bluuebunny/crossref_metadata_2025_split huggingface dataset:
Active Efficient Coding Explains the Development of Binocular Vision and its Failure in Amblyopia
Authors: Eckmann Samuel , Klimmasch Lukas , Shi Bertram E. , Triesch Jochen 
Published: March 2019

The development of vision during the first months of life is an active process that comprises the learning of appropriate neural representations and the learning of accurate eye movements. While it has long been suspected that the two learning processes are coupled, there is still no widely accepted theoretical framework describing this joint development. Here we propose a computational model of the development of active binocular vision to fill this gap. The model is based on a new formulation of the Active Efficient Coding theory, which proposes that eye movements, as well as stimulus encoding, are jointly adapted to maximize the overall coding efficiency. Under healthy conditions, the model self-calibrates to perform accurate vergence and accommodation eye movements. It exploits disparity cues to deduce the direction of defocus, which leads to co-ordinated vergence and accommodation responses. In a simulated anisometropic case, where the refraction power of the two eyes differs, an amblyopia-like state develops, in which the foveal region of one eye is suppressed due to inputs from the other eye. After correcting for refractive errors, the model can only reach healthy performance levels if receptive fields are still plastic, in line with findings on a critical period for binocular vision development. Overall, our model offers a unifying conceptual framework for understanding the development of binocular vision.Significance StatementBrains must operate in an energy-efficient manner. The efficient coding hypothesis states that sensory systems achieve this by adapting neural representations to the statistics of sensory input signals. Importantly, however, these statistics are shaped by the organism’s behavior and how it samples information from the environment. Therefore, optimal performance requires jointly optimizing neural representations and behavior, a theory called Active Efficient Coding. Here we test the plausibility of this theory by proposing a computational model of the development of binocular vision. The model explains the development of accurate binocular vision under healthy conditions. In the case of refractive errors, however, the model develops an amblyopia-like state and suggests conditions for successful treatment.
Using deep learning algorithm to surmount drawbacks of statistical method in the procession of identify related genes with cancer
Authors: Zou Bohao 
Published: March 2019

AbstractFinding key genes which are relative with cancer is the first essential step to understand what has taken place in the tumor cell. At present, the most methods which can discover key genes make a contrast between normal samples and tumor samples and base on the statistical test. However, those methods face on some problems like the insufficient of statistical test in unbalanced samples, defect of only using single data that can not display the holistic situation in tumor cell. For solving those issues, i proposed a innovation method that uses semi-supervised and unsupervised algorithm to discover key genes which are linked with cancer. The genes in the final result list are not only in the double category but with distinct hierarchy and those genes are all detected from diversity data like methylation, gene expression RNA-Seq, exon expression RNA-Seq and so on. At last, for comparing the result of this method and traditional statistical method, i used the conception of information gain ratio to prove the advantage of this deep learning method in mathematical.
CRISPR disruption and UK Biobank analysis of a highly conserved polymorphic enhancer suggests a role in male anxiety and ethanol intake
Authors: McEwan Andrew R. , Davidson Connor , Hay Elizabeth , Turnbull Yvonne , Erickson Johanna Celene , Marini Pietro , Wilson Dana , McIntosh Andrew M. , Adams Mark J. , Murgatroyd Chris , Barrett Perry , Delibegovic Mirela , Clarke Toni-Kim , MacKenzie Alasdair 
Published: March 2019

