Screw lifetime prediction based on wavelet neural network and empirical mode decomposition

To predict residual lifetime of ball screw, screw lifetime prediction technology based on wavelet neural network (WNN) and empirical mode decomposition (EMD) is proposed. Screw accelerated lifetime test platform is introduced. Accelerometers are installed to monitor ball screw lifetime. With the method of principal component analysis (PCA), high dimension features are mapped to low dimensional space and stored into sample library together with screw expected remaining lifetime. Training samples and testing samples are randomly selected from the sample library to train and test the WNN. Then EMD is used to extract output tendency of WNN. Finally, screw lifetime prediction model can be obtained. The experimental results show that the maximum error of the training samples is 602 hours while the maximum error of the testing samples is 652 hours, which meet the need of screw lifetime prediction

A Survey of DeFi Security: Challenges and Opportunities

DeFi, or Decentralized Finance, is based on a distributed ledger called blockchain technology. Using blockchain, DeFi may customize the execution of predetermined operations between parties. The DeFi system use blockchain technology to execute user transactions, such as lending and exchanging. The total value locked in DeFi decreased from \$200 billion in April 2022 to \$80 billion in July 2022, indicating that security in this area remained problematic. In this paper, we address the deficiency in DeFi security studies. To our best knowledge, our paper is the first to make a systematic analysis of DeFi security. First, we summarize the DeFi-related vulnerabilities in each blockchain layer. Additionally, application-level vulnerabilities are also analyzed. Then we classify and analyze real-world DeFi attacks based on the principles that correlate to the vulnerabilities. In addition, we collect optimization strategies from the data, network, consensus, smart contract, and application layers. And then, we describe the weaknesses and technical approaches they address. On the basis of this comprehensive analysis, we summarize several challenges and possible future directions in DeFi to offer ideas for further research

LIM-domain binding protein 2 was down-regulated by miRNA-96-5p inhibited the proliferation, invasion and metastasis of lung cancer H1299 cells

  Objectives: Lung cancer was one of the most common malignancies around the world. It has great significance in to search for the mechanism of occurrence and development of lung cancer. LIM Domain Binding protein 2 (LDB2) belongs to the LIM-domain binding family, it can be used as a binding protein that combined with other transcription factors to form the transcription complex for regulating the expression of target genes. The expression of microRNA-96-5p (miR-96-5p) has been investigated in various tumors. The aim of this study is to investigate the potential role of LDB2 and miR-96-5p in lung cancer. Methods: Real-time quantitative PCR was applied to detect the expression of LDB2 and miR-96-5p. The proliferation, invasion, and metastasis of H1299 cells were analyzed by CCK8, transwell, and wound healing assay after LDB2 or miR-96-5p transfection. Luciferase activities assay and western blot were used to reveal the targeted regulation between LDB2 and miR-96-5p. Results: Here the authors found LDB2 was down-regulated in lung cancer tissues and negatively correlated with miR-96-5p expression, it could promote or inhibit the proliferation, invasion and metastasis of H1299 cells after LDB2 knockdown or overexpression and regulate the expression of cyclinD1, MMP9, Bcl-2, and Bax via ERK1/2 signaling pathway. Furthermore, miR-96-5p exerted its function by directly binding to 3′-UTR of LDB2 and regulating expression of LDB2. miR-96-5p could promote the proliferation, invasion, and metastasis of H1299 cells. Conclusion: These findings demonstrate that LDB2 can act as a new regulator to inhibit cell proliferation, invasion, and metastasis via the ERK1/2 signaling pathway, and miR-96-5p may be a potential promising molecular by targeting LDB2 in lung cancer

Non-Fragile Observer-Based Adaptive Integral Sliding Mode Control for a Class of T-S Fuzzy Descriptor Systems With Unmeasurable Premise Variables

The issue of non-fragile observer-based adaptive integral sliding mode control for a class of Takagi–Sugeno (T-S) fuzzy descriptor systems with uncertainties and unmeasurable premise variables is investigated. General nonlinear systems are represented by nonlinear T-S fuzzy descriptor models, because premise variables depend on unmeasurable system states and fuzzy models have different derivative matrices. By introducing the system state derivative as an auxiliary state vector, original fuzzy descriptor systems are transformed into augmented systems for which system properties remain the same. First, a novel integral sliding surface, which includes estimated states of the sliding mode observer and controller gain matrices, is designed to obtain estimation error dynamics and sliding mode dynamics. Then, some sufficient linear matrix inequality (LMI) conditions for designing the observer and the controller gains are derived using the appropriate fuzzy Lyapunov functions and Lyapunov theory. This approach yields asymptotically stable sliding mode dynamics. Corresponding auxiliary variables are introduced, and the Finsler's lemma is employed to eliminate coupling of controller gain matrices, observer gain matrices, Lyapunov function matrices, and/or observer gain perturbations. An observer-based integral sliding mode control strategy is obtained to assure that reachability conditions are satisfied. Moreover, a non-fragile observer and a non-fragile adaptive controller are developed to compensate for system uncertainties and perturbations in both the observer and the controller. Finally, example results are presented to illustrate the effectiveness and merits of the proposed method

