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In order to alleviate the impact of the old bridge by the environment and its own life and the problem of increasing maintenance cost, the intelligent building integrated evaluation system based on BIM technology is proposed. Firstly, based on BIM technology, an all-weather hardware monitoring system is constructed by using multiple sensors laid on the bridge. The software communicates with the monitoring system through 5G public network and applies the unique advantages of deep neural network in classification to the assessment of the health status of old bridges with the help of the multiclassification convolutional neural network embedded in the software. The test results show that the preprocessed data is imported into the health diagnosis module for training, and the training accuracy reaches about 70%, and the loss curve is stable at about 0.5. Conclusion. The system meets the design requirements and solves the problem of difficult health diagnosis caused by long monitoring period and low efficiency of old bridges. The system has advantages of low difficulty in obtaining parameters, high precision, and accurate health assessment.

Deep neural networks have achieved remarkable success in various fields. However, training an effective deep neural network still poses challenges. This paper aims to propose a method to optimize the training effectiveness of deep neural networks, with the goal of improving their performance. Firstly, based on the observation that parameters (weights and bias) of deep neural network change in certain rules during training process, the potential of parameters prediction for improving training efficiency is discovered. Secondly, the potential of parameters prediction to improve the performance of deep neural network by noise injection introduced by prediction errors is revealed. And then, considering the limitations comprehensively, a deep neural network Parameters Linear Prediction method is exploit. Finally, performance and hyperparameter sensitivity validations are carried out on some representative backbones. Experimental results show that by employing proposed Parameters Linear Prediction method, as opposed to SGD, has led to an approximate 1% increase in accuracy for optimal model, along with a reduction of about 0.01 in top-1/top-5 error. Moreover, it also exhibits stable performance under various hyperparameter settings, shown the effectiveness of the proposed method and validated its capacity in enhancing network’s training efficiency and performance.

Detecting common cylindrical hole defects (corrosion defects) in locomotive traction motor shafts is essential to ensure equipment reliability and safety. This paper conducts an in-depth study of rotating shafts with a rich magnetic field surrounding them, with the goal of identifying cylindrical hole defects and diagnosing corrosion defects. During eddy current testing, to investigate the influence of variable-parameter (bottom diameter and depth) cylindrical hole defects on the electromagnetic properties of traction motor shafts, an equivalent model of a variable-parameter defect detection system for cylindrical hole defects (corrosion defects) was established using COMSOL software, based on eddy current testing theory, and simulation analysis was performed. By studying the horizontal and vertical magnetic induction intensities(HMII, VMII) and their respective phases(PHMII, PVMII), the magnetic field distribution around the cylindrical hole defect under variable parameters was analyzed. The results show that HMII and its phase have a significant geometric correspondence, allowing for quantitative assessment of the defect bottom diameter through characteristic peak spacing or phase lag width. Simultaneously, the amplitude variations of HMII and VMII can qualitatively determine the presence of defects and evaluate their relative depth (limited by the saturation threshold). Furthermore, PVMII is insensitive to the defect’s geometric parameters.

To study the influence of the geometric structure of the probe coil on the electromagnetic characteristics of the eddy current probe in the process of eddy current testing, based on the principle of eddy current testing, different probe coil models were established using finite element software. These geometric structure parameters include the difference between the inner and outer radius, thickness, and equivalent radius. The magnetic field distribution around the probe is simulated and analyzed under different parameters, and the detection performance of the probe is judged in combination with the change rate of the magnetic field around the probe coil. The simulation results show that at a closer position, increasing the difference between the inner and outer radii, reducing the thickness, and reducing the equivalent radius are beneficial to improve the resolution of the probe coil. At a far position, reducing the difference between the inner and outer radii, increasing the thickness, and reducing the equivalent radius are beneficial to improve the resolution of the probe coil. At the same time, the accuracy of the simulation data is verified by comparing the theoretical values with the simulated values under different conditions. Therefore, the obtained conclusions can provide a reference and basis for the optimal design of the probe structure.

Emerging evidence suggests that astrocyte loss is one of the most important pathological features in the hippocampus of patients with major depressive disorder (MDD) and depressive mice. Pyroptosis is a recently discovered form of programmed cell death depending on Caspase–gasdermin D (Casp-GSDMD), which is involved in multiple neuropsychiatric diseases. However, the involvement of pyroptosis in the onset of MDD and glial pathological injury remains obscure. Here, we observed that depressive mice showed astrocytic pyroptosis, which was responsible for astrocyte loss, and selective serotonin reuptake inhibitor (SSRI) treatment could attenuate the pyroptosis induced by the chronic mild stress (CMS) model. Genetic KO of GSDMD, Casp-1, and astrocytic NOD-like receptor protein 3 (NLRP3) inflammasome in mice alleviated depression-like behaviors and inhibited the pyroptosis-associated protein expression. In contrast, overexpression of astrocytic GSDMD–N-terminal domain (GSDMD-N) in the hippocampus could abolish the improvement of behavioral alterations in GSDMD-deficient mice. This work illustrates that targeting the NLRP3/Casp-1/GSDMD–mediated pyroptosis may provide potential therapeutic benefits to stress-related astrocyte loss in the pathogenesis of depression.

