Improved object detection method for unmanned driving based on Transformers.

The object detection method serves as the core technology within the unmanned driving perception module, extensively employed for detecting vehicles, pedestrians, traffic signs, and various objects. However, existing object detection methods still encounter three challenges in intricate unmanned driving scenarios: unsatisfactory performance in multi-scale object detection, inadequate accuracy in detecting small objects, and occurrences of false positives and missed detections in densely occluded environments. Therefore, this study proposes an improved object detection method for unmanned driving, leveraging Transformer architecture to address these challenges. First, a multi-scale Transformer feature extraction method integrated with channel attention is used to enhance the network's capability in extracting features across different scales. Second, a training method incorporating Query Denoising with Gaussian decay was employed to enhance the network's proficiency in learning representations of small objects. Third, a hybrid matching method combining Optimal Transport and Hungarian algorithms was used to facilitate the matching process between predicted and actual values, thereby enriching the network with more informative positive sample features. Experimental evaluations conducted on datasets including KITTI demonstrate that the proposed method achieves 3% higher mean Average Precision (mAP) than that of the existing methodologies.

Resilience Training for Nurses: A Meta-analysis.

Nurses frequently face stressful situations during work, which makes resilience an essential quality of their personality to cope with professional stress and to prevent burnout. Resilience can be improved by training and practice. To analyze the effect of resilience training in nurses, studies reporting the changes in resilience before and after resilience training were identified by conducting the literature search in electronic databases. Meta-analyses of standardized mean differences (SMDs) between postintervention and preintervention scores of resilience and other related variables were performed. Thirteen studies (576 nurse participants) were included. Resilience training improved the resilience scores of the participants (SMD, 0.58; 95% confidence interval [CI], 0.23-0.94; P = .001), whereas there was no improvement in the resilience scores of nurses who did not participate in resilience training (SMD, -0.13; 95% CI, -0.54 to 0.27; P = .523). The stress (SMD, -0.60; 95% CI, -0.80 to -0.40; P < .00001), anxiety (SMD, -0.50; 95% CI, -0.80 to -0.20; P = .001), depression (SMD, -0.43; 95% CI, -0.67 to -0.19; P < .0001), and burnout (SMD, -1.01; 95% CI, -1.25 to -0.76; P <˂ .0001) scores of the participants were also decreased after resilience training. In conclusion, resilience training improved the resilience scores of nurses, which was also associated with improvements in stress, depression, anxiety, and burnout scores. However, because of the variations in training contents and measuring tools, only generalized assessments could be made.

LncRNA HOXA11-AS Aggravates Keloid Progression by the Regulation of HOXA11-AS-miR-205-5p-FOXM1 Pathway.

BACKGROUND: Keloid is troublesome for patients' skin appearance and mental health, although it is a benign tumor. Long noncoding RNA (lncRNA) troubling keloid is frequently reported. The purpose of this study was to investigate the role of lncRNA homeobox (HOX) A11 antisense (HOXA11-AS) and related action mechanisms during the development of keloid. METHODS: The expression of HOXA11-AS, miR-205-5p, and forkhead box M1 (FOXM1) was measured by quantitative real-time polymerase chain reaction (qRT-PCR). Cell proliferation or apoptosis was assessed using 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium (MTT) assay or flow cytometry assay. Cell migration and invasion were monitored by transwell assay. The protein levels of extracellular matrix (ECM) proteins (collagen I and collagen III), fibronectin, glucose transporter 1 (GLUT1), lactate dehydrogenase A (LDHA), and FOXM1 were quantified by Western blot. Glycolysis processes were investigated by the glycolysis stress test, glucose consumption, and lactate production. The relationship between miR-205-5p and HOXA11-AS or FOXM1 was predicted by the online tool MIRcode or starBase v2.0 and verified by dual-luciferase reporter assay or RNA immunoprecipitation (RIP). RESULTS: HOXA11-AS and FOXM1 were significantly upregulated in keloid tissues and keloid fibroblasts, while miR-205-5p was downregulated. HOXA11-AS knockdown or miR-205-5p enrichment inhibited proliferation, migration, invasion, ECM accumulation, and glycolysis but accelerated apoptosis of keloid fibroblasts. MiR-205-5p was targeted by HOXA11-AS, and its inhibition overturned the effects of HOXA11-AS knockdown. Moreover, FOXM1 was a target of miR-205-5p, and HOXA11-AS regulated the expression of FOXM1 by adsorbing miR-205-5p. FOXM1 overexpression abolished the role of miR-205-5p enrichment. CONCLUSIONS: The HOXA11-AS-miR-205-5p-FOXM1 pathway may be an active mode in which HOXA11-AS participates in the progression of keloid.