AbstractExcessive alcohol intake is associated with 5.9% of global deaths. However, this figure is especially acute in men such that 7.6% of deaths can be attributed to alcohol intake. Previous studies identified a significant interaction between genotypes of the galanin (GAL) gene with anxiety and alcohol abuse in different male populations but were unable to define a mechanism. To address these issues the current study analysed the human UK Biobank cohort and identified a significant interaction (n=115,865; p=0.0007) between allelic variation (GG or CA genotypes) in the highly conserved human GAL5.1 enhancer, alcohol intake (AUDIT questionnaire scores) and anxiety in men that was consistent with these previous studies. Critically, disruption of GAL5.1 in mice using CRISPR genome editing significantly reduced GAL expression in the amygdala and hypothalamus whilst producing a corresponding reduction in ethanol intake in KO mice. Intriguingly, we also found evidence of reduced anxiety-like behaviour in male GAL5.1KO animals mirroring that seen in humans. Using bioinformatic analysis and co-transfection studies we further identified the EGR1 transcription factor, that is co expressed with GAL in amygdala and hypothalamus, as being important in the protein kinase C (PKC) supported activity of the GG genotype of GAL5.1 but less so in the CA genotype. Our unique study uses a novel combination of human association analysis, CRISPR genome editing in mice, animal behavioural analysis and cell culture studies to identify a highly conserved regulatory mechanism linking anxiety and alcohol intake that might contribute to increased susceptibility to anxiety and alcohol abuse in men.
3D spatially-resolved geometrical and functional models of human liver tissue reveal new aspects of NAFLD progression
Authors: Segovia-Miranda Fabián , Morales-Navarrete Hernán , Kücken Michael , Moser Vincent , Seifert Sarah , Repnik Urska , Rost Fabian , Hendriks Alexander , Hinz Sebastian , Röcken Christoph , Lüthjohann Dieter , Kalaidzidis Yannis , Schafmayer Clemens , Brusch Lutz , Hampe Jochen , Zerial Marino 
Published: March 2019

AbstractEarly disease diagnosis is key for the effective treatment of diseases. It relies on the identification of biomarkers and morphological inspection of organs and tissues. Histopathological analysis of human biopsies is the gold standard to diagnose tissue alterations. However, this approach has low resolution and overlooks 3D structural changes that are consequence of functional alterations. Here, we applied multiphoton imaging, 3D digital reconstructions and computational simulations to generate spatially-resolved geometrical and functional models of human liver tissue at different stages of non-alcoholic fatty liver disease (NAFLD). We identified a set of new morphometric cellular parameters correlated with disease progression. Moreover, we found profound topological defects in the 3D bile canaliculi (BC) network. Personalized biliary fluid dynamic simulations predicted an increased pericentral biliary pressure and zonated cholestasis, consistent with elevated cholestatic biomarkers in patients’ sera. Our spatially-resolved models of human liver tissue can contribute to high-definition medicine by identifying quantitative multi-parametric cellular and tissue signatures to define disease progression and provide new insights into NAFLD pathophysiology.
A General Statistic to Test an Optimally Weighted Combination of Common and/or Rare Variants
Authors: Zhang Jianjun , Wu Baolin , Sha Qiuying , Zhang Shuanglin , Wang Xuexia 
Published: March 2019

AbstractBoth genome-wide association study and next generation sequencing data analyses are widely employed in order to identify disease susceptible common and/or rare genetic variants in many large scale genetic studies. Rare variants generally have large effects though they are hard to detect due to their low frequency. Currently, many existing statistical methods for rare variants association studies employ a weighted combination scheme, which usually puts subjective weights or suboptimal weights based on some ad hoc assumptions (e.g. ignoring dependence between rare variants). In this study, we analytically derive optimal weights for both common and rare variants and propose a General and novel approach to Test association between an Optimally Weighted combination of variants (G-TOW) in a gene or pathway for a continuous or dichotomous trait while easily adjusting for covariates. We conduct extensive simulation studies to evaluate the performance of G-TOW. Results of the simulation studies show that G-TOW has properly controlled type I error rates and it is the most powerful test among the methods we compared, when testing effects of either both rare and common variants or rare variants only. We also illustrate the effectiveness of G-TOW using the Genetic Analysis Workshop 17 (GAW17) data. In addition, we applied G-TOW and other competitive methods to test association for schizophrenia. The G-TOW have successfully verified genes FYN and VPS39 which are associated with schizophrenia reported in existing publications. Both of these genes are missed by the weighted sum statistic (WSS) and the sequence kernel association test (SKAT). G-TOW also showed much stronger significance (p-value=0.0037) than our previously developed method named Testing the effect of an Optimally Weighted combination of variants (TOW) (p-value=0.0143) on gene FYN. FYN is a member of the protein-tyrosine kinase oncogene family that phosphorylates glutamate metabotropic receptors and ionotropic N-methyl-d-aspartate (NMDA) receptors. NMDA modulates trafficking, subcellular distribution and function. It is involved in neuronal apoptosis, brain development and synaptic transmission and lower expression, which has been observed in the platelets of schizophrenic patients compared with controls. The application for schizophrenia indicates that G-TOW is a powerful tool in genome-wide association studies.
Bid as a novel interacting partner of IRE1 differentially regulating its RNAse activity
Authors: Bashir Samirul , Pal Debnath , Banday Mariam , Qadri Ozaira , Bashir Arif , Hazari Younis Mohammad , Hilal Nazia , Altaf Mohammad , Fazili Khalid Majid 
Published: March 2019

