Orientador: Arício Xavier Linhares / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Biologia / Made available in DSpace on 2018-08-19T01:31:06Z (GMT). No. of bitstreams: 1 Fernandes_FernandaRodrigues_D.pdf: 1975248 bytes, checksum: d23780751c399131a44ca6f88c69adb5 (MD5) Previous issue date: 2011 / Resumo: Parasitismo é considerado uma interação interespecífica antagonística, em que uma espécie (parasito) vive dentro ou sobre outra espécie (hospedeiro), obtendo dela parte de, ou todos seus nutrientes e em geral, causa algum grau de injúria à espécie hospedeira. Por um processo coevolutivo, os artrópodes parasitos desenvolveram mecanismos para aumentar sua aptidão explorando seus hospedeiros mamíferos, por outro lado, estes se esforçam para maximizar sua própria aptidão, minimizando o efeito negativo da infestação por parasitos. Devido muitos padrões da ecologia dos parasitos estarem intimamente ligados à ecologia do hospedeiro, o presente trabalho teve como objetivo investigar a influência do sexo, tamanho corporal e local de ocorrência dos hospedeiros na abundância de ectoparasitos, bem como, a influência da filogenia e sobreposição geográfica dos hospedeiros na similaridade de fauna de ectoparasitos. Os principais resultados obtidos foram: 1. A abundância de ectoparasitos Anoplura em Oligoryzomys nigripes é influenciada pelo sexo e local de ocorrência do hospedeiro; 2. A abundância de ectoparasitos Acari (Mesostigmata) em Oligoryzomys nigripes é influenciada pela massa corporal do hospedeiro; 3. As similaridades das faunas de ectoparasitos estão correlacionadas com a filogenia dos hospedeiros / Abstract: Parasitism is considered an antagonistic interspecific interaction where one species (the parasite) lives in or on another species (the host) obtaining part of or all of its nutrients and normally causing some degree of injury to the host. Through the coevolutionary process, arthropod parasites have developed mechanisms to increase their fitness by exploiting their mammalian hosts, on the other hand, the hosts strive to maximize its own fitness by minimizing negative effects of infestation by parasites. Because many patterns of parasite ecology is intimately related to the ecology of the host, this study aimed to investigate the influence of host sex, body size and locality in ectoparasite abundance, as well as, the influence of phylogeny and geographical overlap of the hosts in ectoparasite fauna similarity. The main results are: 1. The abundance of Anoplura ectoparasites on Oligoryzomys nigripes is influenced by host sex and locality; 2. The abundance of Acari (Mesostigmata) ectoparasites on Oligoryzomys nigripes is influenced by host body mass; 3. The similarities of ectoparasite fauna is correlated with host phylogeny / Doutorado / Doutor em Ecologia

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Air pollution may be due to various economic activities, as well as natural phenomena. Regardless of the generating factor, it influences the environment and quality of life of human beings. In addition to damage the fauna and flora, air pollution can affect people's health. In Brazil, the researchers are concentrated in the state of São Paulo, with few studies in other regions of the country. This study was realized in the city of Canoas/RS and aimed to analyze the association of pollutants (PM10, O3, CO, SO2) with the number of hospitalizations, in 2014, for respiratory diseases in under 14 and over 60 years. Negative Binomial regression was used due to the presence in over dispersed in the data. The relative risks were estimated (RR) for lags (Lag) up to seven days, and the relative percentage risk (RR%) to the increase of 10 mg/m³ of PM10 and increase of difference interquartile of other pollutants. It was observed that occurred in county of Canoas, 3080 hospitalizations by respiratory diseases, in greater numbers in the elderly, which is the main cause of death in the city. With the results of this study, it was possible to verify the association of PM10 (Lag 0, Lag 1 and Lag 3), O3 (Lag 0) and SO2 (Lag 5) with hospitalizations by respiratory diseases in children and adolescents, and CO (Lag 0, Lag 4, Lag 6 and Lag 7) and O3 (Lag 3), with the number of hospitalizations for respiratory diseases in the elderly. These results confirm strong evidence that air pollution is an important risk factor for the onset and worsening of respiratory problems in county of Canoas, stressing the need for effective action to reduce emissions. / A poluição atmosférica pode ser decorrente de diversas atividades econômicas, como também de fenômenos naturais. Independentemente do fator gerador, ela exerce influência no meio ambiente e na qualidade de vida dos seres humanos. Além de prejudicar a fauna e a flora, a poluição atmosférica é capaz de afetar a saúde das pessoas. No Brasil, as pesquisas analisando os efeitos da poluição do ar na saúde se concentram no estado de São Paulo, sendo poucos os estudos desenvolvidos em outras regiões do país. Esta pesquisa foi realizada no município de Canoas/RS e teve o objetivo de analisar a associação de poluentes (MP10, O3, CO, SO2) com o número de internações, em 2014, por doenças respiratórias em menores de 14 anos e maiores de 60 anos. Foi utilizada a regressão Binomial Negativa devido à presença de sobredispersão nos dados. Foram estimados os riscos relativos (RR) para defasagens (Lag) de até sete dias, bem como o risco relativo percentual (RR%) para o incremento de 10 μg/m³ de PM10 e do incremento da diferença interquartílica dos demais poluentes. Foi observado que ocorreram, no município de Canoas, 3080 internações por doenças respiratórias, em maior número nos idosos, sendo esta a principal causa de óbito na cidade, no período analisado. Com os resultados deste estudo, foi possível verificar a associação do PM10 (Lag 0, Lag 1 e Lag 3), do O3 (Lag 0) e do SO2 (Lag 5) com internações por doenças respiratórias em crianças e adolescentes, e do CO (Lag 0, Lag 4, Lag 6 e Lag 7) e do O3 (Lag 3), com o número de internações por doenças respiratórias em idosos. Tais resultados confirmam fortes indícios de que a poluição do ar é fator de risco importante para o surgimento e agravo de problemas respiratórios no município de Canoas, alertando para a necessidade de ações efetivas para a redução da emissão de poluentes.

