Explore the vast range of titles available.

In recent years, intelligent driving technology based on vehicle–road cooperation has gradually become a research hotspot in the field of intelligent transportation. There are many studies regarding vehicle perception, but fewer studies regarding roadside perception. As sensors are installed at different heights, the roadside object scale varies violently, which burdens the optimization of networks. Moreover, there is a large amount of overlapping and occlusion in complex road environments, which leads to a great challenge of object distinction. To solve the two problems raised above, we propose RD-YOLO. Based on YOLOv5s, we reconstructed the feature fusion layer to increase effective feature extraction and improve the detection capability of small targets. Then, we replaced the original pyramid network with a generalized feature pyramid network (GFPN) to improve the adaptability of the network to different scale features. We also integrated a coordinate attention (CA) mechanism to find attention regions in scenarios with dense objects. Finally, we replaced the original Loss with Focal-EIOU Loss to improve the speed of the bounding box regression and the positioning accuracy of the anchor box. Compared to the YOLOv5s, the RD-YOLO improves the mean average precision (mAP) by 5.5% on the Rope3D dataset and 2.9% on the UA-DETRAC dataset. Meanwhile, by modifying the feature fusion layer, the weight of RD-YOLO is decreased by 55.9% while the detection speed is almost unchanged. Nevertheless, the proposed algorithm is capable of real-time detection at faster than 71.9 frames/s (FPS) and achieves higher accuracy than the previous approaches with a similar FPS.

To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required.

BackgroundEmerging studies have shown that HOTAIR acts as an oncogene in gastric cancer (GC). However, its role in the extracellular matrix and in tumor immune infiltration remains unknown.MethodsHOTAIR and COL5A1 levels were analyzed by bioinformatics analysis and validated by qRT-PCR, western blotting and immunohistochemistry assays. The regulatory relationships between components of the HOTAIR/miR-1277-5p/COL5A1 axis and the role of this axis in GC were predicted by bioinformatics analysis, and validated by in vitro and in vivo experiments. The correlation between COL5A1 and GC immune infiltration was assessed by bioinformatics analysis and a COL5A1-based predictive nomogram was established using the Stomach Adenocarcinoma dataset from The Cancer Genome Atlas.ResultsWe found that HOTAIR and COL5A1 were overexpressed in GC compared to normal controls, which predicted poor prognosis. The regulatory relationship of the HOTAIR/miR-1277-5p/COL5A1 axis in GC was demonstrated, and HOTAIR and COL5A1 were found to promote GC growth while miR-1277-5p exerted the reverse effects. In addition, COL5A1 was negatively associated with tumor purity but positively associated with immune infiltration, which suggested that COL5A1-mediated GC growth may be partially mediated by the regulation of immune infiltration. Additionally, the established COL5A1-based nomogram showed that COL5A1 can serve as a prognostic biomarker in GC.ConclusionsHOTAIR regulates GC growth by sponging miR-1277-5p and upregulating COL5A1, and COL5A1-mediated GC cell proliferation may be mediated by effects on the tumor microenvironment, which provides novel targets for GC treatment.

Gastric cancer (GC) is one of the most common malignant tumors worldwide, and its treatment has been a focus of medical research. Herein we systematically review the current status of and advancements in targeted therapy and immunotherapy for GC, which have emerged as important treatment strategies in recent years with great potential, and summarize the efficacy and safety of such treatments. Targeted therapies against key targets in GC, including epidermal growth factor receptor (EGFR), human epidermal growth factor receptor 2 (HER2), and vascular endothelial growth factor (VEGF)/VEGF receptor (VEGFR), have shown remarkable therapeutic efficacies by inhibiting tumor progression and/or blood supply. In particular, markable breakthroughs have been made in HER2-targeting drugs for HER2-positive GC patients. To address intrinsic and acquired resistances to HER2-targeting drugs, novel therapeutic agents including bispecific antibodies and antibody–drug conjugates (ADC) targeting HER2 have been developed. Immunotherapy enhances the recognition and elimination of cancer cells by activating body anticancer immune system. Programmed cell death protein 1 (PD-1) and programmed cell death-ligand 1 (PD-L1) antibodies are the most commonly used immunotherapeutic agents and have been used with some success in GC treatment. Innovative immunotherapy modalities, including adoptive immune cell therapy, tumor vaccines, and non-specific immunomodulators therapy, and oncolytic viruses have shown promise in early-stage clinical trials for GC. Clinical trials have supported that targeted therapy and immunotherapy can significantly improve the survival and quality of life of GC patients. However, the effects of such therapies need to be further improved and more personalized, with advancement in researches on tumor immune microenvironment. Further studies remain needed to address the issues of drug resistance and adverse events pertaining to such therapies for GC. The combined application of such therapies and individualized treatment strategies should be further explored with novel drugs developed, to provide more effective treatments for GC patients.

