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。     

^137Cs,^134Cs在人工海洋小生境中的行为

在自行建立的人工海洋小生境中,采用示踪法综合地研究~(137)Cs、~(134)Cs在人工小生境中的行为。结果表明,~(137)Cs和~(134)Cs具有共同的生理生态行为,并表现出相似的规律、沉积物对~(137)Cs、~(134)Cs的吸附能力甚低,~(137)Cs、~(134)Cs在海洋动物体内趋于全身性的分布。各主要生化物质均能检出~(137)Cs、~(134)Cs。排泄实验后,海洋动物的胃肠、肝(消化腺)~(137)Cs、~(134)Cs损失显著。沉积物表现为解吸-重吸附的过程。     

草鱼出血病病毒对其它鱼的感染性研究

用草鱼出血病病鱼分离出的草鱼出血病病毒(Grass carp hemorrhage virus,GCHV)感染其它常见鱼并用ELISA方法检查感染鱼组织提取液,结果表明:青鱼、鲢鱼、布氏鳌条对GCHV抗体呈阳性反应;鲤鱼、鳙鱼、鲫鱼、团头鲂、泥鳅则呈阴性反应。综合感染鱼发病症状及死亡特征,初步认为:青鱼对GCHV是易感的,GCHV能在鲢鱼、布氏蟹条体内增值,但毒力较低,鳙鱼、鲫鱼、团头鲂、鲤鱼、泥鳅能抗GCHV感染。     

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。     

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.     

Towards a Drosophila genome map.

A physical map of the genome of Drosophila melanogaster has been created using 965 yeast artificial chromosome (YAC) clones assigned to locations in the cytogenetic map by in situ hybridization with the polytene salivary gland chromosomes. Clones with insert sizes averaging about 200 kb, totaling 1.7 genome equivalents, have been mapped. More than 80% of the euchromatic genome is included in the mapped clones, and 75% of the euchromatic genome is included in 161 cytological contigs ranging in size up to 2.5 Mb (average size 510 kb). On the other hand, YAC coverage of the one-third of the genome constituting the heterochromatin is incomplete, and clones containing long tracts of highly repetitive simple satellite DNA sequences have not been recovered.