Nous proposons de modéliser les données issues des technologies de séquençage du transcriptome (RNA-Seq) à l'aide de la loi binomiale négative, et nous construisons des modèles de segmentation adaptés à leur étude à différentes échelles biologiques, dans le contexte où ces technologies sont devenues un outil précieux pour l'annotation de génome, l'analyse de l'expression des gènes, et la détection de nouveaux transcrits. Nous développons un algorithme de segmentation rapide pour analyser des séries à l'échelle du chromosome, et nous proposons deux méthodes pour l'estimation du nombre de segments, directement lié au nombre de gènes exprimés dans la cellule, qu'ils soient précédemment annotés ou détectés à cette même occasion. L'objectif d'annotation précise des gènes, et plus particulièrement de comparaison des sites de début et fin de transcription entre individus, nous amène naturellement à nous intéresser à la comparaison des localisations de ruptures dans des séries indépendantes. Nous construisons ainsi dans un cadre de segmentation bayésienne des outils de réponse à nos questions pour lesquels nous sommes capable de fournir des mesures d'incertitude. Nous illustrons nos modèles, tous implémentés dans des packages R, sur des données RNA-Seq provenant d'expériences sur la levure, et montrons par exemple que les frontières des introns sont conservées entre conditions tandis que les débuts et fin de transcriptions sont soumis à l'épissage différentiel. / We propose to model the output of transcriptome sequencing technologies (RNA-Seq) using the negative binomial distribution, as well as build segmentation models suited to their study at different biological scales, in the context of these technologies becoming a valuable tool for genome annotation, gene expression analysis, and new-transcript discovery. We develop a fast segmentation algorithm to analyze whole chromosomes series, and we propose two methods for estimating the number of segments, a key feature related to the number of genes expressed in the cell, should they be identified from previous experiments or discovered at this occasion. Research on precise gene annotation, and in particular comparison of transcription boundaries for individuals, naturally leads us to the statistical comparison of change-points in independent series. To address our questions, we build tools, in a Bayesian segmentation framework, for which we are able to provide uncertainty measures. We illustrate our models, all implemented in R packages, on an RNA-Seq dataset from a study on yeast, and show for instance that the intron boundaries are conserved across conditions while the beginning and end of transcripts are subject to differential splicing.

Vehicles being driven for work purposes represent a large proportion of road collision and deaths in the workplace. These observations mean that people driving for work can impose a large burden on organisations and on society. In addition, previous studies identified a fleet driver effect for which there was greater collision risk for those who drive for work compared to the general driving population, even after controlling for exposure. This accentuates the need for both organisational and government policy makers to take steps to reduce the impact of these collisions. No single intervention has been found to solve issues around work-related road safety therefore a range of initiatives have been directed towards the risks associated with drivers, vehicles, journeys and organisations. Many of the interventions, however, lack robust evidence to support their use. The aim of this thesis is to assess organisational interventions to improve work-related road safety by using econometric models on real-world data. The data represents driving claims made between 2005 and 2012 by employees of a large UK company, with a fleet of approximately 35,000 vehicles. The drivers were employed in a variety of roles such as working in technical positions at customer sites or making sales visits. The company has applied a range of strategies to road safety resulting in annual claim reductions of 7.7% compared to only a 4.5% reduction in collisions nationally. The company s data are used to undertake three studies which focused on driver training, manager training and claim segmentation. Statistical models were employed to investigate the effect of two different driver training courses on the frequency of claims while controlling for other factors. The results indicated that driver training courses significantly reduced both the total number of claims and the claim types targeted by the training. The impacts of the interventions were also adjusted for the effects of non-random driver selection and other safety improvements initiated by the company or other agencies. An important finding of this work was that randomly inflated pre-training events accounted for between a third and a quarter of the observed reduction in claims following training. The second study evaluated the impact of management training on claims using multilevel models which allowed for correlation between observations. The study could not confirm that this training was an effective safety intervention. This null result provides an incentive to re-evaluate the implementation of the scheme. The final study identified homogeneous claim segments using statistical models and the impact of training was evaluated on these segments. Such claims were estimated to be reduced by between 32% to 55% following existing driver training courses. This thesis has helped close important gaps and contributed to knowledge in terms of both intervention methodology and the understanding of the effectiveness of work-related road safety interventions. The results, which are already being applied in the case study organisation, demonstrated that training employees in either safe and fuel efficient driving, or low speed manoeuvring, reduced vehicle insurance claims. Further work is necessary to verify the safety value of manager training including gathering detailed information on interactions between managers and drivers.

Development of the Jordan Performance Appraisal System (JPAS) was completed in 1996. This study examined the factor structure of the classroom observation instrument used in the JPAS. Using observed classroom instructional quality ratings of 1220 elementary teachers of Grades 1-6 in the Jordan School District, this study estimated the factor structure of the data and the rater effect on relevant structural parameters. This study also tested for measurement invariance at the within and between levels across teachers of two grade-level groups (a) lower grades: Grades 1-3 and (b) upper grades: Grades 4-6. Factor structure was estimated using complex exploratory factor analysis (EFA) conducted on a subset of the original data. The analysis provided evidence of a three-factor model for the combined groups. The results of multiple confirmatory factor analyses (CFA) conducted using a different subset of the data cross-validated EFA results. Results from multilevel confirmatory factor analysis (MCFA) indicated the three-factor model fit best at both the within and the between levels, and that the intraclass correlation (ICC) was high (.699), indicating significant rater-level variance. Results from a multilevel multigroup confirmatory factor analysis (MLMG-CFA) indicated that the ICC was not significantly different between groups. Results also indicated configural, metric (weak factorial), and scalar (strong factorial) equivalence between groups. This study provided one of the first examples of how to estimate the impact of cluster-level variables such as rater on grouping variables nested at the within level. It provided an example of how to conduct a multilevel multigroup analysis on count data. It also disproved the assumption that counting classroom teaching behaviors was less subjective than using a categorical rating scale. These results will provide substantial information for future developments made to the classroom observation instrument used in the JPAS.

This study examined differences in sexual risk behaviors by gender and over time among 1281 patients (777 males and 504 females) from 12 community-based substance use disorder treatment programs throughout the United States participating in CTN-0032, a randomized control trial conducted within the National Drug Abuse Treatment Clinical Trials Network. Zero-inflated negative binomial and negative binomial models were used in the statistical analysis. Results indicated significant reductions in most types of sexual risk behaviors among substance users regardless of the intervention arms. There were also significant gender differences in sexual risk behaviors. Men (compared with women) reported more condomless sex acts with their non-primary partners (IRR = 1.80, 95 % CI 1.21–2.69) and condomless anal sex acts (IRR = 1.74, 95 % CI 1.11–2.72), but fewer condomless sex partners (IRR = 0.87, 95 % CI 0.77–0.99), condomless vaginal sex acts (IRR = 0.83, 95 % CI 0.69–1.00), and condomless sex acts within 2 h of using drugs or alcohol (IRR = 0.70, 95 % CI 0.53–0.90). Gender-specific intervention approaches are called for in substance use disorder treatment.

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This thesis develops and evaluates a probabilistic model that estimates the stoppage time in football. Stoppage time represents the additional minutes of play given after the original matchtime is over. It is crucial in determining the course of events during the remainder of a match, thereby affecting the odds of live sports betting. The proposed approach uses the negative binomial distribution to model events in football and stoppage time is viewed as a convolution of these distributions. The parameters of the negative binomials are estimated using machine learning methods in Python, with TensorFlow as the underlying framework. The data used for the analysis consists of event data for thousands of football matches with corresponding stoppage time, as well as the duration of pauses that have occurred in these games. The negative binomial distribution is shown to be a good fit and can be adapted to the data using scaling and resolution techniques. The model allows us to see how different events contribute to the stoppage time, and the results indicate that injuries, VAR checks, and red cards have the most significant impact on stoppage time. The model has potential for use in live sports betting and can enhance the accuracy of odds calculation. This work was conducted in collaboration with xAlgo which is a department of Kambi, a business-to-business provider of sports betting services.

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