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。     

Speaking fundamental frequency characteristics as a function of age and professional singing

The purposes of this project were to discover (1) if the speaking fundamental frequency (SFF) levels of professional singers differ significantly from those of nonsingers and (2) if the age-related SFF patterns are similar for these two classes of individuals. Sixty professional singers and 94 nonsingers were recorded reading the first paragraph of the “Rainbow Passage;” both males and females were included. Three paired groups (young, middle, and old age) were studied; they were selected on the basis of health and age. The professional singer groups were further divided by a binary voice classification system, specifically that of soprano/alto for women and tenor/baritone for men. It was found that the sopranos and tenors exhibited significantly higher SFF levels then did the age-matched nonsingers, whereas the altos and baritones did not differ significantly from the controls. Relationships within the performer groups were mixed. For example, there appeared to be a systemic trend for the sopranos and tenors to exhibit higher SFF levels than the altos and baritones. Finally, although the nonsinger SFF levels varied significantly as a function of age, those for the professional singers did not.     

The effect of infertile semen on the mRNA-based body fluid identification

In the past decade, mRNA markers have been well demonstrated as promising molecular markers in forensic body fluid identification (BFI), and successfully used in wide applications. Several studies have assessed the performance of semen-specific mRNA markers in distinguishing semen from other common body fluids at the crime scene. Infertility has been reported as a global health problem that is affecting approximately 15% of couples worldwide. Therefore, it is important for forensic researchers to consider the impact of infertility on semen identification. This study aimed to explore the effect of semen from infertile men (hereinafter “infertile semen”) on BFI and to identify semen-specific mRNAs that can efficiently and accurately distinguish normal and infertile semen samples from other body fluids. Results showed that the selected five mRNAs (KLK3, TGM4, SEMG1, PRM1, and PRM2) performed a significantly high semen specificity in normal semen. Moreover, KLK3 was slightly influenced by infertile semen samples with over 98% positive results in all semen samples. The accuracy to predict normal semen reached up to 96.6% using the discrimination function Y1 with KLK3 and PRM1. However, when the infertile semen samples were included in discrimination function (function Y2 with KLK3), the accuracy rate of semen identification (including the normal and infertile semen) was down to 89.5%. Besides, the sensitivity of multiplex assay could reach down to 50pg. Our results suggest that it is important to consider the presence of infertile semen when using mRNAs to identify semen samples, which would have a far-reaching impact in forensic identification.     

CYP19 gene variant confers susceptibility to endometriosis-associated infertility in Chinese women

An aromatase encoded by the CYP19 gene catalyzes the final step in the biosynthesis of estrogens, which is related to endometriosis development. To assess the association of CYP19 gene polymorphisms with the risks of endometriosis, chocolate cysts and endometriosis-related infertility, a case–control study was conducted in Chinese Han women by recruiting 225 healthy control females, 146 patients with endometriosis, 94 endometriosis women with chocolate cyst and 65 women with infertility resulting from endometriosis, as diagnosed by both pathological and laparoscopic findings. Individual genotypes at rs2236722:T>C, rs700518:A>G, rs10046:T>C and [TTTA]n polymorphisms were identified. Allelic and genotypic frequencies were compared between the control group and case groups by chi-square analysis. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were determined by logistic regression analysis to predict the association of CYP19 gene polymorphisms with the risk of endometriosis, the related chocolate cysts and infertility. The genotype distributions of the tested CYP19 gene polymorphisms were not significantly different between the healthy control group and the endometriosis/endometriosis with the chocolate cyst group. However, the CYP19 rs700518AA genotype was significantly associated with an increased risk of endometriosis-related infertility (55.4% in the infertility group vs 25.3% in the control group, P<0.001; OR (95% CI): 3.66 (2.06–6.50)) under the recessive form of the A allele. Therefore, we concluded that in Chinese Han females CYP19 gene polymorphisms are not associated with susceptibility to endometriosis or chocolate cysts, whereas CYP19 rs700518AA genotype confers genetic susceptibility to endometriosis-related infertility.