A non-sequential method for constructing trees and hierarchical classifications

Summary A procedure is presented that forms an unrooted tree-like structure from a matrix of pairwise differences. The tree is not formed a portion at a time, as methods now in use generally do, but is formed en toto without intervening estimates of branch lengths. The method is based on a relaxed additivity (four-point metric) constraint. From the tree, a classification may be formed.     

Finding edging genes from microarray data

MOTIVATION: A set of genes and their gene expression levels are used to classify disease and normal tissues. Due to the massive number of genes in microarray, there are a large number of edges to divide different classes of genes in microarray space. The edging genes (EGs) can be co-regulated genes, they can also be on the same pathway or deregulated by the same non-coding genes, such as siRNA or miRNA. Every gene in EGs is vital for identifying a tissue's class. The changing in one EG's gene expression may cause a tissue alteration from normal to disease and vice versa. Finding EGs is of biological importance. In this work, we propose an algorithm to effectively find these EGs. RESULT: We tested our algorithm with five microarray datasets. The results are compared with the border-based algorithm which was used to find gene groups and subsequently divide different classes of tissues. Our algorithm finds a significantly larger amount of EGs than does the border-based algorithm. As our algorithm prunes irrelevant patterns at earlier stages, time and space complexities are much less prevalent than in the border-based algorithm. AVAILABILITY: The algorithm proposed is implemented in C++ on Linux platform. The EGs in five microarray datasets are calculated. The preprocessed datasets and the discovered EGs are available at http://www3.it.deakin.edu.au/~phoebe/microarray.html.     

研究报告: 第五版《世界卫生组织中枢神经系统肿瘤分类指南》下成人胶质瘤诊断及预后

目的 脑胶质瘤是最常见的恶性原发性中枢神经系统肿瘤,近年来分子病理的快速发展对胶质瘤诊断及分级带来了重要影响,在2021年发布的《世界卫生组织中枢神经系统肿瘤分类指南》（第五版）引入了更多分子指标对肿瘤的诊断和分级进行指导。本研究旨在临床队列中比较最新版指南和上一版指南对肿瘤诊断及预后评估的影响,以期为临床实践活动中新版指南的应用提供数据参考和依据。方法 回顾性纳入了癌症基因组图谱数据库512例胶质瘤样本,分别依据2016版和2021版《世界卫生组织中枢神经系统肿瘤分类指南》进行诊断、通过Kaplan-Meier进行生存曲线绘制和中位总生存期计算和生存差异分析。结果 对512例样本分别完成了上一版指南和最新版指南的诊断及分级。在新版指南下分别有53和72例异柠檬酸脱氢酶（IDH）突变型和IDH野生型的胶质瘤诊断级别升级为了4级,且这些诊断级别升高的胶质瘤的预后更差。结论 最新版指南较上一版指南可对胶质瘤进行更为精准地分类及分级,在有条件的情况下应尽快依据最新版指南开展诊断及分级。     

Do mental health professionals use diagnostic classifications the way we think they do? A global survey

We report on a global survey of diagnosing mental health professionals, primarily psychiatrists, conducted as a part of the development of the ICD‐11 mental and behavioural disorders classification. The survey assessed these professionals' use of various components of the ICD‐10 and the DSM, their attitudes concerning the utility of these systems, and usage of “residual” (i.e., “other” or “unspecified”) categories. In previous surveys, most mental health professionals reported they often use a formal classification system in everyday clinical work, but very little is known about precisely how they are using those systems. For example, it has been suggested that most clinicians employ only the diagnostic labels or codes from the ICD‐10 in order to meet administrative requirements. The present survey was conducted with clinicians who were members of the Global Clinical Practice Network (GCPN), established by the World Health Organization as a tool for global participation in ICD‐11 field studies. A total of 1,764 GCPN members from 92 countries completed the survey, with 1,335 answering the questions with reference to the ICD‐10 and 429 to the DSM (DSM‐IV, DSM‐IV‐TR or DSM‐5). The most frequent reported use of the classification systems was for administrative or billing purposes, with 68.1% reporting often or routinely using them for that purpose. A bit more than half (57.4%) of respondents reported often or routinely going through diagnostic guidelines or criteria systematically to determine whether they apply to individual patients. Although ICD‐10 users were more likely than DSM‐5 users to utilize the classification for administrative purposes, other differences were either slight or not significant. Both classifications were rated to be most useful for assigning a diagnosis, communicating with other health care professionals and teaching, and least useful for treatment selection and determining prognosis. ICD‐10 was rated more useful than DSM‐5 for administrative purposes. A majority of clinicians reported using “residual” categories at least sometimes, with around 12% of ICD‐10 users and 19% of DSM users employing them often or routinely, most commonly for clinical presentations that do not conform to a specific diagnostic category or when there is insufficient information to make a more specific diagnosis. These results provide the most comprehensive available information about the use of diagnostic classifications of mental disorders in ordinary clinical practice.     

藏猴（Macaca thibetana）皮纹的研究

四川省峨眉山的18只(♀:10;♂:8)及凉山雷波县的19只(♀:7;♂:12)藏猴手足面上皮纹显示,该种的皮纹与其他猕猴属动物基本相同:其指趾端上的花纹几乎全是原始花纹;掌面上,大小鱼际主要为箕形和开放形花纹,指间Ⅰ—Ⅳ区主要为斗形纹;跖面上,大小鱼际及趾间Ⅰ—Ⅱ区则主要是箕形纹,趾间Ⅲ—Ⅳ区主要是斗形纹。左右端指间Ⅱ—Ⅳ区及趾间Ⅲ—Ⅳ区花纹分布有一定差异。性别之间在指间Ⅰ区有显著差异。两地猴则在掌面大鱼际、指及趾间Ⅰ区具(极)显著差异,可能与地理和社会隔离相关,趾间Ⅳ区的特征性Da花纹及弓形纹缺失似可作为藏猴皮纹的一个特征。     

An improved alignment-free model for dna sequence similarity metric

Background

DNA Clustering is an important technology to automatically find the inherent relationships on a large scale of DNA sequences. But the DNA clustering quality can still be improved greatly. The DNA sequences similarity metric is one of the key points of clustering. The alignment-free methodology is a very popular way to calculate DNA sequence similarity. It normally converts a sequence into a feature space based on words’ probability distribution rather than directly matches strings. Existing alignment-free models, e.g. k-tuple, merely employ word frequency information and ignore many types of useful information contained in the DNA sequence, such as classifications of nucleotide bases, position and the like. It is believed that the better data mining results can be achieved with compounded information. Therefore, we present a new alignment-free model that employs compounded information to improve the DNA clustering quality.

Results

This paper proposes a Category-Position-Frequency (CPF) model, which utilizes the word frequency, position and classification information of nucleotide bases from DNA sequences. The CPF model converts a DNA sequence into three sequences according to the categories of nucleotide bases, and then yields a 12-dimension feature vector. The feature values are computed by an entropy based model that takes both local word frequency and position information into account. We conduct DNA clustering experiments on several datasets and compare with some mainstream alignment-free models for evaluation, including k-tuple, DMk, TSM, AMI and CV. The experiments show that CPF model is superior to other models in terms of the clustering results and optimal settings.

Conclusions

The following conclusions can be drawn from the experiments. (1) The hybrid information model is better than the model based on word frequency only. (2) For DNA sequences no more than 5000 characters, the preferred size of sliding windows for CPF is two which provides a great advantage to promote system performance. (3) The CPF model is able to obtain an efficient stable performance and broad generalization.