Dynamical Behavior of Nonautonomous Stochastic Reaction-Diffusion Neural Network Models

This brief investigates nonautonomous stochastic reaction-diffusion neural-network models with S-type distributed delays. First, the existence and uniqueness of mild solution are studied under the Lipschitz condition without the linear growth condition. Due to the existence of a nonautonomous reaction-diffusion term and the infinite dimensional Wiener process, the criteria for the well-posedness of the models are established based on the evolution system theory. Then, the S-type distributed delay, which is an infinite delay, is handled by the truncation method, and sufficient conditions for the global exponential stability are obtained by constructing a simple Lyapunov-Krasovskii functional candidate. Finally, neural-network examples and an illustrative example are given to show the applications of the obtained results.</p

Learning non-negativity constrained variation for image denoising and deblurring

This paper presents a heuristic Learning-based Non-Negativity Constrained Variation (L-NNCV) aiming to search the coefficients of variational model automatically and make the variation adapt different images and problems by supervised-learning strategy. The model includes two terms: a problem-based term that is derived from the prior knowledge, and an image-driven regularization which is learned by some training samples. The model can be solved by classical o-constraint method. Experimental results show that: the experimental effectiveness of each term in the regularization accords with the corresponding theoretical proof; the proposed method outperforms other PDE-based methods on image denoising and deblurring.</p

Finite-Time Boundedness of Impulsive Delayed Reaction–Diffusion Stochastic Neural Networks

Considering the impulsive delayed reaction&amp;#x2013;diffusion stochastic neural networks (IDRDSNNs) with hybrid impulses, the finite-time boundedness (FTB) and finite-time contractive boundedness (FTCB) are investigated in this article. First, a novel delay integral inequality is presented. By integrating this inequality with the comparison principle, some sufficient conditions that ensure the FTB and FTCB of IDRDSNNs are obtained. This study demonstrates that the FTB of neural networks with hybrid impulses can be maintained, even in the presence of impulsive perturbations. And for a system that is not FTB due to impulsive perturbations, achieving FTB is possible through the implementation of appropriate impulsive control and optimization of the average impulsive intervals. In addition, to validate the practicality of our results, three illustrative examples are provided. In the end, these theoretical findings are successfully applied to image encryption.</p

Stability of stochastic impulsive reaction-diffusion neural networks with S-type distributed delays and its application to image encryption

In this paper, we study stochastic impulsive reaction-diffusion neural networks with S-type distributed delays, aiming to obtain the sufficient conditions for global exponential stability. First, an impulsive inequality involving infinite delay is introduced and the asymptotic behaviour of its solution is investigated by the truncation method. Then, global exponential stability in the mean-square sense of the stochastic impulsive reaction-diffusion system is studied by constructing a simple Lyapunov-Krasovskii functional where the S-type distributed delay is handled by the impulsive inequality. Numerical examples are also given to verify the effectiveness of the proposed results. Finally, the obtained theoretical results are successfully applied to an image encryption scheme based on bit-level permutation and the stochastic neural networks.</p

Finite-Time Boundedness of Impulsive Delayed Reaction–Diffusion Stochastic Neural Networks

Considering the impulsive delayed reaction&amp;#x2013;diffusion stochastic neural networks (IDRDSNNs) with hybrid impulses, the finite-time boundedness (FTB) and finite-time contractive boundedness (FTCB) are investigated in this article. First, a novel delay integral inequality is presented. By integrating this inequality with the comparison principle, some sufficient conditions that ensure the FTB and FTCB of IDRDSNNs are obtained. This study demonstrates that the FTB of neural networks with hybrid impulses can be maintained, even in the presence of impulsive perturbations. And for a system that is not FTB due to impulsive perturbations, achieving FTB is possible through the implementation of appropriate impulsive control and optimization of the average impulsive intervals. In addition, to validate the practicality of our results, three illustrative examples are provided. In the end, these theoretical findings are successfully applied to image encryption.</p

Real Time Cardan Shaft State Estimation of High-Speed Train Based on Ensemble Empirical Mode Decomposition

Due to the special location and structure of transmission system on high-speed train named CRH5, dynamic unbalance state of the cardan shaft will pose a threat to the train servicing safety, so effective methods that test the cardan shaft operating information and estimate the performance state in real time are needed. In this study a useful estimation method based on ensemble empirical mode decomposition (EEMD) is presented. By using this method, time-frequency characteristic of cardan shaft can be extracted effectively by separating the gearbox vibration acceleration data. Preliminary analysis suggests that the pinions rotating vibration separated from gearbox vibration by EEMD can be used as important assessment basis to estimate cardan shaft state. With two sets gearbox vibration signals collected from the in-service train at different running speed, the comparative analysis verifies that the proposed method has high effectiveness for cardan-shaft state estimate. Of course, it needs further research to quantify the performance state of cardan shaft based on this method

Integrative GWAS and Mendelian randomization study of rheumatoid arthritis based on the 2019 UK Biobank questionnaire

