Incidence, characteristics, and predictive factors for dysphagia after pediatric traumatic brain injury

Objective: (1) To establish an incidence figure for dysphagia in a population of pediatric traumatic brain injury (TBI) cases; (2) to provide descriptive data on the admitting characteristics, patterns of resolution, and outcomes of children with and without dysphagia after TBI; and (3) to identify any factors present at admission that may predict dysphagia. Participants: A total of 1, 145 children consecutively admitted to an acute care setting for traumatic brain injury between July 1995 and July 2000. Main outcome measure: Medical parameters relating to dysphagia based on medical chart review. Results: (1) Dysphagia incidence figure of 5.3% across all pediatric head injury admissions. Incidence figures of 68% for severe TBI, 15% for moderate TBI, and only 1% for mild brain injury. (2) Statistically significant differences were found between the dysphagic and nondysphagic subgroups on the variables of length of stay, length of ventilation, Glasgow Coma Scale (GCS), computed tomography classification, duration of speech pathology intervention, supplemental feeding duration, duration until initiation of oral intake (DIOF), duration to total oral intake (DTOF), and period of time from the initiation of intake until achievement of total oral intake (DI-TOF). (3) Significant predictive factors for dysphagia included GCS < 8.5 and a ventilation period in excess of 1.5 days. Conclusion: The provision of incidence data and predictive factors for dysphagia will enable clinicians in acute care settings to allocate resources necessary to deal with the predicted number of dysphagia cases in a pediatric population, and assist in predicting patients who are at risk for dysphagia following TBI. Early detection of patients with swallowing dysfunction will be aided by these data, in turn helping to facilitate effective medical and speech pathology intervention via assisting the reduction of medical complications such as aspiration pneumonia.

An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data

We consider a mixture model approach to the regression analysis of competing-risks data. Attention is focused on inference concerning the effects of factors on both the probability of occurrence and the hazard rate conditional on each of the failure types. These two quantities are specified in the mixture model using the logistic model and the proportional hazards model, respectively. We propose a semi-parametric mixture method to estimate the logistic and regression coefficients jointly, whereby the component-baseline hazard functions are completely unspecified. Estimation is based on maximum likelihood on the basis of the full likelihood, implemented via an expectation-conditional maximization (ECM) algorithm. Simulation studies are performed to compare the performance of the proposed semi-parametric method with a fully parametric mixture approach. The results show that when the component-baseline hazard is monotonic increasing, the semi-parametric and fully parametric mixture approaches are comparable for mildly and moderately censored samples. When the component-baseline hazard is not monotonic increasing, the semi-parametric method consistently provides less biased estimates than a fully parametric approach and is comparable in efficiency in the estimation of the parameters for all levels of censoring. The methods are illustrated using a real data set of prostate cancer patients treated with different dosages of the drug diethylstilbestrol. Copyright (C) 2003 John Wiley Sons, Ltd.

Methodologies for calibration and predictive analysis of a watershed model

The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.

Usabilidade e qualidade da informação : avaliação do Portal do aluno da Universidade Federal do Espírito Santo

O presente trabalho tem por objetivo avaliar o Portal do Aluno da Universidade Federal do Espírito Santo, identificando fatores associados à usabilidade que afetam a percepção de qualidade da informação e diferenciando grupos de usuários em função da usabilidade do Portal. Para tanto, foi realizado um levantamento por meio de um questionário aplicado aos usuários, com abordagem predominantemente quantitativa. Foi descrito o perfil dos alunos, o contexto em que utilizam o Portal e qual avaliação fazem dos relatórios e atividades. A partir do modelo elaborado no estudo, no qual a usabilidade contou com seis dimensões (facilidade de aprendizado, facilidade de memorização, eficiência, satisfação, suporte a erros e utilidade), constatou-se que, com exceção da facilidade de aprendizado, as demais dimensões possuem significância estatística na percepção da qualidade da informação, sendo a mais influente a dimensão satisfação. Por meio da análise de conglomerados, foram identificados três grupos, com base nas avaliações dos fatores associados ao atendimento de suas expectativas e aos resultados do uso do portal (satisfação, eficiência e utilidade): o primeiro grupo composto por alunos que avaliaram melhor estes fatores bem como as atividades, os relatórios e a qualidade da informação do Portal; o segundo que avaliaram medianamente; e o terceiro que avaliaram pior. Através da regressão simples utilizando a média das dimensões de usabilidade, verificou-se também que a qualidade da informação está fortemente correlacionada (r=0,7) com a usabilidade do Portal, tendo um poder de predição de 49%. Também foram obtidas respostas acerca do que era mais confuso e sugestões de melhorias que ajudaram a formular, conjuntamente com as demais análises, as propostas do Plano de Intervenção. Embora ainda apresente deficiências, em uma escala de 0 a 5, o Portal foi avaliado acima da média na dimensão satisfação (3,42), bem como obteve média geral de qualidade da informação de 3,31 e de usabilidade de 3,69 . Desta forma, pode-se dizer que ele tem cumprido sua função.

