Search

BackgroundOsteosarcoma (OS) poses a significant clinical challenge, necessitating a comprehensive exploration of its molecular underpinnings.MethodsThis study explored the roles of PTTG family genes (PTTG1, PTTG2, and PTTG3P) in OS, employing a multifaceted approach encompassing molecular experiments, including OS cell lines culturing, RT-qPCR, bisulfite and Whole Exome Sequencing (WES) and in silico experiments, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets-based validation, overall survival, gene enrichment, functional assays, and molecular docking analyses.ResultsOur findings reveal a consistent up-regulation of PTTG genes in OS cell lines, supported by RT-qPCR experiments and corroborated across various publically available expression datasets databases. Importantly, ROC curve analyses highlight their potential as diagnostic markers. Moving beyond expression profiles, we unveil the epigenetic landscape by demonstrating significant hypomethylation of CpG islands associated with PTTG genes in OS. The negative correlation between methylation status and mRNA expression emphasizes the regulatory role of promoter methylation in PTTG gene expression. Contrary to expectations, genetic mutations in PTTG genes are rare in OS, with only benign mutations observed. Moreover, functional assays also confirmed the oncogenic roles of the PTTG gene in the development of OS. Lastly, we also revealed that Calcitriol is the most appropriate drug that can be utilized to treat OS in the context of PTTG genes.ConclusionThe identification of PTTG genes as potential diagnostic markers and their association with epigenetic alterations opens new avenues for understanding OS pathogenesis and developing targeted therapies. As we navigate the complex landscape of OS, this study contributes essential insights that may pave the way for improved diagnostic and therapeutic strategies in its management.

R&D strategies play a decisive role in the promotion of enterprise innovation output and innovation ability. In order to thoroughly investigate the R&D strategies of iron and steel enterprises, an R&D strategy analysis framework based on R&D semantic topic analysis and outlier behavior detection was proposed. Additionally, empirical research on R&D layout and direction, R&D quality, and the achievement maintenance strategy of enterprises, from both macro and micro perspectives, was conducted. The feasibility of the R&D strategy analysis framework was verified. Additionally, the results show that, in terms of R&D topic layout strategy, most enterprises adopted a stable maintenance strategy after quickly completing the layout; regarding the R&D focus strategy, most enterprises focused on R&D fields and carried out strategic management; for R&D quality control strategy, some enterprises adopted a strategy of prolonging the duration of invention patents, and high-quality outputs with a long lifetime were developed rapidly. These research results have reference value for Chinese enterprises, to adjust their R&D strategies, and for the government, to formulate supporting policies.

This study focuses on the lateral safety of autonomous vehicles. The aim is to provide greater safety margins for the motion controller under hazardous conditions by improving the planning methods. The proposed lateral planning method employs a hierarchical structure. First, a graph search method that considers road curvature is presented. This method benefits from the design of jump nodes and can generate a safe corridor in real-time by backtracking. Next, a concise and effective optimization problem is constructed in the Frenet frame. It is designed to optimize the path by incorporating the peak, integral, and differential of curvature into the objective function. By solving this problem using CasADi, the path within the safe corridor is obtained. Finally, the proposed planning method is combined with a model predictive motion controller and tested through simulation and experimentation on a real vehicle platform. This planning method has good scalability, adaptability, and real-time performance. Simulation and experimental results show that this method can help avoid exceeding lateral limits of road surface friction, improve path-following accuracy, and enhance the safety of autonomous vehicles in dangerous conditions. [ABSTRACT FROM AUTHOR]

Abstract Although using machine learning techniques to solve computer security challenges is not a new idea, the rapidly emerging Deep Learning technology has recently triggered a substantial amount of interests in the computer security community. This paper seeks to provide a dedicated review of the very recent research works on using Deep Learning techniques to solve computer security challenges. In particular, the review covers eight computer security problems being solved by applications of Deep Learning: security-oriented program analysis, defending return-oriented programming (ROP) attacks, achieving control-flow integrity (CFI), defending network attacks, malware classification, system-event-based anomaly detection, memory forensics, and fuzzing for software security.