Introduction: Rheumatoid arthritis (RA) is the most prevalent autoimmune inflammatory joint disorder worldwide. We aimed to identify the genetic variants contributing to RA and investigate the potential influence of related diseases on RA risk. Methods: We performed genome-wide association studies (GWAS) on RA using the 2019 UK Biobank pain questionnaire. We conducted a primary GWAS (9,389 RA cases; 132,108 controls) and separate sex-stratified GWAS for females (4,832 cases; 75,184 controls) and males (4,557 cases; 56,924 controls). We incorporated 12 phenotypes from downstream analyses, such as genetic correlation analyses, transcriptome-wide association studies (TWAS), phenome-wide association studies (PheWAS), and Mendelian randomization (MR) studies to determine causal relationships with RA. Results: Two loci reached genome-wide significance in the primary GWAS. The top SNP, rs35139284 (p = 3.67 × 10−25) in the HLA-DRB1 gene on chromosome 6, exhibited a robust replication. Another locus, harboring the top SNP rs539837 (p = 6.26 × 10−9) near the LINC01680 gene on chromosome 1, also showed a significant association. In the female-specific GWAS, rs35139284 (p = 1.91 × 10−22) remained the top signal, whereas the male-specific GWAS revealed a suggestive significance at rs9267989 (p = 5.28 × 10−8) in TSBP1-AS1. TWAS and tissue specificity studies pointed to the spleen, lung, and small intestine as key tissues implicated in RA. PheWAS and MR analyses highlighted asthma and eosinophils associated with RA. Conclusion: Our findings confirmed an RA locus at chromosome 6 and highlighted associations between RA and a spectrum of immune-related and inflammatory phenotypes. Further analyses may provide greater insights into the genetic architecture of RA. (Table presented.)</p

Adaptive synchronization of stochastic complex dynamical networks and its application

This paper investigates exponential synchronization for stochastic complex dynamical networks with reaction–diffusion terms and S-type distributed delays. Based on a generalized Halanay inequality and Poincaré inequality, adaptive control strategies for exponential synchronization are established by constructing a simple Lyapunov–Krasovskii functional candidate and utilizing the truncation method. Some numerical examples are provided to demonstrate the effectiveness of the obtained results. Finally, the proposed adaptive synchronization theoretical results are successfully applied to image encryption.</p

The Use of Human Serum Samples to Study Malignant Transformation: A Pilot Study

Obesity and excess adiposity account for approximately 20% of all cancer cases; however, biomarkers of risk remain to be elucidated. While fibroblast growth factor-2 (FGF2) is emerging as an attractive candidate biomarker for visceral adipose tissue mass, the role of circulating FGF2 in malignant transformation remains unknown. Moreover, functional assays for biomarker discovery are limited. We sought to determine if human serum could stimulate the 3D growth of a non-tumorigenic cell line. This type of anchorage-independent 3D growth in soft agar is a surrogate marker for acquired tumorigenicity of cell lines. We found that human serum from cancer-free men and women has the potential to stimulate growth in soft agar of non-tumorigenic epithelial JB6 P+ cells. We examined circulating levels of FGF2 in humans in malignant transformation in vitro in a pilot study of n = 33 men and women. Serum FGF2 levels were not associated with colony formation in epithelial cells (r = 0.05, p = 0.80); however, a fibroblast growth factor receptor-1 (FGFR1) selective inhibitor significantly blocked serum-stimulated transformation, suggesting that FGF2 activation of FGFR1 may be necessary, but not sufficient for the transforming effects of human serum. This pilot study indicates that the FGF2/FGFR1 axis plays a role in JB6 P+ malignant transformation and describes an assay to determine critical serum factors that have the potential to promote tumorigenesis

Fusobacterium nucleatum Abundance is Associated with Cachexia in Colorectal Cancer Patients: The ColoCare Study

Background: Cachexia accounts for about 20% of all cancer‐related deaths and indicates poor prognosis. The impact of Fusobacterium nucleatum (Fn), a microbial risk factor for colorectal cancer (CRC), on the development of cachexia in CRC has not been established. Methods: We evaluated the association between Fn abundance in pre‐surgical stool samples and onset of cachexia at 6 months post‐surgery in n = 87 patients with stages I–III CRC in the ColoCare Study. Results: High fecal Fn abundance compared to negative/low fecal Fn abundance was associated with 4‐fold increased risk of cachexia onset at 6 months post‐surgery (OR = 4.82, 95% CI = 1.15, 20.10, p = 0.03). Conclusion: Our findings suggest that high fecal Fn abundance was associated with an increased risk of cachexia at 6 months post‐surgery in CRC patients. This is the first study to link Fn abundance with cachexia in CRC patients, offering novel insights into biological mechanisms and potential management of cancer cachexia. Due to the small sample size, our results should be interpreted with caution. Future studies with larger sample sizes are needed to validate these findings