Zygosity Diagnosis in the Absence of Genotypic Data: An Approach Using Latent Class Analysis

For zygosity diagnosis in the absence of genotypic data, or in the recruitment phase of a twin study where only single twins from same-sex pairs are being screened, or to provide a test for sample duplication leading to the false identification of a dizygotic pair as monozygotic, the appropriate analysis of respondents' answers to questions about zygosity is critical. Using data from a young adult Australian twin cohort (N = 2094 complete pairs and 519 singleton twins from same-sex pairs with complete responses to all zygosity items), we show that application of latent class analysis (LCA), fitting a 2-class model, yields results that show good concordance with traditional methods of zygosity diagnosis, but with certain important advantages. These include the ability, in many cases, to assign zygosity with specified probability on the basis of responses of a single informant (advantageous when one zygosity type is being oversampled); and the ability to quantify the probability of misassignment of zygosity, allowing prioritization of cases for genotyping as well as identification of cases of probable laboratory error. Out of 242 twins (from 121 like-sex pairs) where genotypic data were available for zygosity confirmation, only a single case was identified of incorrect zygosity assignment by the latent class algorithm. Zygosity assignment for that single case was identified by the LCA as uncertain (probability of being a monozygotic twin only 76%), and the co-twin's responses clearly identified the pair as dizygotic (probability of being dizygotic 100%). In the absence of genotypic data, or as a safeguard against sample duplication, application of LCA for zygosity assignment or confirmation is strongly recommended.

Perceived stress as a predictor of the self-reported new diagnosis of symptomatic CHD in older women

This article describes one aspect of a prospective cohort study of 10,432 women aged between 70 and 75 years. After a 3-year period, 503 women self-reported a new diagnosis by a doctor of angina or myocardial infarction (symptomatic coronary heart disease [CHD]). Time one psychosocial variables (Duke Social Support Index, time pressure, Perceived Stress Scale, Mental Health Index, having a partner, educational attainment, and location of residence) were analyzed using univariate binary logistic regression for their ability to predict subsequent symptomatic CHD. Of these variables, the Duke Social Support Index, Perceived Stress Scale and the Mental Health Index were found to be significant predictors of symptomatic CHID diagnosis. Only the Perceived Stress Scale, however, proved to be a significant independent predictor. After controlling for time one nonpsychosocial variables, as well as the frequency of family doctor visits, perceived stress remained a significant predictor of the new diagnosis of symptomatic CHD in this cohort of older women over a 3-year period.

Diagnosis of Helicobacter pylori infection in adults with intellectual disability

Helicobacter pylori infection is common among adults with intellectual disability. The acceptabilities and accuracies of different diagnostic tests in this population are unknown. We aimed to determine (i) patient acceptability and (ii) performance characteristics of serology, fecal-antigen, and urea breath tests among adults with intellectual disability. One hundred sixty-eight such adults underwent H. pylori testing with serology and fecal-antigen tests, and a portion underwent treatment. One year later, the participants were retested with fecal-antigen, serology, and urea breath tests. The numbers of specimens obtained and difficulties in collection reported by caregivers were noted. Test performance characteristics were assessed among participants and 65 of their caregivers, using serology as the reference. All participants provided at least one specimen, despite reported collection difficulties for 23% of fecal and 27% of blood specimens. Only 25% of the participants provided breath specimens; failure to perform this test was associated with lower intellectual ability and higher maladaptive behavior. The sensitivity, specificity, and positive and negative predictive values of the fecal test (baseline and 12 months versus caregivers) were 70 and 63 versus 81, 93 and 95 versus 98, 96 and 92 versus 93, and 53 and 74 versus 93%, respectively; those of the urea breath test (12 months versus caregivers) were 86 versus 100, 88 versus 95, 75 versus 89, and 94 versus 100%, respectively. With assistance, fecal or blood specimens for H. pylori assessment can be provided by most patients with intellectual disability regardless of their level of function or behavior. Only those with greater ability can perform the urea breath test. Using serology as the reference test, the limitations of performance characteristics of the fecal-antigen and urea breath tests are similar to those among a control group of caregivers.

A new species of Solepyris Azevedo (Hymenoptera, Bethylidae) from Brazil, with amended diagnosis of the genus

Solepyris Azevedo é um gênero neotropical raramente coletado. O gênero é reconhecido dentre os gêneros de Sclerodermini por possuir a nervação das asas anteriores com apenas uma célula fechada grande. É descrita e ilustrada uma segunda espécie, Solepyris montuosus, sp. nov., do Brasil. Esta espécie é caracterizada por possuir a nervura radial da asa anterior longa. É incluída uma diagnose emendada de Solepyris unicus Azevedo. Foi fornecida uma chave para as duas espécies de Solepyris.

Application of Gage R&R methodology for improving the training and evaluation of automatic optical inspection machine operators

The current level of demand by customers in the electronics industry requires the production of parts with an extremely high level of reliability and quality to ensure complete confidence on the end customer. Automatic Optical Inspection (AOI) machines have an important role in the monitoring and detection of errors during the manufacturing process for printed circuit boards. These machines present images of products with probable assembly mistakes to an operator and him decide whether the product has a real defect or if in turn this was an automated false detection. Operator training is an important aspect for obtaining a lower rate of evaluation failure by the operator and consequently a lower rate of actual defects that slip through to the following processes. The Gage R&R methodology for attributes is part of a Six Sigma strategy to examine the repeatability and reproducibility of an evaluation system, thus giving important feedback on the suitability of each operator in classifying defects. This methodology was already applied in several industry sectors and services at different processes, with excellent results in the evaluation of subjective parameters. An application for training operators of AOI machines was developed, in order to be able to check their fitness and improve future evaluation performance. This application will provide a better understanding of the specific training needs for each operator, and also to accompany the evolution of the training program for new components which in turn present additional new difficulties for the operator evaluation. The use of this application will contribute to reduce the number of defects misclassified by the operators that are passed on to the following steps in the productive process. This defect reduction will also contribute to the continuous improvement of the operator evaluation performance, which is seen as a quality management goal.

