact with elevated temperature to alter heat stress signaling via the Unfolded Protein Response in maize
Authors: Neill Elena M. , Byrd Michael C. R. , Billman Thomas , Brandizzi Federica , Stapleton Ann E. 
Published: January 2019

ABSTRACTPlants are increasingly exposed to high temperatures, which can cause accumulation of unfolded protein in the endoplasmic reticulum (ER). This condition, known as ER stress, evokes the unfolded protein response (UPR), a cytoprotective signaling pathway. One important branch of the UPR is regulated by splicing of bZIP60 mRNA by the IRE1 stress sensor. There is increasing evidence that commercial plant growth regulators may protect against abiotic stressors including heat stress and drought, but there is very little mechanistic information about these effects or about the regulatory pathways involved. We evaluated evidence in the B73 Zea mays inbred for differences in the activity of the UPR between permissive and elevated temperature in conjunction with plant growth regulator application. Treatment with elevated temperature and plant growth regulators increased UPR activation, as assessed by an increase in splicing of the mRNA of the IRE1 target bZIP60 following paclobutrazol treatment. We propose that plant growth regulator treatment induces bZIP60 mRNA splicing which ‘primes’ plants for rapid adaptive response to subsequent endoplasmic reticulum-stress inducing conditions.
Plumbagin-induced oxidative stress leads to inhibition of Na+/K+-ATPase (NKA) in canine cancer cells
Authors: Alharbi Yousef , Kapur Arvinder , Felder Mildred , Barroilhet Lisa , Stein Timothy , Pattnaik Bikash R. , Patankar Manish S. 
Published: January 2019

AbstractThe Na+/K+-ATPase (NKA) complex is the master regulator of membrane potential and a target for anti-cancer therapies. Here, we investigate the effect of drug-induced oxidative stress on NKA activity. The natural product, plumbagin increases oxygen radicals through inhibition of oxidative phosphorylation. As a result, plumbagin treatment results in decreased production of ATP and a rapid increase in intracellular oxygen radicals. We show that plumbagin induces apoptosis in canine cancer cells via oxidative stress. We use this model to test the effect of oxidative stress on NKA activity. Using whole-cell patch-clamp electrophysiology we demonstrate that short-term exposure (4 min) to plumbagin results in 48% decrease in outward current at +50 mV. Even when exogenous ATP was supplied to the cells, plumbagin treatment resulted in 46% inhibition outward current through NKA at +50 mV. In contrast, when the canine cancer cells were pre-treated with the oxygen radical scavenger, N-acetylcysteine, the NKA inhibitory activity of plumbagin was abrogated. These experiments demonstrate that the oxidative stress-causing agents such as plumbagin and its analogues, are a novel avenue to regulate NKA activity in tumors.
Quantifying the effect of experimental perturbations at single-cell resolution
Authors: Burkhardt Daniel B. , Stanley Jay S. , Tong Alexander , Perdigoto Ana Luisa , Gigante Scott A. , Herold Kevan C. , Wolf Guy , Giraldez Antonio J. , van Dijk David , Krishnaswamy Smita 
Published: January 2019

AbstractCurrent methods for comparing scRNA-seq datasets collected in multiple conditions focus on discrete regions of the transcriptional state space, such as clusters of cells. Here, we quantify the effects of perturbations at the single-cell level using a continuous measure of the effect of a perturbation across the transcriptomic space. We describe this space as a manifold and develop a relative likelihood estimate of observing each cell in each of the experimental conditions using graph signal processing. This likelihood estimate can be used to identify cell populations specifically affected by a perturbation. We also develop vertex frequency clustering to extract populations of affected cells at the level of granularity that matches the perturbation response. The accuracy of our algorithm to identify clusters of cells that are enriched or depleted in each condition is on average 57% higher than the next best-performing algorithm tested. Gene signatures derived from these clusters are more accurate compared to six alternative algorithms in ground-truth comparisons.
The SUMO ligase Su(var)2-10 links piRNA-guided target recognition to chromatin silencing
Authors: Ninova Maria , Chen Yung-Chia Ariel , Godneeva Baira , Rogers Alicia K. , Luo Yicheng , Aravin Alexei A. , Tóth Katalin Fejes 
Published: January 2019

