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Bearings are widely used in various electrical and mechanical equipment. As their core components, failures often have serious consequences. At present, most parameter adjustment methods are still manual adjustments of parameters. This adjustment method is easily affected by prior knowledge, easily falls into the local optimal solution, cannot obtain the global optimal solution, and requires a lot of resources. Therefore, this paper proposes a new method for bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by a simulated annealing algorithm. Firstly, the original bearing vibration signal is extracted by wavelet packet transform to obtain the spectrogram, and then the obtained spectrogram is sent to the convolutional neural network for parameter adjustment, and finally the simulated annealing algorithm is used to adjust the parameters. To verify the effectiveness of the method, the bearing database of Case Western Reserve University is used for testing, and the traditional intelligent bearing fault diagnosis methods are compared. The results show that the new method for bearing fault diagnosis proposed in this paper has a better and more reliable diagnosis effect than the existing machine learning and deep learning methods.

Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery. Prediction of drug-target interactions (DTI) plays a vital role in drug development through applications in various areas, such as virtual screening for lead discovery, drug repurposing and identification of potential drug side effects. Here, the authors develop a unified framework for DTI prediction by combining a knowledge graph and a recommendation system.

During the COVID-19 pandemic, SARS-CoV-2 variants have emerged and spread worldwide. The Delta (B.1.617.2) variant was first reported in India in October 2020 and was classified as a “variant of concern (VOC)” by the WHO on 11 May, 2021. Compared to the wild-type strain, several studies have shown that the Delta variant is more transmissible and has higher viral loads in infected samples. COVID-19 patients infected with the Delta variant have a higher risk of hospitalization, intensive care unit (ICU) admission, and mortality. The Delta variant is becoming the dominant strain in many countries around the world. This review summarizes and analyses the biological characteristics of key amino acid mutations, the epidemic characteristics, and the immune escape of the Delta variant. We hope to provide scientific reference for the monitoring and prevention measures of the SARS-CoV-2 Delta variant and the development strategy of a second-generation vaccine.

Background:There is growing evidence that the internet has beneficial effects on the mental health of middle-aged and older people (≥45 years), but the evidence is inconclusive, and the underlying mechanisms are less known.Objective:This study aims to explore the relationship between multidimensional (devices, frequency, and purpose) internet use and depression in middle-aged and elderly Chinese, as well as the mediating effect of social participation. Moreover, this study will explore the moderating effect of the regional informatization development level (RIDL) on the relationships between individual internet use, social participation, and depression.Methods:Data on 17,676 participants aged 45 years or older were obtained from the China Health and Retirement Longitudinal Study (CHARLS) 2018 data set. The 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10) was used to identify the presence of depression. Logistic regression was used to explore the relationship between each dimension of internet use and depression. Multiple linear regression was used to explore the mediating effect of social participation and the moderating effect of the RIDL.Results:The results showed that 28.33% (5008/17,676) of the total population had depression. In terms of regional subgroups, respondents living in the western region exhibited the highest proportion of depression (2041/5884, 34.69%). Internet use was negatively associated with depression (odds ratio 0.613, 95% CI 0.542-0.692; P<.001). Various dimensions of internet use positively contributed to individual social participation and reduced individual depression (devices: β=–.170, 95% CI –0.209 to –0.127; frequency: β=–.065, 95% CI –0.081 to –0.047; and purpose: β=–.043, 95% CI –0.053 to –0.031). In addition, the RIDL weakened the relationship between individual-level internet use and social participation (internet use: F74.12,9.82=7.55, P<.001; devices: F51.65/9.88=5.23, P=.005; frequency: F66.74/10.08=6.62, P=.001; and purpose: F66.52/9.78=6.80, P=.001), and negatively moderated the relationship between the frequency of internet use and depression (frequency: F662.67/188.79=3.51, P=.03).Conclusions:This study found that different dimensions of internet use are associated with lower levels of depression. Social participation partially mediates the association between multidimensional internet use and depression in the eastern, central, and western regions, respectively. Additionally, the RIDL helps individuals further their internet use and social participation, reducing the impact of depression. However, this effect weakens sequentially from the western region to the central region and then to the eastern region.

Distributed fiber acoustic sensing (DAS) technology can continuously spatially detect disturbances along the sensing fiber over long distance in real time. It has many unique advantages, including, large coverage, high time-and-space resolution, convenient implementation, strong environment adaptability, etc. Nowadays, DAS becomes a versatile technology in many fields, such as, intrusion detection, railway transportation, seismology, structure health monitoring, etc. In this paper, the sensing principle and some common performance indexes are introduced, and a brief overview of recent DAS researches in Shanghai Institute of Optics and Fine Mechanics (SIOM) is presented. Some representative research advances are explained, including, quantitative demodulation, interference fading suppression, frequency response boost, high spatial resolution, and distributed multi-dimension localization. The engineering applications of DAS, carried out by SIOM and other groups, are summarized and reviewed. Finally, possible future directions are discussed and concluded. It is believed that, DAS has great development potential and application prospect.