Applications of artificial intelligence to condition-based maintenance

In this paper we discuss interesting developments of expert systems for machine diagnosis and condition-based maintenance. We review some elements of condition-based maintenance and its applications, expert systems for machine diagnosis, and an example of machine diagnosis. In the last section we note some problems to be resolved so that expert systems for machine diagnosis may gain wider acceptance in the future.

O desenvolvimento dos "Diagnosis Related Groups"- DRGs. Metodologia de classificação de pacientes hospitalares

É descrito o processo de desenvolvimento do sistema de classificação de pacientes internados em hospitais que atendem casos agudos, denominada Diagnosis Relatd Group - DRGs, desenvolvido e difundido por pesquisadores da Universidade de Yale, USA. Esse sistema vem a ser um instrumento que permite a mensuração do produto hospitalar, principalmente sob o ponto de vista gerencial. São apresentadas considerações acerca do que é entendido como produto hospitalar, seguindo nos meandros do desenvolvimento dos primeiros DRGs, até a mais recente revisão do sistema. É descrita sua utilização em alguns países e diversos usos potenciais desse sistema, que abrangem desde o uso para pagamento a instrumento de controle de qualidade.

Comportamento dinâmico da rede eléctrica da ilha de São Vicente em Cabo Verde: estabilidade de tensão

Neste trabalho é efectuado, não só o diagnóstico em regime permanente, mas também o estudo, simulação e análise do comportamento dinâmico da rede eléctrica da ilha de São Vicente em Cabo Verde. Os estudos de estabilidade transitória desempenham um importante papel, tanto no planeamento como na operação dos sistemas de potência. Tais estudos são realizados, em grande parte, através de simulação digital no domínio do tempo, utilizando integração numérica para resolver as equações não-lineares que modelam a dinâmica do sistema e dependem da existência de registos reais de perturbação (ex: osciloperturbografia). O objectivo do trabalho será também verificar a aplicabilidade dos requisitos técnicos que as unidades geradoras devem ter, no que concerne ao controlo de tensão, estabelecidos na futura regulamentação europeia desenvolvida pela ENTSO-E (European Network Transmission System Operator for Electricity). De entre os requisitos analisou-se a capacidade das máquinas existentes suportarem cavas de tensão decorrentes de curto-circuitos trifásicos simétricos, Fault Ride Through, no ponto de ligação à rede. Identificaram-se para o efeito os factores que influenciam a estabilidade desta rede, em regime perturbado nomeadamente: (i) duração do defeito, (ii) caracterização da carga, com e sem a presença do sistema de controlo de tensão (AVR) em unidades de geração síncronas. Na ausência de registos reais sobre o comportamento do sistema, conclui-se que este é sensível à elasticidade das cargas em particular do tipo potência constante, existindo risco de perda de estabilidade, neste caso, para defeitos superiores a 5ms sem AVR. A existência de AVR nesta rede afigura-se como indispensável para garantir estabilidade de tensão sendo contudo necessário proceder a uma correcta parametrização.

Power Electronics Didactic Modules for Direct Current Machine Control

Several didactic modules for an electric machinery laboratory are presented. The modules are dedicated for DC machinery control and get their characteristic curves. The didactic modules have a front panel with power and signal connectors and can be configurable for any DC motor type. The three-phase bridge inverter proposed is one of the most popular topologies and is commercially available in power package modules. The control techniques and power drives were designed to satisfy static and dynamic performance of DC machines. Each power section is internally self-protected against misconnections and short-circuits. Isolated output signals of current and voltage measurements are also provided, adding versatility for use either in didactic or research applications. The implementation of such modules allowed experimental confirmation of the expected performance.

DITRANS - a multi-agent system for integrated diagnosis of power transformers

This paper describes the development and the implementation of a multi-agent system for integrated diagnosis of power transformers. The system is divided in layers which contain a number of agents performing different functions. The social ability and cooperation between the agents lead to the final diagnosis and to other relevant conclusions through integrating various monitoring technologies, diagnostic methods and data sources, such as the dissolved gas analysis.

Training control centers' operators in incident diagnosis and power restoration using intelligent tutoring systems

The activity of Control Center operators is important to guarantee the effective performance of Power Systems. Operators’ actions are crucial to deal with incidents, especially severe faults like blackouts. In this paper, we present an Intelligent Tutoring approach for training Portuguese Control Center operators in tasks like incident analysis and diagnosis, and service restoration of Power Systems. Intelligent Tutoring System (ITS) approach is used in the training of the operators, having into account context awareness and the unobtrusive integration in the working environment. Several Artificial Intelligence techniques were criteriously used and combined together to obtain an effective Intelligent Tutoring environment, namely Multiagent Systems, Neural Networks, Constraint-based Modeling, Intelligent Planning, Knowledge Representation, Expert Systems, User Modeling, and Intelligent User Interfaces.