An Enhanced Low-Resolution Image Recognition Method for Traffic Environments

Currently, low-resolution image recognition is confronted with a significant challenge in the field of intelligent traffic perception. Compared to high-resolution images, low-resolution images suffer from small size, low quality, and lack of detail, leading to a notable decrease in the accuracy of traditional neural network recognition algorithms. The key to low-resolution image recognition lies in effective feature extraction. Therefore, this paper delves into the fundamental dimensions of residual modules and their impact on feature extraction and computational efficiency. Based on experiments, we introduce a dual-branch residual network structure that leverages the basic architecture of residual networks and a common feature subspace algorithm. Additionally, it incorporates the utilization of intermediate-layer features to enhance the accuracy of low-resolution image recognition. Furthermore, we employ knowledge distillation to reduce network parameters and computational overhead. Experimental results validate the effectiveness of this algorithm for low-resolution image recognition in traffic environments

A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis

Road traffic accident databases provide the basis for road traffic accident analysis, the data inside which usually has a radial, multidimensional, and multilayered structure. Traditional data mining algorithms such as association rules, when applied alone, often yield uncertain and unreliable results. An improved association rule algorithm based on Particle Swarm Optimization (PSO) put forward by this paper can be used to analyze the correlation between accident attributes and causes. The new algorithm focuses on characteristics of the hyperstereo structure of road traffic accident data, and the association rules of accident causes can be calculated more accurately and in higher rates. A new concept of Association Entropy is also defined to help compare the importance between different accident attributes. T-test model and Delphi method were deployed to test and verify the accuracy of the improved algorithm, the result of which was a ten times faster speed for random traffic accident data sampling analyses on average. In the paper, the algorithms were tested on a sample database of more than twenty thousand items, each with 56 accident attributes. And the final result proves that the improved algorithm was accurate and stable

Vision-Based Bicycle Detection Using Multiscale Block Local Binary Pattern

Bicycle traffic has heavy proportion among all travel modes in some developing countries, which is crucial for urban traffic control and management as well as facility design. This paper proposes a real-time multiple bicycle detection algorithm based on video. At first, an effective feature called multiscale block local binary pattern (MBLBP) is extracted for representing the moving object, which is a well-classified feature to distinguish between bicycles and nonbicycles; then, a cascaded bicycle classifier trained by AdaBoost algorithm is proposed, which has a good computation efficiency. Finally, the method is tested with video sequence captured from the real-world traffic scenario. The bicycles in the test scenario are successfully detected

The KLF4–p62 axis prevents vascular endothelial cell injury via the mTOR/S6K pathway and autophagy in diabetic kidney disease

Introduction: Diabetic kidney disease (DKD) is a complication of systemic diabetic microangiopathy, which has a high risk of developing into end-stage renal disease and death. This study explored the mechanism underlying autophagy in DKD vascular endothelial cell injury. Material and methods: DKD and vascular endothelial cell injury models were established using Sprague Dawley rats and human umbilical vein endothelial cells (HUVECs). HUVECs overexpressing Kruppel-like factor 4 (KLF4) were constructed by transient transfection of plasmids. Biochemical determination of urinary protein and blood urea nitrogen (BUN), superoxide dismutase (SOD), and creatinine (Scr) levels was performed. Renal pathology was observed by periodic acid–Schiff (PAS) staining. Cell Counting Kit-8 (CCK8), terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL), and immunocytochemistry (ICC) were used to analyse the growth and apoptosis of HUVECs. Microtubule-associated protein light chain 3 (LC3) expression was observed by immunofluorescence (IF). The reactive oxygen species (ROS) levels were measured using flow cytometry. Monocyte chemoattractant protein-1 (MCP-1), KLF4, and tumour necrosis factor alpha (TNF-α) levels were detected using enzyme-linked immunosorbent assay (ELISA). The expression of KLF4, p62 protein, and LC3 was analysed using reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR). S6 kinase (S6K), p70 ribosomal S6 kinase (p-S6K), Beclin1, ATG5, LC3, p62, Caspase-3, mammalian target of rapamycine (mTOR), and phsophorylated mTOR (p-mTOR) expressions were detected by western blotting. Results: PAS-positive substances (polysaccharide and glycogen) and S6K protein levels increased, and LC3 protein expression decreased in DKD rats. The levels of urinary protein, BUN, and Scr increased, and KLF4 decreased in DKD rats. High glucose (HG) levels decreased the proliferation and increased the apoptosis rate of HUVECs. The expression of ROS, TNF-α, MCP-1, and p62 increased, while the expression of SOD, KLF4, Beclin1, ATG5, and LC3 decreased in HG-induced HUVECs. KLF4 overexpression significantly increased Beclin1, ATG5, and LC3 protein expression and decreased p62 protein expression compared to the oe-NC group in HG-induced HUVECs. KLF4 overexpression inhibits the expression of Caspase-3, p-mTOR, and p-S6K in HG-induced HUVECs. Conclusions: KLF4–p62 axis improved vascular endothelial cell injury by regulating inflammation and the mTOR/S6K pathway in DKD

