Stability of Stochastic Discrete-Time Neural Networks with Discrete Delays and the Leakage Delay

This paper investigates the stability of stochastic discrete-time neural networks (NNs) with discrete time-varying delays and leakage delay. As the partition of time-varying and leakage delay is brought in the discrete-time system, we construct a novel LyapunovKrasovskii function based on stability theory. Furthermore sufficient conditions are derived to guarantee the global asymptotic stability of the equilibrium point. Numerical example is given to demonstrate the effectiveness of the proposed method and the applicability of the proposed method

JMJ704 positively regulates rice defense response against Xanthomonas oryzae pv. oryzae infection via reducing H3K4me2/3 associated with negative disease resistance regulators

A schematic representation showing the genomic regions of the three genes for ChIP-PCR assay. White box indicates untranslated region, black box indicates coding sequence, line through the box indicates intron region of the genes, lines and numbers above the gene indicate the regions and positions used for ChIP-PCR assay. (TIF 2795 kb

State Estimation for Discrete-Time Stochastic Neural Networks with Mixed Delays

This paper investigates the analysis problem for stability of discrete-time neural networks (NNs) with discrete- and distribute-time delay. Stability theory and a linear matrix inequality (LMI) approach are developed to establish sufficient conditions for the NNs to be globally asymptotically stable and to design a state estimator for the discrete-time neural networks. Both the discrete delay and distribute delays employ decomposing the delay interval approach, and the Lyapunov-Krasovskii functionals (LKFs) are constructed on these intervals, such that a new stability criterion is proposed in terms of linear matrix inequalities (LMIs). Numerical examples are given to demonstrate the effectiveness of the proposed method and the applicability of the proposed method

Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representations on Sequence Labelling Tasks

Word embeddings -- distributed word representations that can be learned from unlabelled data -- have been shown to have high utility in many natural language processing applications. In this paper, we perform an extrinsic evaluation of five popular word embedding methods in the context of four sequence labelling tasks: POS-tagging, syntactic chunking, NER and MWE identification. A particular focus of the paper is analysing the effects of task-based updating of word representations. We show that when using word embeddings as features, as few as several hundred training instances are sufficient to achieve competitive results, and that word embeddings lead to improvements over OOV words and out of domain. Perhaps more surprisingly, our results indicate there is little difference between the different word embedding methods, and that simple Brown clusters are often competitive with word embeddings across all tasks we consider

Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representations on Sequence Labelling Tasks

Word embeddings — distributed word representations that can be learned from unlabelled data — have been shown to have high utility in many natural language processing applications. In this paper, we perform an extrinsic evaluation of four popular word embedding methods in the context of four sequence labelling tasks:part-of-speech tagging, syntactic chunking, named entity recognition, and multiword expression identification. A particular focus of the paper is analysing the effects of task-based updating of word representations. We show that when using word embeddings as features, as few as several hundred training instances are sufficient to achieve competitive results, and that word embeddings lead to improvements over out-of-vocabulary words and also out of domain. Perhaps more surprisingly, our results indicate there is little difference between the different word embedding methods, and that simple Brown clusters are often competitive with word embeddings across all tasks we conside

Integrating single-cell and bulk transcriptomic analyses to develop a cancer-associated fibroblast-derived biomarker for predicting prognosis and therapeutic response in breast cancer

BackgroundCancer-associated fibroblasts (CAFs) contribute to the progression and treatment of breast cancer (BRCA); however, risk signatures and molecular targets based on CAFs are limited. This study aims to identify novel CAF-related biomarkers to develop a risk signature for predicting the prognosis and therapeutic response of patients with BRCA.MethodsCAF-related genes (CAFRGs) and a risk signature based on these genes were comprehensively analyzed using publicly available bulk and single-cell transcriptomic datasets. Modular genes identified from bulk sequencing data were intersected with CAF marker genes identified from single-cell analysis to obtain reliable CAFRGs. Signature CAFRGs were screened via Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. Multiple patient cohorts were used to validate the prognosis and therapeutic responsiveness of high-risk patients stratified based on the CAFRG-based signature. In addition, the relationship between the CAFRG-based signature and clinicopathological factors, tumor immune landscape, functional pathways, chemotherapy sensitivity and immunotherapy sensitivity was examined. External datasets were used and sample experiments were performed to examine the expression pattern of MFAP4, a key CAFRG, in BRCA.ResultsIntegrated analyses of single-cell and bulk transcriptomic data as well as prognostic screening revealed a total of 43 prognostic CAFRGs; of which, 14 genes (TLN2, SGCE, SDC1, SAV1, RUNX1, PDLIM4, OSMR, NT5E, MFAP4, IGFBP6, CTSO, COL12A1, CCDC8 and C1S) were identified as signature CAFRGs. The CAFRG-based risk signature exhibited favorable efficiency and accuracy in predicting survival outcomes and clinicopathological progression in multiple BRCA cohorts. Functional enrichment analysis suggested the involvement of the immune system, and the immune infiltration landscape significantly differed between the risk groups. Patients with high CAF-related risk scores (CAFRSs) exhibited tumor immunosuppression, enhanced cancer hallmarks and hyposensitivity to chemotherapy and immunotherapy. Five compounds were identified as promising therapeutic agents for high-CAFRS BRCA. External datasets and sample experiments validated the downregulation of MFAP4 and its strong correlation with CAFs in BRCA.ConclusionsA novel CAF-derived gene signature with favorable predictive performance was developed in this study. This signature may be used to assess prognosis and guide individualized treatment for patients with BRCA

Microarray and bioinformatic analysis reveal the parental genes of m6A modified circRNAs as novel prognostic signatures in colorectal cancer

BackgroundAccumulating evidences have revealed that the abnormal N6-methyladenosine (m6A) modification is closely associated with the occurrence, development, progression and prognosis of cancer. It is noteworthy that m6A modification is widely existed in circRNAs and found its key biological functions in regulating circRNAs metabolism. However, the role of m6A modified circRNAs in colorectal cancer (CRC) remains unknown. To better understand the role of circRNAs in the pathogenesis of CRC, we focus on the relationship between m6A-modified circRNAs and their parental genes.MethodsArraystar m6A-circRNA epitranscriptomic microarray was used to identify differentially m6A modified circRNAs between CRC and the control group. In addition, TCGA-COAD and GSE106582 cohort were used to identify differentially expressed mRNAs. In this study, we screened the parental genes for which both circRNAs and mRNAs were down-regulated further to analyze, including gene expression, survival prognosis, enrichment analysis. Additionally, Western Blotting was used to further validate the role of the parental gene in CRC.ResultsWe found that 1405 significantly downregulated circRNAs in CRC by our microarray data. Moreover, we obtained 113 parental genes for which both circRNAs and mRNAs were down-regulated to analyze the relationship with the prognosis of CRC based on TCGA-COAD cohort. And we identified nine potential prognostic genes, including ABCD3, ABHD6, GAB1, MIER1, MYOCD, PDE8A, RPS6KA5, TPM1 and WDR78. And low expression of these genes was associated with poor survival prognosis of the patients with CRC. In addition, we found that TPM1 is downregulated in CRC by western blotting experiment. And the calcium-signaling pathway may involve the process of the CRC progression.ConclusionsWe identified nine potential prognostic genes, after analyzed the relationship between the parental genes of m6A modified circRNAs and the progression of CRC. Above all, our study further validated TPM1 can serve as a potentail signature for CRC patients