AbstractRegulation of transcription is the main mechanism responsible for precise control of gene expression. While the majority of transcriptional regulation is mediated by a multitude of DNA-binding transcription factors that bind to regulatory gene regions, an elegant alternative strategy employs small RNA guides, piwi-interacting RNAs (piRNAs) to identify targets of transcriptional repression. Here we show that in Drosophila the small ubiquitin-like protein SUMO and the SUMO E3 ligase Su(var)2-10 are required for piRNA-guided deposition of repressive chromatin marks and transcriptional silencing of piRNA targets. Su(var)2-10 links the piRNA-guided target recognition complex to the silencing effector by binding the piRNA/Piwi complex and inducing SUMO-dependent recruitment of the SetDB1/Wde histone methyltransferase effector. We propose that in Drosophila, the nuclear piRNA pathway has co-opted a conserved mechanism of SUMO-dependent recruitment of the SetDB1/Wde chromatin modifier to confer repression of genomic parasites.Highlights-piRNA-induced transposon silencing requires SUMO and the SUMO E3 ligase Su(var)2-10-Su(var)2-10 links the target recognition complex to the silencing effector-Su(var)2-10 binds the piRNA-guided target recognition complex and deposits SUMO on target chromatin-Su(var)2-10 induces SUMO-dependent recruitment of the SetDB1/Wde histone methyltransferase to target loci
Extinction-resistant attention to long-term conditioned threat is indexed by selective visuocortical alpha suppression in humans
Authors: Panitz Christian , Keil Andreas , Mueller Erik M. 
Published: January 2019

AbstractWhile ERP studies have shown heightened early visual attention to conditioned threat, it is unknown whether this attentional prioritization is sustained throughout later processing stages and whether it is robust to extinction. To investigate sustained visual attention, we assessed visuocortical alpha suppression in response to conditioned and extinguished threat. N = 87 participants underwent a two-day threat conditioning paradigm with acquisition and extinction on one day and a critical recall test one day later. EEG time-frequency analyses revealed that, on Day 2, threat-conditioned vs. safety cues evoked stronger occipital alpha power suppression from 600 to 1200 ms. Notably, this suppression was resistant to extinction. The present study showed for the first time that threat conditioning enhances sustained modulation of visuocortical attention to threat in the long term. The long-term stability and extinction resistance of alpha suppression suggests a crucial role of visuocortical attention mechanisms in the maintenance of learned fears.
Pharmacological inhibition of GPR4 remediates intestinal inflammation in a mouse colitis model
Authors: Sanderlin Edward J. , Marie Mona , Velcicky Juraj , Loetscher Pius , Yang Li V. 
Published: January 2019

AbstractInflammatory bowel disease (IBD) is characterized by chronic, recurring inflammation of the digestive tract. Current therapeutic approaches are limited and include biologics and steroids such as anti-TNFα monoclonal antibodies and corticosteroids, respectively. Significant adverse drug effects can occur for chronic usage and include increased risk of infection in some patients. GPR4, a pH-sensing G protein-coupled receptor, has recently emerged as a potential therapeutic target for intestinal inflammation. We have assessed the effects of a GPR4 antagonist, 2-(4-((2-Ethyl-5,7-dimethylpyrazolo[1,5-a]pyrimidin-3-yl)methyl)phenyl)-5-(piperidin-4-yl)-1,3,4-oxadiazole (GPR4 antagonist 13, also known as NE 52-QQ57) in the dextran sulfate sodium (DSS)-induced acute colitis mouse model. The GPR4 antagonist 13 inhibited intestinal inflammation. The clinical parameters such as body weight loss and fecal score were reduced in the GPR4 antagonist 13 treatment group compared to vehicle control. Macroscopic disease indicators such as colon shortening, splenic expansion, and mesenteric lymph node enlargement were all reduced in severity in the GPR4 antagonist 13 treated mice. Histopathological features of active colitis were alleviated in GPR4 antagonist 13 treatment groups compared to vehicle control. Finally, inflammatory gene expression in the colon tissues and vascular adhesion molecule expression in the intestinal endothelia were attenuated by GPR4 antagonist 13. Our results indicate that GPR4 antagonist 13 provides a protective effect in the DSS-induced acute colitis mouse model, and inhibition of GPR4 can be explored as a novel anti-inflammatory approach.
Tubulin mRNA stability is sensitive to change in microtubule dynamics caused by multiple physiological and toxic cues
Authors: Gasic Ivana , Boswell Sarah A. , Mitchison Timothy J. 
Published: January 2019

