959 resultados para Defeasible conditional
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BACKGROUND: Epidemiological and clinical studies suggest comorbidity between prostate cancer (PCA) and cardiovascular disease (CVD) risk factors. However, the relationship between these two phenotypes is still not well understood. Here we sought to identify shared genetic loci between PCA and CVD risk factors.
METHODS: We applied a genetic epidemiology method based on conjunction false discovery rate (FDR) that combines summary statistics from different genome-wide association studies (GWAS), and allows identification of genetic overlap between two phenotypes. We evaluated summary statistics from large, multi-centre GWA studies of PCA (n=50 000) and CVD risk factors (n=200 000) [triglycerides (TG), low-density lipoprotein (LDL) cholesterol and high-density lipoprotein (HDL) cholesterol, systolic blood pressure, body mass index, waist-hip ratio and type 2 diabetes (T2D)]. Enrichment of single nucleotide polymorphisms (SNPs) associated with PCA and CVD risk factors was assessed with conditional quantile-quantile plots and the Anderson-Darling test. Moreover, we pinpointed shared loci using conjunction FDR.
RESULTS: We found the strongest enrichment of P-values in PCA was conditional on LDL and conditional on TG. In contrast, we found only weak enrichment conditional on HDL or conditional on the other traits investigated. Conjunction FDR identified altogether 17 loci; 10 loci were associated with PCA and LDL, 3 loci were associated with PCA and TG and additionally 4 loci were associated with PCA, LDL and TG jointly (conjunction FDR <0.01). For T2D, we detected one locus adjacent to HNF1B.
CONCLUSIONS: We found polygenic overlap between PCA predisposition and blood lipids, in particular LDL and TG, and identified 17 pleiotropic gene loci between PCA and LDL, and PCA and TG, respectively. These findings provide novel pathobiological insights and may have implications for trials using targeting lipid-lowering agents in a prevention or cancer setting.
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Background: European regional variation in cancer survival was reported in the EUROCARE-4 study for patients diagnosed in 1995-1999. Relative survival (RS) estimates are here updated for patients diagnosed with cancer of the oesophagus, stomach and small intestine from 2000 to 2007. Trends in RS from 1999-2001 to 2005-2007 are presented to monitor and discuss improvements in patient survival in Europe. Materials and methods: EUROCARE-5 data from 29 countries (87 cancer registries) were used to investigate 1- and 5-year RS. Using registry-specific life-tables stratified by age, gender and calendar year, age-standardised 'complete analysis' RS estimates by country and region were calculated for Northern, Southern, Eastern and Central Europe, and for Ireland and United Kingdom (UK). Survival trends of patients in periods 1999-2001, 2002-2004 and 2005-2007 were investigated using the 'period' RS approach. We computed the 5-year RS conditional on surviving the first year (5-year conditional survival), as the ratio of age-standardised 5-year RS to 1-year RS. Results Oesophageal cancer 1- and 5-year RS (40% and 12%, respectively) remained poor in Europe. Patient survival was worst in Eastern (8%), Northern (11%) and Southern Europe (10%). Europe-wide, there was a 3% improvement in oesophageal cancer 5-year survival by 2005-2007, with Ireland and the UK (3%), and Central Europe (4%) showing large improvements. Europe-wide, stomach cancer 5-year RS was 25%. Ireland and UK (17%) and Eastern Europe (19%) had the poorest 5-year patient survival. Southern Europe had the best 5-year survival (30%), though only showing an improvement of 2% by 2005-2007. Small intestine cancer 5-year RS for Europe was 48%, with Central Europe having the best (54%), and Ireland and UK the poorest (37%). Five-year patient survival improvement for Europe was 8% by 2005-2007, with Central, Southern and Eastern Europe showing the greatest increases (≥9%). Conclusions Survival for these cancer sites, particularly oesophageal cancer, remains poor in Europe with wide variation. Further investigation into the wide variation, including analysis by histology and anatomical sub-site, will yield insights to better monitor and explain the improvements in survival observed over time.
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Bone Mineral Density (BMD) is a highly heritable trait, but genome-wide association studies have identified few genetic risk factors. Epidemiological studies suggest associations between BMD and several traits and diseases, but the nature of the suggestive comorbidity is still unknown. We used a novel genetic pleiotropy-informed conditional False Discovery Rate (FDR) method to identify single nucleotide polymorphisms (SNPs) associated with BMD by leveraging cardiovascular disease (CVD) associated disorders and metabolic traits. By conditioning on SNPs associated with the CVD-related phenotypes, type 1 diabetes, type 2 diabetes, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein, triglycerides and waist hip ratio, we identified 65 novel independent BMD loci (26 with femoral neck BMD and 47 with lumbar spine BMD) at conditional FDR < 0.01. Many of the loci were confirmed in genetic expression studies. Genes validated at the mRNA levels were characteristic for the osteoblast/osteocyte lineage, Wnt signaling pathway and bone metabolism. The results provide new insight into genetic mechanisms of variability in BMD, and a better understanding of the genetic underpinnings of clinical comorbidity.
