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Decabromodiphenyl ethane (DBDPE) is a novel retardant. DBDPE is used in various flammable consumer products such as electronics, building materials, textiles, and children's toys. The presence of DBDPE in humans makes it extremely urgent to assess the health effects of DBDPE exposure. Here, we used female mice as an animal model to investigate the effects of DBDPE on embryonic development and offspring health. The results showed that 50 μg/kg bw/day of DBDPE exposure did not affect spindle rotation in oocytes after fertilization, but led to a decrease of pronuclei (PN) in zygotes. Further investigation found that DBDPE interferes with the self‐assembly of F‐actin in PN, resulting in PN reduction, DNA damage, and reduced expression of zygotic genome activating genes, and finally leading to abnormal embryonic development. More importantly, we found that maternal DBDPE exposure did not affect the growth and development of the first generation of offspring (F1) mice, but resulted in behavioral defects in F1 mice. Female F1 mice from DBDPE‐exposed mothers exhibited increased motor activity and deficits in social behavior. Both female and male F1 mice from DBDPE‐exposed mothers exhibited cognitive memory impairment. These results suggest that DBDPE has developmental toxicity on embryos and has a cross‐generational interference effect. It is suggested that people should pay attention to the reproductive toxicity of DBDPE. In addition, it also provides a reference for studying the origin of neurological diseases and indicates that adult diseases caused by environmental pollutants may have begun in the embryonic stage.

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In vivo transparent vessel segmentation is important to life science research. However, this task remains very challenging because of the fuzzy edges and the barely noticeable tubular characteristics of vessels under a light microscope. In this paper, we present a new machine learning method based on blood flow characteristics to segment the global vascular structure in vivo. Specifically, the videos of blood flow in transparent vessels are used as input. We use the machine learning classifier to classify the vessel pixels through the motion features extracted from moving red blood cells and achieve vessel segmentation based on a region-growing algorithm. Moreover, we utilize the moving characteristics of blood flow to distinguish between the types of vessels, including arteries, veins, and capillaries. In the experiments, we evaluate the performance of our method on videos of zebrafish embryos. The experimental results indicate the high accuracy of vessel segmentation, with an average accuracy of 97.98%, which is much more superior than other segmentation or motion-detection algorithms. Our method has good robustness when applied to input videos with various time resolutions, with a minimum of 3.125 fps.