Syndromic Surveillance Using Veterinary Laboratory Data: Algorithm Combination and Customization of Alerts

Background

Syndromic surveillance research has focused on two main themes: the search for data sources that can provide early disease detection; and the development of efficient algorithms that can detect potential outbreak signals.

Methods

This work combines three algorithms that have demonstrated solid performance in detecting simulated outbreak signals of varying shapes in time series of laboratory submissions counts. These are: the Shewhart control charts designed to detect sudden spikes in counts; the EWMA control charts developed to detect slow increasing outbreaks; and the Holt-Winters exponential smoothing, which can explicitly account for temporal effects in the data stream monitored. A scoring system to detect and report alarms using these algorithms in a complementary way is proposed.

Results

The use of multiple algorithms in parallel resulted in increased system sensitivity. Specificity was decreased in simulated data, but the number of false alarms per year when the approach was applied to real data was considered manageable (between 1 and 3 per year for each of ten syndromic groups monitored). The automated implementation of this approach, including a method for on-line filtering of potential outbreak signals is described.

Conclusion

The developed system provides high sensitivity for detection of potential outbreak signals while also providing robustness and flexibility in establishing what signals constitute an alarm. This flexibility allows an analyst to customize the system for different syndromes.     

Laboratory Test of an Automated Cell Analysis System For Cervical Screening

An automated cell analysis system (Autoplan-MIAC) for the early detection of precancerous lesions of the cervix was tested under semi-routine conditions in a clinical cytology laboratory. A set of 1500 specimens, highly enriched with abnormal cases, was analysed. Cervical scrapings were collected in suspension and processed by cytocentrifugation for microscopy. Two slides were prepared from each sample: one for staining according to Papanicolaou for the visual reference diagnosis and one for Feulgen staining for automated analysis. the specimens were evaluated in two ways: the first one, which is referred to as the automated machine classification system (AMC), classifies the specimens according to the number and ratio of selected objects (alarms) and is a fully automated system. the second system classifies the specimens after visual evaluation of the stored alarms as they are displayed on a TV monitor, and is designated the interactive machine classification system (IMC). the AMC results showed a false positive rate of 16.5% when the cut-off threshold was selected so that all 117 positively diagnosed specimens were classified ‘positive’ by the system. In that case 87.4% of the CINI and 96.9% of the CINII cases were AMC-positive. the IMC results showed a false positive rate of 2.5%, when 86.3% of the CIN I cases, 96.9% of the CIN II cases and all CIN III and invasive carcinoma cases were positively classified.     

热分析方法及裂解气相色谱在树木分类上的试探性研究

热分析是在程序控制温度下自动连续跟踪物质的物理性质与温度关系的一类技术。尤其是差热分析和热重法与其他手段联合,可用于化合物稳定性、分解机理和相转变等方面的研究。由于种子是一个复杂化合物的有机体,我们期望发现各不同种子化合物之间在成份上和组成方式上的共性和差异性,从热化学的角度对木本植物进行热化学分类。我们选用针叶类两个科(松科、杉木科)的种子进行了DTA、TG、550℃裂解气相色谱分析;300℃逸出成份分析,发现松科种子DTA图上在240±5℃有一个共同的吸热峰,而杉木则出现在320±5 ℃。TG曲线同科的不同种之间非常相似,逸出成份分析和裂解气相色谱分析同科的不同种之间也具共同特征,且又显示出一定的差异性。这种分类方法是可靠的,且可为树木(或植物)分类学提供更有价值的信息。     

用于变应原疫苗分析和质控的实验技术

建立和应用一种可靠的质量分析方法是所有生物制品质量和工艺一致性的保障。一种准确反应药物中特异组成成分的活性和结构,并且优化和验证了的方法,是所有生物医药领域研发和新药上市的一个核心目标。变应原疫苗是天然物质的混合物,每一种变应原疫苗含有多种活性成分和与变应原无关未知的非活性成分,其中,具有变应原活性的物质只占一小部分,而且主要变应原成分作用在患者与患者之间差异性很大。本文介绍了一种变应原疫苗分析和质控的方法,包括变应原疫苗总蛋白含量、总蛋白组分、主要致敏蛋白组成、主要致敏蛋白含量、总生物活性几方面。     