Although β-arrestins (ARRBs) regulate diverse physiological and pathophysiological processes, their functions and regulation in Parkinson’s disease (PD) remain poorly defined. In this study, we show that the expression of β-arrestin 1 (ARRB1) and β-arrestin 2 (ARRB2) is reciprocally regulated in PD mouse models, particularly in microglia. ARRB1 ablation ameliorates, whereas ARRB2 knockout aggravates, the pathological features of PD, including dopaminergic neuron loss, neuroinflammation and microglia activation in vivo, and microglia-mediated neuron damage in vitro. We also demonstrate that ARRB1 and ARRB2 produce adverse effects on inflammation and activation of the inflammatory STAT1 and NF-κB pathways in primary cultures of microglia and macrophages and that two ARRBs competitively interact with the activated form of p65, a component of the NF-κB pathway. We further find that ARRB1 and ARRB2 differentially regulate the expression of nitrogen permease regulator-like 3 (Nprl3), a functionally poorly characterized protein, as revealed by RNA sequencing, and that in the gain- and loss-of-function studies, Nprl3 mediates the functions of both ARRBs in microglia inflammatory responses. Collectively, these data demonstrate that two closely related ARRBs exert opposite functions in microglia-mediated inflammation and the pathogenesis of PD which are mediated at least in part through Nprl3 and provide novel insights into the understanding of the functional divergence of ARRBs in PD.

Background No previous prospective research has explored the association of the TyG (fasting triglyceride-glucose) index and TG/HDL-C ratio as insulin resistance markers with the risk of colorectal cancer (CRC) incidence in the Northern Chinese population. Methods In this prospective cohort study, we included 93,659 cancer-free participants with the measurements of TyG index and TG/HDL-C ratio. Participants were divided by the quartiles of the TyG index or TG/HDL-C ratio. The associations of TyG index, TG/HDL-C ratio, and their components with CRC risk were assessed using Cox proportional hazards regression models. Results During a median follow-up of 13.02 years, 593 incident CRC cases were identified. Compared with the lowest quartile of the TyG index (Q1), the risk of CRC was higher in persons in the third (Q3) and highest quartiles (Q4) of the TyG index, with corresponding multivariable-adjusted HRs (95% CI) of 1.36 (1.06, 1.76) and 1.50 (1.19, 1.91), respectively. The elevated risks of CRC incidence were observed in people in the second, third, and highest quartiles of the TG/HDL-C ratio groups, with corresponding multivariable-adjusted HRs (95% CI) of 1.33 (1.05, 1.70), 1.36 (1.07, 1.73) and 1.37 (1.07, 1.75), respectively. Conclusions Elevated TyG index and TG/HDL-C ratio were associated with a higher risk of developing CRC among adults in Northern China.

Small cell lung cancer (SCLC) is a highly malignant tumor which is eventually refractory to any treatment. Intratumoral heterogeneity (ITH) may contribute to treatment failure. However, the extent of ITH in SCLC is still largely unknown. Here, we subject 120 tumor samples from 40 stage I-III SCLC patients to multi-regional whole-exome sequencing. The most common mutant genes are TP53 (88%) and RB1 (72%). We observe a medium level of mutational heterogeneity (0.30, range 0.0~0.98) and tumor mutational burden (TMB, 10.2 mutations/Mb, range 1.1~51.7). Our SCLC samples also exhibit somatic copy number variation (CNV) across all patients, with an average CNV ITH of 0.49 (range 0.02~0.99). In terms of mutation distribution, ITH, TMB, mutation clusters, and gene signatures, patients with combined SCLC behave roughly the same way as patients with pure SCLC. This condition also exists in smoking patients and patients with EGFR mutations. A higher TMB per cluster is associated with better disease-free survival while single-nucleotide variant ITH is linked to worse overall survival, and therefore these features may be used as prognostic biomarkers for SCLC. Together, these findings demonstrate the intratumoral genetic heterogeneity of surgically resected SCLC and provide insights into resistance to treatment. Multi-region sequencing of small cell lung cancers (SCLC) can improve our understanding of the disease. Here the authors analyse 120 multi-region samples from 40 SCLC patients with whole exome sequencing and characterise their mutational burden, evolution, heterogeneity, and potential prognostic biomarkers.