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Hidden Markov models (HMMs) are widely used probabilistic models of sequential data. As with other probabilistic models, they require the specification of local conditional probability distributions, whose assessment can be too difficult and error-prone, especially when data are scarce or costly to acquire. The imprecise HMM (iHMM) generalizes HMMs by allowing the quantification to be done by sets of, instead of single, probability distributions. iHMMs have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. In this paper, we consider iHMMs under the strong independence interpretation, for which we develop efficient inference algorithms to address standard HMM usage such as the computation of likelihoods and most probable explanations, as well as performing filtering and predictive inference. Experiments with real data show that iHMMs produce more reliable inferences without compromising the computational efficiency.
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Recently there has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and architectural complexity). Once one has learned a model based on their devised method, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Unfortunately, the standard tests used for this purpose are not able to jointly consider performance measures. The aim of this paper is to resolve this issue by developing statistical procedures that are able to account for multiple competing measures at the same time. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameter of such models, as usually the number of studied cases is very reduced in such comparisons. Real data from a comparison among general purpose classifiers is used to show a practical application of our tests.
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The association between oral bisphosphonate use and upper gastrointestinal cancer has been controversial. Therefore, we examined the association with esophageal and gastric cancer within the Kaiser Permanente, Northern California population. A total of 1,011 cases of esophageal (squamous cell carcinoma and adenocarcinoma) and 1,923 cases of gastric adenocarcinoma (cardia, non-cardia and other) diagnosed between 1997 and 2011 from the Kaiser Permanente, Northern California cancer registry were matched to 49,886 and 93,747 controls, respectively. Oral bisphosphonate prescription fills at least one year prior to the index date were extracted. Conditional logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (95% CI) for the associations between prospectively evaluated oral bisphosphonate use with incident esophageal and gastric cancer diagnoses with adjustment for potential confounders. After adjustment for potential confounders, no significant associations were found for esophageal squamous cell carcinoma (OR 0.88; 95% CI: 0.51, 1.52), esophageal adenocarcinoma (OR 0.68; 95% CI: 0.37, 1.24), or gastric non-cardia adenocarcinoma (OR 0.83, 95% CI: 0.59, 1.18), but we observed an adverse association with gastric cardia adenocarcinoma (OR 1.64; 95% CI: 1.07, 2.50). In conclusion, we observed no association between oral bisphosphonate use and esophageal cancer risk within a large community-based population. A significant association was detected with gastric cardia and other adenocarcinoma risk, although this needs to be replicated.
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BACKGROUND & AIMS: Individuals who began taking low-dose aspirin before they were diagnosed with colorectal cancer were reported to have longer survival times than patients who did not take this drug. We investigated survival times of patients who begin taking low-dose aspirin after a diagnosis of colorectal cancer in a large population-based cohort study.
METHODS: We performed a nested case-control analysis using a cohort of 4794 patients diagnosed with colorectal cancer from 1998 through 2007, identified from the UK Clinical Practice Research Datalink and confirmed by cancer registries. There were 1559 colorectal cancer-specific deaths, recorded by the Office of National Statistics; these were each matched with up to 5 risk-set controls. Conditional logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI), based on practitioner-recorded aspirin usage.
RESULTS: Overall, low-dose aspirin use after a diagnosis of colorectal cancer was not associated with colorectal cancer-specific mortality (adjusted OR = 1.06; 95% CI: 0.92-1.24) or all-cause mortality (adjusted OR = 1.06; 95% CI: 0.94-1.19). A dose-response association was not apparent; for example, low-dose aspirin use for more than 1 year after diagnosis was not associated with colorectal cancer-specific mortality (adjusted OR = 0.98; 95% CI: 0.82-1.19). There was also no association between low-dose aspirin usage and colon cancer-specific mortality (adjusted OR = 1.02; 95% CI: 0.83-1.25) or rectal cancer-specific mortality (adjusted OR = 1.10; 95% CI: 0.88-1.38).
CONCLUSIONS: In a large population-based cohort, low-dose aspirin usage after diagnosis of colorectal cancer did not increase survival time.
