Vaccination against the Influenza A virus (IAV) is often an important and critical task for much of the population, as IAV causes yearly epidemics, and can cause even deadlier pandemics. Designing the vaccine requires an understanding of the current major circulating strains of Influenza, as well as an understanding of how those strains could change over time to become either less harmful or more deadly, or simply die out completely. An error in the prediction process can lead to a non-immunized population at risk of epidemics, or even a pandemic. Presented here is a posterior predictive approach to generate emerging influenza strains based on a realistic genomic model that incorporates natural features of viral evolution such as selection and recombination. Also introduced is a sequence sampling scheme to relieve the computational burden of the posterior predictive analysis by clustering sequences based on their pairwise similarity. Finally, the impact of “evolutionary accidents” that take the form of bursts of evolution and or of recombination on the predictive power of our procedure is tested. An analysis of the impact of these bursts is carried out in a retrospective study that focuses on the unexpected emergence of a new H3N2 strain in the 2007-08 influenza season. Measuring the R2 values of both pairwise and patristic distances, the model reaches a predictive power of ∼40%, but is not able to simulate the emergence of the target Brisbane/10/2007 sequence with a high probability. The inclusion of “evolutionary accidents” improved the algorithm’s ability to predict HA sequences, but the prediction power of the NA gene remained low.

Vaccination against the Influenza A virus (IAV) is often an important and critical task for much of the population, as IAV causes yearly epidemics, and can cause even deadlier pandemics. Designing the vaccine requires an understanding of the current major circulating strains of Influenza, as well as an understanding of how those strains could change over time to become either less harmful or more deadly, or simply die out completely. An error in the prediction process can lead to a non-immunized population at risk of epidemics, or even a pandemic. Presented here is a posterior predictive approach to generate emerging influenza strains based on a realistic genomic model that incorporates natural features of viral evolution such as selection and recombination. Also introduced is a sequence sampling scheme to relieve the computational burden of the posterior predictive analysis by clustering sequences based on their pairwise similarity. Finally, the impact of “evolutionary accidents” that take the form of bursts of evolution and or of recombination on the predictive power of our procedure is tested. An analysis of the impact of these bursts is carried out in a retrospective study that focuses on the unexpected emergence of a new H3N2 strain in the 2007-08 influenza season. Measuring the R2 values of both pairwise and patristic distances, the model reaches a predictive power of ∼40%, but is not able to simulate the emergence of the target Brisbane/10/2007 sequence with a high probability. The inclusion of “evolutionary accidents” improved the algorithm’s ability to predict HA sequences, but the prediction power of the NA gene remained low.

Thesis (M.S.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains vii, 68 p. : ill. Includes abstract. Includes bibliographical references (p. 66-68).

Background: Predictive models allow clinicians to more accurately identify higher- and lower-risk patients and make more targeted treatment decisions, which can help improve efficiency in health systems. Microalbuminuria (MA) is a condition characterized by the presence of albumin in the urine below the threshold detectable by a standard dipstick. Its presence is understood to be an early marker for cardiovascular disease. Therefore, identifying patients at risk for MA and intervening to treat or prevent conditions associated with MA, such as high blood pressure or high blood glucose, may support cost-effective treatment. Methods: The National Health and Nutrition Examination Survey (NHANES) was utilized to create predictive models for MA. This database includes clinical, medical and laboratory data. The dataset was split into thirds; one-third was used to develop the model, while the other two-thirds were utilized to validate the model. Univariate logistic regression was performed to identify variables related with MA. Stepwise multivariate logistic regression was performed to create the models. Model performance was evaluated using three criteria: 1) receiver operator characteristic (ROC) curves; 2) pseudo R-squared; and 3) goodness of fit (Hosmer-Lemeshow). The predictive models were then used to develop risk-scores. Results: Two models were developed using variables that had significant correlations in the univariate analysis (p-value<0.05). For Model A, variables included in the final model were: systolic blood pressure (SBP); fasting glucose; C-reactive protein; blood urea nitrogen (BUN); and alcohol consumption. For Model B, the variables were: SBP; glycohemoglobin; BUN; smoking status; and alcohol consumption. Both models performed well in the creation dataset and no significant difference between the models was found when they were evaluated in the validation set. A 0-18 risk score was developed utilizing Model A, and the predictive probability of developing MA was calculated. Conclusion: The predictive models developed provide new evidence about which variables are related with MA and may be used by clinicians to identify at-risk patients and to tailor treatment. Furthermore, the risk score developed using Model A may allow clinicians to more easily measure patient risk. Both predictive models will require external validation before they can be applied to other populations.

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Vaccination against the Influenza A virus (IAV) is often an important and critical task for much of the population, as IAV causes yearly epidemics, and can cause even deadlier pandemics. Designing the vaccine requires an understanding of the current major circulating strains of Influenza, as well as an understanding of how those strains could change over time to become either less harmful or more deadly, or simply die out completely. An error in the prediction process can lead to a non-immunized population at risk of epidemics, or even a pandemic. Presented here is a posterior predictive approach to generate emerging influenza strains based on a realistic genomic model that incorporates natural features of viral evolution such as selection and recombination. Also introduced is a sequence sampling scheme to relieve the computational burden of the posterior predictive analysis by clustering sequences based on their pairwise similarity. Finally, the impact of “evolutionary accidents” that take the form of bursts of evolution and or of recombination on the predictive power of our procedure is tested. An analysis of the impact of these bursts is carried out in a retrospective study that focuses on the unexpected emergence of a new H3N2 strain in the 2007-08 influenza season. Measuring the R2 values of both pairwise and patristic distances, the model reaches a predictive power of ∼40%, but is not able to simulate the emergence of the target Brisbane/10/2007 sequence with a high probability. The inclusion of “evolutionary accidents” improved the algorithm’s ability to predict HA sequences, but the prediction power of the NA gene remained low.

