作用于钾离子通道蝎毒素的结构特征及活性表面研究进展

对作用于钾离子通道的蝎毒素的空间结构特点进行了简要归纳,发现高含量碱性残基在不同结构单元广泛分布而少量酸性残基特征性分布等新特点。蝎毒素活性表面研究进展表明,利用空间结构的分子模建结合残基突变是确定活性表面的有效方法。基于少量酸性残基特征分布与活性表面取向的相关性,提出酸性残基为活性调节残基的新观点和简单的“拇指”规则预测钾毒素活性表面的方法,从而可望加速蝎毒素的结构与功能关系研究。     

论生态安全的基本概念和研究内容

安全是风险的反函数,通常指评价对象对于期望值状态的保障程度,或防止非理想的不确定性事件发生的可靠性。生态安全可定义为人类在生产、生活与健康等方面不受生态破坏与环境污染等影响的保障程度,包括饮用水与食物安全、空气质量与绿色环境等基本要素。生态安全研究的主要内容包括生态系统健康诊断、区域生态风险分析、景观安全格局、生态安全监测与预警以及生态安全管理、保障等方面。区域生态安全研究具有宏观性和针对性的特点,评价标准则具有相对性和发展性,生态安全预警与设计要针对某一生态问题体现人类活动的能动性。最后,以干旱内流区为例,对绿洲景观的生态安全分析和内陆河流域的生态安全保障措施进行了讨论。     

从非同源蛋白质的一级序列预测其结构类

对基于氨基酸组成、自相关函数和自协方差函数提取特征的蛋白质结构类预测算法进行分析比较,对氨基酸组成和自相关函数相结合的方法,以及氨基酸组成和自协放差函数相结合的方法的预测算法进行了研究。结果表明：对非同源蛋白质,因氨基酸和自相关函数相结合的方法中,采用Miyazawa和Jernigan的疏水值时,训练的自检验的总精度为95．34％,其Jackknife检验的总精度为81．92％,检验加的他检验的总精工为86．61％。在氨基酸组成和自协方差函数相结合的方法中,采用Wold等的疏水值时,训练库的自检验的总精度为96．71％,其Jackknife检验的总精度为82．18％,检验加的他检验的总精工为86．88％。这说明氨基酸组成和自相关函数相结合的方法,以及氨基酸组成和自协方差函数相结合的方法可有效提高结构类预测精度,表明提取更多有效的序列信息是提高分类精度的关键。     

A predictive model relating North American plant formations and climate

A discriminant model was produced that predicts North American plant formations with basic climatic variables (monthly mean temperatures, monthly precipitation, and latitude). The model is based on data from 176 weather stations. Climatic variables from 30 additional randomly-selected weather stations were used to test the model. The predicted formation and actual formation at each site were compared; four sites were classified into the wrong formations (87% accuracy). This predictive model indicates a strong correlation between climate and formations in North America. Vegetation-climate models produced by canonical discriminant analysis may be useful in detecting geographical localities where non-climatic factors are particularly influential.     

Bayesian Inference for Smoking Cessation with a Latent Cure State

Summary .  We present a Bayesian approach to modeling dynamic smoking addiction behavior processes when cure is not directly observed due to censoring. Subject-specific probabilities model the stochastic transitions among three behavioral states: smoking, transient quitting, and permanent quitting (absorbent state). A multivariate normal distribution for random effects is used to account for the potential correlation among the subject-specific transition probabilities. Inference is conducted using a Bayesian framework via Markov chain Monte Carlo simulation. This framework provides various measures of subject-specific predictions, which are useful for policy-making, intervention development, and evaluation. Simulations are used to validate our Bayesian methodology and assess its frequentist properties. Our methods are motivated by, and applied to, the Alpha-Tocopherol, Beta-Carotene Lung Cancer Prevention study, a large (29,133 individuals) longitudinal cohort study of smokers from Finland.     

ARIMA模型在杭州市中小学生咳嗽症状监测中的应用

目的：探讨时间序列ARIMA模型在时间序列资料分析中的应用,建立咳嗽症状监测数据的预测模型.方法：采用条件最小二乘方法估计模型参数.通过对数转换及差分方法使原始序列平稳,按照残差不相关原则、简洁原则确定模型结构,依据AIC和SBC准则确定模型阶数,最终建立起ARIMA预测模型.结果：ARIMA（1,1,1）模型拟合效果较好,方差估计值为0.7361,AIC=95.6092,SBC=98.8310,对模型进行白噪声残差检验,提示残差为白噪声.结论：症状监测这种具有时间序列特点的资料可以用ARIMA模型来进行拟合估计.本文中预测结果可信区间比较宽,可能是因为时间序列比较短,还未能考虑到季节趋势.另外,所用监测数据是在中小学生在校发生症状的人数,故在节假日会出现缺失值,样本量和时间长度均有限,可能影响模型估计的准确性,本研究的结论还有待于将来资料积累后进行修正和深化.