CpGIF: an algorithm for the identification of CpG islands

CpG islands (CGIs) play a fundamental role in genome analysis and annotation, and contribute to improving the accuracy of promoter prediction. Besides, CGIs in promoter regions are abnormally methylated in cancer cells and thus can be used as tumor markers. However, current methods for identifying CGIs suffer from various drawbacks. We present a new algorithm for detecting CGIs, called CpG Island Finder (CpGIF), which combines the best features in the most commonly used algorithms and avoids their disadvantages as much as possible. Five public tools for CpG island searching are used to compare with CpGIF for the assessment of accuracy and computational efficiency. The results reveal that CpGIF has higher performance coefficient and correlation coefficient than these previous methods, which indicates that CpGIF is able to provide high sensitivity and specificity at the same time. CpGIF is also faster than those methods with comparable prediction accuracy.     

中国小麦白粉病发生地域分布的气候分区

根据全国１２０个气象站点小麦拔节一成熟期间的多年平均（１９７１—１９９８）旬≥０．１ｍｍ雨日出现机率、旬降雨量、旬平均相对湿度、旬平均温度、旬日照时效和白杨病病害资料,采用系统聚类分析法将中国小麦白杨病发生地域分布划分为发生气候带（１）和不发生气候带（ｌ）。Ｉ又分为常发气候亚带（１Ａ）、易发气候亚带（１Ｂ）、次易发气候亚带（１Ｃ）、偶发气候亚带（１Ｄ）。ＩＡ分为冬小麦白杨病西南常发气候区（１Ａ１）、冬小麦白杨病长江流域常发气候区（１Ａ２）、冬小麦白杨病淮河流域常发气候区（１Ａ３）共３个区５ＩＢ为冬小麦白杨病黄河流域易发气候区（１Ｂ区）１个区５ＩＣ分为南方冬小麦白杨病次易发气候区（１Ｃ１）、北方春小麦白杨病次易发气候区（１Ｃ２）、北疆春小麦白杨病次易发气候区（１Ｃ３）、西藏南部冬春小麦白杨病次易发气候区（１Ｃ４）共４个区５ＩＤ分为南方冬小麦白杨病偶发气候区（１Ｄ１）、北方春小麦白杨病偶发气候区（１Ｄ２）共２个区。评述了不同气候区小麦白杨病发生流行的气候生态特征。该分区结果客观揭示了小麦白杨病的发生地域分布规律,是针对不同气候条件下进行小麦白杨病分区预测预报和综合防治的基础性工作。     

鄂西青少年体质发育初步研究

目前,研究青少年体质发育的方法较多,作者试图用逐步回归分析法对此作些研究。 按照我国正常儿童青少年体格发育调查研究实施方案(儿童体格发育调查研究工作学习班,1976)和活体测量方法(黄新美,1983)的有关规定,对鄂西7—17岁的1533名青少年(男863人,女670人)进行了直接测量:1.身高;2.体重;3.上肢长;4.下肢长(由地面至大转子尖的距离)(靳仕信,1981);5.上半身长(身高—下肢长);6.上半身长与下肢长的比值。以上各项均值和标准差见表1.另外,还计算了下肢长指数（下肢长/身高×100）和上半身长指数（上半身长/身高×100）。将以上数据按性别、年龄逐个输入AppleII电子计算机（Basic语言）,对数据进行分析处理,用回归分析法（上海第二医学院数学教研室,1979;杨树勤,1985）分析青少年体重的变化与年龄、身高等七个因素的相关关系（见表2）,并对已进入方程式中的各项指标进行反复检查,最后,建立了推算青少年体重变化与年龄、身高的多元回归方程。     

实施当日出院制度对平均住院日的影响及意义

目的 探讨实施当日出院制度对缩短平均住院日指标的效果,分析其作用和意义。方法 收集医院2013年度医疗数据,运用描述性统计学方法和线性回归当日出院和平均住院日的相关性进行分析。结果 2012年11月—2013年10月,全院平均住院日呈逐月下降趋势;同时住院患者当日出院比例增长明显。平均住院日和当日出院比例散点图显示相关性,线性回归分析具有统计学意义,两者呈高度负相关（r＝-0.739,P＝0.006）。结论 住院患者当日出院比例对平均住院日有显著影响。为进一步缩短平均住院日,相关职能部门可以采取优化流程,加快出院结算,放开住院病患当日出院服务的政策措施。     

Unsupervised clustering of fMRI and MRI time series

Unsupervised clustering represents a powerful technique for self-organized segmentation of biomedical image time series data describing groups of pixels exhibiting similar properties of local signal dynamics. The theoretical background is presented in the beginning, followed by several medical applications demonstrating the flexibility and conceptual power of these techniques. These applications range from functional MRI data analysis to dynamic contrast-enhanced perfusion MRI and breast MRI. For fMRI, these methods can be employed to identify and separate time courses of interest, along with their associated spatial patterns. When applied to dynamic perfusion MRI, they identify groups of voxels associated with time courses that are clinically informative and straightforward to interpret. In breast MRI, a segmentation of the lesion is achieved and in addition a subclassification is obtained within the lesion with regard to regions characterized by different MRI signal time courses. In the present paper, we conclude that unsupervised clustering techniques provide a robust method for blind analysis of time series image data in the important and current field of functional and dynamic MRI.     

Effective feature selection framework for cluster analysis of microarray data

The microarray technique has become a standard means in simultaneously examining expression of all genes measured in different circumstances. As microarray data are typically characterized by high dimensional features with a small number of samples, feature selection needs to be incorporated to identify a subset of genes that are meaningful for biological interpretation and accountable for the sample variation. In this article, we present a simple, yet effective feature selection framework suitable for two-dimensional microarray data. Our correlation-based, nonparametric approach allows compact representation of class-specific properties with a small number of genes. We evaluated our method using publicly available experimental data and obtained favorable results.