bluuebunny/crossref_metadata_2025_split huggingface dataset:
Decoding the auditory brain with canonical component analysis
Authors: de Cheveigné Alain , Wong Daniel , Di Liberto Giovanni M. , Hjortkjaer Jens , Slaney Malcolm , Lalor Edmund 
Published: November 2017

AbstractThe relation between a stimulus and the evoked brain response can shed light on perceptual processes within the brain. Signals derived from this relation can also be harnessed to control external devices for Brain Computer Interface (BCI) appli-cations. While the classic event-related potential (ERP) is appropriate for isolated stimuli, more sophisticated “decoding” strategies are needed to address continuous stimuli such as speech, music or environmental sounds. Here we describe an approach based on Canonical Correlation Analysis (CCA) that finds the optimal transform to apply to both the stimulus and the response to reveal correlations between the two. Compared to prior methods based on forward or backward models for stimulus-response mapping, CCA finds significantly higher correlation scores, thus providing increased sensitivity to relatively small effects, and supports classifier schemes that yield higher classification scores. CCA strips the brain response of variance unrelated to the stimulus, and the stimulus representation of variance that does not affect the response, and thus improves observations of the relation between stimulus and response.
Transposable Element Expression Predicts Prognosis in Acute Myeloid Leukemia
Authors: Colombo Anthony , Triche Timothy , Ramsingh Giridharan 
Published: November 2017

AbstractOver half of the human genome is comprised of transposable elements (TE). TE have been implicated in cancer pathogenesis. Despite large-scale studies of the transcriptome in cancer, a comprehensive look at TE expression investigating its relationship to various mutations and its role in predicting prognosis has not been performed. We characterized TE expression in 178 adult acute myeloid leukemia (AML) patients using transcriptome data from The Cancer Genome Atlas (TCGA). We identified significant dysregulation of TE, with distinct patterns of TE expression correlated to specific mutations and distinct coding gene networks. TP53 mutated AML had a unique TE expression signature and was associated with significantly suppressed expression of TE and various classes of non-coding RNA. We identified 17 candidate prognostic TE transcripts that can classify AML subtypes as either high or low risk. These 17 TE were able to further sub-stratify low risk AML (based on mutational profile and coding gene expression) into favorable and unfavorable prognostic categories. The expression signature of the 17 TE was able to predict prognosis in an independent cohort of 284 pediatric AML patients, and was also able to predict time to relapse in an independent dataset of relapsed adult cases. This first comprehensive study of TE expression in AML demonstrates that TE expression can be used as a biomarker for predicting prognosis in AML and also provides novel insights into the biology of TP53 mutated AML. Studies characterizing its role in other cancers are warranted.
Efficient and accurate detection of splice junctions from RNAseq with Portcullis
Authors: Mapleson Daniel , Venturini Luca , Kaithakottil Gemy , Swarbreck David 
Published: November 2017

ABSTRACTNext generation sequencing (NGS) technologies enable rapid and cheap genome-wide transcriptome analysis, providing vital information about gene structure, transcript expression and alternative splicing. Key to this is the the accurate identification of exon-exon junctions from RNA sequenced (RNA-seq) reads. A number of RNA-seq aligners capable of splitting reads across these splice junctions (SJs) have been developed, however, it has been shown that while they correctly identify most genuine SJs available in a given sample, they also often produce large numbers of incorrect SJs. Herein we describe the extent of this problem using popular RNA-seq mapping tools, and present a new method, called Portcullis, to rapidly filter false SJs junctions from spliced alignments produced by any RNA-seq mapper capable of creating SAM/BAM files. We show that Portcullis distinguishes between genuine and false positive junctions to a high-degree of accuracy across different species, samples, expression levels, error profiles and read lengths. Portcullis makes efficient use of memory and threading and, to our knowledge, is currently the only SJ prediction tool that reliably scales for use with large RNAseq datasets and large highly fragmented genomes, whilst delivering highly accurate SJs.AvailabilityPortcullis is available under the GPLv3 license at: http://maplesond.github.io/portcullis/Contactdaniel.mapleson@earlham.ac.uk
Phytophthora methylomes modulated by expanded 6mA methyltransferases are associated with adaptive genome regions
Authors: Chen Han , Shu Haidong , Wang Liyuan , Zhang Fan , Li Xi , Ochola Sylvans Ochieng , Mao Fei , Ma Hongyu , Ye Wenwu , Gu Tingting , Jiang Lubing , Wu Yufeng , Wang Yuanchao , Kamoun Sophien , Dong Suomeng 
Published: November 2017

