AC484 was developed by designing compounds based on the PTPN2 protein structure. AC484 enhances antitumor immunity through multiple mechanisms: increasing tumor sensitivity to IFN-γ, improving T-cell functions, stimulating tumor microenvironment inflammation, expanding TCR diversity, and preventing T-cell exhaustion. Interestingly, the efficacy of AC484 was also mediated by CD8+ and NK cells.
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http://dx.doi.org/10.1002/mco2.567 | DOI Listing |
Plant Methods
March 2025
College of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, China.
Remarkable inter-class similarity and intra-class variability of tomato leaf diseases seriously affect the accuracy of identification models. A novel tomato leaf disease identification model, DWTFormer, based on frequency-spatial feature fusion, was proposed to address this issue. Firstly, a Bneck-DSM module was designed to extract shallow features, laying the groundwork for deep feature extraction.
View Article and Find Full Text PDFPeerJ Comput Sci
February 2025
Earthquake Administration of Jiangsu Province, Nanjing, China.
With the rapid advancement of Internet of Things (IoT) technology, the volume of sensor data collection has increased significantly. These data are typically presented in the form of time series, gradually becoming a crucial component of big data. Traditional time series analysis methods struggle with complex patterns and long-term dependencies, whereas deep learning technologies offer new solutions.
View Article and Find Full Text PDFThe increasing effect of Internet of Things (IoT) unlocks the massive volume of the availability of Big Data in many fields. Generally, these Big Data may be in a non-independently and identically distributed fashion (non-IID). In this paper, we have contributions in such a way enable multi-view k-means (MVKM) clustering to maintain the privacy of each database by allowing MVKM to be operated on the local principle of clients' multi-view data.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
December 2024
Fast and reliable interpretation of high-dimensional hyperspectral images (HSIs) can provide great support to remote sensing-based Earth observations. Targets of interest in HSI can be detected using deep neural networks (DNNs) for background learning on an acquired image where the occurrence probability of background samples is much greater than that of targets, accounting for more than 95% of the whole scene. However, there is an increasing gap between theory and feasible application, because of the contradiction between massive hyperspectral data and resource-limited Internet of Things (IoT)/edge device hardware like satellite.
View Article and Find Full Text PDFSci Rep
March 2025
The College of Big Data and Internet of Things, Chongqing Vocational Institute of Engineering, Chongqing, 402260, China.
The safe and efficient operation of smart Intelligent vehicles relies heavily on accurate trajectory prediction techniques. Existing methods improve prediction accuracy by introducing scene context information, but lack the causal perspective to explain why scene context improves prediction performance. In addition, current multimodal trajectory prediction methods are mostly target-driven and implicitly fused, relying too much on the density of candidate targets, as well as ignoring the road rule constraints, which leads to a lack of anthropomorphic properties in the model's prediction results.
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