746 research outputs found

    Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors

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    Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical "template" ICA model where source signals---including known population brain networks and subject-specific signals---are represented as latent variables. For estimation, we derive an expectation maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75-250% higher intra-subject reliability

    A qualitative study of how self-harm starts and continues among Chinese adolescents

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    Background It is essential to investigate the experiences behind why adolescents start and continue to self-harm in order to develop targeted treatment and prevent future self-harming behaviours. Aims The aims of this study are to understand the motivations for initiating and repeating nonfatal self-harm, the different methods used between first-time and repeated self-harm and the reasons that adolescents do not seek help from health services. Methods Adolescents with repeated nonfatal self-harm experiences were recruited to participate in individual, semi-structured qualitative interviews. The interviews were analysed with interpretative phenomenological analysis. Results We found that nonfatal self-harm among adolescents occurred comparatively early and was often triggered by specific reasons. However, the subsequent nonfatal self-harm could be causeless, with repeated self-harm becoming a maladaptive coping strategy to handle daily pressure and negative emotions. The choice of tools used was related to the ease of accessibility, the life-threatening risk and the size of the scars. Adolescents often concealed their scars on purpose, which made early identification insufficient. Peer influence, such as online chat groups encouraging self-harm by discussing and sharing self-harm pictures, could also lead to increased self-harm. The results also included participants’ opinions on how to stop nonfatal self-harm and their dissatisfaction with the current healthcare services. Conclusions The current study provides important implications both for early identification and interventions for adolescents who engage in repeated nonfatal self-harm, and for individualising treatment planning that benefits them. It is also worthwhile to further investigate how peer influence and social media may affect self-harm in adolescents

    A multi-tissue atlas of regulatory variants in cattle:Cattle Genotype-Tissue Expression Atlas

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    Characterization of genetic regulatory variants acting on the livestock gene expression is essential for interpreting the molecular mechanisms underlying traits of economic value and for increasing the rate of genetic gain through artificial selection. Here we build a Cattle Genotype-Tissue Expression atlas (CattleGTEx) as part of the pilot phase of Farm animal GTEx (FarmGTEx) project for the research community based on publicly available 7,180 RNA-Seq samples. We describe the transcriptomic landscape of over 100 tissues/cell types and report hundreds of thousands of genetic associations with gene expression and alternative splicing for 23 distinct tissues. We evaluate the tissue-sharing patterns of these genetic regulatory effects, and functionally annotate them using multi-omics data. Finally, we link gene expression in different tissues to 43 economically important traits using both transcriptome-wide association and colocalization analyses to decipher the molecular regulatory mechanisms underpinning such agronomic traits in cattle

    Prevalence and prognostic significance of DNMT3A- and TET2- clonal haematopoiesis-driver mutations in patients presenting with ST-segment elevation myocardial infarction

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    Background Clonal haematopoiesis driven by mutations in DNMT3A or TET2 has recently been identified as a new risk factor for cardiovascular disease. Experimental studies suggest that these mutations may enhance inflammation which accelerates the disease progression. We aim to investigate the prevalence of mutations in DNMT3A and TET2 and their association with prognosis of patients with ST-segment elevation myocardial infarction (STEMI). Methods Targeted deep sequencing for DNMT3A and TET2 and inflammatory cytokines (IL-1β, IL-6, TNF-α, INF-γ) were analyzed in 485 patients with STEMI. Major adverse cardiac events (MACE) was a composite of death, myocardial infarction, stroke, or hospitalization due to heart failure. Findings Patients carrying DNMT3A- or TET2-CH-driver mutations with a variant allele frequency (VAF) ≥2% were found in 12.4% (60 of 485) of STEMI patients and experienced an increased incidence of the death (30.9% vs 15.5%, P = 0.001) and MACE (44.5% vs 21.8%, P < 0.001) compared to those who did not, during a median follow up of 3.0 (interquartile range: 2.4–3.4) years. After adjusting for confounders, mutation remained an independent predictor of death (HR = 1.967, 95% CI 1.103–3.507, P = 0.022) and MACE (HR = 1.833, 95% CI 1.154–2.912, P = 0.010). Concentrations of plasma IL-1β (P = 0.010) and IL-6 (P = 0.011) were significantly elevated in DNMT3A/TET2 VAF≥2% group. Interpretation DNMT3A- or TET2-CH-driver mutations with a VAF≥2% were observed in over 10% STEMI patients, and were significantly associated with poorer prognosis, which might be explained by higher levels of inflammatory cytokines in mutations carriers

    Gene-Expression Signatures Can Distinguish Gastric Cancer Grades and Stages

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    Microarray gene-expression data of 54 paired gastric cancer and adjacent noncancerous gastric tissues were analyzed, with the aim to establish gene signatures for cancer grades (well-, moderately-, poorly- or un-differentiated) and stages (I, II, III and IV), which have been determined by pathologists. Our statistical analysis led to the identification of a number of gene combinations whose expression patterns serve well as signatures of different grades and different stages of gastric cancer. A 19-gene signature was found to have discerning power between high- and low-grade gastric cancers in general, with overall classification accuracy at 79.6%. An expanded 198-gene panel allows the stratification of cancers into four grades and control, giving rise to an overall classification agreement of 74.2% between each grade designated by the pathologists and our prediction. Two signatures for cancer staging, consisting of 10 genes and 9 genes, respectively, provide high classification accuracies at 90.0% and 84.0%, among early-, advanced-stage cancer and control. Functional and pathway analyses on these signature genes reveal the significant relevance of the derived signatures to cancer grades and progression. To the best of our knowledge, this represents the first study on identification of genes whose expression patterns can serve as markers for cancer grades and stages
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