AbstractFilamentous plant pathogen genomes often display a bipartite architecture with gene sparse, repeat-rich compartments serving as a cradle for adaptive evolution. However, the extent to which this “two-speed” genome architecture is associated with genome-wide epigenetic modifications is unknown. Here, we show that the oomycete plant pathogens Phytophthora infestans and Phytophthora sojae possess functional adenine N6- methylation (6mA) methyltransferases that modulate patterns of 6mA marks across the genome. In contrast, 5-methylcytosine (5mC) could not be detected in the two Phytophthora species. Methylated DNA IP Sequencing (MeDIP-seq) of each species revealed that 6mA is depleted around the transcriptional starting sites (TSS) and is associated with low expressed genes, particularly transposable elements. Remarkably, genes occupying the gene-sparse regions have higher levels of 6mA compared to the remainder of both genomes, possibly implicating the methylome in adaptive evolution of Phytophthora. Among three putative adenine methyltransferases, DAMT1 and DAMT3 displayed robust enzymatic activities. Surprisingly, single knockouts of each of the 6mA methyltransferases in P. sojae significantly reduced in vivo 6mA levels, indicating that the three enzymes are not fully redundant. MeDIP-seq of the damt3 mutant revealed uneven patterns of 6mA methylation across genes, suggesting that PsDAMT3 may have a preference for gene body methylation after the TSS. Our findings provide evidence that 6mA modification is an epigenetic mark of Phytophthora genomes and that complex patterns of 6mA methylation by the expanded 6mA methyltransferases may be associated with adaptive evolution in these important plant pathogens.
Mining the forest: uncovering biological mechanisms by interpreting Random Forests
Authors: de Ruiter Julian , Knijnenburg Theo , de Ridder Jeroen 
Published: November 2017

AbstractBiological datasets are large and complex. Machine learning models are therefore essential to capture relationships in the data. Unfortunately, the inferred complex models are often difficult to understand and interpretation is limited to a list of features ranked on their importance in the model.We propose a computational approach, called Foresight, that enables interpretation of the patterns uncovered by Random Forest models trained on biological datasets. Foresight exploits the correlation structure in the data to uncover relevant groups of features and the interactions between them. This facilitates interpretation of the computational model and can provide more detailed insight in the underlying biological relationships than simply ranking features. We demonstrate Foresight on both an artificial dataset and a large gene expression dataset of breast cancer patients. Using the latter dataset we show that our approach retrieves biologically relevant features and provides a rich description of the interactions and correlation structure between these features.
The Block Object Storage Service (bossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery
Authors: Hider Robert , Kleissas Dean M. , Pryor Derek , Gion Timothy , Rodriguez Luis , Matelsky Jordan , Gray-Roncal William , Wester Brock 
Published: November 2017

AbstractLarge volumetric neuroimaging datasets have grown in size over the past ten years from gigabytes to terabytes, with petascale data becoming available and more common over the next few years. Current approaches to store and analyze these emerging datasets are insufficient in their ability to scale in both cost-effectiveness and performance. Additionally, enabling large-scale processing and annotation is critical as these data grow too large for manual inspection. We provide a new cloud-native managed service for large and multi-modal experiments, with support for data ingest, storage, visualization, and sharing through a RESTful Application Programming Interface (API) and web-based user interface. Our project is open source and can be easily and cost-effectively used for a variety of modalities and applications.
A genome-wide association study finds novel genetic associations with broadly-defined headache in UK Biobank (N = 223,773)
Authors: Meng Weihua , Adams Mark J , Hebert Harry L , Deary Ian J , McIntosh Andrew M , Smith Blair H 
Published: November 2017