The Use of Global Rating Scales for OSCEs in Veterinary Medicine

OSCEs (Objective Structured Clinical Examinations) are widely used in health professions to assess clinical skills competence. Raters use standardized binary checklists (CL) or multi-dimensional global rating scales (GRS) to score candidates performing specific tasks. This study assessed the reliability of CL and GRS scores in the assessment of veterinary students, and is the first study to demonstrate the reliability of GRS within veterinary medical education. Twelve raters from two different schools (6 from University of Calgary [UCVM] and 6 from Royal (Dick) School of Veterinary Studies [R(D)SVS] were asked to score 12 students (6 from each school). All raters assessed all students (video recordings) during 4 OSCE stations (bovine haltering, gowning and gloving, equine bandaging and skin suturing). Raters scored students using a CL, followed by the GRS. Novice raters (6 R(D)SVS) were assessed independently of expert raters (6 UCVM). Generalizability theory (G theory), analysis of variance (ANOVA) and t-tests were used to determine the reliability of rater scores, assess any between school differences (by student, by rater), and determine if there were differences between CL and GRS scores. There was no significant difference in rater performance with use of the CL or the GRS. Scores from the CL were significantly higher than scores from the GRS. The reliability of checklist scores were .42 and .76 for novice and expert raters respectively. The reliability of the global rating scale scores were .7 and .86 for novice and expert raters respectively. A decision study (D-study) showed that once trained using CL, GRS could be utilized to reliably score clinical skills in veterinary medicine with both novice and experienced raters.     

Using Generalized Distances in Classification of Groups

The Mahalanobis generalized distance can advantageously be used to achieve the hierarchical clustering of groups of individuals. With a set of nearly 12,000 biometrical data, comprising populations of 14 different species of clover, we tried four methods to cluster those populations, in order to compare their results and to see whether the numerical classification obtained agrees with the botanical taxonomy. One of those methods is a conventional hierarchical clustering technique, based upon the Euclidean distances between the means of the populations, while the three other methods make use, with an increasing degree of complexity, of the generalized distances. These methods gave obviously better results.     

QRS波群自动分类技术研究进展

本文综述了QRS波群自动分类技术的关键技术及其研究方向,内容涉及QRS波群的描述方法、分类方法和训练数据集这三个重要影响因素。     

兽医中药在宠物临床上的应用

本文系统综述了我国兽医中药在宠物临床上的应用历史及概况。从兽医中药起源,到中药在宠物临床上的古今应用。重点针对宠物临床中常见病、多发病如犬猫皮肤寄生虫病、皮肤真菌病、胃肠道疾病、传染病、肝胆疾病、心肺疾病、腰病以及虚弱性疾病等等进行了归纳总结。同时指出,随着宠物饲养社会功能的变化,宠物中药将会伴随着宠物市场的扩大迎来更大的发展空间。     

Effective Automated Feature Construction and Selection for Classification of Biological Sequences

Background

Many open problems in bioinformatics involve elucidating underlying functional signals in biological sequences. DNA sequences, in particular, are characterized by rich architectures in which functional signals are increasingly found to combine local and distal interactions at the nucleotide level. Problems of interest include detection of regulatory regions, splice sites, exons, hypersensitive sites, and more. These problems naturally lend themselves to formulation as classification problems in machine learning. When classification is based on features extracted from the sequences under investigation, success is critically dependent on the chosen set of features.

Methodology

We present an algorithmic framework (EFFECT) for automated detection of functional signals in biological sequences. We focus here on classification problems involving DNA sequences which state-of-the-art work in machine learning shows to be challenging and involve complex combinations of local and distal features. EFFECT uses a two-stage process to first construct a set of candidate sequence-based features and then select a most effective subset for the classification task at hand. Both stages make heavy use of evolutionary algorithms to efficiently guide the search towards informative features capable of discriminating between sequences that contain a particular functional signal and those that do not.

Results

To demonstrate its generality, EFFECT is applied to three separate problems of importance in DNA research: the recognition of hypersensitive sites, splice sites, and ALU sites. Comparisons with state-of-the-art algorithms show that the framework is both general and powerful. In addition, a detailed analysis of the constructed features shows that they contain valuable biological information about DNA architecture, allowing biologists and other researchers to directly inspect the features and potentially use the insights obtained to assist wet-laboratory studies on retainment or modification of a specific signal. Code, documentation, and all data for the applications presented here are provided for the community at http://www.cs.gmu.edu/~ashehu/?q=OurTools.     