The indirect efficacy comparison of DNA methylation in sputum for early screening and auxiliary detection of lung cancer: A meta-analysis

Background: DNA methylation in sputum has been an attractive candidate biomarker for the non-invasive screening and detection of lung cancer. Materials and Methods: Databases including PubMed, Ovid, Cochrane library, Web of Science databases, Chinese Biological Medicine (CBM), Chinese National Knowledge Infrastructure (CNKI), Wanfang, Vip Databases and Google Scholar were searched to collect the diagnostic trials on aberrant DNA methylation in the screening and detection of lung cancer published until 1 December 2016. Indirect comparison meta-analysis was used to evaluate the diagnostic value of the included candidate genes. Results: The systematic literature search yielded a total of 33 studies including a total of 4801 subjects (2238 patients with lung cancer and 2563 controls) and covering 32 genes. We identified that methylated genes in sputum samples for the early screening and auxiliary detection of lung cancer yielded an overall sensitivity of 0.46 (0.41–0.50) and specificity of 0.83 (0.80–0.86). Combined indirect comparisons identified the superior gene of SOX17 (sensitivity: 0.84, specificity: 0.88), CDO1 (sensitivity: 0.78, specificity: 0.67), ZFP42 (sensitivity: 0.87, specificity: 0.63) and TAC1 (sensitivity: 0.86, specificity: 0.75). Conclusions: The present meta-analysis demonstrates that methylated SOX17, CDO1, ZFP42, TAC1, FAM19A4, FHIT, MGMT, p16, and RASSF1A are potential superior biomarkers for the screening and auxiliary detection of lung cancer

Curdlan Prevents the Cognitive Deficits Induced by a High-Fat Diet in Mice via the Gut-Brain Axis

A high-fat (HF) diet is a major predisposing factor of neuroinflammation and cognitive deficits. Recently, changes in the gut microbiota have been associated with neuroinflammation and cognitive impairment, through the gut-brain axis. Curdlan, a bacterial polysaccharide widely used as food additive, has the potential to alter the composition of the microbiota and improve the gut-brain axis. However, the effects of curdlan against HF diet-induced neuroinflammation and cognitive decline have not been investigated. We aimed to evaluate the neuroprotective effect and mechanism of dietary curdlan supplementation against the obesity-associated cognitive decline observed in mice fed a HF diet. C57Bl/6J male mice were fed with either a control, HF, or HF with curdlan supplementation diets for 7 days (acute) or 15 weeks (chronic). We found that acute curdlan supplementation prevented the gut microbial composition shift induced by HF diet. Chronic curdlan supplementation prevented cognitive declines induced by HF diet. In addition, curdlan protected against the HF diet-induced abnormities in colonic permeability, hyperendotoxemia, and colonic inflammation. Furthermore, in the prefrontal cortex (PFC) and hippocampus, curdlan mitigated microgliosis, neuroinflammation, and synaptic impairments induced by a HF diet. Thus, curdlan-as a food additive and prebiotic-can prevent cognitive deficits induced by HF diet via the colon-brain axis

Periodic changes in the N-glycosylation of immunoglobulin G during the menstrual cycle

Immunoglobulin G (IgG) is the most abundant plasma glycoprotein and a prominent humoral immune mediator. Glycan composition affects the affinity of IgG to ligands and consequent immune responses. The modification of IgG N-glycosylation is considered to be one of the various mechanisms by which sex hormones modulate the immune system. Although the menstrual cycle is the central sex hormone-related physiological process in most women of reproductive age, IgG N-glycosylation dynamics during the menstrual cycle have not yet been investigated. To fill this gap, we profiled the plasma IgG N-glycans of 70 healthy premenopausal women at 12 time points during their menstrual cycles (every 7 days for 3 months) using hydrophilic interaction ultra-performance liquid chromatography (HILIC-UPLC). We observed cyclic periodic changes in the N-glycosylation of IgG in association with the menstrual cycle phase and sex hormone concentration in plasma. On the integrated cohort level, the modeled average menstrual cycle effect on the abundance of IgG N-glycosylation traits was low for each trait, with the highest being 1.1% for agalactosylated N-glycans. However, intrapersonal changes were relatively high in some cases; for example, the largest difference between the minimum and maximum values during the menstrual cycle was up to 21% for sialylated N-glycans. Across all measurements, the menstrual cycle phase could explain up to 0.72% of the variation in the abundance of a single IgG glycosylation trait of monogalactosylation. In contrast, up to 99% of the variation in the abundance of digalactosylation could be attributed to interpersonal differences in IgG N-glycosylation. In conclusion, the average extent of changes in the IgG N-glycopattern that occur during the menstrual cycle is small; thus, the IgG N-glycoprofiling of women in large sample-size studies can be performed regardless of menstrual cycle phase