The COVID-19 pandemic is a significant public health problem globally, which causes difficulty and trouble for both people’s travel and public transport companies’ management. Improving the accuracy of bus passenger flow prediction during COVID-19 can help these companies make better decisions on operation scheduling and is of great significance to epidemic prevention and early warnings. This research proposes an improved STL-LSTM model (ISTL-LSTM), which combines seasonal-trend decomposition procedure based on locally weighted regression (STL), multiple features, and three long short-term memory (LSTM) neural networks. Specifically, the proposed ISTL-LSTM method consists of four procedures. Firstly, the original time series is decomposed into trend series, seasonality series, and residual series through implementing STL. Then, each sub-series is concatenated with new features. In addition, each fused sub-series is predicted by different LSTM models separately. Lastly, predicting values generated from LSTM models are combined in a final prediction value. In the case study, the prediction of daily bus passenger flow in Beijing during the pandemic is selected as the research object. The results show that the ISTL-LSTM model could perform well and predict at least 15% more accurately compared with single models and a hybrid model. This research fills the gap of bus passenger flow prediction under the influence of the COVID-19 pandemic and provides helpful references for studies on passenger flow prediction.

Nanofiber scaffolds have emerged as a revolutionary drug delivery platform for promoting wound healing, due to their unique properties, including high surface area, interconnected porosity, excellent breathability, and moisture absorption, as well as their spatial structure which mimics the extracellular matrix. However, the use of nanofibers to achieve controlled drug loading and release still presents many challenges, with ongoing research still exploring how to load drugs onto nanofiber scaffolds without loss of activity and how to control their release in a specific spatiotemporal manner. This comprehensive study systematically reviews the applications and recent advances related to drug-laden nanofiber scaffolds for skin-wound management. First, we introduce commonly used methods for nanofiber preparation, including electrostatic spinning, sol–gel, molecular self-assembly, thermally induced phase separation, and 3D-printing techniques. Next, we summarize the polymers used in the preparation of nanofibers and drug delivery methods utilizing nanofiber scaffolds. We then review the application of drug-loaded nanofiber scaffolds for wound healing, considering the different stages of wound healing in which the drug acts. Finally, we briefly describe stimulus-responsive drug delivery schemes for nanofiber scaffolds, as well as other exciting drug delivery systems.

The rapid industrial development has led to serious cadmium (Cd) pollution. Cd is a toxic heavy metal placing severe health threat to human. Cd can enter the body through the atmosphere, water, soil and food, and has a long half-life (10–30 years), it largely accumulates in kidneys, liver, bone and other organs and causes irreversible damage to the target organs. Cd pollution has also further caused certain carcinogenic and non-carcinogenic health risk. This study summarizes the current situation of Cd pollution, the toxicity of specific target organs, carcinogenic risk and non-carcinogenic risk in the general population, as well as dietary supplements to prevent and mitigate Cd toxication, which aims to focus on the adverse effects of Cd to human from both individual and population perspectives, hoping that not only the health risk of Cd poisoning can be reduced, but also the accurate prevention and control of Cd poisoning can be achieved in the future.

