Dynamic Circular Network-Based Federated Dual-View Learning for Multivariate Time Series Anomaly Detection

Multivariate time-series data exhibit intricate correlations in both temporal and spatial dimensions. However, existing network architectures often overlook dependencies in the spatial dimension and struggle to strike a balance between long-term and short-term patterns when extracting features from the data. Furthermore, industries within the business community are hesitant to share their raw data, which hinders anomaly prediction accuracy and detection performance. To address these challenges, the authors propose a dynamic circular network-based federated dual-view learning approach. Experimental results from four open-source datasets demonstrate that the method outperforms existing methods in terms of accuracy, recall, and F1_score for anomaly detection

Contemporary survival and anticoagulation of patients with atrial fibrillation: A community based cohort study in China

BackgroundsThe understanding of death in patients with atrial fibrillation (AF) in China is limited. This study aimed to assess the contemporary survival of AF patients in China and to explore risk factors for deaths.MethodsThis was a prospective community-based cohort study including 559 AF patients, who were followed-up from July 2015 to December 2020.ResultsDuring 66-month follow-up, there were 200 deaths (56.5% cardiovascular, 40.0% non-cardiovascular, and 3.5% unknown causes) among 559 AF patients with the median age of 76 years. The top three causes of death were heart failure (33.0%), ischemic stroke (17.0%) and cancer (16.5%). Multivariate Cox regression analysis indicated baseline variables positively associated with all-cause death were age (HR: 1.10, 95% CI: 1.08–1.13), AF subtype (HR: 1.37, 95% CI: 1.08–1.73), prior myocardial infarction (HR: 3.40, 95% CI: 1.48–7.78), previous tumor (HR: 2.61, 95% CI: 1.37–4.98), hypoglycemic therapy at baseline (HR: 1.81, 95% CI: 1.13–2.91), but body weight (HR: 0.98, 95% CI: 0.97–1.00) and use of calcium channel blocker (CCB) (HR: 0.62, 95% CI: 0.41–0.95) played a protective role to all-cause death. Of patients who were alive at the end of follow-up, 24.0% were on oral anticoagulants (OAC) alone, 4.5% on dual antithrombotic therapy, 33.1% on antiplatelet agents alone and 38.4% weren't on any antithrombotic medication.ConclusionIschemic stroke still remains one of the leading causes of death and OAC is seriously underused in AF patients in China. Independent risk factors for death are age, AF subtype, previous tumor, prior myocardial infarction, hypoglycemic therapy, low body weight and no CCB use.Clinical Trial Registrationhttp://www.chictr.org.cn/ (ChiCTR-ICR-15007036)

Demand dispatch in cyber-physical load aggregation system with multilevel incentives

Abstract This paper presents a demand dispatch strategy of aggregated electric water heaters (EWHs) for a load aggregation system at demand side, based on the theory of cyber-physical system. The objective is to solve the problem of water heater load control when the cyber-physical load aggregation system participates in demand dispatch of the power grid. First, an implementation framework of the demand dispatch strategy is designed between the cyber space and the physical space, including state awareness, real-time analysis, scientific decision-making and precise execution. Second, a multilevel incentive model, an EWH appliance model and a thermostat setpoint control rule are introduced. Next, based on the models and the rule, the state awareness logic, real-time analysis logic, scientific decision-making logic and precise execution logic of the strategy are designed to implement demand dispatch of aggregated EWHs. Finally, simulation results confirm the effectiveness, the advantage and excellent scalability of the proposed strategy

A XML-BASED BOOTSTRAPPING METHOD FOR PATTERN ACQUISITION

Abstract: Extensible Markup Language (XML) has been widely used as a middleware because of its flexibility. Fixed domain is one of the bottlenecks of Information Extraction (IE) technologies. In this paper we present a XML-based domain-adaptable bootstrapping method of pattern acquisition, which focuses on minimizing the cost of domain migration. The approach starts from a seed corpus with some seed patterns; extends the corpus based on the seed corpus through the Internet and acquires the new patterns from extended corpus. Positive and negative examples classified from training corpus are used to evaluate the patterns acquired. The result shows our method is a practical way in pattern acquisitions.

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies

<div><p>Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART). With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study.</p></div

Heatmap displays the rank of 105 tissues (y-axis) in terms of their relevance for each of the 43 GWAS traits (x-axis).

<p>Traits are organized by hierarchical clustering. Tissues are organized into ten tissue groups.</p

Association results for SNP set tests in WTCCC using different SNP weights.

<p>Results are shown for 17 genes identified to be significant by at least one SNP weighting option in four dieseases from the WTCCC data (CD, RA, T1D and T2D). All these genes have been previously identified to be associated with the corresponding trait (cited references). Approaches that yield a p-value passing the genome-wide significance threshold (8.95x10^-6) are highlighted in bold.</p

Simulation results for comparing using multiple annotations versus a single annotation.

<p>(A) Power to detect trait-relevant tissues by different approaches in various settings at a fixed FDR of 0.1. x-axis shows the values of the two annotation coefficients used in the simulations. Settings where at least one annotation coefficient is zero are shaded in grey. The setting where the annotation coefficients equal to the median estimates from real data (i.e. <b><i>α</i></b> = <b>(0.1, 0.05)</b>) is shaded in gold. The first number for each method in the figure legend represents the number of times each method is ranked as the best in 25 simulation settings where none of the annotations have zero coefficients; while the second number represents the number of times each method is ranked as the best in 11 simulation settings where at least one annotation has a zero coefficient. (B) Annotation coefficient estimates by SMART are centered around the truth (horizontal dotted gold lines). (C) Mean power (y-axis) to detect trait-relevant tissues by different approaches at different FDR values (x-axis). Error bar shows the standard deviation computed across 10 simulation groups, each of which contains 1,000 simulation replicates (i.e. a total of 10,000 simulations). <i>p</i>-values from the paired t-test are used to compare methods at different FDR cutoffs. Note that the error bar is large due to the small number of simulation replicates within each simulation group. For (B) and (C), simulations were done at <b><i>α</i></b> = <b>(0.1, 0.05)</b>. FDR, false discovery rate.</p

SNP set test results on Crohn’s disease (CD) using different SNP weights.

<p>(A) Manhattan plot shows association signal across genes (x-axis) detected by SNP set tests using three different sets of SNP weights. EqualWeight (black): equal SNP weights. HC (red): SNP weights constructed using the estimated coefficient parameters for continuous histone mark based annotations in the GWAS consortium study. HB (green): SNP weights constructed using the estimated coefficient parameters for binary histone mark based annotations in the GWAS consortium study. The gold dashed line represents genome-wide significance threshold (8.95x10^-6). (B) The same results are displayed with QQ plot of -log10 p-values. Grey shaded area represents the 95% point-wise confidence interval.</p

Simulation results for using different weights to construct SNP set tests.

<p>(A) QQ plot of -log10 p values from SNP set tests using different SNP weights under the null simulations. Tests using different weights all control type I error well. (B) Power to detect causal blocks by SNP set tests using different SNP weights in the simulation setting where <b>α</b> = <b>(0.4, 0.4)</b>. (C) Power to detect causal blocks by SNP set tests using different SNP weights in the simulation setting where <b>α</b> = <b>(0.4, 0).</b> For both (B) and (C), Power are evaluated at a genome-wide significance threshold of 1x10^-4. Standard errors are computed across 1,000 simulation replicates. The x-axis shows the proportion of causal SNPs that have identical values for the two annotations, which measures correlation between the two annotations.</p