Changes in adiponectin levels of subclinical ketosis cows and their effects on steroid hormone secretion and proliferation in follicular granulosa cells.

In dairy cows, the occurrence of subclinical ketosis (SCK) is particularly high during early lactation. Previously, we documented alterations in the abundance of adiponectin (ADPN) in anestrus cows with SCK in comparison to cows in estrus. In the present study, 60 cows were divided into two groups: control (C, n = 30) and SCK (n = 30). Based on cow's estrus situation in two group at 55-60 days postpartum, 15 anestrus SCK cows and estrus cows were designated the SCK-A group and C-E group, respectively. The SCK-A group had downregulated serum and follicular fluid ADPN levels compared with the C-E group. The serum ADPN level was positively correlated with the insulin level and follicle growth rate, and there was a positive correlation between ADPN and glucose in the follicular fluid. Primary culture of dairy cow granulosa cells (GCs) was established to observe the effect of low glucose (Glu) and/or ADPN on GCs cyclins and proteins important for steroid synthesis. The results showed that the addition of 1 µg/mL ADPN alleviated the negative effects of low Glu treatment on the proliferation of GCs and the expression of steroid secretion related protein proteins. Treatment with LY294002 (PI3K inhibitor) four experimental GCs groups: control (0 µg/mL ADPN), 1 µg/mL ADPN, LY294002 inhibitor, and 1 µg/mL ADPN+LY294002. The results showed that ADPN promotes the secretion of steroid hormones by GCs through the PI3K-AKT. In summary, ADPN plays a crucial role in ameliorating postpartum anestrus in dairy cows with SCK.

A Highly Sensitive and Stretchable Core-Shell Fiber Sensor for Gesture Recognition and Surface Pressure Distribution Monitoring.

This work reports a highly-strain flexible fiber sensor with a core-shell structure utilizes a unique swelling diffusion technique to infiltrate carbon nanotubes (CNTs) into the surface layer of Ecoflex fibers. Compared with traditional blended Ecoflex/CNTs fibers, this manufacturing process ensures that the sensor maintains the mechanical properties (923% strain) of the Ecoflex fiber while also improving sensitivity (gauge factor is up to 3716). By adjusting the penetration time during fabrication, the sensor can be customized for different uses. As an application demonstration, the fiber sensor is integrated into the glove to develop a wearable gesture language recognition system with high sensitivity and precision. Additionally, the authors successfully monitor the pressure distribution on the curved surface of a soccer ball by winding the fiber sensor along the ball's surface.

Fusion PCAM R-CNN of Automatic Segmentation for Magnetic Flux Leakage Defects.

Magnetic leakage detection technology plays an important role in the long-oil pipeline. Automatic segmentation of defecting images is crucial for the detection of magnetic flux leakage (MFL) works. At present, accurate segmentation for small defects has always been a difficult problem. In contrast to the state-of-the-art MFL detection methodologies based on convolution neural network (CNN), an optimization method is devised in our study by integrating mask region-based CNN (Mask R-CNN) and information entropy constraint (IEC). To be precise, the principal component analysis (PCA) is utilized to improve the feature learning and network segmentation ability of the convolution kernel. The similarity constraint rule of information entropy is proposed to be inserted into the convolution layer in the Mask R-CNN network. The Mask R-CNN optimizes the convolutional kernel with similar weights or higher similarity, meanwhile, the PCA network reduces the dimension of the feature image to reconstruct the original feature vector. As such, the feature extraction of MFL defects is optimized in the convolution check. The research results can be applied in the field of MFL detection.