Assessment and prediction of asthma and its severity in the pediatric community

Seventy four asthmatic children aged 7 to 11 years were examined along with controls matched by age and sex. Clinical and laboratory investigations preceded a 28-day follow-up where data about morning and evening peak expiratory flow rate (PEF), symptoms and treatment were recorded. The coefficient of variation of PEF was found to be an objective measurement of asthma severity that has statistically significant correlation with both symptoms (r s= .36) and treatment (r s= .60). Moreover, it separates mild and severe asthmatics, as confirmed by statistically significant differences (p= .008 or less) in symptoms, treatment, skin allergy and airways response to exercise. Skin allergy and airways responsiveness to exercise were found to be predictors of both disease and severity. By means of logistic regression analysis it was possible to establish the probabilities for both asthma and severe asthma when children presenting and not presenting these characteristics are compared. One single positive skin test represent a probability of 88% for the development of asthma and a probability of 70% for severe disease. A PEF reduction of 10% after an exercise test implies a probability of 73% for disease and a probability of 64% for severe disease. Increases in these variables imply geometrically increased risks and their presence together have a multiplicative effect in the final risk.

Predictive Optimal Matrix Converter Control for a Dynamic Voltage Restorer with Flywheel Energy Storage

This paper presents a predictive optimal matrix converter controller for a flywheel energy storage system used as Dynamic Voltage Restorer (DVR). The flywheel energy storage device is based on a steel seamless tube mounted as a vertical axis flywheel to store kinetic energy. The motor/generator is a Permanent Magnet Synchronous Machine driven by the AC-AC Matrix Converter. The matrix control method uses a discrete-time model of the converter system to predict the expected values of the input and output currents for all the 27 possible vectors generated by the matrix converter. An optimal controller minimizes control errors using a weighted cost functional. The flywheel and control process was tested as a DVR to mitigate voltage sags and swells. Simulation results show that the DVR is able to compensate the critical load voltage without delays, voltage undershoots or overshoots, overcoming the input/output coupling of matrix converters.

Occupational exposure to environmental tobacco smoke in hospitality venues: are genetic- or proteomics-based biomarkers predictive of respiratory diseases?

Environmental tobacco smoke (ETS) is recognized as an occupational hazard in the hospitality industry. Although Portuguese legislation banned smoking in most indoor public spaces, it is still allowed in some restaurants/bars, representing a potential risk to the workers’ health, particularly for chronic respiratory diseases. The aims of this work were to characterize biomarkers of early genetic effects and to disclose proteomic signatures associated to occupational exposure to ETS and with potential to predict respiratory diseases development. A detailed lifestyle survey and clinical evaluation (including spirometry) were performed in 81 workers from Lisbon restaurants. ETS exposure was assessed through the level of PM 2.5 in indoor air and the urinary level of cotinine. The plasma samples were immunodepleted and analysed by 2D-SDSPAGE followed by in-gel digestion and LC-MS/MS. DNA lesions and chromosome damage were analysed innlymphocytes and in exfoliated buccal cells from 19 cigarette smokers, 29 involuntary smokers, and 33 non-smokers not exposed to tobacco smoke. Also, the DNA repair capacity was evaluated using an ex vivo challenge comet assay with an alkylating agent (EMS). All workers were considered healthy and recorded normal lung function. Interestingly, following 2D-DIGE-MS (MALDI-TOF/TOF), 61 plasma proteins were found differentially expressed in ETS-exposed subjects, including 38 involved in metabolism, acute-phase respiratory inflammation, and immune or vascular functions. On the other hand, the involuntary smokers showed neither an increased level of DNA/chromosome damage on lymphocytes nor an increased number of micronuclei in buccal cells, when compared to non-exposed non-smokers. Noteworthy, lymphocytes challenge with EMS resulted in a significantly lower level of DNA breaks in ETS-exposed as compared to non-exposed workers (P<0.0001) suggestive of an adaptive response elicited by the previous exposure to low levels of ETS. Overall, changes in proteome may be promising early biomarkers of exposure to ETS. Likewise, alterations of the DNA repair competence observed upon ETS exposure deserves to be further understood. Work supported by Fundação Calouste Gulbenkian, ACSS and FCT/Polyannual Funding Program.