AbstractHeadache is the most common neurological symptom and a leading cause of years lived with disability. We sought to identify the genetic variants associated with a broadly-defined headache phenotype in 223,773 subjects from the UK Biobank cohort. We defined headache based on a specific question answered by the UK Biobank participants. We performed a genome-wide association study of headache as a single entity, using 74,461 cases and 149,312 controls. We identified 3,343 SNPs which reached the genome-wide significance level of P < 5 × 10−8. The SNPs were located in 28 loci, with the top SNP of rs11172113 in the LRP1 gene having a P value of 4.92 × 10−47. Of the 28 loci, 14 have previously been associated with migraine. Among 14 new loci, rs77804065 with a P value of 5.87 × 10−15 in the LINC02210-CRHR1 gene was the top SNP.Positive relationships (P < 0.001) between multiple brain tissues and genetic associations were identified through tissue expression analysis, whereas no vascular related tissues showed significant relationships. We identified several significant positive genetic correlations between headache and other psychological traits including neuroticism, depressive symptoms, insomnia, and major depressive disorder.Our results suggest that brain function is closely related to broadly-defined headache. In addition, we also found that many psychological traits have genetic correlations with headache.
Dynamics of human telomerase recruitment depend on template-telomere base-pairing
Authors: Schmidt Jens C. , Zaug Arthur J. , Kufer Regina , Cech Thomas R. 
Published: November 2017

AbstractThe reverse transcriptase telomerase adds telomeric repeats to chromosome ends to counteract telomere shortening and thereby assures genomic stability in dividing human cells. Key variables in telomere homeostasis are the frequency with which telomerase engages the chromosome end and the number of telomeric repeats it adds during each association event. To study telomere elongation in vivo we have established a live-cell imaging assay to track individual telomerase RNPs in HeLa cells. Using this assay and the drug imetelstat, which is a competitive inhibitor of telomeric DNA binding, we demonstrate that stable association of telomerase with the single-stranded overhang of the chromosome end requires telomerase-DNA base-pairing. Furthermore, we show that telomerase processivity contributes to telomere elongation in vivo. Together, these findings provide new insight into the dynamics of telomerase recruitment and the importance of processivity in maintaining telomere length in human cancer cells.
White Matter Connectivity Reflects Success in Musical Improvisation
Authors: Zeng Tima , Przysinda Emily , Pfeifer Charles , Arkin Cameron , Loui Psyche 
Published: November 2017

AbstractCreativity is the ability to produce work that is novel, high in quality, and appropriate to an audience. One domain of creativity comes from musical improvisation, in which individuals spontaneously create novel auditory-motor sequences that are aesthetically rewarding. Here we test the hypothesis that individual differences in creative behavior are subserved by mesial and lateral differences in white matter connectivity. We compare jazz improvising musicians against classical (non-improvising) musicians and non-musician control subjects in musical performance and diffusion tensor imaging. Subjects improvised on short musical motifs and underwent DTI. Statistical measures of fluency and entropy for musical performances predicted expert ratings of creativity for each performance. Tract-Based Spatial Statistics (TBSS) showed higher Fractional Anisotropy (FA) in the cingulate cortex and corpus callosum in jazz musicians. FA in the cingulate also correlated with entropy. Probabilistic tractography from these mesial regions to lateral seed regions of the arcuate fasciculus, a pathway known to be involved in sound perception and production, showed mesial-to-lateral connectivity that correlated with improvisation training. Results suggest that white matter connectivity between lateral and mesial structures may integrate domain-general and domain-specific components of creativity.
Laccaria bicolor MiSSP8 is a small-secreted protein decisive for the establishment of the ectomycorrhizal symbiosis
Authors: Pellegrin Clément , Daguerre Yohann , Ruytinx Joske , Guinet Frédéric , Kemppainen Minna , Frei dit Frey Nicolas , Puech-Pagès Virginie , Hecker Arnaud , Pardo Alejandro G. , Martin Francis M. , Veneault-Fourrey Claire 
Published: November 2017

