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Abstract Background Investigate the AMPK (protein kinase AMP-activated catalytic subunit alpha 1)/YAP (Yes1 associated transcriptional regulator)/NLRP3 (NLR family pyrin domain containing 3) signaling pathway's role in ankylosing spondylitis (AS) development using public database analysis, in vitro and in vivo experiments. Methods Retrieve AS dataset, analyze differential gene expression in R, conduct functional enrichment analysis, collect 30 AS patient and 30 normal control samples, and construct a mouse model. ELISA, IP, and knockdown experiments were performed to detect expression changes. Results NLRP3 was identified as a significant AS-related gene. Caspase-1, IL-1β, IL-17A, IL-18, IL-23, YAP, and NLRP3 were upregulated in AS patients. Overexpressing AMPK inhibited YAP's blockade on NLRP3 ubiquitination, reducing ossification in fibroblasts. Inhibiting AMPK exacerbated AS symptoms in AS mice. Conclusion AMPK may suppress YAP expression, leading to NLRP3 inflammasome inhibition and AS alleviation.

Malleable iron pipes are widely used in construction, manufacturing, aerospace, and many other fields. Cast malleable iron pipes need to be treated flat to meet the needs of different shapes and sizes. This process is usually completed manually, which is low efficiency and is subject to potential safety risks. To solve this problem, a machine vision method is proposed to detect and localize malleable iron pipes. Point cloud images of malleable iron pipes are obtained by the Random Sample Consensus (RANSAC) algorithm, and precise matching is completed by the Iterative Closest Point (ICP) algorithm to obtain more accurate positions, so as to realize robot grasping. The grasping experiments of malleable iron pipes with the same and different specifications were carried out using a specially designed experimental platform. The results show that malleable iron pipes can be identified effectively and that the corresponding grasping success rate is more than 85 %. The target detection and localization method can obtain the three-dimensional (3D) position of malleable iron pipes to improve grasping efficiency, which provided a certain theoretical basis and guiding significance to improve production efficiency in practice.

Community structure has a more intuitive, physical meaning than how it has traditionally been perceived; this representation plays an important role in networks and is accordingly becoming widely adapted. Consequently, community detection has attracted increasing attention. Although scholars have proposed many community detection methods from different perspectives, due to the complexity, diversity and dynamic characteristics of networks, efficient community detection in many real networks remains a challenge. Inspired by fish school effect in real life, this paper envisions networks as an ecosystem and proposes a novel dynamic model that aims to reveal the communities in a more intuitive way. Relying on the new model, we design a community detection algorithm, known as community detection based on fish school effect (CDFSE). CDFSE has plentiful desirable properties: high-quality community detection, parameters free and notable scalability. To evaluate the performance of CDFSE, this paper employs two widely utilized evaluation metrics and twelve representative algorithms to test the effectiveness of the algorithm in both synthetic and real-world networks. The experimental results show that in most cases, CDFSE is superior to the comparison methods in terms of the quality of community detection.

Abstract Background The purpose of the present study is to evaluate the prevalence of temporomandibular disorders (TMD) and their associated biological and psychological factors in Chinese university students. Methods A total of 754 students were included from Zunyi Medical University, each participant completed questionnaires and clinical examinations according to the Diagnostic Criteria for Temporomandibular Disorders. Results The overall prevalence of TMD was 31.7% among medical students. Subjects with TMD had a high prevalence of bruxism, empty chewing, unilateral chewing, chewing gum, anterior teeth overbite, anterior teeth overjet, depression, anxiety, and sleep disturbance. Moreover, sleep bruxism, empty chewing, unilateral chewing, anterior teeth overbite, depression, and anxiety were the strongest risk factors for TMD. Conclusions Individuals with TMD have a high prevalence of psychological distress and oral parafunctional habits. Except for the psychological factors associated with TMD, bruxism, abnormal chewing, and malocclusion also shared similar risks for TMD.

This article presents a human–robot cooperation controller towards the lower extremity exoskeleton which aims to improve the tracking performance of the exoskeleton and reduce the human–robot interaction force. Radial basis function neural network is introduced to model the human–machine interaction which can better approximate the non-linear relationship than the general impedance model. A new method to calculate the inverse Jacobian matrix is presented. Compared to traditional damped least squares method, the novel method is proved to be able to avoid the orientation change of the velocity of the human–robot interaction point by the simulation result. This feature is very important in human–robot system. Then, an improved non-linear robust sliding mode controller is designed to promote the tracking performance considering system uncertainties and model errors, where a new non-linear integral sliding surface is given. The stability analysis of the proposed controller is performed using Lyapunov stability theory. Finally, the novel methods are applied to the swing leg control of the lower extremity exoskeleton, its effectiveness is validated by simulation and comparative experiments.