Bayesian consensus clustering for multivariate longitudinal data

In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.     

Statistical analysis of gait patterns of persons with cerebral palsy

Patients at Boston's Children's Hospital diagnosed as having cerebral palsy were filmed walking. These films were digitized and translated into measurements associated with leg motion. In this paper we use the gait measurements of 128 such patients to illustrate that the kth nearest neighbour clustering procedure results in a gait typology for patients with cerebral palsy. The procedure identifies four subpopulations from the sample data; the membership of a patient within this typology is mostly determined by the patient's motor control. The developed typology differs from the present diagnostic system which classifies a cerebral palsy patient as either quadriplegic, diaplegic or hemiplegic.     

Clustering of autoimmune disease in parents of siblings from the Type 1 diabetes Warren repository.

AIMS: Autoimmune disorders co-exist in the same individuals and in families, implying a shared aetiology. The aim of this study was to compare the prevalence of the common autoimmune diseases in the parents of siblings from the Type 1 diabetes Warren repository with the general population. METHODS: Between 1989 and 1996, 505 British families with at least two siblings affected by Type 1 diabetes were recruited. Clinical information was collected regarding the presence of autoimmune disease in the parents and the prevalence of disease in the parents was compared with that expected in the general population. RESULTS: The prevalence of autoimmune disease in the parents was significantly higher in the repository compared with that expected in the general population [P-value = 1.98 x 10(-5) (female), P-value = 1.1 x 10(-8) (male)]. Type 1 diabetes was recorded in 63/1010 (6.2%) parents with a marked paternal preponderance (9.5 vs. 3%P = 0.002). Other autoimmune diseases affected 27% of parents with diabetes and 13.2% of parents without diabetes (P < 0.01). CONCLUSION: These data confirm the importance of family history as a significant risk factor for the development of Type 1 diabetes and support the hypothesis that the common autoimmune diseases share at least some aetiological mechanisms.     

微乳对难溶性药物增溶机理的研究

目的:初步探讨微乳对难溶性药物的增溶机理.方法:经处方筛选及相图研究确定(BL-9/正丁醇3∶1)/油/水比例为9∶1∶40的微乳处方;以二氢吡啶类药物为模型药物,测定其在微乳、胶束和油相中的溶解度及油/水分配系数;根据化学结构,采用计算机软件计算药物的特性参数,并结合模型药物的特性分析微乳增溶难溶性药物的机理.结果:微乳对药物的增溶效果远大于油相和胶束,并随药物的脂溶性和空间结构的改变而变化.结论:微乳可用于增溶难溶性药物,且脂溶性越大,空间位阻越小,增溶效果越明显.     

基于实验均方误差F值的基因表达数据最佳聚类评判方法研究

目的 近年来产生了一些用于分析基因表达数据的聚类算法，却很少有关于评价聚类算法方法的研究。本研究的目的是尝试建立一个定量的评价基因表达数据聚类结果的方法。方法 本研究提供了一个系统的评价聚类结果的方法，利用我们提出的实验均方误差F值对几个常见的聚类算法进行比较。结果 利用F值对类质量的评价和利用已有的生物学知识对类进行分析的结果一致。结论 实验均方误差F值可以定量地评判用于基因表达数据的聚类算法。     

Determination of needed radiographic room capacity to serve an emergency department

This study describes a method for determining the number of radiographic rooms devoted to emergency radiology that would be required to keep mean patient waiting time at a desired level. A desired mean waiting time for patients must be determined. In our setting, a mean waiting time of 8 minutes resulted in few complaints. The waiting time then sets the required utilization rate of available capacity. Daily and hourly volume and variability in volume of examinations were measured over a 3-month period. This represents the demand. The needed number of rooms is determined by comparing demand with effective available capacity for different numbers of rooms. To maintain an 8-minute mean waiting time, 50% utilization of capacity is required. Mean demand on Sundays is 176 examinations. Five rooms are required, since this gives a 180-examination effective capacity. Using waiting time as the primary decision criterion for making capacity decisions in emergency radiology has several advantages: the method is easy to use, volume variability is taken into account, and the focus is on service to patients.     

