紫外吸收光谱加权相关分析法对若干中成药的鉴别

目的：评估加权相关分析法（以相关分析为基础，在UV吸收光谱的峰和谷附近设置更大的权重）对中成药的鉴别价值。方法：使用紫外分光光度计扫描不同厂家的复方丹参片、滴丸，以及其他不同品种中成药的UV吸收光谱（水为溶剂）。利用加权相关分析对以上光谱数据进行分析，得到各产品之间的Pearson’s R和AR（Pearson’s R与其镜像位置值的绝对差值），并以此进行各产品的光谱相似性比较。结果：相同厂家不同批号产品之间的UV吸收光谱的Pearsan’s R=0．995—0．9999，△R=0～0．00006，光谱相似度极高，而不同厂家产品之间的Pearson’s R=0．92—0．997，△R=0．00020—0．02，光谱相似度高；与复方丹参片（滴丸）的UV吸收光谱相比，其他中成药光谱的pearsan’s R=0．25～0．98，通常伴有△R〉0．02，光谱相似度相对较低。结论：加权相关分析可以准确地测量UV吸收光谱的相似度，可将其用于中成药的鉴别和质量控制。

Evolution of COP9 Signalosome and Proteasome Lid Complex

The COP9 signalosome and the regulatory lid of the 26S proteasome are both eight-subunit protein complexes which are present in most eukaryotes. There is a one-to-one relationship between the corresponding subunits of the two protein complexes in terms of their size and amino acid sequences. Eight groups of subunits from the COP9 signalosome and the proteasome lid complex of different organisms are collected from all the databases at the NCBI website. The corresponding subunits of COP9 signalosome and proteasome lid complex share at least 12% amino acid identity and some conserved regions, and the conserved sites spread evenly over the entire length of the subunits, suggesting that the two complexes have a common evolutionary ancestor. Phylogenetic analyses based on the amino acid sequences of the corresponding subunits of two protein complexes indicate that every tree consists of two clades. The subunits from one of the two protein complexes of different organisms are grouped into one of the two clades respectively. The sequences of single-cell organisms are always the basal groups to that of multi-cell animal and plant species. These results imply that the duplication/divergence events of COP9 signalosome and regulatory lid of the proteasome genes have occurred before the divergence of single-cell and multi-cell eukaryotes, and the genes of the two complexes are independently evolved. The analyses of dN/dS correlation show significant Pearson's correlations between 21 and 15 pairs of subunit-encoding sequences within the COP9 signalosome and the proteasome lid complex respectively, suggesting that those subunits pairs might have related functions and interacted with one another, and resulted in co-evolution.

相关性和一致性:这对相仿概念和测量方法的回顾与阐明（英文）

一致性(agreement)和相关性(correlation)是两个广泛使用的概念,用来评估变量之间的关联。虽然二者相似且相关,但是它们代表关联完全不同的概念。评估变量之间的一致性假设变量测量的是相同的结构,而在变量测量完全不同的结构时也可以评估它们之间的相关性。这种概念上的差异就要求使用不同的统计方法,并且当评估一致性或相关性时,统计方法根据数据的分布和研究者的兴趣可能会有所不同。例如,Pearson相关性,作为评估连续变量之间相关性的一种普遍测量方法,只有用于符合线性关系的变量时才能提供有用的信息;当用于不符合线性关系的变量时就无法提供准确信息甚至会产生误导。同样地,内部相关性,作为一种评估连续变量之间一致性的常用方法,如果一致性不好的实质正好是研究兴趣所在,那么该测量就不能为研究者提供充分的信息。本报告回顾了一致性和相关性的概念,并讨论了几种常用方法在应用中的差异。

Temporal patterns of dengue epidemics:The case of recent outbreaks in Kaohsiung

Objective:To investigate whether major dengue outbreaks in the last two decades in Kaohsiung follow a precise temporal pattern.Methods:Government daily lab-confirmed dengue case data from three major dengue outbreaks occurring during the last two decades in Kaohsiung in2002,2014 and 2015,is utilized to compute the corresponding weekly cumulative percentage of total case numbers.We divide each of the three time series data into two periods to examine the corresponding weekly cumulative percentages of case numbers for each period.Pearson’s correlation coefficient was calculated to compare quantitatively the similarity between the temporal patterns of these three years.Results:Three cutoff points produce the most interesting comparisons and the most different outcomes.Pearson’s correlation coefficient indicates quantitative discrepancies in the similarity between temporal patterns of the three years when using different cutoff points.Conclusions:Temporal patterns in 2002 and 2014 are comparatively more similar in early stage.The 2015 outbreak started late in the year,but ended more like the outbreak in 2014,both with record-breaking number of cases.The retrospective analysis shows that the temporal dynamics of dengue outbreaks in Kaohsiung can strongly vary from one year to another,making it difficult to identify any common predictor.

Independence of functional connectivity analysis in fMRI research does not rely on whether seeds are exogenous or endogenous

Defining seeds is the first step in assessing functional connectivity between separate brain regions.Circular analysis is often a serious concern when defining ROIs.It was considered as circular analysis that seeds were localized on the basis of activations in fMRI data and the functional connectivity among these seeds was measured by using the same data.Such circularity was reported to contaminate results because the margin of detected activations can be affected by noise in the data.Therefore,using independent data for seed selection was advocated.For example,suppose that datasets A and B are independent.Activations are first detected in dataset A through statistical mapping,and then these activations are used to localize seeds for assessing functional connectivity in dataset B.However,localizing seeds only on the basis of an independent source without referring to the intrinsic features of the fMRI data to be analyzed may be inappropriate for assessing functional connectivity.This study demonstrates how different spatial locations and seed scopes influence the results of functional connectivity in task-state fMRI data when these choices meet independence requirements.The independence of analysis does not rely on whether seeds are exogenous or endogenous for the data to be analyzed.Activation and connectivity analyses have inherently independent natures.