基因芯片数据分析方法研究进展

基因芯片技术的出现改变了生物医学研究的前景,其产生的海量数据是限制其发展的瓶颈问题。为提取其中所隐含的有价值的信息,在基因芯片数据分析的复杂计算工具和方法方面近年来有很多尝试。本文对近5年来基因芯片表达数据的分类分析方法进行综述,既分类比较了以聚类分析为基础的分类方法,也吸收了当前应用数据挖掘、信息融合等系统生物学思路的研究技术,并对数据的分析结果进行评价。     

银杏品种类群的模糊聚类划分

对４２个银杏核用品种的１３个性状特征进行系统聚类分析和主成份分析．根据系统聚类分析树状图和主成份坐标点图,将银杏品种划分为５个主要类群,并对其进行了简要分析。     

Non-specificity of Laboratory Test for Diagnosis of Multiple Sclerosis

It has been claimed that the inhibiting effect of linoleic acid on the macrophage electrophoretic mobility test provides a specific laboratory method for the diagnosis of multiple sclerosis (M.S.) and may also enable susceptible relatives of M.S. patients to be identified. Three trials of the method under double-blind conditions have failed to confirm that the test is diagnostically useful.     

肝功能检验建议性报告软件临床分析管理的优势

介绍一种方便于临床分析、鉴别诊断和分析管理的临床检验专家系统。利用人工神经网络（Artificial Neural. Networks,ANN）挖掘患者基本信息和实验室数据,进行综合统计分析,对已确诊的患者（肝炎、肝癌、肝硬化和胆囊结石）的生化检验项目进行梳理,得出相应疾病生化指标的临床阳性预测值,然后以各项生化指标为多因素变量、诊断为输出变量建立ANN预测模型。另外抽取肝功异常并已明确诊断的患者60例进行生化指标测定,利用ANN系统,综合分析预测临床符合率,同时构建对初诊病人的树状筛查程序和直观判读报告软件的开发。利用专家诊断系统对肝脏疾病预测的准确率分别为：肝炎80.0%、肝硬化86.7%、肝癌66.7%和胆囊结石73.3%,优化后的报告格式直观反映患者的病情变化。借助于建议性报告软件可快速简易地作出对肝胆系统疾病鉴别诊断,达到医疗资源优化利用的目的。     

生物技术在兽医领域的研究应用

当今环境污染日益严重,危害人类的疾病时有发生,这对畜牧兽医业的现状与发展影响越来越大,对兽医工作是严峻的挑战。对此,采用现代生物技术研究和解决兽医领域的问题是兽医行业的发展方向。从预防兽医、临床兽医和兽医制药三方面综述生物技术在兽医领域的研究应用与发展趋势。     

Methods for Generating Year-Round Access to Amphioxus in the Laboratory

Cephalochordates, commonly known as amphioxus, are key to understanding vertebrate origins. However, laboratory work suffers from limited access to adults and embryonic material. Here we report the design and experimental validation of an inland marine facility that allows establishing stable amphioxus colonies in the laboratory and obtaining embryos at any time of day and over almost the entire year, far exceeding natural conditions. This is achieved by mimicking the natural benthic environment, natural day- and moon- light, natural substrate and by providing a strictly controlled and seasonally fluctuating temperature regimen. Moreover, supplemented algae diets allow animals to refill their gonads in consecutive years. Spontaneous spawning, a major problem in previous setups, no longer occurs in our facility; instead, all breeding is induced and fertilization occurs fully in vitro. Our system makes amphioxus a standard laboratory animal model.     

A Comparative Survey of Automated Parameter-Search Methods for Compartmental Neural Models

One of the most difficult and time-consuming aspects of building compartmental models of single neurons is assigning values to free parameters to make models match experimental data. Automated parameter-search methods potentially represent a more rapid and less labor-intensive alternative to choosing parameters manually. Here we compare the performance of four different parameter-search methods on several single-neuron models. The methods compared are conjugate-gradient descent, genetic algorithms, simulated annealing, and stochastic search. Each method has been tested on five different neuronal models ranging from simple models with between 3 and 15 parameters to a realistic pyramidal cell model with 23 parameters. The results demonstrate that genetic algorithms and simulated annealing are generally the most effective methods. Simulated annealing was overwhelmingly the most effective method for simple models with small numbers of parameters, but the genetic algorithm method was equally effective for more complex models with larger numbers of parameters. The discussion considers possible explanations for these results and makes several specific recommendations for the use of parameter searches on neuronal models.     

Prototype of a Fully Automated Device for Determination of Bacterial Antibiotic Susceptibility in the Clinical Laboratory

A completely automated system for the performance of antibiotic susceptibility tests in the clinical laboratory is described. With a modicum of personnel involvement, data on 40 specimens tested against 13 antibiotics are obtained every hour after an initial 3-hr period. The step by step explanation of the functioning of this prototype system, based on a thoroughly tested manual model, is presented. The system compares well with the standard diffusion test and has a potential for application to other endeavors of the clinical microbiology laboratory with a comparable saving in time and labor.