Improved sports image classification using deep neural network and novel tuna swarm optimization.

Sports image classification is a complex undertaking that necessitates the utilization of precise and robust techniques to differentiate between various sports activities. This study introduces a novel approach that combines the deep neural network (DNN) with a modified metaheuristic algorithm known as novel tuna swarm optimization (NTSO) for the purpose of sports image classification. The DNN is a potent technique capable of extracting high-level features from raw images, while the NTSO algorithm optimizes the hyperparameters of the DNN, including the number of layers, neurons, and activation functions. Through the application of NTSO to the DNN, a finely-tuned network is developed, exhibiting exceptional performance in sports image classification. Rigorous experiments have been conducted on an extensive dataset of sports images, and the obtained results have been compared against other state-of-the-art methods, including Attention-based graph convolution-guided third-order hourglass network (AGTH-Net), particle swarm optimization algorithm (PSO), YOLOv5 backbone and SPD-Conv, and Depth Learning (DL). According to a fivefold cross-validation technique, the DNN/NTSO model provided remarkable precision, recall, and F1-score results: 97.665 ± 0.352%, 95.400 ± 0.374%, and 0.8787 ± 0.0031, respectively. Detailed comparisons reveal the DNN/NTSO model's superiority toward various performance metrics, solidifying its standing as a top choice for sports image classification tasks. Based on the practical dataset, the DNN/NTSO model has been successfully evaluated in real-world scenarios, showcasing its resilience and flexibility in various sports categories. Its capacity to uphold precision in dynamic settings, where elements like lighting, backdrop, and motion blur are prominent, highlights its utility. The model's scalability and efficiency in analyzing images from live sports competitions additionally validate its suitability for integration into real-time sports analytics and media platforms. This research not only confirms the theoretical superiority of the DNN/NTSO model but also its pragmatic effectiveness in a wide array of demanding sports image classification assignments.

The Crucial Role of PPARγ-Egr-1-Pro-Inflammatory Mediators Axis in IgG Immune Complex-Induced Acute Lung Injury.

Background: The ligand-activated transcription factor peroxisome proliferator-activated receptor (PPAR) γ plays crucial roles in diverse biological processes including cellular metabolism, differentiation, development, and immune response. However, during IgG immune complex (IgG-IC)-induced acute lung inflammation, its expression and function in the pulmonary tissue remains unknown. Objectives: The study is designed to determine the effect of PPARγ on IgG-IC-triggered acute lung inflammation, and the underlying mechanisms, which might provide theoretical basis for therapy of acute lung inflammation. Setting: Department of Pathogenic Biology and Immunology, Medical School of Southeast University. Subjects: Mice with down-regulated/up-regulated PPARγ activity or down-regulation of Early growth response protein 1 (Egr-1) expression, and the corresponding controls. Interventions: Acute lung inflammation is induced in the mice by airway deposition of IgG-IC. Activation of PPARγ is achieved by using its agonist Rosiglitazone or adenoviral vectors that could mediate overexpression of PPARγ. PPARγ activity is suppressed by application of its antagonist GW9662 or shRNA. Egr-1 expression is down-regulated by using the gene specific shRNA. Measures and Main Results: We find that during IgG-IC-induced acute lung inflammation, PPARγ expression at both RNA and protein levels is repressed, which is consistent with the results obtained from macrophages treated with IgG-IC. Furthermore, both in vivo and in vitro data show that PPARγ activation reduces IgG-IC-mediated pro-inflammatory mediators' production, thereby alleviating lung injury. In terms of mechanism, we observe that the generation of Egr-1 elicited by IgG-IC is inhibited by PPARγ. As an important transcription factor, Egr-1 transcription is substantially increased by IgG-IC in both in vivo and in vitro studies, leading to augmented protein expression, thus amplifying IgG-IC-triggered expressions of inflammatory factors via association with their promoters. Conclusion: During IgG-IC-stimulated acute lung inflammation, PPARγ activation can relieve the inflammatory response by suppressing the expression of its downstream target Egr-1 that directly binds to the promoter regions of several inflammation-associated genes. Therefore, regulation of PPARγ-Egr-1-pro-inflammatory mediators axis by PPARγ agonist Rosiglitazone may represent a novel strategy for blockade of acute lung injury.