High levels of mitochondrial dynamics, autophagy, and apoptosis contribute to stable testicular status in hibernating Daurian ground squirrels.

Daurian ground squirrels (Spermophilus dauricus) experience various stress states during winter hibernation, but the impact on testicular function remains unclear. This study focused on the effects of changes in testicular autophagy, apoptosis, and mitochondrial homeostasis signaling pathways at various stages on the testes of Daurian ground squirrels. Results indicated that: (1) During winter hibernation, there was a significant increase in seminiferous tubule diameter and seminiferous epithelium thickness compared to summer. Spermatogonia number and testosterone, follicle-stimulating hormone (FSH), and luteinizing hormone (LH) levels were higher during inter-bout arousal, suggesting that the testes remained stable during hibernation. (2) An increased number of mitochondria with intact morphology were observed during hibernation, indicating that mitochondrial homeostasis may contribute to testicular stability. (3) DNA fragmentation was evident in the testes during the hibernation and inter-bout arousal stages, with the highest level of caspase3 enzyme activity detected during inter-bout arousal, together with elevated levels of Bax/Bcl-2 and Lc3 II/Lc3 I, indicating an up-regulation of apoptosis and autophagy signaling pathways during hibernation. (4) The abundance of DRP1, MFF, OPA1, and MFN2 proteins was increased, suggesting an up-regulation of mitochondrial dynamics-related pathways. Overall, testicular autophagy, apoptosis, and mitochondrial homeostasis-related signaling pathways were notably active in the extreme winter environment. The well-maintained mitochondrial morphology may favor the production of reproductive hormones and support stable testicular morphology.

Deep Learning-Based Defects Detection in Keyhole TIG Welding with Enhanced Vision.

Keyhole tungsten inert gas (keyhole TIG) welding is renowned for its advanced efficiency, necessitating a real-time defect detection method that integrates deep learning and enhanced vision techniques. This study employs a multi-layer deep neural network trained on an extensive welding image dataset. Neural networks can capture complex nonlinear relationships through multi-layer transformations without manual feature selection. Conversely, the nonlinear modeling ability of support vector machines (SVM) is limited by manually selected kernel functions and parameters, resulting in poor performance for recognizing burn-through and good welds images. SVMs handle only lower-level features such as porosity and excel only in detecting simple edges and shapes. However, neural networks excel in processing deep feature maps of "molten pools" and can encode deep defects that are often confused in keyhole TIG. Applying a four-class classification task to weld pool images, the neural network adeptly distinguishes various weld states, including good welds, burn-through, partial penetration, and undercut. Experimental results demonstrate high accuracy and real-time performance. A comprehensive dataset, prepared through meticulous preprocessing and augmentation, ensures reliable results. This method provides an effective solution for quality control and defect prevention in keyhole TIG welding process.