bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transcriptome-wide association supplements genome-wide association in Zea mays
Authors: Kremling Karl A. G. , Diepenbrock Christine H. , Gore Michael A. , Buckler Edward S. , Bandillo Nonoy B. 
Published: July 2018

AbstractModern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The potential of using endophenotypes for dissecting traits of interest remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299 genotype and 7 tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation for agronomic and seed quality (carotenoid, tocochromanol) traits is regulatory. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits, beating the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This improves not only the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.Author summaryWe examined the ability to associate variability in gene expression directly with terminal phenotypes of interest, as a supplement linking genotype to phenotype. We found that transcriptome-wide association studies (TWAS) are a useful accessory to genome-wide association studies (GWAS). In a combined test with GWAS results, TWAS improves the capacity to re-detect genes known to underlie quantitative trait loci for kernel and agronomic phenotypes. This improves not only the capacity to link genes to phenotypes, but also illustrates the widespread importance of regulation for phenotype.
Predicting Growth and Carcass Traits in Swine Using Metagenomic Data and Machine Learning Algorithms
Authors: Maltecca Christian , Lu Duc , Schillebeeckx Costantino , McNulty Nathan P , Schwab Clint , Schull Caleb , Tiezzi Francesco 
Published: July 2018

ABSTRACTIn this paper, we evaluated the power of metagenome measures taken at three time points over the growth test period (weaning, 15 and 22 weeks) to foretell growth and carcass traits in 1039 individuals of a line of crossbred pigs. We measured prediction accuracy as the correlation between actual and predicted phenotypes in a five-fold cross-validation setting. Phenotypic traits measured included live weight measures and carcass composition obtained during the trial as well as at slaughter. We employed a null model excluding microbiome information as a baseline to assess the increase in prediction accuracy stemming from the inclusion of operational taxonomic units (OTU) as predictors. We further contrasted performance of models from the Bayesian alphabet (Bayesian Lasso) as well machine learning approaches (Random Forest and Gradient Boosting) and semi-parametric kernel models (Reproducing Kernel Hilbert space). In most cases, prediction accuracy increased significantly with the inclusion of microbiome data. Accuracy was more substantial with the inclusion of metagenomic information taken at week 15 and 22, with values ranging from approximately 0.30 for loin traits to more than 0.50 for back-fat. Conversely, microbiome composition at weaning resulted in most cases in marginal gains of prediction accuracy, suggesting that later measures might be more useful to include in predictive models. Model choice affected predictions marginally with no clear winner for any model/trait/time point. We, therefore, suggest average prediction across models as a robust strategy in fitting metagenomic information. In conclusion, microbiome composition can effectively be used as a predictor of growth and composition traits, particularly for fatness traits. The inclusion of OTU predictors could potentially be used to promote fast growth of individuals while limiting fat accumulation. Early microbiome measures might not be good predictors of growth and OTU information might be best collected at later life stages. Future research should focus on the inclusion of both microbiome as well as host genome information in predictions, as well as the interaction between the two. Furthermore, the influence of microbiome on feed efficiency as well as carcass and meat quality should be investigated.
Phosphorylation of LXRα impacts atherosclerosis regression by modulating monocyte/macrophage trafficking
Authors: Shrestha Elina , Voisin Maud , Barrett Tessa J. , Nishi Hitoo , Cantor David J. , Hussein Maryem A. , David Gregory , Pineda-Torra Inés , Fisher Edward A. , Garabedian Michael J. 
Published: July 2018

AbstractLXRα activation in macrophages enhances regression of atherosclerotic plaques in mice by regulating genes crucial for cholesterol efflux, cell motility and inflammation. Diabetes, however, impairs plaque regression in mice. LXRα is phosphorylated at serine 198 (pS198), which affects the expression of genes controlling inflammation, lipid metabolism and cell movement. We hypothesize that LXRα function is affected by hyperglycemia through changes in LXRα pS198. Indeed, macrophages cultured in diabetes relevant high glucose versus normal glucose display alterations in LXR-dependent gene expression and increased LXRα pS198. We therefore examined the consequence of disrupting LXRα phosphorylation (S196A in mouse LXRα) during regression of atherosclerosis in normal and diabetic mice. We find that phosphorylation deficient LXRα S196A reduces macrophage retention in plaques in diabetes, which is predicted to be anti-atherogenic and enhance plaque regression. However, this favorable effect on regression is masked by increased monocyte infiltration in the plaque attributed to leukocytosis in LXRα S196A mice. RNA-seq of plaque macrophages from diabetic S196A mice shows increased expression of chemotaxis and decreased expression of cell adhesion genes, consistent with reduced macrophage retention by LXRα S196A. Thus, the non-phosphorylated form of LXRα precludes macrophage retention in the plaque. Our study provides the first evidence for a physiological role of LXRα phosphorylation in modulating atherosclerosis regression. Compounds that prevent LXRα phosphorylation or ligands that induce the conformation of non-phosphorylated LXRα may selectively enhance macrophage emigration from atherosclerotic plaques.
Combining citizen science and deep learning to amplify expertise in neuroimaging
Authors: Keshavan Anisha , Yeatman Jason D. , Rokem Ariel 
Published: July 2018

