铜绿假单胞菌外毒素A的生产,分离纯化和鉴定

通过对培养基组成、种子活化、接种量和培养条件进行优化,使铜绿假单胞菌外毒素Ａ（ＰＥ）产量达到每毫升５－１０μｇ和１９２小鼠ＬＤ５０,不低于国外报道水平。经二步纯化,ＰＥ蛋白回收率为３３．３３％（ＰＥ）和１６．６７％（ＬＤ５０）,提纯系数为４３８．５（ＰＥ）或２１８．５（ＬＤ５０）,ＳＤＳ－ＰＡＧＥ呈现一条带,相对分子质量为６６０００,琼脂糖扩散鉴定与兔抗ＰＥ产生一条沉淀线,小鼠半数致死量为０．１５     

PKC、PKA和TPK在血小板激活中的作用

利用~（３２）Ｐ－ＮａＨ_２ＰＯ_４标记猪血小板,然后以ＰＭＡ、凝血酶、ＰＧＥ_１、腺苷等处理,结果表明,随着ＰＭＡ激活ＰＫＣ,血小板发生聚集。３５μｍｏｌ／ＬＰＧＥ_１或１ｍｍｏｌ／ＬｄｂｃＡＭＰ不能抑制５０ｎｍｏｌ／ＬＰＭＡ诱导的血小板聚集,腺苷却能抑制ＰＭＡ诱导的血小板聚集（ＥＣ_（５０）＝０．１ｍｍｏｌ／Ｌ）,ｄｂ－ｃＡＭＰ、腺苷都不能抑制１００ｎｍｏｌ／ＬＰＭＡ诱导的４０ｋＤ蛋白磷酸化。ＰＫＡ激活不能抑制ＰＭＡ激活的ＰＫＣ。在ＰＭＡ、凝血酶激活的血小板中,ＰＫＣ、ＴＰＫ都发生激活,４０ｋＤ底物既是ＰＫＣ的底物又是ＴＰＫ的底物,ＰＫＣ和ＴＰＫ在血小板聚集中起着重要的调节作用。     

南方鲇碱性磷酸酶的分离纯化及部分性质的研究

用正丁醇抽提,硫酸铵分级沉淀,ＤＥＡＥ－纤维素和ＳｅｐｈａｃｒｙｌＳ－２００柱层析,从南方鲇（Ｓｉｌｕｒｕｓ　ｍｅｒｉｄｉｏｎａｌｉｓ　Ｃｈｅｎ）肠粘膜中提取出碱性磷酸酶（ＡＫＰ）。提纯倍数为３９．５０倍,比活为６８．３５μ／ｍｇ蛋白,提取酶液经ＰＡＧＥ和ＳＤＳ－ＰＡＧＥ只呈现一条区带。该酶的分子量为１３２１４０,Ｎ末端氨基酸为门冬氨酸,最适ｐＨ为１０．１０,７．５＞ｐＨ＞１１．５时不稳定,最适温度为４０℃左右,对热不很稳定,以磷酸苯二钠为底物其Ｋ_ｍ值为１．７２×１０~（－３）ｍｏｌ／Ｌ。Ｍｇ~（２＋）、Ｍｎ~（２＋）为该酶的激活剂,ＫＨ_２ＰＯ_４、Ｌ－ＣｙＳ、ＭＥ、ＤＦＰ、ＥＤＴＡ－Ｎａ_２为抑制剂。选用ＫＨ_２ＰＯ_４和ＤＦＰ作抑制类型的判断,结果表明,ＫＨ_２ＰＯ_４属竞争性掏剂,其抑制常数为２．３ｍｍｏｌ／Ｌ;ＤＦＰ为非竞争性抑制剂,抑制常数为１．０５ｍｍｏｌ／Ｌ。     

