Around the world, tuberculosis (TB) remains one of the most common causes of morbidity and mortality. The molecular mechanism ofMycobacterium tuberculosis(Mtb) infection is still unclear. Extracellular vesicles (EVs) play a key role in the onset and progression of many disease states and can serve as effective biomarkers or therapeutic targets for the identification and treatment of TB patients. We analysed the expression profile to better clarify the EVs characteristics of TB and explored potential diagnostic markers to distinguish TB from healthy control (HC). Twenty EVs‐related differentially expressed genes (DEGs) were identified, and 17 EVs‐related DEGs were up‐regulated and three DEGs were down‐regulated in TB samples, which were related to immune cells. Using machine learning, a nine EVs‐related gene signature was identified and two EVs‐related subclusters were defined. The single‐cell RNA sequence (scRNA‐seq) analysis further confirmed that these hub genes might play important roles in TB pathogenesis. The nine EVs‐related hub genes had excellent diagnostic values and accurately estimated TB progression. TB's high‐risk group had significantly enriched immune‐related pathways, and there were substantial variations in immunity across different groups. Furthermore, five potential drugs were predicted for TB using CMap database. Based on the EVs‐related gene signature, the TB risk model was established through a comprehensive analysis of different EV patterns, which can accurately predict TB. These genes could be used as novel biomarkers to distinguish TB from HC. These findings lay the foundation for further research and design of new therapeutic interventions aimed at treating this deadly infectious disease.

利用热丝辅助双偏压氢等离子体对化学气相沉积金刚石薄膜进行了纳米尺度上的表面改装，制造出锥状金刚石列阵.金刚石薄膜内在的柱状结构使氢离子在刻蚀薄膜时产生非均匀的刻蚀速率，对锥状表面的形成起着重要作用.另一方面，溅射出的含碳粒子会发生二次沉积，最终的特征表面形貌取决于刻蚀与含碳基团再沉积之间的相互竞争.栅极的使用影响基底区域放电的伏安特性，改变栅极电流可以对形成的金刚石特征表面结构进行有效调节.在处理过程中少量掺入甲烷，提高了金刚石表面附近的含碳基团浓度，促进二次成核，进而诱发均匀分布的锥状列阵.

To improve the accuracy and real-time performance of vehicle recognition in an advanced driving-assistance system (ADAS), a vehicle recognition method based on radar and camera information fusion is proposed. Firstly, the millimeter-wave radar and camera are calibrated jointly, the radar recognition information is mapped on the camera image, and the region of interest is established. Then, based on operator edge detection, global threshold binarization is performed on the image of the region of interest (ROI) to obtain the contour information of the vehicle in front, and Hough transform is used to fit the vehicle contour edge straight line. Finally, a sliding window is established according to the symmetry characteristics of the fitting line, which can find the vehicle region with the highest symmetry and complete the identification of the vehicle. The experimental results show that compared to the original recognition region of the radar, the mean square error of this algorithm is reduced by 13.4 and the single frame detection time is reduced to 28 ms. It is proven that the algorithm has better accuracy and a faster detection rate, and it can solve the problem of an inaccurate recognition region caused by radar error.