Youth offending is an increasingly major problem in many countries and cultures. Several theories imply that a subset of young people display delinquent behaviour at a young age and go on to have an extensive and serious criminal career. Recently, there has been interest in the literature in identifying these young people early on and carrying out interventions in order to deter them from a criminal career. Many studies have examined the development and usefulness of actuarial measures of risk of future violence or recidivism in adult offenders. However, the same attention has not been paid to the youth offender population. The present study gathered data from the population (N = 4307) of all young persons in New Zealand whose antisocial behaviour resulted in a Youth Justice intake from the Department of Child, Youth, and Family (CYF) in 2002. Information was obtained about this population from the CYF database, CYRAS, and from the Police National Intelligence Application database for a stratified random sample (N = 500). Three models were developed using Hierarchical Cox regression to predict recidivism, and they each used a different definition of recidivism. The performance of the models was assessed using ROC analysis and they were found to predict recidivism with a moderately good level of accuracy. A validation sample (N = 500), different from the sample on which the models were developed, was used to further assess the performance of the models by showing that they were able to generalize to a new data set and continue to perform at an adequate level. An actuarial model, like the one developed in the present study, could be used to help make decisions about which young people within the Youth Justice System require intervention in order to reduce the likelihood of subsequent reoffending.

Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Mavris, Dimitri; Committee Member: German, Brian; Committee Member: Healy, Tim; Committee Member: Rosson, Randy; Committee Member: Tai, Jimmy. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Made available in DSpace on 2014-06-11T19:28:35Z (GMT). No. of bitstreams: 0 Previous issue date: 2005-05Bitstream added on 2014-06-13T20:58:36Z : No. of bitstreams: 1 oliveira_rjg_me_guara.pdf: 1311511 bytes, checksum: 7c57fbdb099a4b3d6123bb38b37813e3 (MD5) / Universidade Estadual Paulista (UNESP) / Na indústria moderna o monitoramento da condição de operação de máquinas rotativas é essencial para se determinar o surgimento de falhas em mancais de rolamentos. Este trabalho apresenta uma técnica de análise adotada para a identificação de falhas em mancais de rolamento em seus estágios iniciais, utilizando procedimentos de análise de sinais no domínio do tempo e da freqüência, com especial atenção para a técnica do HFRT (High Frequency Resonance Technique), também conhecida como Técnica do Envelope. Este método de análise de sinais foi escolhido em razão de ser uma ferramenta apropriada para identificar falhas em mancais de rolamentos na sua fase inicial. A teoria das técnicas foi discutida e os passos para a implementação computacional foram apresentados. As rotinas foram implementadas através da linguagem de programação MATLAB e um sinal simulado representativo de um sinal coletado de um mancal de rolamento com defeito pontual na pista externa foi desenvolvido para verificar a eficácia dos métodos implementados. Os experimentos foram desenvolvidos utilizando-se uma bancada de testes aplicada para testar mancais de rolamento com defeitos pontuais produzidos em laboratório. A aquisição dos dados foi desenvolvida com instrumentação comercial. Os resultados obtidos mostraram ser efetivos para identificar falhas em rolamentos para os dados simulados e dados experimentais. / In the modern industries, the condition monitoring of the rotational machinery operation is important to evidence the beginning of the fails in bearings. This work presents a technique of analysis applied to identify fails in bearing during the initial phases, using techniques of signal analysis in time and frequency domain with special attention for the High Frequency Resonance Technique, also called envelope technique. This method for signal analysis was chosen because is an appropriated tool to identify fails in bearings during initial phases. The theory for the techniques was discussed and the steps for the computational implementation were showed. The routines were implemented through MATLAB programming language and it was prepared a representative signal of a bearing with a single point defect in the outer race in order to verify the capability of the method implemented in the routine. The experiments were performed using a experimental test rig applied to test bearings with single point defects performed in laboratory. The data acquisition were performed with commercial instrumentation. The results obtained shown to be effective to identify fails in bearings for both numerically simulated data and experimental data.

Vaccination against the Influenza A virus (IAV) is often an important and critical task for much of the population, as IAV causes yearly epidemics, and can cause even deadlier pandemics. Designing the vaccine requires an understanding of the current major circulating strains of Influenza, as well as an understanding of how those strains could change over time to become either less harmful or more deadly, or simply die out completely. An error in the prediction process can lead to a non-immunized population at risk of epidemics, or even a pandemic. Presented here is a posterior predictive approach to generate emerging influenza strains based on a realistic genomic model that incorporates natural features of viral evolution such as selection and recombination. Also introduced is a sequence sampling scheme to relieve the computational burden of the posterior predictive analysis by clustering sequences based on their pairwise similarity. Finally, the impact of “evolutionary accidents” that take the form of bursts of evolution and or of recombination on the predictive power of our procedure is tested. An analysis of the impact of these bursts is carried out in a retrospective study that focuses on the unexpected emergence of a new H3N2 strain in the 2007-08 influenza season. Measuring the R2 values of both pairwise and patristic distances, the model reaches a predictive power of ∼40%, but is not able to simulate the emergence of the target Brisbane/10/2007 sequence with a high probability. The inclusion of “evolutionary accidents” improved the algorithm’s ability to predict HA sequences, but the prediction power of the NA gene remained low.