固氮粪产碱菌谷氨酰胺合成酶的分离纯化及其特性

联合固氮细菌粪产碱菌（Ａｌｃａｌｉｇｅｎｅｓｆａｅｃａｌｉｓ）Ａ１５０１菌体经超声破碎后,无细胞粗提液以ＰＥＧ－６０００分级沉淀,丙酮沉淀,再经蓝琼脂糖（ＢｌｕｅＳｅｐｈａｒｏｓｅＣＬ－６８）亲和层析分离、纯化。获得的纯谷氨酰胺合成酶（ＧＳ）在ＳＤＳ－ＰＡＧＥ和４－３０％梯度ＰＡＧＥ上均呈均一的一条带。ＧＳ亚基及整酶分子量分别为５５ｋＤ和６４５ｋＤ,亚基由４５６个氨基酸残基组成。ＧＳ的Ｋｍ值,在以Ｇｌｕ为氮源的介质中培养时分别为２０ｍｍｏｌ／Ｌ（Ｇｌｕ）,５０ｍｍｏｌ／Ｌ（ＡＴＰ）和４５ｍｍｏｌ／Ｌ（ＮＨ~＋_４）;在以ＮＨ~＋_４为氮源的介质中培养时则分别为７０ｍｍｏｌ／Ｌ（Ｇｌｕ）,４９ｍｍｏｌ／Ｌ（ＡＴＰ）和８０ｍｍｏｌ／Ｌ（ＮＨ~＋_４）,表明ＮＨ~＋_４培养下形成高度腺苷化的ＧＳ对Ｇｌｕ及ＮＨ~＋_４的亲和力有所下降。     

蟾蜍血清梭曼水解酶（somanase）的纯化及性质的研究

从蟾蜍血清中发现并纯化出了一种与血清载脂蛋白有关的可催化神经性军用毒剂梭曼（０－１,２,２－ｔｒｉｍｅｔｈｙｌｐｒｏｐｙｌｍｅｔｈｙｌｐｈｏｓｐｈｏｆｌｕｏｒｉｄａｔｅ,ｓｏｍａｎ）的水解酶,此酶全部与高密度脂蛋白（ＨＤＬ）以复合形式存在于血清中。我们经过多种纯化手段,从ＨＤＬ中分离纯化出了酯酶的最小分子量组分,得到了高活性、高纯度的酶蛋白,在ＳＤＳ－聚丙烯酰胺凝胶电泳及等电聚焦电泳中呈单一区带,分子量４２ｋＤ,等电点ｐＨ５．２,并证明此酶不是ＨＤＬ中的任何一种已知载脂蛋白,也不是磷脂酶Ａ_２和磷脂酶Ｃ以及存在于ＨＤＬ中的卵磷脂胆固醇酰基转移酶（ＬＣＡＴ）。它是与ＨＤＬ中的主要载脂蛋白ａｐｏＡ_１比较牢固地连接在一起并含量甚微的一种酶蛋白,水解梭曼的Ｋｍ值ｐＨ７．２、３７℃时为３．１３ｍｍｏｌ／Ｌ。可被对氯汞苯甲酸、ＨＣｌ－胍和ＥＤＴＡ－Ｎａ_２所抑制,二硫苏糖醇对酶活性有恢复作用。     

黄花棘豆种子有毒成分分析

用荧光法测黄花棘豆种子含晒量为１５．８１μｇ／ｇ,并从中提取一种植物碱,对α－甘露糖苷酶有可逆抑制作用。从黄花棘豆萃取液,硫酸铵４０－８０％饱和度盐析级分中,分离到一溶血活生蛋白质,分子量６００００,ｐＩ为５．３;对家兔和小白鼠２％红细胞悬液半溶血活性Ｈｕ５０为０．２９ｍｇ／ｍｌ和０．５０ｍｇ／ｍｌ,对小白鼠半致死量ＬＤ５０为６２．３５ｍｇ／ｋｇ;对体外培养的肺癌细胞蛋白质合成抑制率达５０％。在硫     

鼠伤寒沙门氏菌外膜蛋白诱导BALB／C小鼠免疫保护的实验研究

本实验采用超声破碎、ＴｒｉｔｏｎＸ－１００处理和超速离心技术提取了鼠伤寒沙门氏菌（Ｓａｌｍｏｎｅｌｌａｔｙｈｉｍｕｒｉｕｍ,ＳＴＭ）的外膜蛋白（Ｏｕｔｅｒｍｅｍｂｒａｎｅｐｒｏｔｅｉｎｓ,ＯＭＰｓ）,其中脂多糖（ＬＰＳ）的含量约为５％。ＯＭＰｓ经ＳＤＳ—ＰＡＧＥ显示１０余条蛋白带。对ＯＭＰｓ诱发ＢＡＬＢ／Ｃ小鼠产生典型的迟发型变态反应（ＤＴＨ）进行了检测。经腹腔免疫的ＢＡＬＢ／Ｃ小鼠用５００ＬＤ５０鼠伤寒沙门氏菌（５０１１５）攻击,１００％可得到保护;用５００ＬＤ５０伤寒杆菌（Ｅ６８６）攻击     

CO2浓度倍增对小麦生育性状和产量构成的影响

ＣＯ_２浓度倍增对小麦生育性状和产量构成的影响王修兰,徐师华,李佑祥（中国农业科学院农业气象研究所,北京,１０００８１）ＴＨＥＥＦＦＥＣＴＳＯＦＣＯ_２ＤＯＵＢＬＩＮＧＯＮＧＲＯＷＩＮＧＡＮＤＤＥＶＥＬＯＰＩＮＧＣＨＡＲＡＣＴＥＲＳＡＮＤＹＩＥＬＤＦＯ...     

