An Empirical Study of Low-carbon Lifestyle

This paper mainly organizes the theory of lifestylefrom the fields of sociology and consumer behavior, it alsooutlines the situation of domestic scholars on the study of thelow-carbon lifestyle. Then this paper does an empiricalresearch, with the method of structural equation, onlow-carbon lifestyle in which residents of Dalian are theobjects of study. And it defines low-carbon lifestyle as asustainable way of life which includes low-carbon publicbehavior, daily low-carbon behavior, social behavior,environmental attitude and low-carbon acknowledgment. Thispaper also finds out that the environmental attitude andlow-carbon acknowledgment affect people’s low-carbonbehavior, includes low-carbon public behavior, social behaviorand daily low0carbon behavior. In the end of the paper, it putsforward the limitations of this study as well as a researchdirection for lifestyle in the future

Efficient Teacher: Semi-Supervised Object Detection for YOLOv5

Semi-Supervised Object Detection (SSOD) has been successful in improving the performance of both R-CNN series and anchor-free detectors. However, one-stage anchor-based detectors lack the structure to generate high-quality or flexible pseudo labels, leading to serious inconsistency problems in SSOD. In this paper, we propose the Efficient Teacher framework for scalable and effective one-stage anchor-based SSOD training, consisting of Dense Detector, Pseudo Label Assigner, and Epoch Adaptor. Dense Detector is a baseline model that extends RetinaNet with dense sampling techniques inspired by YOLOv5. The Efficient Teacher framework introduces a novel pseudo label assignment mechanism, named Pseudo Label Assigner, which makes more refined use of pseudo labels from Dense Detector. Epoch Adaptor is a method that enables a stable and efficient end-to-end semi-supervised training schedule for Dense Detector. The Pseudo Label Assigner prevents the occurrence of bias caused by a large number of low-quality pseudo labels that may interfere with the Dense Detector during the student-teacher mutual learning mechanism, and the Epoch Adaptor utilizes domain and distribution adaptation to allow Dense Detector to learn globally distributed consistent features, making the training independent of the proportion of labeled data. Our experiments show that the Efficient Teacher framework achieves state-of-the-art results on VOC, COCO-standard, and COCO-additional using fewer FLOPs than previous methods. To the best of our knowledge, this is the first attempt to apply Semi-Supervised Object Detection to YOLOv5.Comment: 14 page

Radar-STDA: A High-Performance Spatial-Temporal Denoising Autoencoder for Interference Mitigation of FMCW Radars

With its small size, low cost and all-weather operation, millimeter-wave radar can accurately measure the distance, azimuth and radial velocity of a target compared to other traffic sensors. However, in practice, millimeter-wave radars are plagued by various interferences, leading to a drop in target detection accuracy or even failure to detect targets. This is undesirable in autonomous vehicles and traffic surveillance, as it is likely to threaten human life and cause property damage. Therefore, interference mitigation is of great significance for millimeter-wave radar-based target detection. Currently, the development of deep learning is rapid, but existing deep learning-based interference mitigation models still have great limitations in terms of model size and inference speed. For these reasons, we propose Radar-STDA, a Radar-Spatial Temporal Denoising Autoencoder. Radar-STDA is an efficient nano-level denoising autoencoder that takes into account both spatial and temporal information of range-Doppler maps. Among other methods, it achieves a maximum SINR of 17.08 dB with only 140,000 parameters. It obtains 207.6 FPS on an RTX A4000 GPU and 56.8 FPS on an NVIDIA Jetson AGXXavier respectively when denoising range-Doppler maps for three consecutive frames. Moreover, we release a synthetic data set called Ra-inf for the task, which involves 384,769 range-Doppler maps with various clutters from objects of no interest and receiver noise in realistic scenarios. To the best of our knowledge, Ra-inf is the first synthetic dataset of radar interference. To support the community, our research is open-source via the link \url{https://github.com/GuanRunwei/rd_map_temporal_spatial_denoising_autoencoder}

Unraveling the Prognostic Significance of Rgs Gene Family in Gastric Cancer and the Potential Implication of Rgs4 in Regulating Tumor-infiltrating Fibroblast

Regulator of G-protein signaling (RGS) proteins are regulators of signal transduction mediated by G protein-coupled receptors (GPCRs). Current studies have shown that some molecules in the RGS gene family are related to the occurrence, development and poor prognosis of malignant tumors. However, the RGS gene family has been rarely studied in gastric cancer. In this study, we explored the mutation and expression profile of RGS gene family in gastric cancer, and evaluated the prognostic value of RGS expression. Then we established a prognostic model based on RGS gene family and performed functional analysis. Further studies showed that RGS4, as an independent prognostic predictor, may play an important role in regulating fibroblasts in the immune microenvironment. In conclusion, this study explores the value of RGS gene family in gastric cancer, which is of great significance for predicting the prognosis and guiding the treatment of gastric cancer

Dual Immunotherapy in advanced or Metastatic Non-Small Cell Lung Cancer: a Network Meta-Analysis