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Tendo em conta que um dos principais objectivos da União Europeia é a aproximação dos níveis de vida dos cidadãos europeus, este trabalho testa a hipótese de convergência entre as regiões NUTS II da União Europeia no período de 1990 a 2001, através da análise da dispersão e estimação de regressões “tipo Barro” que relacionam o crescimento com o nível de rendimento inicial e outras variáveis. Identifica os factores que explicam as diferenças regionais no produto per capita, produtividade e produto por pessoa com idade para trabalhar, mostrando as diferenças de resultados consoante a variável utilizada. Os resultados mostram a existência de convergência do produto per capita e do produto por trabalhador, mas não do produto por pessoa com idade para trabalhar, uma vez que a evolução da demografia tem-se mostrado favorável à redução das disparidades, mas o emprego não. Procura também avaliar se a eligibilidade das regiões como “objectivo 1”, no âmbito da política comunitária, permitiu um maior crescimento das mesmas. Encontra ainda evidência de convergência condicional entre as regiões da UE, com o dinamismo das regiões vizinhas a terem um impacto positivo na velocidade de convergência regional, mostrando-nos a importância do investimento em acessibilidades que tornem as regiões periféricas cada vez mais próximas dos grandes centros económicos.
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The introduction of chemicals into the environment by human activities may represent a serious risk to environmental and human health. Environmental risk assessment requires the use of efficient and sensitive tools to determine the impact of contaminants on the ecosystems. The use of zebrafish for the toxicity assessment of pharmaceuticals, drugs, and pollutants, is becoming well accepted due to zebrafish unique advantages for the screening of compounds for hazard identification. The aim of the present work is to apply toxicogenomic approaches to identify novel biomarkers and uncovered potential modes of action of classic and emergent contaminants able to disrupt endocrine systems, such as the Retinoic Acid Receptor, Retinoid X Receptor and the Aryl Hydrocarbon Receptor. This study relies on different nuclear and cytosolic protein receptors and other conditional (ligand- or stress- activated) transcriptional factors that are intimately involved in the regulation of defensome genes and in mechanisms of chemical toxicity. The transcriptomic effects of organic compounds, endogenous compounds, and nanoparticles were analysed during the early stages of zebrafish development. Studying the gene expression profiles of exposed and unexposed organisms to pollutants using microarrays allowed the identification of specific gene markers and to establish a "genetic code" for the tested compounds. Changes in gene expression were observed at toxicant concentrations that did not cause morphological effects. Even at low toxicant concentrations, the observed changes in transcript levels were robust for some target genes. Microarray responses of selected genes were further complemented by the real time quantitative polymerase chain reaction (qRT-PCR) methodology. The combination of bio-informatic, toxicological analyses of differential gene expression profiles, and biochemical and phenotypic responses across the treatments allowed the identification of uncovered potential mechanisms of action. In addition, this work provides an integrated set of tools that can be used to aid management-decision making by improving the predictive capability to measure environmental stress of contaminants in freshwater ecosystems. This study also illustrates the potential of zebrafish embryos for the systematic, large-scale analysis of chemical effects on developing vertebrates.
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As comunicações quânticas aplicam as leis fundamentais da física quântica para codificar, transmitir, guardar e processar informação. A mais importante e bem-sucedida aplicação é a distribuição de chaves quânticas (QKD). Os sistemas de QKD são suportados por tecnologias capazes de processar fotões únicos. Nesta tese analisamos a geração, transmissão e deteção de fotões únicos e entrelaçados em fibras óticas. É proposta uma fonte de fotões única baseada no processo clássico de mistura de quatro ondas (FWM) em fibras óticas num regime de baixas potências. Implementamos essa fonte no laboratório, e desenvolvemos um modelo teórico capaz de descrever corretamente o processo de geração de fotões únicos. O modelo teórico considera o papel das nãolinearidades da fibra e os efeitos da polarização na geração de fotões através do processo de FWM. Analisamos a estatística da fonte de fotões baseada no processo clássico de FWM em fibras óticas. Derivamos um modelo teórico capaz de descrever a estatística dessa fonte de fotões. Mostramos que a estatística da fonte de fotões evolui de térmica num regime de baixas potências óticas, para Poissoniana num regime de potências óticas moderadas. Validamos experimentalmente o modelo teórico, através do uso de fotodetetores de avalanche, do método estimativo da máxima verossimilhança e do algoritmo de maximização de expectativa. Estudamos o processo espontâneo de FWM como uma fonte condicional de fotões únicos. Analisamos a estatística dessa fonte em termos da função condicional de coerência de segunda ordem, considerando o espalhamento de Raman na geração de pares de fotões, e a perda durante a propagação de fotões numa fibra ótica padrão. Identificamos regimes apropriados onde a fonte é quase ideal. Fontes de pares de fotões implementadas em fibras óticas fornecem uma solução prática ao problema de acoplamento que surge quando os pares de fotões são gerados fora da fibra. Exploramos a geração de pares de fotões através do processo espontâneo de FWM no interior de guias de onda com suceptibilidade elétrica de terceira ordem. Descrevemos a geração de pares de fotões em meios com elevado coeficiente de absorção, e identificamos regimes ótimos para o rácio contagens coincidentes/acidentais (CAR) e para a desigualdade de Clauser, Horne, Shimony, and Holt (CHSH), para o qual o compromisso entre perda do guia de onda e não-linearidades maximiza esses parâmetros.