具不同淋巴道转移潜能小鼠肝癌瘤株细胞膜鞘糖脂比较研究

采用高效薄层层析（ＨＰＴＬＣ）对两株具有不同淋巴道转移潜能的小鼠腹水型肝癌瘤株细胞膜鞘糖脂组分进行了比较分析．低转移的ＣＬ－Ａ_２瘤株神经节苷脂以ＧＭ_３为主,高转移的ＣＬ－１６Ａ_３瘤株则以ＧＭ_２为主．两细胞株中性鞘糖脂各组分相对百分含量无较显著差异．脂结合唾液酸含量测定表明,ＣＬ－１６Ａ_３瘤株脂结合唾液酸含量约为ＣＬ－Ａ_２瘤株的三倍．提示,具有不同淋巴道转移潜能的瘤细胞,其质膜鞘糖脂的组成也不同．     

乙醛磺酸的制备及对中国仓鼠卵巢细胞生长的影响

以２－溴－１,１－二乙氧基乙烷为起始物,制备了牛磺酸的一种类似物乙醛磺酸（ｓｕｌｆｏｎｉｃａｌｄｅｈｙｄｅ;ＳＡＤ）,并对其部分化学性质、光吸收以及细胞半致死量进行了测定。所合成的ＳＡＤ在２０３ｎｍ和２５０ｎｍ分别有吸收,在ＨＰＬＣ分离谱上为单一的峰。ＳＡＤ在碱性溶液中不稳定,在酸性条件下相对稳定。ＳＡＤ对中国仓鼠卵巢细胞（ＣＨＯ）的生长具有抑制和毒性作用,其半致死量为３００μｍｏｌ／Ｌ。由于ＳＡＤ含有较为活泼的醛基,可与肼及氨基反应,因此,ＳＡＤ的合成为我们制备新的牛磺酸类化合物打下了基础,同时对进一步研究牛磺酸的生理学功能提供了信息     

苦参碱的提取分离及对小鼠的毒性研究

采用酸性乙醇提取、乙醚萃取、硅胶柱层析分离等方法从苦参中分离到苦参碱单体.以小鼠为实验动物进行毒性测定,小鼠的死亡主要集中在48h内,48h后无小鼠的死亡现象.小鼠对苦参碱的耐受量大于30mg.k-g1,小于140mg.k-g1,致死中量LD50为64.01mg.kg-1,回归方程Y=-3.2370+4.5602X,LD50标准误差SE=6.14.适口性的测定表明,苦参碱对小鼠有较好的适口性,可以作为杀鼠剂使用.     

鸡血清与卵黄中抗中华眼镜蛇毒IgY动态变化研究

目的探索特异性IgY的产生和变化规律。方法用眼镜蛇毒原毒免疫产蛋母鸡,ELISA定期检测卵黄中的抗体效价变化,小鼠体外中和实验检测其生物活性。第1次免疫40周后,眼镜蛇毒攻击已免疫母鸡,检测攻击前后鸡血清中抗体效价变化情况,未经眼镜蛇毒免疫的母鸡作阴性对照。结果经免疫后第7天蛋黄中即可检测到抗体,经多次加强免疫,40周时蛋黄中还能保持高效价的抗体,通过分离纯化,此抗体可保护实验小鼠免受4 LD50眼镜蛇毒的攻击;同时,鸡血清中也保留着较高效价的抗体,可中和4 LD50以上的眼镜蛇毒。结论用眼镜蛇毒免疫鸡,经多次加强免疫,卵黄和鸡血清中可持久保持高效价的特异性抗体,初步检测此抗体可中和4 LD50的蛇毒。     

Generalized Linear Mixed Models for Binary Data: Are Matching Results from Penalized Quasi-Likelihood and Numerical Integration Less Biased?

Background

Over time, adaptive Gaussian Hermite quadrature (QUAD) has become the preferred method for estimating generalized linear mixed models with binary outcomes. However, penalized quasi-likelihood (PQL) is still used frequently. In this work, we systematically evaluated whether matching results from PQL and QUAD indicate less bias in estimated regression coefficients and variance parameters via simulation.