Background Fluoxetine, a selective serotonin reuptake inhibitor, has been reported to directly bind with 5-HT 2B receptor (5-HT 2B R), but the precise mechanisms, whereby fluoxetine confers the anti-depressive actions via 5-HT 2B R is not fully understood. Although neuroinflammation-induced A1 astrocytes are involved in neurodegenerative diseases, the role of A1 astrocyte in the pathogenesis and treatment of major depressive disorder (MDD) remains unclear. Methods Mice were subjected to chronic mild stress (CMS) for 6 weeks and subsequently treated with fluoxetine for 4 weeks. The depressive-like and anxiety-like behaviors and the activation of A1 reactive astrocyte in hippocampus and cortex of mice were measured. Primary astrocytes were stimulated with A1 cocktail (tumor necrosis factor (TNF)-α, interleukin (IL)-1α and C1q), activated (LPS) microglia-conditioned medium (MCM) or IL-6 for 24 h and the expression of A1-special and A2-special markers were determined using RT-qPCR and western blot. The role of 5-HT 2B R in the effects of fluoxetine on A1 reactive astrocyte was measured using 5-HT 2B R inhibitor and siRNA in vitro and AAVs in vivo. The functions of downstream signaling Gq protein and β-arrestins in the effects of fluoxetine on the activation of A1 astrocyte were determined using pharmacological inhibitor and genetic knockout, respectively. Results In this study, we found that fluoxetine inhibited the activation of A1 reactive astrocyte and reduced the abnormal behaviors in CMS mice, as well as ameliorated A1 astrocyte reactivity under three different stimulators in primary astrocytes. We also showed that astrocytic 5-HT 2B R was required in the inhibitory effects of fluoxetine on A1 reactive astrocyte in MDD in vivo and in vitro. We further found that the functions of fluoxetine in the activation of A1 astrocyte were independent of either Gq protein or β-arrestin1 in vitro. β-arrestin2 pathway was the downstream signaling of astrocytic 5-HT 2B R mediated the inhibitory effects of fluoxetine on A1 astrocyte reactivity in primary astrocytes and CMS mice, as well as the improved roles of fluoxetine in behavioral impairments of CMS mice. Conclusions These data demonstrate that fluoxetine restricts reactive A1 astrocyte via astrocytic 5-HT 2B R/β-arrestin2 pathway in a mouse model of MDD and provide a novel therapeutic avenue for MDD.

Backgrounds Malnutrition and systemic inflammatory responses are associated with poor overall survival (OS) in lung cancer patients, but it remains unclear which biomarkers are better for predicting their prognosis. This study tried to determine the best one among the existing common nutrition/inflammation‐based indicators of OS for patients with lung cancer. Materials and methods There were 16 nutrition or systemic inflammation‐based indicators included in this study. The cut‐off points for the indicators were calculated using maximally selected rank statistics. The OS was evaluated using the Kaplan–Meier estimator, and univariate and multivariate Cox proportional hazard models were used to determine the relationship between the indicators and OS. A time‐dependent receiver operating characteristic curves (time‐ROC) and C‐index were calculated to assess the predictive ability of the different indicators. Results There were 1772 patients with lung cancer included in this study. In univariate analysis, all 16 indicators were significantly associated with OS of the patients (all P < 0.001). Except for platelet‐to‐lymphocyte ratio, all other indicators were independent predictors of OS in multivariate analysis (all P < 0.05). Low advanced lung cancer inflammation index (ALI) was associated with higher mortality risk of lung cancer [hazard ratio, 1.30; 95% confidence interval (CI), 1.13–1.49]. The results of the time‐AUC and C‐index analyses indicated that the ALI (C‐index: 0.611) had the best predictive ability on the OS in patients with lung cancer. In different sub‐groups, the ALI was the best indicator for predicting the OS of lung cancer patients regardless of sex (C‐index, 0.609 for men and 0.613 for women) or smoking status (C‐index, 0.629 for non‐smoker and 0.601 for smoker) and in patients aged <65 years (C‐index, 0.613). However, the modified Glasgow prognostic score was superior to the other indicators in non‐small cell lung cancer patients (C‐index, 0.639) or patients aged ≥65 years (C‐index, 0.610), and the glucose‐to‐lymphocyte ratio performed better prognostic ability in patients with small cell lung cancer (C‐index, 0.601). Conclusions The prognostic ability of the ALI is superior to the other inflammation/nutrition‐based indicators for all patients with lung cancer.