催乳素对培养的绵羊睾丸间质细胞睾酮分泌的影响

在绵羊睾丸间质细胞体外无血清长期培养的条件下,研究了催乳素对睾丸间质细胞睾酮分泌的调节作用。实验结果表明,催乳素可增强细胞对人绒毛膜促性腺激素(hCG)刺激的反应。催乳素的这种作用呈双相调节。睾酮分泌量显著高于hCG和催乳素单独作用时的总和。在hCG存在下,不同的底物转化为睾酮的量不同。其中雄烯二酮和孕酮转化为睾酮的方式存在着双相性。脱氢表雄酮转为睾酮的量少,不存在双相性,而与其剂量成正比。催乳素在hCG存在下可调节底物转化为睾酮。低剂量的催乳素(1ng/ml)可使一定剂量的孕酮(10～30ng/ml)转化为睾酮的量明显增加,而高剂量的催乳素(>10ng/ml)却明显地抑制孕酮转化为睾酮。催乳素可明显地抑制雄烯二酮转化为睾酮,与剂量无关。可见催乳素对于孕酮和雄烯二酮这两个关键底物转化为睾酮的调节是不同的。催乳素增强hCG刺激睾酮分泌的作用可能部分是通过其促进孕酮转化为睾酮来实现的。     

病毒单糖的研究 Ⅰ.颗粒体病毒(GV)单糖的气相色谱分析

本文介绍了颗粒体病毒中单糖的气相色谱分析法,对所得病毒单糖色谱图中的主要组分进行了分析鉴定,证明组成病毒的单糖成分有:鼠李糖、核糖、木糖、甘露糖、葡萄糖和阿拉伯糖。并用相应单糖峰高比值,区分了它们间的异同。病毒单糖分析结果。能与血清学和裂解气相色谱法分析鉴定病毒的结果相符。     

解淀粉芽胞杆菌启动基因的克隆及其对地衣杆菌α-淀粉酶基因的调控

用大肠杆菌启动基因探针质粒pHE5克隆了解淀粉芽胞杆菌的启动基因。供体菌DNA的HindⅢ片段与pHE5重组后转化大肠杆菌,获得一批带启动基因的抗四环素转化子,其中四株的抗性达到200μg/mL,从这四株高抗性转化子中提取质粒,并对其中的一个重组质粒pAE23的插入片段进行限制性图谱分析和缺失研究,获得了一个缺失了部份片段,四环素抗性仍达到200μg/mL的衍生质粒pAED23,经酶切分析证明其上的启动基因位于0.8kb的EcoR Ⅰ—HindⅢ片段上。点杂交分析证实该片段来自解淀粉芽胞杆菌的DNA。将地衣杆菌的α-淀粉酶基因亚克隆至pAED23启动基因下游的HindⅢ位点上能增强该基因在大肠杆菌中的表达。     

HL-60细胞诱导分化期间细胞表面ConA受体结合量变化的研究

 <正> 在用二甲亚砜(DMSO)诱导人急性早幼粒细胞性白血病细胞系(HL-60 Cell)沿粒系统分化成熟的实验基础上,我们用受体荧光标记技术和荧光分光光度法,进一步观察了HL-60细胞诱导分化期间,细胞膜流动性动态降低对膜上伴刀豆球蛋白(ConA)受体结合量的影响。     

Quantitation of Rat Dopamine Transporter mRNA: Effects of Cocaine Treatment and Withdrawal

Dopamine transporter mRNA levels in the rat substantia nigra were quantified using a sensitive nuclease protection assay with a highly homologous human dopamine transporter cDNA clone. The same probe was also used to visualize dopamine transporter mRNA in the substantia nigra by in situ hybridization. Repeated cocaine administration (15 mg/kg, twice a day for 6.5 days) resulted in a greater than 40% decrease in nigral dopamine transporter mRNA levels. In contrast, dopamine transporter mRNA levels were unchanged after either acute treatment (4 h before death) or repeated cocaine treatment followed by a 72-h withdrawal period. Thus, blockade of the dopamine transporter by repeated cocaine administration may result in the down-regulation of dopamine transporter gene expression in dopamine neurons.     

Predicting protein secondary structure using neural net and statistical methods.

A comparison of neural network methods and Bayesian statistical methods is presented for prediction of the secondary structure of proteins given their primary sequence. The Bayesian method makes the unphysical assumption that the probability of an amino acid occurring in each position in the protein is independent of the amino acids occurring elsewhere. However, we find the predictive accuracy of the Bayesian method to be only minimally less than the accuracy of the most sophisticated methods used to date. We present the relationship of neural network methods to Bayesian statistical methods and show that, in principle, neural methods offer considerable power, although apparently they are not particularly useful for this problem. In the process, we derive a neural formalism in which the output neurons directly represent the conditional probabilities of structure class. The probabilistic formalism allows introduction of a new objective function, the mutual information, which translates the notion of correlation as a measure of predictive accuracy into a useful training measure. Although a similar accuracy to other approaches (utilizing a mean-square error) is achieved using this new measure, the accuracy on the training set is significantly and tantalizingly higher, even though the number of adjustable parameters remains the same. The mutual information measure predicts a greater fraction of helix and sheet structures correctly than the mean-square error measure, at the expense of coil accuracy, precisely as it was designed to do. By combining the two objective functions, we obtain a marginally improved accuracy of 64.4%, with Matthews coefficients C alpha, C beta and Ccoil of 0.40, 0.32 and 0.42, respectively. However, since all methods to date perform only slightly better than the Bayes algorithm, which entails the drastic assumption of independence of amino acids, one is forced to conclude that little progress has been made on this problem, despite the application of a variety of sophisticated algorithms such as neural networks, and that further advances will require a better understanding of the relevant biophysics.     

Modeling the Transmission of Middle East Respirator Syndrome Corona Virus in the Republic of Korea

The 2015 epidemic of Middle East respiratory syndrome (MERS) in the Republic of Korea has been the largest outbreak outside Middle East. This epidemic had caused 185 laboratory-confirmed cases and 36 deaths in the Republic of Korea until September 2, 2015, which attracted public’s attention. Based on the detailed data of patients released by World Health Organization (WHO) and actual propagation of the epidemic, we construct two dynamical models to simulate the propagation processes from May 20 to June 8 and from June 9 to July 10, 2015, respectively and find that the basic reproduction number R ₀ reaches up to 4.422. The numerical analysis shows that the reasons of the outbreak spread quickly are lack of self-protection sense and targeted control measures. Through partial correction analysis, the parameters β ₁ and γ have strong correlations with R ₀, i.e., the infectivity and proportion of the asymptomatic infected cases have much influence on the spread of disease. By sensitivity analysis, strengthening self-protection ability of susceptible and quickly isolating or monitoring close contacts are effective measures to control the disease.