Methods

We performed a simulation study in which we varied the size of the data set, probability of the outcome, variance of the random effect, number of clusters and number of subjects per cluster, etc. We estimated bias in the regression coefficients, odds ratios and variance parameters as estimated via PQL and QUAD. We ascertained if similarity of estimated regression coefficients, odds ratios and variance parameters predicted less bias.

Results

Overall, we found that the absolute percent bias of the odds ratio estimated via PQL or QUAD increased as the PQL- and QUAD-estimated odds ratios became more discrepant, though results varied markedly depending on the characteristics of the dataset

Conclusions

Given how markedly results varied depending on data set characteristics, specifying a rule above which indicated biased results proved impossible.This work suggests that comparing results from generalized linear mixed models estimated via PQL and QUAD is a worthwhile exercise for regression coefficients and variance components obtained via QUAD, in situations where PQL is known to give reasonable results.     

Correcting the Bias in Estimation of Genetic Variances Contributed by Individual QTL

In addition to locating chromosomal positions of quantitative trait loci (QTL), estimating the sizes of identified QTL is also an important component in QTL mapping. The size of a QTL is usually measured by the proportion of the phenotypic variance contributed by the QTL. However, the genetic variance may be overestimated in a small line crossing experiment. In this study, we investigate this bias and develop a simple method to correct the bias. The bias correction, however, requires the error of the estimated genetic effect, which is not trivial if the genetic effect is estimated using the Expectation and Maximization (EM) algorithm. Therefore, we also develop a simple method to estimate the standard error of the estimated genetic effect, which is subsequently used to correct the bias in the variance estimate.     

THE IMPORTANCE OF CONSIDERING PREDICTION VARIANCE IN ANALYSES USING PHOTOGRAMMETRIC MASS ESTIMATES

Development and application of photogrammetric mass-estimation techniques in marine mammal studies is becoming increasingly common. When a photogrammetrically estimated mass is used as a covariate in regression modeling, the error associated with estimating mass induces bias in regression statistics and decreases model explanatory power. Thus, it is important to understand and account for prediction variance when addressing ecological questions that require use of estimated mass values. In a simulation study based on data collected from Weddell seals, we developed regression models of pup weaning mass as a function of maternal postparturition mass where maternal mass was directly measured and second where maternal mass was photogrammetrically estimated. We demonstrate that when estimated mass was used, the regression coefficient was biased toward zero and the coefficient of determination was 30% less than the value obtained when using maternal postparturition mass obtained from direct measurement. After applying bias correction procedures, however, the regression coefficient and coefficient of determination were within 2% of their true values. To effectively use photogrammetrically estimated masses, prediction variance should be understood and accounted for in all analyses. The methods presented in this paper are effective and simple techniques to explore and account for prediction variance.     

Accounting for Sampling Error When Inferring Population Synchrony from Time-Series Data: A Bayesian State-Space Modelling Approach with Applications

Background

Data collected to inform time variations in natural population size are tainted by sampling error. Ignoring sampling error in population dynamics models induces bias in parameter estimators, e.g., density-dependence. In particular, when sampling errors are independent among populations, the classical estimator of the synchrony strength (zero-lag correlation) is biased downward. However, this bias is rarely taken into account in synchrony studies although it may lead to overemphasizing the role of intrinsic factors (e.g., dispersal) with respect to extrinsic factors (the Moran effect) in generating population synchrony as well as to underestimating the extinction risk of a metapopulation.

Methodology/Principal findings

The aim of this paper was first to illustrate the extent of the bias that can be encountered in empirical studies when sampling error is neglected. Second, we presented a space-state modelling approach that explicitly accounts for sampling error when quantifying population synchrony. Third, we exemplify our approach with datasets for which sampling variance (i) has been previously estimated, and (ii) has to be jointly estimated with population synchrony. Finally, we compared our results to those of a standard approach neglecting sampling variance. We showed that ignoring sampling variance can mask a synchrony pattern whatever its true value and that the common practice of averaging few replicates of population size estimates poorly performed at decreasing the bias of the classical estimator of the synchrony strength.

Conclusion/Significance

The state-space model used in this study provides a flexible way of accurately quantifying the strength of synchrony patterns from most population size data encountered in field studies, including over-dispersed count data. We provided a user-friendly R-program and a tutorial example to encourage further studies aiming at quantifying the strength of population synchrony to account for uncertainty in population size estimates.     