AbstractThe ectomycorrhizal symbiosis is a predominant tree-microbe interaction in forest ecosystems sustaining tree growth and health. Its establishment and functioning implies a long-term and intimate relationship between the soil-borne fungi and the roots of trees. Mycorrhiza-induced Small Secreted Proteins (MiSSPs) are hypothesized as keystone symbiotic proteins, required to set up the symbiosis by modifying the host metabolism and/or building the symbiotic interfaces.L. bicolor MiSSP8 is the third most highly induced MiSSPs in symbiotic tissues and it is also expressed in fruiting bodies. The MiSSP8-RNAi knockdown mutants are strongly impaired in their mycorrhization ability with Populus, with the lack of fungal mantle and Hartig net development due to a lack of hyphal aggregation. MiSSP8 C-terminus displays a repetitive motif containing a kexin cleavage site, recognized by KEX2 in vitro. This suggests MiSSP8 protein might be cleaved into small peptides. Moreover, the MiSSP8 repetitive motif is found in other proteins predicted secreted by both saprotrophic and ectomycorrhizal fungi. Thus, our data indicate that MiSSP8 is a small-secreted protein involved at early stages of ectomycorrhizal symbiosis, likely by regulating hyphal aggregation and pseudoparenchyma formation.
Using Optimal F-Measure and Random Resampling in Gene Ontology Enrichment Calculations
Authors: Ge Weihao , Fazal Zeeshan , Jakobsson Eric 
Published: November 2017

AbstractBackgroundA central question in bioinformatics is how to minimize arbitrariness and bias in analysis of patterns of enrichment in data. A prime example of such a question is enrichment of gene ontology (GO) classes in lists of genes. Our paper deals with two issues within this larger question. One is how to calculate the false discovery rate (FDR) within a set of apparently enriched ontologies, and the second how to set that FDR within the context of assessing significance for addressing biological questions, to answer these questions we compare a random resampling method with a commonly used method for assessing FDR, the Benjamini-Hochberg (BH) method. We further develop a heuristic method for evaluating Type II (false negative) errors to enable utilization of F-Measure binary classification theory for distinguishing “significant” from “non-significant” degrees of enrichment.ResultsThe results show the preferability and feasibility of random resampling assessment of FDR over the analytical methods with which we compare it. They also show that the reasonableness of any arbitrary threshold depends strongly on the structure of the dataset being tested, suggesting that the less arbitrary method of F-measure optimization to determine significance threshold is preferable.ConclusionTherefore, we suggest using F-measure optimization instead of placing an arbitrary threshold to evaluate the significance of Gene Ontology Enrichment results, and using resampling to replace analytical methods
AQMM: Enabling Absolute Quantification of Metagenome and Metatranscriptome
Authors: Jiang Xiao-Tao , Yu Ke , Li Li-Guan , Yin Xiao-Le , Li An-Dong , Zhang Tong 
Published: November 2017

AbstractMetatranscriptome has become increasingly important along with the application of next generation sequencing in the studies of microbial functional gene activity in environmental samples. However, the quantification of target active gene is hindered by the current relative quantification methods, especially when tracking the sharp environmental change. Great needs are here for an easy-to-perform method to obtain the absolute quantification. By borrowing information from the parallel metagenome, an absolute quantification method for both metagenomic and metatranscriptomic data to per gene/cell/volume/gram level was developed. The effectiveness of AQMM was validated by simulated experiments and was demonstrated with a real experimental design of comparing activated sludge with and without foaming. Our method provides a novel bioinformatic approach to fast and accurately conduct absolute quantification of metagenome and metatranscriptome in environmental samples. The AQMM can be accessed from https://github.com/biofuture/aqmm.
A semi-quantitative analysis of lupus arthritis using contrasted high-field MRI
Authors: Zollars Eric S , Chapin Russell 
Published: November 2017

AbstractObjectiveArthritis in systemic lupus erythematosus is poorly described and there is no objective measure for quantification of the arthritis. We aim to develop MRI as a research tool for the quantification of lupus arthritis.MethodsPatients were eligible for entry into the study if they were evaluated at the MUSC Lupus Center and determined by their treating physician to have active hand arthritis due to SLE. Standard of care lupus activity measures were collected along with a detailed physical exam. MRI images were obtained using standard musculoskeletal sequences with gadolinium contrast. Semi-quantitative scoring of the images used the OMERACT RAMRIS system.ResultsRAMRIS demonstrates large amounts of synovitis, tenosynovitis, bone marrow edema and erosive disease in only a minority of patients. Some patients were not scored as having any synovitis or tenosynovits. We describe potential features of lupus arthritis that are not captured in the RAMRIS scores and may be contributing to symptoms.ConclusionLupus arthritis is an entity separate from rheumatoid arthritis and requires the development of new quantitative methods to describe it. MRI findings suggest the inadequacy of a typical lupus musculoskeletal measures and even swollen/tender joint counts to assess the level of disease activity.
BayCount: A Bayesian Decomposition Method for Inferring Tumor Heterogeneity using RNA-Seq Counts
Authors: Xie Fangzheng , Zhou Mingyuan , Xu Yanxun 
Published: November 2017