AbstractUnfolded protein response is a dynamic signalling pathway, which is involved in the maintenance of proteostasis and cellular homeostasis. IRE1, a transmembrane signalling protein represents the start point of a highly conserved UPR signalling cascade. IRE1 is endowed with kinase and endoribonuclease activities. The activation of the kinase domain of IRE1 by trans-autophosphorylation leads to the activation of its RNAse domain. RNAse domain performs atypical splicing of Xbp1 mRNA and degradation of mRNAs by an effector function known as Regulated IRE1 Dependent Decay (RIDD). The regulation of the distinctive nature of the IRE1 ribonuclease function is potentially mediated by a dynamic protein structure UPRosome that is an assembly of a huge number of proteins on IRE1. Here, we reported that Bid is a novel recruit to UPRosome, which directly interacts with the cytoplasmic domain of IRE1. Bid controls the auto-phosphorylation of IRE1 in a negative manner where Bid overexpression conditions displayed reduced phosphorylation levels of IRE1 and Bid knockdown cells showed slightly enhanced IRE1 phosphorylation. This effect was reciprocated with JNK, a downstream target of IRE1. Our Insilico analysis revealed that Bid binding to IRE1 dimer averts its structural flexibility and thereby preventing its trans-autophosphorylation activity. We found that the effect of Bid is specific to the IRE1 branch of UPR signalling and competitive in nature. The highlighting observation of the study was that Bid stimulated a differential activity of the IRE1 RNAse domain towards Xbp1 splicing and RIDD. These results together establish that Bid is a part of the UPRosome and modulates IRE1 in a way to differentially regulate its RNAse outputs.
Aberrant Visual Population Receptive Fields in Human Albinism
Authors: Duwell Ethan J. , Woertz Erica N. , Mathis Jedidiah , Carroll Joseph , DeYoe Edgar A. 
Published: March 2019

ABSTRACTRetinotopic organization is a fundamental feature of visual cortex thought to play a vital role in encoding spatial information. One important aspect of normal retinotopy is the representation of the right and left hemifields in contralateral visual cortex. However, in human albinism, many temporal retinal afferents decussate pathologically at the optic chiasm resulting in partially superimposed representations of opposite hemifields in each hemisphere of visual cortex. Previous fMRI studies in human albinism suggest that the right and left hemifield representations are superimposed in a mirror-symmetric manner. This should produce imaging voxels which respond to two separate regions in visual space mirrored across the vertical meridian. However, it is not yet clear how retino-cortical miswiring in albinism manifests at the level of single voxel population receptive fields. Here we used fMRI retinotopic mapping in conjunction with population receptive field (pRF) modeling to fit both single and dual pRF models to the visual responses of voxels in visual areas V1-V3 of five subjects with albinism. We found that subjects with albinism (but not controls) have sizable clusters of voxels with dual pRFs consistently corresponding to, but not fully coextensive with regions of hemifield overlap. These dual pRFs were typically positioned at roughly mirror image locations across the vertical meridian but were uniquely clustered within the visual field for each subject. We also found that single pRFs are larger in albinism than controls, and that single pRF sizes in the central visual field were anti-correlated with subjects’ foveal cone densities. Finally, dual pRF and aberrant hemifield representation characteristics varied significantly across subjects with albinism suggesting more central heterogeneity than previously appreciated.
Revealing new therapeutic opportunities through drug target prediction via class imbalance-tolerant machine learning
Authors: Liang Siqi , Yu Haiyuan 
Published: March 2019

