Sperm detection performance is particularly critical for sperm motility tracking. However, there are a large number of non-sperm objects, sperm occlusion and poorly detailed texture features in semen images, which directly affect the accuracy of sperm detection. To solve the problem of false detection and missed detection in sperm detection, a multi-sperm target detection model, Yolov5s-SA, with an SA attention mechanism is proposed based on the YOLOv5s algorithm. Firstly, a depthwise, separable convolution structure is used to replace the partial convolution of the backbone network, which can ensure stable precision and reduce the number of model parameters. Secondly, a new multi-scale feature fusion module is designed to enhance the perception of feature information to supplement the positional information and high-resolution of the deep feature map. Finally, the SA attention mechanism is integrated into the neck network before the output of the feature map to enhance the correlation between the feature map channels and improve the fine-grained feature fusion ability of YOLOv5s. Experimental results show that compared with various YOLO algorithms, the proposed algorithm improves the detection accuracy and speed to a certain extent. Compared with the YOLOv3, YOLOv3-spp, YOLOv5s and YOLOv5m models, the average accuracy increases by 18.1%, 15.2%, 6.9% and 1.9%, respectively. It can effectively reduce the missed detection of occluded sperm and achieve lightweight and efficient multi-sperm target detection.

Deoxynivalenol (DON) is one of the most common mycotoxins in animal feed worldwide and causes significant threats to the animal health. Increased use of plant ingredients in aquaculture feeds increased the risk of mycotoxin contamination. To evaluate the effects of dietary deoxynivalenol (DON) on growth performance, immune response and intestinal health of turbot and the mitigation efficacy of yeast cell wall extract (YCWE) toward DON, nine isonitrogenous and isolipidic diets were formulated: Diet 1 (control): No DON added; Diets 2-5 or Diets 6-9: 0.5 or 3.0 mg added DON/kg diet + 0%, 0.1%, 0.2%, or 0.4% YCWE, respectively. Results showed that Diet 6 (3 mg/kg DON, 0% YCWE) significantly decreased weight gain, specific growth rate and feed efficiency ratio of fish and reduced immunoglobulin M and complement 4 concentrations in serum. Fish fed Diet 6 presented morphological alterations, lower activity of superoxide dismutase, catalase and total antioxidant capacity but higher malondialdehyde content, lower claudin-4 and occludin expression but higher interleukin-1β expression in intestine. Besides, Diet 6 decreased the abundance of potential helpful bacteria but increased the abundance of potential pathogens in intestine. While, dietary YCWE, especially Diet 8 (3 mg/kg DON, 0.2% YCWE) and 9 (3 mg/kg DON, 0.4% YCWE), markedly improved growth performance and immune response and enhanced the intestinal health of turbot. In conclusion, dietary YCWE could mitigate the toxic effects induced by DON in turbot, and could be used as an effective strategy to control DON contamination in fish feed.

Sperm concentration and motility are generally analysed only in the discrete state in microscopic videos. As for sperm nonspecific aggregation areas, it brings difficulties to accurate sperm detection. In this paper, an algorithm for nonspecific aggregates automatic segmentation, detection and tracking of sperm is proposed. A grid model commensurate with the size of a sperm head is created to segment nonspecific aggregation areas. Multi‐scale edge function and new energy functional are designed based on the level set method to realize sperm head segmentation. In the sperm tracking stage, we improve the weight condition and the standard of trust flow quantization based on graph theory method, and simplify the sperm tracking to the vertex matching between two frames to solve the matching failure problem of adjacent frames with small space distance. The proposed method achieves accurate segmentation of sperm non‐specific aggregation regions, which outperforms the level set methods of LBF and SBGFR. At the same time, our method can real‐time calculation of sperm concentration and motility during sperm tracking. It is compared with four state‐of‐the‐art algorithms, and it gives lower tracking error rates, which has potential applications in male fertility field.