Identification of osteoarthritis-characteristic genes and immunological micro-environment features through bioinformatics and machine learning-based approaches

Abstract Background Osteoarthritis (OA) is a multifaceted chronic joint disease characterized by complex mechanisms. It has a detrimental impact on the quality of life for individuals in the middle-aged and elderly population while also imposing a significant socioeconomic burden. At present, there remains a lack of comprehensive understanding regarding the pathophysiology of OA. The objective of this study was to examine the genes, functional pathways, and immune infiltration characteristics associated with the development and advancement of OA. Methods The Gene Expression Omnibus (GEO) database was utilized to acquire gene expression profiles. The R software was employed to conduct the screening of differentially expressed genes (DEGs) and perform enrichment analysis on these genes. The OA-characteristic genes were identified using the Weighted Gene Co-expression Network Analysis (WGCNA) and the Lasso algorithm. In addition, the infiltration levels of immune cells in cartilage were assessed using single-sample gene set enrichment analysis (ssGSEA). Subsequently, a correlation analysis was conducted to examine the relationship between immune cells and the OA-characteristic genes. Results A total of 80 DEGs were identified. As determined by functional enrichment, these DEGs were associated with chondrocyte metabolism, apoptosis, and inflammation. Three OA-characteristic genes were identified using WGCNA and the lasso algorithm, and their expression levels were then validated using the verification set. Finally, the analysis of immune cell infiltration revealed that T cells and B cells were primarily associated with OA. In addition, Tspan2, HtrA1 demonstrated a correlation with some of the infiltrating immune cells. Conclusions The findings of an extensive bioinformatics analysis revealed that OA is correlated with a variety of distinct genes, functional pathways, and processes involving immune cell infiltration. The present study has successfully identified characteristic genes and functional pathways that hold potential as biomarkers for guiding drug treatment and facilitating molecular-level research on OA

Security Analysis of Dynamic SDN Architectures Based on Game Theory

Security evaluation of SDN architectures is of critical importance to develop robust systems and address attacks. Focused on a novel-proposed dynamic SDN framework, a game-theoretic model is presented to analyze its security performance. This model can represent several kinds of players’ information, simulate approximate attack scenarios, and quantitatively estimate systems’ reliability. And we explore several typical game instances defined by system’s capability, players’ objects, and strategies. Experimental results illustrate that the system’s detection capability is not a decisive element to security enhancement as introduction of dynamism and redundancy into SDN can significantly improve security gain and compensate for its detection weakness. Moreover, we observe a range of common strategic actions across environmental conditions. And analysis reveals diverse defense mechanisms adopted in dynamic systems have different effect on security improvement. Besides, the existence of equilibrium in particular situations further proves the novel structure’s feasibility, flexibility, and its persistent ability against long-term attacks

Simulation system of mine unmanned vehicle based on parallel control theory

The test of mine unmanned vehicle has problems of great danger, long test time, high test cost and narrow test coverage. In order to solve the above problems, the simulation system of unmanned mine vehicle based on parallel control theory is studied. The system adopts key technologies such as mine vehicle dynamics modeling, high-fidelity scene reconstruction, and virtual sensor modeling. The system realizes the functions of comprehensive deduction of the unmanned driving algorithm, system integration reliability test, mining area production prediction simulation, and virtual and actual interactive parallel deduction. The main step of dynamic modeling of the mine vehicle is divided into two parts: vehicle model building and visual scene creation. The vehicle dynamic model is associated with the virtual scene. The simulation data generated by the vehicle model is used for driving the vehicle in the virtual scene to move in real-time. In view of the complex and irregular characteristics of the large-scale open-pit mine scene, the high-precision 3D model data of the mine is obtained by means of UAV aerial mapping and laser radar 3D scanning. Based on the virtual micro polygon geometry technology, high pixel virtual texture technology, and 3D scene real-time rendering technology, a high-fidelity virtual 3D scene is constructed. The virtual sensor mainly comprises virtual laser radar, virtual millimeter wave radar, virtual inertial navigation device and virtual vision camera. The virtual sensor is carried on the virtual mine car. It is responsible for generating virtual data information in a simulated mining area scene, and sending the data to the automatic driving controller for processing. Based on the simulation system, single-vehicle test, multi-vehicle scheduling test and intelligent scheduling algorithm test can be carried out. The dynamic virtual-reality interaction between on-site vehicles and virtual vehicles can be tested. The system is used to provide a verification platform for stable transportation of the whole mining area, deduction simulation of complex intersections and optimal decision of intelligent scheduling algorithm. The system ensures the efficiency and safety of unmanned driving test and accelerates the upgrading of unmanned driving technology in the mining area

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field