Unsupervised fuzzy clustering analysis supports behavioral cutoff criteria in an animal model of posttraumatic stress disorder.

BACKGROUND: Unsupervised fuzzy clustering (UFC) analysis is a mathematical technique that groups together objects in the multidimensional feature space according to a specified similarity measurement, thereby yielding clusters of similar data points that can be represented by a set of prototypes or centroids. METHODS: Since clinical studies of mental disorders distinguish between affected and unaffected individuals, we designed an inclusion/exclusion criteria (cutoff behavioral criteria [CBC]) approach for animal behavioral studies. The effect of classifying the study population into clearly affected versus clearly unaffected individuals according to behaviors on two behavioral paradigms was statistically significant. RESULTS: Here the raw data from previous studies were subjected to UFC algorithms as a means of objectively testing the validity of the concept of the CBC for our experimental model. The first UFC algorithm yielded two clearly discrete clusters, found to consist almost exclusively of the exposed animals in the one and unexposed animals in the other. The second algorithm yielded three clusters corresponding to animals designated as clearly affected, partially affected, and clearly unaffected. The algorithm for physiological data in addition to behavioral data failed to elicit discrete clusters. CONCLUSIONS: The UFC analysis yielded data that support the conceptual contention of the CBC and lends additional validity to our previous behavioral studies.     

腮腺间隙良性肿瘤的MRI诊断

目的探讨腮腺间隙常见良性肿瘤的影像特征及其诊断与鉴别诊断。方法良性肿瘤20例，包括多形性腺瘤13例、乳头状淋巴囊腺瘤3例、淋巴管瘤2例、血管瘤1例、脂肪瘤1例。经手术病理证实15例，穿刺活检证实4例，典型MR特征结合病史确诊1例。结果13例多形性腺瘤中有11例位于腮腺浅叶，2例位于深叶；9例表现为均匀等T1长T2信号．4例在T2WI上信号明显不均匀；静脉注射Gd—UIPA后3例瘤实质均匀强化，4例增强后明显不均匀强化。3例Warthin瘤中2例发生于一侧，1例累及双侧腮腺，在T1WI上呈低信号，T2WI上信号等于或低于正常腺实质，增强后无明显强化。2例淋巴管瘤呈明显长T1长T2信号，并有多房现象。1例血管瘤信号不均质，呈明显长T1长T2信号，静1脉注射Gd—DTPA后病变往往显著异常强化。1例脂肪瘤在T1WI及T2WI上均表现为明显高信号强度。结论运用MR成像研究腮腺间隙的良性肿瘤，有利于疾病的定位，有助于病变的诊断与鉴别诊断。     

Simulation of space adaptation syndrome on earth

Summary The three Spacelab D-1 Scientist Astronauts were exposed to a 11/2 h 3 g centrifuge run in the supine position, resulting in a linear 3 g acceleration in the x-direction. They used their space experience to evaluate their readapting to normal gravity and compared their observations with Space Adaptation Syndrome. After the centrifuge runs, the vestibular visual system appeared to be modified in a very specific and reproducible manner. Readapting to the normal 1 g environment took at least 6 h. During this period there was a striking similarity to the astronaut's experience during adaptation to weightlessness in space. A series of vestibular tests were then performed, confirming these subjective findings.     

Distribution of the largest neuron in mouse caudate-putamen nucleus: Its position in large-cell — Medium-cell clusters

Summary The position of the largest striatal neuron within territories delimited by medium-sized clustered neurons was charted in Nissl-stained sections through the mouse caudate-putamen nucleus. Medium-sized neuron somata occur in close proximity to this large cell at some point in the anteroposterior, mediolateral or dorsoventral extent of its soma. The size of the network of medium-sized neurons associated with the large cell may vary from two to 15 neurons. Even when this network is extensive, the large neuron is never completely surrounded. Most often, this cell also borders a fascicle of internal capsule fibers, and the entire cellular island may be aligned either parallel to or perpendicular to the orientation of these fibers. These findings suggest the hypothesis that cellular territories in the caudate-putamen nucleus have a very specific orientation in three dimensional space.