Research on Dynamic Pig Counting Method Based on Improved YOLOv7 Combined with DeepSORT.

A pig inventory is a crucial component of achieving precise and large-scale farming. In complex pigsty environments, due to pigs' stress reactions and frequent obstructions, it is challenging to count them accurately and automatically. This difficulty contrasts with most current deep learning studies, which rely on overhead views or static images for counting. This research proposes a video-based dynamic counting method, combining YOLOv7 with DeepSORT. By utilizing the YOLOv7 network structure and optimizing the second and third 3 × 3 convolution operations in the head network ELAN-W with PConv, the model reduces the computational demand and improves the inference speed without sacrificing accuracy. To ensure that the network acquires accurate position perception information at oblique angles and extracts rich semantic information, we introduce the coordinate attention (CA) mechanism before the three re-referentialization paths (REPConv) in the head network, enhancing robustness in complex scenarios. Experimental results show that, compared to the original model, the improved model increases the mAP by 3.24, 0.05, and 1.00 percentage points for oblique, overhead, and all pig counting datasets, respectively, while reducing the computational cost by 3.6 GFLOPS. The enhanced YOLOv7 outperforms YOLOv5, YOLOv4, YOLOv3, Faster RCNN, and SSD in target detection with mAP improvements of 2.07, 5.20, 2.16, 7.05, and 19.73 percentage points, respectively. In dynamic counting experiments, the improved YOLOv7 combined with DeepSORT was tested on videos with total pig counts of 144, 201, 285, and 295, yielding errors of -3, -3, -4, and -26, respectively, with an average accuracy of 96.58% and an FPS of 22. This demonstrates the model's capability of performing the real-time counting of pigs in various scenes, providing valuable data and references for automated pig counting research.

Old age at diagnosis increases risk of tumor progression in nasopharyngeal cancer.

Age at diagnosis has been found to be a prognostic factor of outcomes in various cancers. However, the effect of age at diagnosis on nasopharyngeal cancer (NPC) progression has not been explored. We retrospectively evaluated the relationship between age and disease progression in 3,153 NPC patients who underwent radiotherapy, chemotherapy, or chemoradiotherapy between 2007 and 2009. Patients were randomly assigned to either a testing cohort or a validation cohort by computer-generated random assignment. X-tile plots determined the optimal cut-point of age based on survival status to be ≤61 vs. >61 years. Further correlation analysis showed that age >61 years was significantly correlated with the tumor progression and therapeutic regimen in both testing and validation cohorts (P <0.05). In the present study, we observed that older age (>61 years) was a strong and independent predictor of poor disease-free survival (DFS) and cancer-specific survival (CSS), in both univariate and multivariate analyses. Age was also found to be a significant prognostic predictor as well (P <0.05) when evaluating patients with the same disease stage. ROC analysis confirmed the predictive value of age on NPC-specific survival in both cohorts (P <0.001) and suggested that age may improve the ability to discriminate outcomes in NPCs, especially regarding tumor progression. In conclusion, our study suggests that older age at NPC diagnosis is associated with a higher incidence of tumor progression and cancer-specific mortality. Age is a strong and independent predictor of poor outcomes and may allow for more tailored therapeutic decision-making and individualized patient counseling.