Coronavirus disease 2019 (COVID-19) is a respiratory infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Droplets and contacts serve as the main route of transmission of SARS-CoV-2. The characteristic of the disease is rather heterogeneous, ranging from no symptoms to critical illness. The factors associated with the outcome of COVID-19 have not been completely characterized to date. Inspired by previous studies on the relevance of infectious diseases, viral and host factors related to clinical outcomes have been identified. The severity of COVID-19 is mainly related to host factors, especially cellular immune responses in patients. Patients with mild COVID-19 and improved patients with severe COVID-19 exhibit a normal immune response to effectively eliminate the virus. The immune response in patients with fatal severe COVID-19 includes three stages: normal or hypofunction, hyperactivation, and anergy. Eventually, the patients were unable to resist viral infection and died. Based on our understanding of the kinetics of immune responses during COVID-19, we suggest that type I interferon (IFN) could be administered to patients with severe COVID-19 in the hypofunctional stage, intravenous immunoglobulin (IVIG) and glucocorticoid therapy could be administered in the immune hyperactivation stage. In addition, low molecular weight heparin (LMWH) anticoagulation therapy and anti-infective therapy with antibiotics are recommended in the hyperactivation stage.

The observational association between gut microbiome and systemic lupus erythematosus (SLE) has been well documented. However, whether the association is causal remains unclear. The present study used publicly available genome-wide association study (GWAS) summary data to perform two-sample Mendelian randomization (MR), aiming to examine the causal links between gut microbiome and SLE. Two sets of MR analyses were conducted. A group of single nucleotide polymorphisms (SNPs) that less than the genome-wide statistical significance threshold (5 × 10 -8 ) served as instrumental variables. To obtain a comprehensive conclusion, the other group where SNPs were smaller than the locus-wide significance level (1 × 10 -5 ) were selected as instrumental variables. Based on the locus-wide significance level, the results indicated that there were causal effects of gut microbiome components on SLE risk. The inverse variance weighted (IVW) method suggested that Bacilli and Lactobacillales were positively correlated with the risk of SLE and Bacillales , Coprobacter and Lachnospira were negatively correlated with SLE risk. The results of weighted median method supported that Bacilli , Lactobacillales , and Eggerthella were risk factors for SLE and Bacillales and Coprobacter served as protective factors for SLE. The estimates of MR Egger suggested that genetically predicted Ruminiclostridium6 was negatively associated with SLE. Based on the genome-wide statistical significance threshold, the results showed that Actinobacteria might reduce the SLE risk. However, Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) detected significant horizontal pleiotropy between the instrumental variables of Ruminiclostridium6 and outcome. This study support that there are beneficial or detrimental causal effects of gut microbiome components on SLE risk.

Dysregulation of polyamine metabolism has been linked to the development of colorectal cancer (CRC), but the underlying mechanism is incompletely characterized. Here, we report that spermine synthase (SMS), a polyamine biosynthetic enzyme, is overexpressed in CRC. Targeted disruption of SMS in CRC cells results in spermidine accumulation, which inhibits FOXO3a acetylation and allows subsequent translocation to the nucleus to transcriptionally induce expression of the proapoptotic protein Bim. However, this induction is blunted by MYC-driven expression of miR-19a and miR-19b that repress Bim production. Pharmacological or genetic inhibition of MYC activity in SMS-depleted CRC cells dramatically induces Bim expression and apoptosis and causes tumor regression, but these effects are profoundly attenuated by silencing Bim . These findings uncover a key survival signal in CRC through convergent repression of Bim expression by distinct SMS- and MYC-mediated signaling pathways. Thus, combined inhibition of SMS and MYC signaling may be an effective therapy for CRC. Polyamine metabolism is frequently dysregulated in cancers. Here, the authors show that a polyamine biosynthetic enzyme, spermine synthase, is overexpressed in colorectal cancers and cooperates with MYC to prevent cancer cell apoptosis by repression of proapoptotic protein, Bim.

It has long been recognized that an increase of reactive oxygen species (ROS) can modify the cell-signaling proteins and have functional consequences, which successively mediate pathological processes such as atherosclerosis, diabetes, unchecked growth, neurodegeneration, inflammation, and aging. While numerous articles have demonstrated the impacts of ROS on various signaling pathways and clarify the mechanism of action of cell-signaling proteins, their influence on the level of intracellular ROS, and their complex interactions among multiple ROS associated signaling pathways, the systemic summary is necessary. In this review paper, we particularly focus on the pattern of the generation and homeostasis of intracellular ROS, the mechanisms and targets of ROS impacting on cell-signaling proteins (NF-κB, MAPKs, Keap1-Nrf2-ARE, and PI3K-Akt), ion channels and transporters (Ca2+ and mPTP), and modifying protein kinase and Ubiquitination/Proteasome System.

STEAP3 (Six-transmembrane epithelial antigen of the prostate 3, TSAP6, dudulin-2) has been reported to be involved in tumor progression in human malignancies. Nevertheless, how it participates in the progression of human cancers, especially HCC, is still unknown. In the present study, we found that STEAP3 was aberrantly overexpressed in the nuclei of HCC cells. In a large cohort of clinical HCC tissues, high expression level of nuclear STEAP3 was positively associated with tumor differentiation and poor prognosis ( p  < 0.001), and it was an independent prognostic factor for HCC patients. In HCC cell lines, nuclear expression of STEAP3 significantly promoted HCC cells proliferation by promoting stemness phenotype and cell cycle progression via RAC1-ERK-STAT3 and RAC1-JNK-STAT6 signaling axes. Through upregulating the expression and nuclear trafficking of EGFR, STEAP3 participated in regulating EGFR-mediated STAT3 transactivity in a manner of positive feedback. In summary, our findings support that nuclear expression of STEAP3 plays a critical oncogenic role in the progression of HCC via modulation on EGFR and intracellular signaling, and it could be a candidate for prognostic marker and therapeutic target in HCC.