Multivariate time series forecasting has long been a research hotspot because of its wide range of application scenarios. However, the dynamics and multiple patterns of spatiotemporal dependencies make this problem challenging. Most existing methods suffer from two major shortcomings: (1) They ignore the local context semantics when modeling temporal dependencies. (2) They lack the ability to capture the spatial dependencies of multiple patterns. To tackle such issues, we propose a novel Transformer-based model for multivariate time series forecasting, called the spatial–temporal convolutional Transformer network (STCTN). STCTN mainly consists of two novel attention mechanisms to respectively model temporal and spatial dependencies. Local-range convolutional attention mechanism is proposed in STCTN to simultaneously focus on both global and local context temporal dependencies at the sequence level, which addresses the first shortcoming. Group-range convolutional attention mechanism is designed to model multiple spatial dependency patterns at graph level, as well as reduce the computation and memory complexity, which addresses the second shortcoming. Continuous positional encoding is proposed to link the historical observations and predicted future values in positional encoding, which also improves the forecasting performance. Extensive experiments on six real-world datasets show that the proposed STCTN outperforms the start-of-the-art methods and is more robust to nonsmooth time series data.

Orchid seed germination is an extremely complex physiological and ecological process that includes seed development and mutualistic interactions with compatible mycorrhizal fungi. Although the mycobiont Coprinellus disseminatus associated with Cremastra appendiculata has been reported, the mutualistic interactions between them remain poorly understood. In this study, we performed physiological and transcriptomic analyses to compare symbiotic germination and ungerminated seeds of Cr. appendiculata , as well as free-living Co. disseminatus , to investigate changes in physicochemical content and identify key genes that regulate the symbiotic germination of Cr. appendiculata seeds. The results revealed the content of auxin (IAA), gibberellic acid (GA 3 ), salicylic acid (SA) and jasmonic acid (JA) of Cr. appendiculata protocorms increased during symbiotic germination, and the content of superoxide dismutase (SOD) and catalase (CAT) in the symbiotically germinating protocorms also higher than ungerminated seeds. However, the content of peroxidases (POD) showed lower in the symbiotically germinating protocorms than ungerminated seeds. A total of 5,913 genes showing different levels of expression (log 2 FPKM ≥ 2.0, FDR < 0.01) in the comparison of CA and SY, and 19,077 differentially expressed genes (DEGs) genes in the comparison of CD and SY. DEGs involved in carbon metabolism, lipid metabolism, plant defense, plant hormone signal transduction and biotin metabolism were identified with significant upregulation or downregulation of genes related to metabolism observed. This work provides that mycorrhizal fungus Co. disseminatus can increase the content of endogenous hormones to promote Cr. appendiculata seed germination, and change the content of SOD, CAT to protect the plant against oxidative damage. These data may contribute to a better understanding of the molecular mechanisms of symbiotic germination and mycorrhizal fungi interactions.

We assessed the influence of the Fukushima nuclear accident (FNA) on the Chinese public’s attitude and acceptance of nuclear power plants in China. Two surveys (before and after the FNA) were administered to separate subsamples of residents near the Tianwan nuclear power plant in Lianyungang, China. A structural equation model was constructed to describe the public acceptance of nuclear power and four risk perception factors: knowledge, perceived risk, benefit, and trust. Regression analysis was conducted to estimate the relationship between acceptance of nuclear power and the risk perception factors while controlling for demographic variables. Meanwhile, we assessed the median public acceptable frequencies for three levels of nuclear events. The FNA had a significant impact on risk perception of the Chinese public, especially on the factor of perceived risk, which increased from limited risk to great risk. Public acceptance of nuclear power decreased significantly after the FNA. The most sensitive groups include females, those not in public service, those with lower income, and those living close to the Tianwan nuclear power plant. Fifty percent of the survey respondents considered it acceptable to have a nuclear anomaly no more than once in 50 y. For nuclear incidents and serious incidents, the frequencies are once in 100 y and 150 y, respectively. The change in risk perception and acceptance may be attributed to the FNA. Decreased acceptance of nuclear power after the FNA among the Chinese public creates additional obstacles to further development of nuclear power in China and require effective communication strategies.