A model to forecast financial failure, in non financial galician SMES

We are concerned with providing more empirical evidence on forecast failure, developing forecast models, and examining the impact of events such as audit reports. A joint consideration of classic financial ratios and relevant external indicators leads us to build a basic prediction model focused in non-financial Galician SMEs. Explanatory variables are relevant financial indicators from the viewpoint of the financial logic and financial failure theory. The paper explores three mathematical models: discriminant analysis, Logit, and linear multivariate regression. We conclude that, even though they both offer high explanatory and predictive abilities, Logit and MDA models should be used and interpreted jointly.

Predicting the revolving door phenomenon among patients with schizophrenic, affective disorders and non-organic psychoses

OBJECTIVE: The aim of the study was to identify the variables that predict the revolving door phenomenon in psychiatric hospital at the moment of a second admission. METHODS: The sample consisted of 3,093 patients who have been followed during 5 to 24 years after their first hospital admission due to schizophrenia, and affective or psychotic disorders. Those who had had four or more admissions during the study period were considered as revolving door patients. Logistic regression analyses were used to assess the impact of gender, age, marital status, urban conditions, diagnosis, mean period of stay on the first admission, interval between the first and second admissions on the patterns of hospitalization. RESULTS: The variables with the highest predictive power for readmission were the interval between first and second admissions, and the length of stay in the first admission. CONCLUSIONS: These data may help public health planners in providing optimal care to a small group of patients with more effective utilization of the available services.

Monitorização da insuficiência cardíaca por parâmetros fisiológicos em portadores de cardioversor-desfibrilhador implantável e/ou sistema de ressincronização cardíaca: HF-PREDICT

Mestrado em Tecnologia de Diagnóstico e Intervenção Cardiovascular. Área de especialização: Intervenção Cardiovascular.

Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

Fast optimum-predictive control and capacitor voltage balancing strategy for bipolar back-to-back NPC converters in high-voltage direct current transmission systems

Multilevel power converters have been introduced as the solution for high-power high-voltage switching applications where they have well-known advantages. Recently, full back-to-back connected multilevel neutral point diode clamped converters (NPC converter) have been used inhigh-voltage direct current (HVDC) transmission systems. Bipolar-connected back-to-back NPC converters have advantages in long-distance HVDCtransmission systems over the full back-to-back connection, but greater difficulty to balance the dc capacitor voltage divider on both sending and receiving end NPC converters. This study shows that power flow control and dc capacitor voltage balancing are feasible using fast optimum-predictive-based controllers in HVDC systems using bipolar back-to-back-connected five-level NPC multilevel converters. For both converter sides, the control strategytakes in account active and reactive power, which establishes ac grid currents in both ends, and guarantees the balancing of dc bus capacitor voltages inboth NPC converters. Additionally, the semiconductor switching frequency is minimised to reduce switching losses. The performance and robustness of the new fast predictive control strategy, and its capability to solve the DC capacitor voltage balancing problem of bipolar-connected back-to-back NPCconverters are evaluated.

HVDC transmission systems: Bipolar back-to-back diode clamped multilevel converter with fastoptimum-predictive control and capacitor balancing strategy

Voltage source multilevel power converter structures are being considered for high power high voltage applications where they have well known advantages. Recently, full back-to-back connected multilevel neutral diode clamped converters (NPC) have been used in high voltage direct current (HVDC) transmission systems. Bipolar back-to-back connection of NPCs have advantages in long distance HVDC transmission systems, but highly increased difficulties to balance the dc capacitor voltage dividers on both sending and receiving end NPCs. This paper proposes a fast optimum-predictive controller to balance the dc capacitor voltages and to control the power flow in a long distance HVDCsystem using bipolar back-to-back connected NPCs. For both converter sides, the control strategy considers active and reactive power to establish ac grid currents on sending and receiving ends, while guaranteeing the balancing of both NPC dc bus capacitor voltages. Furthermore, the fast predictivecontroller minimizes the semiconductor switching frequency to reduce global switching losses. The performance and robustness of the new fast predictive control strategy and the associated dc capacitors voltage balancing are evaluated. (C) 2011 Elsevier B.V. All rights reserved.