A respiratory syncytial virus (RSV) vaccine based on parainfluenza virus 5 (PIV5)

AbstractHuman respiratory syncytial virus (RSV) is a leading cause of severe respiratory disease and hospitalizations in infants and young children. It also causes significant morbidity and mortality in elderly and immune compromised individuals. No licensed vaccine currently exists. Parainfluenza virus 5 (PIV5) is a paramyxovirus that causes no known human illness and has been used as a platform for vector-based vaccine development. To evaluate the efficacy of PIV5 as a RSV vaccine vector, we generated two recombinant PIV5 viruses – one expressing the fusion (F) protein and the other expressing the attachment glycoprotein (G) of RSV strain A2 (RSV A2). The vaccine strains were used separately for single-dose vaccinations in BALB/c mice. The results showed that both vaccines induced RSV antigen-specific antibody responses, with IgG2a/IgG1 ratios similar to those seen in wild-type RSV A2 infection. After challenging the vaccinated mice with RSV A2, histopathology of lung sections showed that the vaccines did not exacerbate lung lesions relative to RSV A2-immunized mice. Importantly, both F and G vaccines induced protective immunity. Therefore, PIV5 presents an attractive platform for vector-based vaccines against RSV infection

Clinical factors of post-chemoradiotherapy as valuable indicators for pathological complete response in locally advanced rectal cancer

OBJECTIVES: Pathological complete response has shown a better prognosis for patients with locally advanced rectal cancer after preoperative chemoradiotherapy. However, correlations between post-chemoradiotherapy clinical factors and pathologic complete response are not well confirmed. The aim of the current study was to identify post-chemoradiotherapy clinical factors that could serve as indicators of pathologic complete response in locally advanced rectal cancer. METHODS: This study retrospectively analyzed 544 consecutive patients with locally advanced rectal cancer treated at Sun Yat-sen University Cancer Center from December 2003 to June 2014. All patients received preoperative chemoradiotherapy followed by surgery. Univariate and multivariate regression analyses were performed to identify post-chemoradiotherapy clinical factors that are significant indicators of pathologic complete response. RESULTS: In this study, 126 of 544 patients (23.2%) achieved pathological complete response. In multivariate analyses, increased pathological complete response rate was significantly associated with the following factors: post-chemoradiotherapy clinical T stage 0-2 (odds ratio=2.098, 95% confidence interval=1.023-4.304, p=0.043), post-chemoradiotherapy clinical N stage 0 (odds ratio=2.011, 95% confidence interval=1.264-3.201, p=0.003), interval from completion of preoperative chemoradiotherapy to surgery of >;7 weeks (odds ratio=1.795, 95% confidence interval=1.151-2.801, p=0.010) and post-chemoradiotherapy carcinoembryonic antigen ≤2 ng/ml (odds ratio=1.579, 95% confidence interval=1.026-2.432, p=0.038). CONCLUSIONS: Post-chemoradiotherapy clinical T stage 0-2, post-chemoradiotherapy clinical N stage 0, interval from completion of chemoradiotherapy to surgery of >;7 weeks and post-chemoradiotherapy carcinoembryonic antigen ≤2 ng/ml were independent clinical indicators for pathological complete response. These findings demonstrate that post-chemoradiotherapy clinical factors could be valuable for post-operative assessment of pathological complete response