Immune checkpoint molecules have been identified as crucial regulators of the immune response, which motivated the emergence of immune checkpoint‐targeting therapeutic strategies. However, the prognostic significance of the immune checkpoint molecules PD‐1, CTLA4, TIM‐3 and LAG‐3 remains controversial. The aim of our study was to conduct a systematic assessment of the expression of these immune checkpoint molecules across different cancers in relation to treatment response, tumor‐infiltrating immune cells and survival. Oncomine and PrognoScan database analyses were used to investigate the expression levels and prognostic values of these immune checkpoint molecule genes across various cancers. Then, we used Kaplan–Meier plotter to validate the associations between the checkpoint molecules and cancer survival identified in the PrognoScan analysis. TIMER analysis was used to evaluate immune cell infiltration data from The Cancer Genome Atlas. Finally, we used Gene Expression Profiling Interactive Analysis to investigate the prognostic value of these four checkpoint molecules and assess the correlations between these four checkpoint molecules and genetic markers. These immune checkpoint molecules may potentially serve as prognostic factors and therapeutic targets in breast cancer, ovarian cancer and lung cancer. The prognostic roles of these checkpoint molecules varied greatly across cancers, which implied a noteworthy amount of heterogeneity among tumors, even within the same molecular subtype. In addition, the expression patterns of these checkpoint molecules were closely associated with treatment response and provided some useful direction when choosing chemotherapeutic drugs. These findings enhance our understanding of these checkpoints in cancer treatment and identify strategies to promote synergistic activities in the context of other immunotherapies.

No abstract

Effective emotion recognition algorithms can help machines better understand people and promote the development of human-computer interaction applications. In recent years, many research efforts have used benchmark expression data to train deep neural network models to achieve state-of-art results. These high-accuracy models usually contain hundreds of layers, so they require complex calculations and may not be suitable for real-world scenarios. This paper proposes a lightweight emotion recognition (LER) model to handle the latency problem under natural conditions. The three main contributions of this paper are as follows. 1) The LER model incorporates a densely connected convolution layer and model compression techniques into a framework that eliminates redundancy parameters. 2) Multichannel input is introduced in our work to preprocess the image data, which improves the learning ability of the model. 3) Experiments show that the proposed LER model has better performance on the FER2013 and FERPLUS datasets compared with other lightweight models. Compared with the VGG13 used in previous work, the LER model achieves higher accuracy and reduces the number of parameters by 97 times. Finally, the FERFIN dataset is created, which had fewer noise data and more accurate labels than the FERPLUS dataset.

No abstract

No abstract