Block Empirical Likelihood for Semiparametric Varying-Coefficient Partially Linear Errors-in-Variables Models with Longitudinal Data

Block empirical likelihood inference for semiparametric varying-coeffcient partially linear errors-in-variables models with longitudinal data is investigated. We apply the block empirical likelihood procedure to accommodate the within-group correlation of the longitudinal data. The block empirical log-likelihood ratio statistic for the parametric component is suggested. And the nonparametric version of Wilk’s theorem is derived under mild conditions. Simulations are carried out to access the performance of the proposed procedure

Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX

This study examines the efficacy of various neural network (NN) models in interpreting mental constructs via electroencephalogram (EEG) signals. Through the assessment of 16 prevalent NN models and their variants across four brain-computer interface (BCI) paradigms, we gauged their information representation capability. Rooted in comprehensive literature review findings, we proposed EEGNeX, a novel, purely ConvNet-based architecture. We pitted it against both existing cutting-edge strategies and the Mother of All BCI Benchmarks (MOABB) involving 11 distinct EEG motor imagination (MI) classification tasks and revealed that EEGNeX surpasses other state-of-the-art methods. Notably, it shows up to 2.1%-8.5% improvement in the classification accuracy in different scenarios with statistical significance (p < 0.05) compared to its competitors. This study not only provides deeper insights into designing efficient NN models for EEG data but also lays groundwork for future explorations into the relationship between bioelectric brain signals and NN architectures. For the benefit of broader scientific collaboration, we have made all benchmark models, including EEGNeX, publicly available at (https://github.com/chenxiachan/EEGNeX).Comment: 19 pages, 6 figure

SilkDB: a knowledgebase for silkworm biology and genomics

The Silkworm Knowledgebase (SilkDB) is a web-based repository for the curation, integration and study of silkworm genetic and genomic data. With the recent accomplishment of a ∼6X draft genome sequence of the domestic silkworm (Bombyx mori), SilkDB provides an integrated representation of the large-scale, genome-wide sequence assembly, cDNAs, clusters of expressed sequence tags (ESTs), transposable elements (TEs), mutants, single nucleotide polymorphisms (SNPs) and functional annotations of genes with assignments to InterPro domains and Gene Ontology (GO) terms. SilkDB also hosts a set of ESTs from Bombyx mandarina, a wild progenitor of B.mori, and a collection of genes from other Lepidoptera. Comparative analysis results between the domestic and wild silkworm, between B.mori and other Lepidoptera, and between B.mori and the two sequenced insects, fruitfly and mosquito, are displayed by using B.mori genome sequence as a reference framework. Designed as a basic platform, SilkDB strives to provide a comprehensive knowledgebase about the silkworm and present the silkworm genome and related information in systematic and graphical ways for the convenience of in-depth comparative studies. SilkDB is publicly accessible at http://silkworm.genomics.org.cn

Selective Inhibition of PKCβ2 Restores Ischemic Postconditioning-Mediated Cardioprotection by Modulating Autophagy in Diabetic Rats

Diabetic hearts are more susceptible to myocardial ischemia/reperfusion (I/R) injury and less sensitive to ischemic postconditioning (IPostC), but the underlying mechanisms remain unclear. PKCβ2 is preferentially overactivated in diabetic myocardium, in which autophagy status is abnormal. This study determined whether hyperglycemia-induced PKCβ2 activation resulted in autophagy abnormality and compromised IPostC cardioprotection in diabetes. We found that diabetic rats showed higher cardiac PKCβ2 activation and lower autophagy than control at baseline. However, myocardial I/R further increased PKCβ2 activation and promoted autophagy status in diabetic rats. IPostC significantly attenuated postischemic infarct size and CK-MB, accompanied with decreased PKCβ2 activation and autophagy in control but not in diabetic rats. Pretreatment with CGP53353, a selective inhibitor of PKCβ2, attenuated myocardial I/R-induced infarction and autophagy and restored IPostC-mediated cardioprotection in diabetes. Similarly, CGP53353 could restore hypoxic postconditioning (HPostC) protection against hypoxia reoxygenation- (HR-) induced injury evidenced by decreased LDH release and JC-1 monomeric cells and increased cell viability. These beneficial effects of CGP53353 were reversed by autophagy inducer rapamycin, but could be mimicked by autophagy inhibitor 3-MA. It is concluded that selective inhibition of PKCβ2 could attenuate myocardial I/R injury and restore IPostC-mediated cardioprotection possibly through modulating autophagy in diabetes