AbstractTumors are heterogeneous - a tumor sample usually consists of a set of subclones with distinct transcriptional profiles and potentially different degrees of aggressiveness and responses to drugs. Understanding tumor heterogeneity is therefore critical for precise cancer prognosis and treatment. In this paper, we introduce BayCount, a Bayesian decomposition method to infer tumor heterogeneity with highly over-dispersed RNA sequencing count data. Using negative binomial factor analysis, BayCount takes into account both the between-sample and gene-specific random effects on raw counts of sequencing reads mapped to each gene. For the posterior inference, we develop an efficient compound Poisson based blocked Gibbs sampler. Simulation studies show that BayCount is able to accurately estimate the subclonal inference, including number of subclones, the proportions of these subclones in each tumor sample, and the gene expression profiles in each subclone. For real-world data examples, we apply BayCount to The Cancer Genome Atlas lung cancer and kidney cancer RNA sequencing count data and obtain biologically interpretable results. Our method represents the first effort in characterizing tumor heterogeneity using RNA sequencing count data that simultaneously removes the need of normalizing the counts, achieves statistical robustness, and obtains biologically/clinically meaningful insights. The R package BayCount implementing our model and algorithm is available for download.
Deregulation of TGF-β1 signaling induces glycolysis by chromatin remodeling in pathogenic TH17 cells
Authors: Yu Xiang , Wang Li , Han Zhijun , Yao Chao , Qiu Rong , Cui Yange , Dai Dai , Jin Wenfei , Shen Nan 
Published: November 2017

SummaryIt is well known that some pathogenic cells have enhanced glycolysis, the regulatory network leading to increased glycolysis are not well characterized. Here, we show that pathogenic TH17 cells specifically upregulate glycolytic pathway genes compared to homeostatic TH17 cells. Bioenergetic assay and metabolomics analyses indicate that pathogenic TH17 cells are highly glycolytic compared to nonpathogenic TH17 cells. Chromatin landscape analyses demonstrate TH17 cells in vivo show distinct chromatin states, and pathogenic TH17 cells show enhanced chromatin accessibility at glycolytic genes with NF-kB binding sites. Mechanistic studies reveal that TGF-β1 signaling induces TH17 cell chromatin remodeling and represses c-Rel-mediated glycolysis. A miR-21-Peli1-c-Rel loop was further identified to be essential for glycolysis of pathogenic TH17 cells. These findings extend our understanding of the regulation TH17 cell glycolysis in vivo and provide insights for future therapeutic intervention to TH17 cell mediated autoimmune diseases.
MapZ forms a stable ring structure that acts as a nano-track for FtsZ treadmilling inStreptococcus mutans
Authors: Li Yongliang , Shao Shipeng , Xu Xiao , Su Xiaodong , Sun Yujie , Wei Shicheng 
Published: November 2017

AbstractBacterial binary division requires the accurate placement of the division machinery. FtsZ, the vital component of the division machinery, can assemble into filaments and self-organize into a ring structure (Z-ring) at the proper site for cell division. Thus, understanding how bacteria control the spatiotemporal formation of the FtsZ ring is crucial for small molecule and nanoparticle antibacterial drug discovery. MapZ, a recently identified FtsZ regulator inStreptococcaceae,has been found to localize at the mid-cell and position the FtsZ ring. However, the mechanism is still unclear. Here, by using total internal reflection fluorescence microscopy, super-resolution imaging, and single molecule tracking, we investigated the mechanism by which MapZ regulates the FtsZ ring position. The results show that FtsZ exhibites dynamic treadmilling motion in S.mutans.Importantly, depletion of MapZ leads to an unconstrained movement of treadmilling FtsZ filaments and a shorter lifetime of the constricting FtsZ ring. Furthermore, by revealing that MapZ forms an immobile ring-like nanostructure at the division site, our study suggests that MapZ forms a stable ring that acts as a nanotrack to guide and restrict treadmilling FtsZ filaments in S.mutans, representing a novel way in which bacteria control the division.