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A modelação e análise de séries temporais de valores inteiros têm sido alvo de grande investigação e desenvolvimento nos últimos anos, com aplicações várias em diversas áreas da ciência. Nesta tese a atenção centrar-se-á no estudo na classe de modelos basedos no operador thinning binomial. Tendo como base o operador thinning binomial, esta tese focou-se na construção e estudo de modelos SETINAR(2; p(1); p(2)) e PSETINAR(2; 1; 1)T , modelos autorregressivos de valores inteiros com limiares autoinduzidos e dois regimes, admitindo que as inovações formam uma sucessão de variáveis independentes com distribuição de Poisson. Relativamente ao primeiro modelo analisado, o modelo SETINAR(2; p(1); p(2)), além do estudo das suas propriedades probabilísticas e de métodos, clássicos e bayesianos, para estimar os parâmetros, analisou-se a questão da seleção das ordens, no caso de elas serem desconhecidas. Com este objetivo consideraram-se algoritmos de Monte Carlo via cadeias de Markov, em particular o algoritmo Reversible Jump, abordando-se também o problema da seleção de modelos, usando metodologias clássica e bayesiana. Complementou-se a análise através de um estudo de simulação e uma aplicação a dois conjuntos de dados reais. O modelo PSETINAR(2; 1; 1)T proposto, é também um modelo autorregressivo com limiares autoinduzidos e dois regimes, de ordem unitária em cada um deles, mas apresentando uma estrutura periódica. Estudaram-se as suas propriedades probabilísticas, analisaram-se os problemas de inferência e predição de futuras observações e realizaram-se estudos de simulação.
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This thesis focuses on the application of optimal alarm systems to non linear time series models. The most common classes of models in the analysis of real-valued and integer-valued time series are described. The construction of optimal alarm systems is covered and its applications explored. Considering models with conditional heteroscedasticity, particular attention is given to the Fractionally Integrated Asymmetric Power ARCH, FIAPARCH(p; d; q) model and an optimal alarm system is implemented, following both classical and Bayesian methodologies. Taking into consideration the particular characteristics of the APARCH(p; q) representation for financial time series, the introduction of a possible counterpart for modelling time series of counts is proposed: the INteger-valued Asymmetric Power ARCH, INAPARCH(p; q). The probabilistic properties of the INAPARCH(1; 1) model are comprehensively studied, the conditional maximum likelihood (ML) estimation method is applied and the asymptotic properties of the conditional ML estimator are obtained. The final part of the work consists on the implementation of an optimal alarm system to the INAPARCH(1; 1) model. An application is presented to real data series.
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The Asymmetric Power Arch representation for the volatility was introduced by Ding et al.(1993) in order to account for asymmetric responses in the volatility in the analysis of continuous-valued financial time series like, for instance, the log-return series of foreign exchange rates, stock indices or share prices. As reported by Brannas and Quoreshi (2010), asymmetric responses in volatility are also observed in time series of counts such as the number of intra-day transactions in stocks. In this work, an asymmetric power autoregressive conditional Poisson model is introduced for the analysis of time series of counts exhibiting asymmetric overdispersion. Basic probabilistic and statistical properties are summarized and parameter estimation is discussed. A simulation study is presented to illustrate the proposed model. Finally, an empirical application to a set of data concerning the daily number of stock transactions is also presented to attest for its practical applicability in data analysis.
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Relatório da Prática de Ensino Supervisionada, Mestrado em Ensino de História e de Geografia no 3º Ciclo do Ensino Básico e Ensino Secundário, Universidade de Lisboa, 2011
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Relatório da Prática de Ensino Supervisionada, Ensino de História e Geografia no 3.º Ciclo do Ensino Básico e Ensino Secundário, Universidade de Lisboa, 2013