AbstractThe localization, mass, and dynamics of microtubules are important in many processes. Cells may actively monitor the state of their microtubules and respond to perturbation, but how this occurs outside mitosis is poorly understood. We used gene expression analysis in quiescent cells to analyze responses to subtle and strong perturbation of microtubules. Genes encoding α-, β, and γ-tubulins, but not δ- or ε-tubulins, exhibited the strongest differential expression response to microtubule-stabilizing versus destabilizing drugs. Q-PCR of exon versus intron sequences confirmed that these changes were caused by regulation of tubulin mRNA stability and not transcription. Using tubulin mRNA stability as a signature to query the GEO database, we find that tubulin genes respond to toxins known to damage microtubules. Importantly, we find many other experimental perturbations, including multiple signaling and metabolic inputs that trigger tubulin differential expression, suggesting their novel role in the regulation of microtubule cytoskeleton. Mechanistic follow up confirms that one important physiological signal, phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) activity, indeed regulates tubulin mRNA stability via changes in microtubule dynamics. We propose that that tubulin gene expression is regulated as part of many coordinated biological responses, with wide implications in physiology and toxicology. Furthermore, we present a new way to discover microtubule regulation using genomics.
Stability of dynamic functional architecture differs between brain networks and states
Authors: Li Le , Lu Bin , Yan Chao-Gan 
Published: January 2019

AbstractStable representation of information in distributed neural connectivity is critical to function effectively in the world. Despite the dynamic nature of the brain’s functional architecture, characterizing its temporal stability has been largely neglected. Here we characterized stability of functional architecture for each brain voxel by measuring the concordance of dynamic functional connectivity (DFC) over time, and explored how stability was modified by movie watching. High-order association regions, especially the default mode network, demonstrated high stability during resting state scans, while primary sensory-motor cortices revealed relatively lower stability. During movie watching, stability in the primary visual cortex was decreased, which was associated with larger DFC variation with neighboring regions. By contrast, higher-order regions in the ventral and dorsal visual stream demonstrated increased stability. The distribution of functional stability and its modification describes a profile of the brain’s stability property, which may be useful reference for examining distinct mental states and disorders.
Flexible Experimental Designs for Valid Single-cell RNA-sequencing Experiments Allowing Batch Effects Correction
Authors: Song Fangda , Chan Ga Ming , Wei Yingying 
Published: January 2019

AbstractDespite their widespread applications, single-cell RNA-sequencing (scRNA-seq) experiments are still plagued by batch effects and dropout events. Although the completely randomized experimental design has frequently been advocated to control for batch effects, it is rarely implemented in real applications due to time and budget constraints. Here, we mathematically prove that under two more flexible and realistic experimental designs—the “reference panel” and the “chain-type” designs—true biological variability can also be separated from batch effects. We develop Batch effects correction with Unknown Subtypes for scRNA-seq data (BUSseq), which is an interpretable Bayesian hierarchical model that closely follows the data-generating mechanism of scRNA-seq experiments. BUSseq can simultaneously correct batch effects, cluster cell types, impute missing data caused by dropout events, and detect differentially expressed genes without requiring a preliminary normalization step. We demonstrate that BUSseq outperforms existing methods with simulated and real data.
Curated BLAST for Genomes
Authors: Price Morgan N. , Arkin Adam P. 
Published: January 2019

Abstract“Curated BLAST for Genomes” finds candidate genes for a process or an enzymatic activity within a genome of interest. In contrast to annotation tools, which usually predict a single activity for each protein, Curated BLAST asks if any of the proteins in the genome are similar to characterized proteins that are relevant. Given a query such as an enzyme’s name or an EC number, Curated BLAST searches the curated descriptions of over 100,000 characterized proteins, and it compares the relevant characterized proteins to the predicted proteins in the genome of interest. In case of errors in the gene models, Curated BLAST also searches the six-frame translation of the genome. Curated BLAST is available at http://papers.genomics.lbl.gov/curated.
Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype
Authors: Yin Bojian , Balvert Marleen , van der Spek Rick A. A. , Dutilh Bas E. , Bohté Sander , Veldink Jan , Schönhuth Alexander 
Published: January 2019

AbstractAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disease-causing variants have been identified, a major part of heritability remains unexplained. ALS is believed to have a complex genetic basis where nonadditive combinations of variants constitute disease, which cannot be picked up using the linear models employed in classical genotype-phenotype association studies. Deep learning on the other hand is highly promising for identifying such complex relations. We therefore developed a deep-learning based approach for the classification of ALS patients versus healthy individuals from the Dutch cohort of the ProjectMinE dataset. Based on recent insight that regulatory regions on the genome play a major role in ALS, we employ a two-step approach: first promoter regions that are likely associated to ALS are identified, and second individuals are classified based on their genotype in the selected genomic regions. Both steps employ a deep convolutional neural network. The network architecture accounts for the structure of genome data by applying convolution only to parts of the data where this makes sense from a genomics perspective.Our approach identifies potential ALS-associated genetic variants, and generally outperforms other classification methods. Test results support the hypothesis that ALS is caused by non-additive combinations of variants. Our method can be applied to large-scale whole genome data. We consider this a first step towards genotype-phenotype association with deep learning that is tailored to genomics and can deal with genome-sized data.
Erosion of phylogenetic diversity in Neotropical bat assemblages: findings from a whole-ecosystem fragmentation experiment
Authors: Aninta Sabhrina G. , Rocha Ricardo , López-Baucells Adrià , Meyer Christoph F. J. 
Published: January 2019

AbstractThe traditional focus on taxonomic diversity metrics for investigating species responses to habitat loss and fragmentation has limited our understanding on how biodiversity is impacted by habitat modification. This is particularly true for taxonomic groups such as bats which exhibit species-specific responses. Here, we investigate phylogenetic alpha and beta diversity of Neotropical bat assemblages across two environmental gradients, one in habitat quality and one in habitat amount. We surveyed bats in 39 sites located across a whole-ecosystem fragmentation experiment in the Brazilian Amazon, representing a gradient of habitat quality (interior-edge-matrix, hereafter IEM) in both continuous forest and forest fragments of different sizes (1, 10, and 100 ha; forest size gradient). For each habitat category, we quantified alpha and beta phylogenetic diversity, then used linear models and cluster analysis to explore how forest area and IEM gradient affect phylogenetic diversity. We found that the secondary forest matrix harboured significantly lower total evolutionary history compared to the fragment interiors, especially the 1 ha fragments, containing bat assemblages with more closely related species. Forest fragments ≥ 10 ha had levels of phylogenetic richness similar to continuous forest, suggesting that large fragments retain considerable levels of evolutionary history. The edge and matrix adjacent to large fragments tend to have closely related lineages nonetheless, suggesting phylogenetic homogenization in these IEM gradient categories. Thus, despite the high mobility of bats, fragmentation still induces considerable levels of erosion of phylogenetic diversity, suggesting that the various evolutionary history might not be able to persist in present-day human-modified landscapes.
Towards a Gamete Matching Platform: Using Immunogenetics and Artificial Intelligence to Predict Recurrent Miscarriage
Authors: Mora-Sánchez Aldo , Aguilar-Salvador Daniel-Isui , Nowak Izabela 
Published: January 2019

AbstractThe degree of Allele sharing of the Human Leukocyte Antigen (HLA) genes has been linked with recurrent miscarriage (RM). However, no clear genetic markers of RM have yet been identified, possibly because of the complexity of interactions between paternal and maternal genes. We propose a methodology to analyse HLA haplotypes from couples either with histories of successful pregnancies or RM. This article describes, for the first time, a method of RM genetic-risk calculation. Novel HLA representation techniques allowed us to create an algorithm (IMMATCH) to retrospectively predict RM with an AUC = 0.71 (p = 0.0035) thanks to high-resolution typing and the use of linear algebra on peptide binding affinity data. The algorithm features an adjustable threshold to increase either sensitivity or specificity. Combining immunogenetics with artificial intelligence could create personalized tools to better understand the genetic causes of unexplained infertility and a gamete matching platform that could increase pregnancy success rates.