A multivariate approach to predicting knee extensor performance

The impact of two predictor variables (estimated knee extensor fast-twitch fiber percentage, body mass) on performance measures (vertical jump power output, leg press peak angular velocity) were examined. Subjects (25 men, 27 women) performed 5 workouts involving 2 vertical jump, leg press, and 50-repetition isokinetic tests (to estimate knee extensor fast-twitch fiber percentage). Multivariate regression determined the following significant (p < 0.05) vertical jump equations: predicted male power output = -59.3464 + 1.566 (estimated knee extensor fast-twitch muscle fiber percent) + 15.7884 (body mass), predicted female power output = 36.1574 + 3.4248 (estimated knee extensor fast-twitch muscle fiber percent) + 9.8633 (body mass). Leg press peak angular velocity equations were insignificant by gender; thus, pooled data yielded the following: predicted leg press peak angular velocity = 18.6187 + 0.235 (estimated knee extensor fast-twitch muscle fiber percent) + 0.3801 (body mass). Body mass explained more variance for each performance measure.     

Accuracy and bias of genomic prediction with different de-regression methods

Genomic selection has become increasingly important in the breeding of animals and plants. The response variable is an important factor, influencing the accuracy of genomic selection. The de-regressed proof (DRP) based on traditional estimated breeding value (EBV) is commonly used as response variable. In the current study, simulated data from 16th QTL-MAS Workshop and real data from Chinese Holstein cattle were used to compare accuracy and bias of genomic prediction with two methods of calculating DRP. Our results with simulated data showed that the correlation between genomic EBV and true breeding value achieved using the Jairath method (DRP_J) was superior to that achieved using the Garrick method (DRP_G) for simulated trait 1 but the reverse was true for simulated trait 3, and these two methods performed comparably for simulated trait 2. For all three simulated traits, DRP_J yielded larger bias of genomic prediction. However, DRP_J outperformed DRP_G in both accuracy and unbiasedness for four milk production traits in Chinese Holstein. In the estimation of genomic breeding value using genomic BLUP model, two methods for weighting diagonal elements of incidence matrix associated with residual error were also compared. With increasing the proportion of genetic variance unexplained by markers, the accuracy of genomic prediction was decreased and the bias was increased. Weighting by the reliability of DRP produced accuracy comparable to the evaluation where the proportion of genetic variance unexplained by markers was considered, but with smaller bias in general.     

Is cross-validation valid for small-sample microarray classification?

MOTIVATION: Microarray classification typically possesses two striking attributes: (1) classifier design and error estimation are based on remarkably small samples and (2) cross-validation error estimation is employed in the majority of the papers. Thus, it is necessary to have a quantifiable understanding of the behavior of cross-validation in the context of very small samples. RESULTS: An extensive simulation study has been performed comparing cross-validation, resubstitution and bootstrap estimation for three popular classification rules-linear discriminant analysis, 3-nearest-neighbor and decision trees (CART)-using both synthetic and real breast-cancer patient data. Comparison is via the distribution of differences between the estimated and true errors. Various statistics for the deviation distribution have been computed: mean (for estimator bias), variance (for estimator precision), root-mean square error (for composition of bias and variance) and quartile ranges, including outlier behavior. In general, while cross-validation error estimation is much less biased than resubstitution, it displays excessive variance, which makes individual estimates unreliable for small samples. Bootstrap methods provide improved performance relative to variance, but at a high computational cost and often with increased bias (albeit, much less than with resubstitution).     

Estimation of heritability from varietal trials data

We present the estimation of heritabilities of an observed trait in situations where evaluation of several pure breeding lines is performed in a trial at a single location and in trials from several locations. For the single location situation, we evaluate exact confidence intervals, the probability of invalid estimates, and the percentage points of the distribution of heritability. Simulations were performed to numerically verify the results. Additionally, approximations to the bias and standard error of the estimate were obtained and are presented along with their simulated values and coefficients of skewness and kurtosis. For trials in several locations, explicit expressions for exact values of confidence limits are not available. Further, one would require knowledge of one more parameter, represented by the ratio of genotype x environment (G x E) interaction variance to error variance, in addition to the number of genotypes, replication and true heritability value. Approximations were made for bias and the standard error of estimates of heritability. The evaluation of the distribution of heritability and its moments was recognized as a problem of the linear function of an independent chi-square. The methods have been illustrated by data from experiments on grain and straw yield of 64 barley genotypes evaluated at three locations.