AbstractResearch in many fields has become increasingly reliant on large and complex datasets. “Big Data” holds untold promise to rapidly advance science by tackling new questions that cannot be answered with smaller datasets. While powerful, research with Big Data poses unique challenges, as many standard lab protocols rely on experts examining each one of the samples. This is not feasible for large-scale datasets because manual approaches are time-consuming and hence difficult to scale. Meanwhile, automated approaches lack the accuracy of examination by highly trained scientists and this may introduce major errors, sources of noise, and unforeseen biases into these large and complex datasets. Our proposed solution is to 1) start with a small, expertly labelled dataset, 2) amplify labels through web-based tools that engage citizen scientists, and 3) train machine learning on amplified labels to emulate expert decision making. As a proof of concept, we developed a system to quality control a large dataset of three-dimensional magnetic resonance images (MRI) of human brains. An initial dataset of 200 brain images labeled by experts were amplified by citizen scientists to label 722 brains, with over 80,000 ratings done through a simple web interface. A deep learning algorithm was then trained to predict data quality, based on a combination of the citizen scientist labels that accounts for differences in the quality of classification by different citizen scientists. In an ROC analysis (on left out test data), the deep learning network performed as well as a state-of-the-art, specialized algorithm (MRIQC) for quality control of T1-weighted images, each with an area under the curve of 0.99. Finally, as a specific practical application of the method, we explore how brain image quality relates to the replicability of a well established relationship between brain volume and age over development. Combining citizen science and deep learning can generalize and scale expert decision making; this is particularly important in emerging disciplines where specialized, automated tools do not already exist.
Generating Realistic Morphologies of Neurons in Rodent Hippocampus with DCGAN
Authors: Hadjiabadi Darian H. 
Published: July 2018

AbstractDendritic size and branching patterns are important features of neural form and function. However, current computational models of neuronal networks use simplistic cylindrical geometries to mimic dendritic arborizations. One reason for this is that current methods to generate dendritic trees have rigid a priori constraints. To address this, a deep convolutional generative adversarial network (DCGAN) trained on images of rodent hippocampal granule and pyramidal dendritic trees. Image features learned by the network were used to generate realistic dendritic morphologies. Results show that DCGANs achieved greater stability∗ and high generalization, as quantified by kernel maximum mean discrepancy, when exposed to instance noise and/or label smoothing during training. Trained models successfully generated realistic morphologies for both neuron types, with high false positive rate reported by expert reviewers. Collectively, DCGANs offer a unique opportunity to advance the geometry of neural modeling, and, therefore, to propel our understanding of neuronal function.∗ A “stable/stabilized DCGAN”, as mentioned throughout this work, is a DCGAN which was stable throughout training.
Is the predation risk of mate-searching different between the sexes?
Authors: Torsekar Viraj R. , Isvaran Kavita , Balakrishnan Rohini 
Published: July 2018

AbstractIn animals that communicate for pair formation, generally one sex invests more effort in mate searching. Differential predation risk of mate searching between the sexes is hypothesised to determine which sex invests more effort in mate searching. Although searching by males is prevalent in most animals, in orthopteran insects and some other taxa females physically move to localise signalling males who are predominantly sedentary. Although the two sexes thus share mate searching effort in orthopterans, their behavioural strategies are different and sexual selection theory predicts that signalling males may be following the riskier strategy and incurring higher costs. However, relative levels of risk posed by the two mate searching strategies remain largely unexplored. Hence, we estimated the relative predation risk experienced in natural populations by signalling males and responding females. We did this by quantifying predation risk as a probability of mortality in the context of acoustic communication in a tree cricket,Oecanthus henryifrom its ecologically relevant predator, a lynx spider,Peucetia viridans. Spiders may perceive calling in males and movement in females by their ability to detect both airborne acoustic cues and substrate-borne vibratory cues. Probability of mortality was quantified by partitioning it into three spatial components at which crickets and spiders interact, using a combination of extensive field observations and manipulative experiments in a semi-natural setup. We found no differences in predation risk faced by calling males and responding females, supporting the prediction that similar sex-specific costs can explain shared mate searching responsibilities. Our findings therefore suggest that direct benefits offered by males to females upon pair formation may better explain shared mate searching effort between the sexes in orthopterans.
Bacterial dynamin-like protein DynA mediates lipid and content mixing and shows phospholipid specificity
Authors: Guo Lijun , Bramkamp Marc 
Published: July 2018