AbstractIn silicodrug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases. Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug-gene phenotype similarity and gene expression profile similarity, that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that target unexplored genes. By providing newly predicted drug-target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes.
ExpansionHunter: A sequence-graph based tool to analyze variation in short tandem repeat regions
Authors: Dolzhenko Egor , Deshpande Viraj , Schlesinger Felix , Krusche Peter , Petrovski Roman , Chen Sai , Emig-Agius Dorothea , Gross Andrew , Narzisi Giuseppe , Bowman Brett , Scheffler Konrad , van Vugt Joke J.F.A. , French Courtney , Sanchis-Juan Alba , Ibáñez Kristina , Tucci Arianna , Lajoie Bryan , Veldink Jan H. , Raymond Lucy , Taft Ryan J. , Bentley David R. , Eberle Michael A. 
Published: March 2019

SummaryWe describe a novel computational method for genotyping repeats using sequence graphs. This method addresses the long-standing need to accurately genotype medically important loci containing repeats adjacent to other variants or imperfect DNA repeats such as polyalanine repeats. Here we introduce a new version of our repeat genotyping software, ExpansionHunter, that uses this method to perform targeted genotyping of a broad class of such loci.Availability and implementationExpansionHunter is implemented in C++ and is available under the Apache License Version 2.0. The source code, documentation, and Linux/macOS binaries are available at https://github.com/Illumina/ExpansionHunter/.Contactmeberle@illumina.com
Transcriptome-wide dynamics of extensive m6A mRNA methylation duringPlasmodium falciparumblood-stage development
Authors: Baumgarten Sebastian , Bryant Jessica M. , Sinha Ameya , Reyser Thibaud , Preiser Peter R. , Dedon Peter C. , Scherf Artur 
Published: March 2019

AbstractMalaria pathogenesis results from the asexual replication ofPlasmodium falciparumwithin human red blood cells, which relies on a precisely timed cascade of gene expression over a 48-hour life cycle. Although substantial post-transcriptional regulation of this hardwired program has been observed, it remains unclear how these processes are mediated on a transcriptome-wide level. To this end, we identified mRNA modifications in theP. falciparumtranscriptome and performed a comprehensive characterization of N6-methyladenosine (m6A) over the course of blood stage development. Using mass spectrometry and m6A RNA sequencing, we demonstrate that m6A is highly developmentally regulated, exceeding m6A levels known in any other eukaryote. We identify an evolutionarily conserved m6A writer complex and show that knockdown of the putative m6A methyltransferase by CRISPR interference leads to increased levels of transcripts that normally contain m6A. In accordance, we find an inverse correlation between m6A status and mRNA stability or translational efficiency. Our data reveal the crucial role of extensive m6A mRNA methylation in dynamically fine-tuning the transcriptional program of a unicellular eukaryote as well as a new ‘epitranscriptomic’ layer of gene regulation in malaria parasites.
Looking for Therapeutic Antibodies in Next Generation Sequencing Repositories
Authors: Krawczyk Konrad , Raybould Matthew , Kovaltsuk Aleksandr , Deane Charlotte M. 
Published: March 2019

AbstractRecently it has become possible to query the great diversity of natural antibody repertoires using Next Generation Sequencing (NGS). These methods are capable of producing millions of sequences in a single experiment. Here we compare Clinical Stage Therapeutic antibodies to the ∼1b sequences from 60 independent sequencing studies in the Observed Antibody Space Database. Of the 242 post Phase I antibodies, we find 16 with sequence identity matches of 95% or better for both heavy and light chains. There are also 54 perfect matches to therapeutic CDR-H3 regions in the NGS outputs, suggesting a nontrivial amount of convergence between naturally observed sequences and those developed artificially. This has potential implications for both the discovery of antibody therapeutics and the legal protection of commercial antibodies.
Cascaded-CNN: Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps
Authors: Moritz Spencer A. , Pfab Jonas , Wu Tianqi , Hou Jie , Cheng Jianlin , Cao Renzhi , Wang Liguo , Si Dong 
Published: March 2019

