基于紫外‐可见透射光谱技术和极限学习机的早期鸡胚雌雄识别

为了对鸡种蛋胚胎进行雌雄识别,探究利用紫外-可见-近红外透射光谱进行鸡胚雌雄识别的可行性,搭建了鸡种蛋透射光谱检测系统,采用横向和竖向大头朝上2种放置方式获取210枚鸡种蛋孵化0~15 d的光谱,光谱范围为360~1 000 nm。构建极限学习机（ELM）鸡胚雌雄识别模型,通过比较不同放置方式和孵化天数下模型的识别准确率,发现竖向放置且孵化第7 d的识别效果最好;将竖向放置孵化第7 d的光谱初步分为紫外（360~380 nm）、可见光（380~780 nm）、近红外（780~1 000 nm）、紫外-可见光（360~780 nm）和全波段（360~1 000 nm）5个不同的波段范围来分析,预测集准确率分别为82.86%,77.14%,75.71%,84.29%和81.43%,筛选出360~780 nm的紫外-可见光波段为有效波段;在紫外-可见光（360~780 nm）波段,采用多元散射校正（MSC）去噪,并用竞争性自适应重加权采样算法（CARS）和连续投影算法（SPA）筛选特征波长降维,建立不经筛选特征波长、CARS筛选特征波长和SPA筛选特征波长的3种ELM模型。其中不经筛选特征波长的ELM模型识别效果最好,但输入变量最多,隐含层神经元为680且激活函数为sig时,预测集准确率为84.29%。SPA筛选特征波长的ELM模型识别效果次之,输入变量有9个,隐含层神经元为840且激活函数为hardlim时,预测集准确率为81.43%。CARS筛选特征波长的ELM模型识别效果最差,输入变量有27个,隐含层神经元为100且激活函数为sig时,预测集准确率为78.57%;用遗传算法（GA）优化ELM模型的权值变量和隐含层阈值,不经筛选特征波长建立的GA-ELM模型,预测集准确率为87.14%,SPA筛选特征波长建立的GA-ELM模型,预测集准确率为87.14%,CARS筛选特征波长建立的GA-ELM模型,预测集准确率为81.43%。紫外-可见光波段不经筛选特征波长的GA-ELM模型识别效果和经SPA筛选特征波长的GA-ELM模型相同,表明SPA筛选的特征波长变量能够有效反映360~780 nm波段的信息,SPA使用的变量数仅占紫外-可见光波段的2.14%,因此,雌雄识别最佳模型为紫外-可见光波段经SPA筛选特征波长的GA-ELM模型,预测集准确率为87.14%,其中,雌性识别率为88.57%,雄性识别率为85.71%,单个样本平均判别时间0.080 ms。结果表明紫外-可见透射光谱技术和ELM模型为孵化早期鸡胚蛋雌雄识别提供了一种可行方法。     

The Effect of Polymer Dots During Mammalian Early Embryo Development and Their Biocompatibility on Maternal Health

Conjugated polymer dots have excellent fluorescence properties in terms of their structural diversity and functional design, showing broad application prospects in the fields of biological imaging and biosensing. Polymer dots contain no heavy metals and are thought to be of low toxicity and good biocompatibility. Therefore, systematic studies on their potential toxicity are needed. Herein, the biocompatibility of poly[(9,9‐dioctylfluorenyl‐2,7diyl)‐co‐(1,4‐benzo‐{2,1′,3}‐thiadiazole)],10% benzothiadiazole(y) (PFBT) and poly[2‐methoxy‐5‐(2‐ethylhexyloxy)‐1,4‐phenylenevinylene] (MEH‐PPV) polymer dots on early embryo development as well as maternal health is studied in detail. The results show that prepared polymer dots are dose‐dependently toxic to preimplantation embryos, and low‐dose polymer dots can be used for cell labeling of early embryos without affecting the normal development of embryos into blastocysts. In addition, the in vivo distribution data show that the polymer dots accumulate mainly in the maternal liver, spleen, kidney, placenta, ovary, and lymph nodes of the pregnant mice. Histopathological examination and blood biochemical tests demonstrate that exposure of the maternal body to polymer dots at a dosage of 14 µg g^?1 does not affect the normal function of the maternal organs and early fetal development. The research provides a safe basis for the wide application of polymer dots.     

Silica Nanoparticles Induced Embryonic Dysplasia and Apoptosis Via Endoplasmic Reticulum Stress in Zebrafish(Danio Rerio)

The possible detrimental effects of nanomaterials on organisms have become a public concern because of the wide applications of nanomaterials in modern society. This study investigates the effects of silica nanoparticles (nano-SiO₂) on zebrafish development. Fluorescent nano-SiO₂ (FITC-nano-SiO₂) is synthesized with an emission wavelength of 517 nm. Due to similar hydrodynamic size, FITC-nano-SiO₂ is used to simulate the distribution of nano-SiO₂ in zebrafish. Nano-SiO₂ regulated col2a1a, col9a2, lect1, and her12 to delay liver development and regulated prox, wnt2bb, mypt1, and hhex caused spinal curvature. The hand2, mef2a, nkx2.5, and atp1a2a are significantly down-regulated. The whole embryo in situ hybridization revealed amhc and flk1 are also down-regulated. This suggests that nano-SiO₂ affected heart and blood vessels development of zebrafish. Furthermore, it is found that cell apoptosis occurred in the heart. The proapoptotic genes (bax, bid) are upregulated, and the antiapoptotic genes (bcl-2, mcl-1b) are down-regulated. Nano-SiO₂ increases the endoplasmic reticulum (ER) stress-related genes (chop, bip, perk, elF2α), indicating that the ER stress is involved in cell apoptosis. This study provides a toxicological basis for evaluating the possible hazards of nano-SiO₂ to aquatic organisms.