ABSTRACTThe dynamins family of GTPases is involved in key cellular processes in eukaryotes, including vesicle trafficking and organelle division. The GTP hydrolysis cycle of dynamin translates to a conformational change in the protein structure, which forces the underlying lipid layer into an energetically unstable conformation that promotes membrane rearrangements. Many bacterial genomes encode dynamin-like proteins, but the biological function of these proteins has remained largely enigmatic. In recent years, our group has reported that the dynamin-like protein DynA fromBacillus subtilismediates nucleotide-independent membrane tethering in vitro and contributes to the innate immunity of bacteria against membrane stress and phage infection. However, so far the mechanism of membrane stress response and the role of GTP hydrolysis remain unclear. Here, we employed content mixing and lipid mixing assays in reconstituted systems to study if the dynamin-like protein DynA fromB. subtilisinduces membrane full fusion, and further test the possibility that GTP hydrolysis of DynA may act on the fusion-through-hemifusion pathway. Our results based on fluorescence resonance energy transfer (FRET) indicated that DynA could induce aqueous content mixing even in absence of GTP. Moreover, DynA-induced membrane fusion in vitro is a thermo-promoted slow response. Surprisingly, digestion of protein mediated an instantl rise of content exchange, supporting the assumption that disassembly of DynA is the fundamental power for fusion-through-hemifusion.
The effect of stride length on lower extremity joint kinetics at various gait speeds
Authors: McGrath Robert L. , Ziegler Melissa L. , Pires-Fernandes Margaret , Knarr Brian A. , Higginson Jill S. , Sergi Fabrizio 
Published: July 2018

AbstractRobot-assisted training is a promising tool under development for improving walking function based on repetitive goal-oriented task practice. The challenges in developing the controllers for gait training devices that promote desired changes in gait is complicated by the limited understanding of the human response to robotic input. A possible method of controller formulation can be based on the principle of bio-inspiration, where a robot is controlled to apply the change in joint moment applied by human subjects when they achieve a gait feature of interest. However, it is currently unclear how lower extremity joint moments are modulated by even basic gaitspatio-temporal parameters.In this study, we investigated how sagittal plane joint moments are affected by a factorial modulation of two important gait parameters: gait speed and stride length. We present the findings obtained from 20 healthy control subjects walking at various treadmill-imposed speeds and instructed to modulate stride length utilizing real-time visual feedback. Implementing a continuum analysis of inverse-dynamics derived joint moment profiles, we extracted the effects of gait speed and stride length on joint moment throughout the gait cycle. Moreover, we utilized a torque pulse approximation analysis to determine the timing and amplitude of torque pulses that approximate the difference in joint moment profiles between stride length conditions, at all gait speed conditions.Our results show that gait speed has a significant effect on the moment profiles in all joints considered, while stride length has more localized effects, with the main effect observed on the knee moment during stance, and smaller effects observed for the hip joint moment during swing and ankle moment during the loading response. Moreover, our study demonstrated that trailing limb angle, a parameter of interest in programs targeting propulsion at push-off, was significantly correlated with stride length. As such, our study has generated assistance strategies based on pulses of torque suitable for implementation via a wearable exoskeleton with the objective of modulating stride length, and other correlated variables such as trailing limb angle.
Both rare and common genetic variants contribute to autism in the Faroe Islands
Authors: Leblond Claire S , Cliquet Freddy , Carton Coralie , Huguet Guillaume , Mathieu Alexandre , Kergrohen Thomas , Buratti Julien , Lemière Nathalie , Cuisset Laurence , Bienvenu Thierry , Boland Anne , Deleuze Jean-François ,   consortium GenMed, Stora Tormodur , Biskupstoe Rannva , Halling Jónrit , Andorsdóttir Guðrið , Billstedt Eva , Gillberg Christopher , Bourgeron Thomas 
Published: July 2018

AbstractThe number of genes associated with autism is increasing, but few studies have been performed on epidemiological cohorts and in isolated populations. Here, we investigated 357 individuals from the Faroe Islands including 36 individuals with autism, 136 of their relatives and 185 non-autism controls. Data from SNP array and whole exome sequencing revealed that individuals with autism compared to controls had a higher burden of copy-number variants (p< 0.05), higher inbreeding status (p< 0.005) and higher load of homozygous deleterious variants (p< 0.01). Our analysis supports the role of several genes/loci associated with autism (e.g.NRXN1, ADNP, 22q11 deletion) and identified new truncating (e.g. GRIK2, ROBO1, NINLandIMMP2L) or recessive deleterious variants (e.g. KIRELL3andCNTNAP2) affecting autism-risk genes. It also revealed three genes involved in synaptic plasticity,RIMS4, KALRNand PLA2G4A, carryingde novodeleterious variants in individuals with autism without intellectual disability. In summary, our analysis provides a better understanding of the genetic architecture of autism in isolated populations by highlighting the role of both common and rare gene variants and pointing at new autism-risk genes. It also indicates that more knowledge about how multiple genetic hits affect neuronal function will be necessary to fully understand the genetic architecture of autism.
Statistical shape analysis of large datasets based on diffeomorphic iterative centroids
Authors: Cury Claire , Glaunès Joan A. , Toro Roberto , Chupin Marie , Shumann Gunter , Frouin Vincent , Poline Jean-Baptiste , Colliot Olivier ,   and the Consortium Imagen
Published: July 2018

AbstractIn this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1000 surfaces, and we are able to analyse this dataset in 26 hours using a diffeomorphic centroid. Our experiments demonstrate that computing diffeomorphic centroids in place of standard variational templates leads to similar shape analysis results and saves around 70% of computation time. Furthermore, the approach is able to adequately capture the variability of hippocampal shapes with a reasonable number of dimensions, and to predict anatomical features of the hippocampus in healthy subjects.
Evolution of Endothelin signaling and diversification of adult pigment pattern inDaniofishes
Authors: Spiewak Jessica E. , Bain Emily J. , Liu Jin , Kou Kellie , Sturiale Samantha L. , Patterson Larissa B. , Diba Parham , Eisen Judith S. , Braasch Ingo , Ganz Julia , Parichy David M. 
Published: July 2018

AbstractFishes of the genusDanioexhibit diverse pigment patterns that serve as useful models for understanding the genes and cell behaviors underlying the evolution of adult form. Among these species, zebrafishD. rerioexhibit several dark stripes of melanophores with sparse iridophores that alternate with light interstripes of dense iridophores and xanthophores. By contrast, the closely related speciesD. nigrofasciatushas an attenuated pattern with fewer melanophores, stripes and interstripes. Here we demonstrate species differences in iridophore development that presage the fully formed patterns. Using genetic and transgenic approaches we identify the secreted peptide Endothelin-3 (Edn3)—a known melanogenic factor of tetrapods—as contributing to reduced iridophore proliferation and fewer stripes and interstripes inD. nigrofasciatus. We further show the locus encoding this factor is expressed at lower levels inD. nigrofasciatusowing tocis-regulatory differences between species. Finally, we show that functions of two paralogous loci encoding Edn3 have been partitioned between skin and non-skin iridophores. Our findings reveal genetic and cellular mechanisms contributing to pattern differences between these species and suggest a model for evolutionary changes in Edn3 requirements across vertebrates.Author SummaryNeural crest derived pigment cells generate the spectacular variation in skin pigment patterns among vertebrates. Mammals and birds have just a single skin pigment cell, the melanocyte, whereas ectothermic vertebrates have several pigment cells including melanophores, iridophores and xanthophores, that together organize into a diverse array of patterns. In the teleost zebrafish,Danio rerio, an adult pattern of stripes depends on interactions between pigment cell classes and between pigment cells and their tissue environment. The close relative,D. nigrofasciatushas fewer stripes and prior analyses suggested a difference between these species that lies extrinsic to the pigment cells themselves. A candidate for mediating this difference is Endothelin-3 (Edn3), essential for melanocyte development in warm-blooded animals, and required by all three classes of pigment cells in an amphibian. We show that Edn3 specifically promotes iridophore development inDanio, and that differences in Edn3 expression contribute to differences in iridophore complements, and striping, betweenD. rerioandD. nigrofasciatus. Our study reveals a novel function for Edn3 and provides new insights into how changes in gene expression yield morphogenetic outcomes to effect diversification of adult form.
polyDFEv2.0: Testing for invariance of the distribution of fitness effects within and across species
Authors: Tataru Paula , Bataillon Thomas 
Published: July 2018

AbstractDistributions of fitness effects (DFE) of mutations can be inferred from site frequency spectrum (SFS) data. There is mounting interest to determine whether distinct genomic regions and/or species share a common DFE, or whether evidence exists for differences among them. polyDFEv2.0 fits multiple SFS datasets at once and provides likelihood ratio tests for DFE invariance across datasets. Simulations show that testing for DFE invariance across genomic regions within a species requires models accounting for heterogeneous genealogical histories underlying SFS data in these regions. Not accounting for these heterogeneities will result in the spurious detection of DFE differences.