AbstractCryo-electron microscopy (cryo-EM) has become a leading technology for determining protein structures. Recent advances in this field have allowed for atomic resolution. However, predicting the backbone trace of a protein has remained a challenge on all but the most pristine density maps (< 2.5Å resolution). Here we introduce a deep learning model that uses a set of cascaded convolutional neural networks (CNNs) to predict Cα atoms along a protein’s backbone structure. The cascaded-CNN (C-CNN) is a novel deep learning architecture comprised of multiple CNNs, each predicting a specific aspect of a protein’s structure. This model predicts secondary structure elements (SSEs), backbone structure, and Cα atoms, combining the results of each to produce a complete prediction map. The cascaded-CNN is a semantic segmentation image classifier and was trained using thousands of simulated density maps. This method is largely automatic and only requires a recommended threshold value for each evaluated protein. A specialized tabu-search path walking algorithm was used to produce an initial backbone trace with Cα placements. A helix-refinement algorithm made further improvements to the α-helix SSEs of the backbone trace. Finally, a novel quality assessment-based combinatorial algorithm was used to effectively map Cα traces to obtain full-atom protein structures. This method was tested on 50 experimental maps between 2.6Å and 4.4Å resolution. It outperformed several state-of-the-art prediction methods including RosettaES, MAINMAST, and a Phenix based method by producing the most complete prediction models, as measured by percentage of found Cα atoms. This method accurately predicted 88.5% (mean) of the Cα atoms within 3Å of a protein’s backbone structure surpassing the 66.8% mark achieved by the leading alternate method (Phenix based fully automatic method) on the same set of density maps. The C-CNN also achieved an average RMSD of 1.23Å for all 50 experimental density maps which is similar to the Phenix based fully automatic method. The source code and demo of this research has been published athttps://github.com/DrDongSi/Ca-Backbone-Prediction.
Fine mapping using whole-genome sequencing confirms anti-Müllerian hormone as a major gene for sex determination in farmed Nile tilapia (Oreochromis niloticus L.)
Authors: Cáceres Giovanna , López María E. , Cadiz María I. , Yoshida Grazyella M. , Jedlicki Ana , Palma-Véjares Ricardo , Travisany Dante , Díaz-Domínguez Diego , Maass Alejandro , Lhorente Jean P. , Soto Jose , Salas Diego , Yáñez José M. 
Published: March 2019

ABSTRACTNile tilapia (Oreochromis niloticus) is one of the most cultivated and economically important species in world aquaculture. Faster male development during grow-out phase is considered a major problem that generate heterogeneous sizes of fish at harvest. Identifying genomic regions associated with sex determination in Nile tilapia is a research topic of great interest. The objective of this study was to identify genomic variants associated with sex determination in three commercial populations of Nile tilapia. Whole-genome sequencing of 326 individuals was performed, and a total of 2.4 million high-quality bi-allelic single nucleotide polymorphisms (SNPs) were identified. A genome-wide association study (GWAS) was conducted to identify markers associated with the binary sexual trait (males = 0; females = 1). A mixed logistic regression GWAS model was fitted and a genome-wide significant signal comprising 36 SNPs, located on chromosome 23 spanning a genomic region of 536 kb, was identified. Ten out of these 36 genetic variants, intercept the anti-Müllerian hormone gene. Other significant SNPs were located in the neighboring Amh gene region. This gene has been strongly associated with sex determination in several vertebrate species, playing an essential role in the differentiation of male and female reproductive tissue in early stages of development. This finding provides useful information to better understand the genetic mechanisms underlying sex determination in Nile tilapia.