OBJECTIVES: Recently, there has been extensive research on dual immunotherapy for advanced or metastatic non-small cell lung cancer (NSCLC), yet a comprehensive evaluation is lacking. This study aimed to rank the available treatment options and assess the efficacy and safety of dual immunotherapy regimens through the implementation of a Bayesian network meta-analysis (NMA). MATERIALS AND METHODS: A thorough search was conducted to recognize eligible randomized controlled trials (RCTs) on March 20, 2023. Overall survival (OS), progression-free survival (PFS), treatment-related adverse events (TRAEs) and grade ≥3 TRAEs were evaluated to identify the efficacy and safety of dual immunotherapy regimens. The surface under the cumulative ranking curve (SUCRA) and RESULTS: Eleven clinical trials involving six different regimens were included in this study. The combination of anti-programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) antibodies with anti-T-cell immunoglobulin and ITIM domain (TIGIT) antibodies emerged as the most promising regimen for improving OS and PFS, followed by anti-PD-1/PD-L1 + anti-cytotoxic T lymphocyte antigen 4 (CTLA-4) + chemotherapy treatment and anti-PD-1/PD-L1 + anti-CTLA-4 treatment. The forest plots demonstrated that these three regimens were all superior to chemotherapy. The above results were observed in both unselected treatment line and first-line settings. The least likely to be associated with TRAEs and grade ≥3 TRAEs were respectively anti-CTLA-4 treatment and anti-PD-1/PD-L1 + anti-TIGIT treatment, with anti-PD-1/PD-L1 + anti-CTLA-4 + chemotherapy treatment to be the worst. CONCLUSIONS: This NMA validated the promising efficacy and safety of dual immunotherapy in advanced or metastatic NSCLC. Among them, anti-PD-1/PD-L1 + anti-TIGIT regimen emerges as a highly potential therapeutic approach. Ongoing research efforts should focus on improving treatment regimens, identifying biomarkers, and managing TRAEs to optimize the patient benefits of dual immunotherapy

B7 family protein glycosylation: Promising novel targets in tumor treatment

Cancer immunotherapy, including the inhibition of immune checkpoints, improves the tumor immune microenvironment and is an effective tool for cancer therapy. More effective and alternative inhibitory targets are critical for successful immune checkpoint blockade therapy. The interaction of the immunomodulatory ligand B7 family with corresponding receptors induces or inhibits T cell responses by sending co-stimulatory and co-inhibitory signals respectively. Blocking the glycosylation of the B7 family members PD-L1, PD-L2, B7-H3, and B7-H4 inhibited the self-stability and receptor binding of these immune checkpoint proteins, leading to immunosuppression and rapid tumor progression. Therefore, regulation of glycosylation may be the “golden key” to relieve tumor immunosuppression. The exploration of a more precise glycosylation regulation mechanism and glycan structure of B7 family proteins is conducive to the discovery and clinical application of antibodies and small molecule inhibitors

Metformin downregulates PD-L1 expression in esophageal squamous cell catrcinoma by inhibiting IL-6 signaling pathway

Purpose: To characterize the mechanism by which metformin inhibits PD-L1 expression in esophageal squamous cell carcinoma (ESCC) and to evaluate the effect of metformin on the antitumor immune response. Methods: The Cancer Genome Atlas (TCGA) database was used to analyze the correlations between IL-6 and prognosis and between IL-6 and PD-L1 gene expression in esophageal cancer. Reverse transcription-quantitative polymerase chain reaction (RT-PCR), Western blotting and immunofluorescence were used to study the mechanism by which metformin affects PD-L1 expression. Additionally, T cell function was assessed in a coculture system containing ESCC cells and peripheral blood mononuclear cells (PBMCs) treated with metformin or IL-6. In an Results: The TCGA esophageal cancer data showed that IL-6 expression was positively correlated with PD-L1 expression and that patients with high IL-6 expression had a significantly lower overall survival rate than patients with low IL-6 expression. PD-L1 expression in ESCC cell lines was significantly inhibited by metformin Conclusions: Metformin downregulated PD-L1 expression by blocking the IL-6/JAK2/STAT3 signaling pathway in ESCC, which enhanced the antitumor immune response

Reactivation of mutant p53 in esophageal squamous cell carcinoma by isothiocyanate inhibits tumor growth

p53 mutations are prevalent in human cancers; approximately half of patients with esophageal cancer present these mutations. Mutant p53 (mutp53) exerts oncogenic functions that promote malignant tumor progression, invasion, metastasis, and drug resistance, resulting in poor prognosis. Some small molecules have been shown to mitigate the oncogenic function of mutp53 by restoring its wild-type activity. Although these molecules have been evaluated in clinical trials, none have been successfully used in the clinic. Here, we investigated the antitumor effects of phenethyl isothiocyanate (PEITC) in p53-mutant esophageal squamous cell carcinoma (ESCC) and elucidated its mechanism to identify new therapeutic strategies. We observed that p53R248Q is a DNA contact mutation and a structural mutation and that PEITC can restore the activity of p53R248Qin vitro and in vivo, further clarifying the antitumor activity of PEITC in cancers with different types of p53 mutations. PEITC can inhibit ESCC growth, induce apoptosis, and arrest cell cycle progression and has a preferential selectivity for ESCC with p53 mutations. Mechanistic studies showed that PEITC induced apoptosis and arrested cells at G2/M transition in cells expressing the p53R248Q mutant by restoring the wild-type conformation and transactivation function of p53; these effects were concentration dependent. Furthermore, PEITC inhibited the growth of subcutaneous xenografts in vivo and restored p53 mutant activity in xenografts. According to these findings, PEITC has antitumor effects, with its ability to restore p53R248Q activity being a key molecular event responsible for these effects

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements