La transplantation d'organe est le meilleur traitement en cas de défaillance terminale d'un organe vital. Cependant, la survie sur le long terme est limitée par la perte inexorable de la fonction des greffons. Cette dernière est attribuée à l'inflammation microvasculaire (1MV) causée par la réponse anticorps contre les alloantigènes (rejet humoral chronique (RHC)). En analysant une cohorte de 129 transplantés rénaux présentant de l'1MV sur une biopsie de greffon, nous avons trouvé que, dans la moitié des cas, les lésions n'étaient pas médiées par les anticorps. Chez ces patients, des études génétiques ont révélé une prévalence plus élevée de « mismatches » entre les molécules HLA de classe 1 (HLA-1) du donneur et les « Killer-cell immunoglobulin-receptors » (K1R) inhibiteurs des NK du receveur. Nous avons émis l'hypothèse que la nature allogénique de l'endothélium du greffon pouvait créer un « pseudo-missing-self ». De ce fait, les NK du receveur, exposés à des stimuli inflammatoires, ne reçoivent plus les signaux inhibiteurs transmis par le HLA-1 de la part des cellules endothéliales du donneur. Dans un modèle de co-culture de cellules endothéliales et de NK humains, nous avons démontré que l'absence d'un ligand HLA-1 du soi sur la cellule endothéliale peut activer les NK. Cette activation dépend de la voie mTOR dans les NK, qui peut être bloquée par la rapamycine, un inhibiteur de mTORC1 disponible en clinique. Enfin, nous avons confirmé l'existence de rejets NK induit par le « missing-self » et leur sensibilité à la rapamycine dans un modèle murin de transplantation cardiaque. Notre travail identifie un nouveau type de rejet chronique, exclusivement médié par l'immunité innée, les NK, ayant le même impact délétère sur la survie des greffons que le RHC. Cependant, alors qu'il n'y a pas de traitement disponible pour le RHC, les inhibiteurs de mTOR préviennent efficacement le développement de lésions dans un modèle murin de rejet vasculaire chronique induit par le « missing self » / Organ transplantation is the best treatment for terminal organ failure. However, long-term outcome of organ transplantation remains limited by inexorable loss of graft function, which the prevalent dogma links to the microvascular inflammation (MVI) triggered by the recipient's antibody response against alloantigens (antibody-mediated chronic rejection, AMR). Analysing a cohort of 129 renal transplant patients with MVI on graft biopsy, we found that, in half of the cases, histological lesions were not mediated by antibodies. In these patients, genetic studies revealed a higher prevalence of mismatches between donor HLA-I and inhibitory Killer-cell immunoglobulin-receptors (KIR) of recipient's NK cells. We hypothesized that the allogeneic nature of graft endothelium could create a "pseudo-missing self" situation, thereby the recipient's NK cells exposed to inflammatory stimuli would not receive HLA I-mediated inhibitory signals from donor endothelial cells. In co-culture experiments with human NK cells and endothelial cells, we demonstrated that the lack of self HLA-I on endothelial cells can activate NK. This activation triggers mTOR pathway in NK, which can be blocked by rapamycin, a commercially available inhibitor of mTORC1. Finally, we confirmed the existence of missing self-induced rejection and its sensitivity to mTOR inhibition in a murine heart transplantation model. Our work identifies a new type of chronic rejection, exclusively mediated by innate NK cells, with the same detrimental impact on graft survival as AMR. However, while no therapy is available for AMR, mTOR inhibitors efficiently prevent the development of lesions in murine models of NK cell-mediated chronic vascular rejection

In this thesis we develop methods for dealing with missing data in a univariate response variable when estimating regression parameters. Missing outcome data is a problem in a number of applications, one of which is follow-up studies. In follow-up studies data is collected at two (or more) occasions, and it is common that only some of the initial participants return at the second occasion. This is the case in Paper II, where we investigate predictors of decline in self reported health in older populations in Sweden, the Netherlands and Italy. In that study, around 50% of the study participants drop out. It is common that researchers rely on the assumption that the missingness is independent of the outcome given some observed covariates. This assumption is called data missing at random (MAR) or ignorable missingness mechanism. However, MAR cannot be tested from the data, and if it does not hold, the estimators based on this assumption are biased. In the study of Paper II, we suspect that some of the individuals drop out due to bad health. If this is the case the data is not MAR. One alternative to MAR, which we pursue, is to incorporate the uncertainty due to missing data into interval estimates instead of point estimates and uncertainty intervals instead of confidence intervals. An uncertainty interval is the analog of a confidence interval but wider due to a relaxation of assumptions on the missing data. These intervals can be used to visualize the consequences deviations from MAR have on the conclusions of the study. That is, they can be used to perform a sensitivity analysis of MAR. The thesis covers different types of linear regression. In Paper I and III we have a continuous outcome, in Paper II a binary outcome, and in Paper IV we allow for mixed effects with a continuous outcome. In Paper III we estimate the effect of a treatment, which can be seen as an example of missing outcome data.

The aim of this thesis is to examine interactivity in the context of performance-based interactive art. The questions asked are: what are the conditions of interactivity, how interactivity happens, and what artistic results it may yield. The method is an analysis based on close studies of three performance-based interactive artworks by applying theories of interactivity, audience participation, and collaboration. First, current theories are outlined, after which, the three artworks are introduced in detail. Next, the artworks are examined, thematically rather than individually, expanding on parameters such as the degree of artistic direction of the artwork, the degree of agency allowed to the spectator in their interaction with the work, and the idea of the “passive” spectator as being “activated” by interactive art. It is shown that the actualization of the interactive gesture inscribed within the work depends on the spectator’s subjectivity and obedience to said gesture. Thus, the conditions of the interaction are not decided by the work/artist alone, but also and equally, by the spectator. Consequently, the actualized interactivity may not immediately correspond with the (possible) authorial intent and may even be unexpected or "infelicitous." The problematic of the thesis falls within the scope of current discourse concerning the striving for interactivity between the work of art and its spectator/audience. While that discourse has often been focused on authorial intent or has implied an ideal spectator, this thesis points to the significance of subjectivity with regards to interactivity and thereby adds increased complexity to the concept of interactivity.

This diploma thesis is focused on the analysis of specific type of Value Added Tax fraud called carousel fraud. The main object is to analyse and quantify tax fraud impact specifically oriented to VAT area, i.e. carousel fraud, and suggest the way of prevention which prevent its emergence and expansion in domestic economy. In the theoretical part the information about VAT, the Czech Law on Value Added Tax, EU directive on the common system of Value Added Tax and carousel fraud is described and furthermore, the losses to carousel fraud are mentioned. The practical part is applied to analysis of profit from carousel fraud as a whole and profits of individual participants in carousel fraud, analysis of the Court of Justice of the European Union judgements and the Supreme Administrativ Court of the Czech Republic judgements and carousel fraud prevention in the EU and at home.

abstract: Accurate data analysis and interpretation of results may be influenced by many potential factors. The factors of interest in the current work are the chosen analysis model(s), the presence of missing data, and the type(s) of data collected. If analysis models are used which a) do not accurately capture the structure of relationships in the data such as clustered/hierarchical data, b) do not allow or control for missing values present in the data, or c) do not accurately compensate for different data types such as categorical data, then the assumptions associated with the model have not been met and the results of the analysis may be inaccurate. In the presence of clustered/nested data, hierarchical linear modeling or multilevel modeling (MLM; Raudenbush & Bryk, 2002) has the ability to predict outcomes for each level of analysis and across multiple levels (accounting for relationships between levels) providing a significant advantage over single-level analyses. When multilevel data contain missingness, multilevel multiple imputation (MLMI) techniques may be used to model both the missingness and the clustered nature of the data. With categorical multilevel data with missingness, categorical MLMI must be used. Two such routines for MLMI with continuous and categorical data were explored with missing at random (MAR) data: a formal Bayesian imputation and analysis routine in JAGS (R/JAGS) and a common MLM procedure of imputation via Bayesian estimation in BLImP with frequentist analysis of the multilevel model in Mplus (BLImP/Mplus). Manipulated variables included interclass correlations, number of clusters, and the rate of missingness. Results showed that with continuous data, R/JAGS returned more accurate parameter estimates than BLImP/Mplus for almost all parameters of interest across levels of the manipulated variables. Both R/JAGS and BLImP/Mplus encountered convergence issues and returned inaccurate parameter estimates when imputing and analyzing dichotomous data. Follow-up studies showed that JAGS and BLImP returned similar imputed datasets but the choice of analysis software for MLM impacted the recovery of accurate parameter estimates. Implications of these findings and recommendations for further research will be discussed. / Dissertation/Thesis / Doctoral Dissertation Educational Psychology 2016

Made available in DSpace on 2015-04-11T13:56:31Z (GMT). No. of bitstreams: 1 Dissertacao Isabela.pdf: 1836486 bytes, checksum: bf6c8c5db338bc806954228e1b7b94fe (MD5) Previous issue date: 2009-07-19 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / As séries históricas de 11 anos de desembarque das espécies Prochilodus nigricans e Semaprochilodus sp., ocorridas no período de janeiro de 1992 a dezembro de 2002 no município de Santarém-PA, foram utilizadas para análise e predição, juntamente com séries de SOI, SST‟s e níveis hidrológicos dos rios Amazonas e Tapajós. Infelizmente, os dados relativos às séries de desembarque de jaraquis e das cotas do Rio tapajós possuíam missing values, o que impossibilitava a realização de análises e predições, porém, o uso da modelagem de Box & Jenkins permitiu completar essas lacunas. Após as estimações dos missing values, promovemos a análise espectral em todas as variáveis citadas, verificamos ciclos relacionados com os fenômenos El Niño e La Niña, com duração de 2 a 7 anos, notamos que esses eventos influenciaram fortemente na variação do nível dos rios e, conseqüentemente, no desembarque dessas espécies. Notamos, também, aumento dos valores de desembarque nos períodos de 2 a 3 anos. Estes períodos podem estar relacionados à ocorrência de fortes cheias que, provavelmente, geraram o sucesso reprodutivo dessas espécies, levando ao aumento das capturas após 2 ou 3 anos. Outras oscilações foram observadas nos desembarques e nível dos rios, tais como oscilações semi-anuais e intra-sazonais. Sabemos que estas oscilações possuem certa influência sobre as precipitações na região amazônica e, portanto, sobre a pesca, mas ainda são necessários estudos mais apurados para o melhor entendimento dessas oscilações sobre o comportamento da pesca dessas espécies. Os modelos de Box & Jenkins também foram usados para a modelagem de desembarque nos anos de 2003 e 2004, a fim de verificar a eficiência desta ferramenta para predições. Empregamos ferramentas métricas que definem o erro das predições, com isso, observamos que os modelos ARIMA são eficientes na predição para médio e curto prazo (12 meses), no qual o modelo demonstrou bom ajuste nas predições para o ano de 2003 em ambas as espécies. / As séries históricas de 11 anos de desembarque das espécies Prochilodus nigricans e Semaprochilodus sp., ocorridas no período de janeiro de 1992 a dezembro de 2002 no município de Santarém-PA, foram utilizadas para análise e predição, juntamente com séries de SOI, SST‟s e níveis hidrológicos dos rios Amazonas e Tapajós. Infelizmente, os dados relativos às séries de desembarque de jaraquis e das cotas do Rio tapajós possuíam missing values, o que impossibilitava a realização de análises e predições, porém, o uso da modelagem de Box & Jenkins permitiu completar essas lacunas. Após as estimações dos missing values, promovemos a análise espectral em todas as variáveis citadas, verificamos ciclos relacionados com os fenômenos El Niño e La Niña, com duração de 2 a 7 anos, notamos que esses eventos influenciaram fortemente na variação do nível dos rios e, conseqüentemente, no desembarque dessas espécies. Notamos, também, aumento dos valores de desembarque nos períodos de 2 a 3 anos. Estes períodos podem estar relacionados à ocorrência de fortes cheias que, provavelmente, geraram o sucesso reprodutivo dessas espécies, levando ao aumento das capturas após 2 ou 3 anos. Outras oscilações foram observadas nos desembarques e nível dos rios, tais como oscilações semi-anuais e intra-sazonais. Sabemos que estas oscilações possuem certa influência sobre as precipitações na região amazônica e, portanto, sobre a pesca, mas ainda são necessários estudos mais apurados para o melhor entendimento dessas oscilações sobre o comportamento da pesca dessas espécies. Os modelos de Box & Jenkins também foram usados para a modelagem de desembarque nos anos de 2003 e 2004, a fim de verificar a eficiência desta ferramenta para predições. Empregamos ferramentas métricas que definem o erro das predições, com isso, observamos que os modelos ARIMA são eficientes na predição para médio e curto prazo (12 meses), no qual o modelo demonstrou bom ajuste nas predições para o ano de 2003 em ambas as espécies.

This dissertation presents a search for dark matter particles produced in association with a Z boson in proton-proton collisions. The dataset consists of 139 fb^{-1} of collision events with centre-of-mass energy of 13 TeV, and was collected by the ATLAS detector from 2015-2018 at the Large Hadron Collider. Signal region events are required to contain a Z boson that decays leptonically to either e^+e^- or μ^+μ^-, and a significant amount of missing transverse momentum, which indicates the presence of undetected particles. Two types of dark matter models are studied: (1) simplified models with an s-channel axial-vector or vector mediator that couples to dark matter Dirac fermions, and (2) two-Higgs-doublet models with an additional pseudo-scalar that couples to dark matter Dirac fermions. The main Standard Model background sources are ZZ, WZ, non-resonant l^+l^-, and Z+jets processes, which are estimated using a combination of data and/or simulation. A new reweighting technique is developed for estimating the Z+jets background using γ+jets events in data; the resulting estimate significantly improves on the statistical and systematic errors compared to the estimate obtained from simulation. The observed data in the signal region are compared to Standard Model prediction using a transverse mass discriminant distribution. No significant excess in data is observed for the simplified models and two-Higgs-doublet models studied. A statistical analysis is performed and several exclusion limits are set on the parameters of the dark matter models. Results are compared to direct detection experiments, the CMS experiment, and other ATLAS searches. Prospects and improvements for future iterations of the search are also presented. / Graduate

BACKGROUND:The extant squamates (>9400 known species of lizards and snakes) are one of the most diverse and conspicuous radiations of terrestrial vertebrates, but no studies have attempted to reconstruct a phylogeny for the group with large-scale taxon sampling. Such an estimate is invaluable for comparative evolutionary studies, and to address their classification. Here, we present the first large-scale phylogenetic estimate for Squamata.RESULTS:The estimated phylogeny contains 4161 species, representing all currently recognized families and subfamilies. The analysis is based on up to 12896 base pairs of sequence data per species (average = 2497 bp) from 12 genes, including seven nuclear loci (BDNF, c-mos, NT3, PDC, R35, RAG-1, and RAG-2), and five mitochondrial genes (12S, 16S, cytochrome b, ND2, and ND4). The tree provides important confirmation for recent estimates of higher-level squamate phylogeny based on molecular data (but with more limited taxon sampling), estimates that are very different from previous morphology-based hypotheses. The tree also includes many relationships that differ from previous molecular estimates and many that differ from traditional taxonomy.CONCLUSIONS:We present a new large-scale phylogeny of squamate reptiles that should be a valuable resource for future comparative studies. We also present a revised classification of squamates at the family and subfamily level to bring the taxonomy more in line with the new phylogenetic hypothesis. This classification includes new, resurrected, and modified subfamilies within gymnophthalmid and scincid lizards, and boid, colubrid, and lamprophiid snakes.

Missing values are a serious problem in surveys. The literature suggests to replace these with realistic values using imputation methods. This master thesis examines four different imputation techniques concerning their ability for handling missing data. Therefore, mean imputation, conditional mean imputation, Expectation-Maximization algorithm and Markov-Chain-Monte-Carlo method are presented. In addition, the three first mentioned methods were simulated by using a large real data set. To analyse the quality of these techniques a metric variable of the original data set was chosen to generate some missing values considering different percentages of missingness and common missing data mechanism. After the replacement of the simulated missing values, several statistical parameters, like quantiles, arithmetic mean and variance of all completed data sets were calculated in order to compare them with the parameters from the original data set. The results, that have been established by empiric data analysis, show that the Expectation-Maximization algorithm estimates all considered statistical parameters of the complete data set far better than the other analysed imputation methods, although the assumption of a multivariate normal distribution could not be achieved. It is found, that the mean as well as the conditional mean imputation produce statistically significant estimator for the arithmetic mean under the supposition of missing completely at random, whereas other parameters as the variance do not show the estimated effects. Generally, the accuracy of all estimators from the three imputation methods decreases with increasing percentage of missingness. The results lead to the conclusion that the Expectation-Maximization algorithm should be preferred over the mean and the conditional mean imputation.

In cluster randomized trials (CRTs), groups of participants are randomized as opposed to individual participants. This design is often chosen to minimize treatment arm contamination or to enhance compliance among participants. In CRTs, we cannot assume independence among individuals within the same cluster because of their similarity, which leads to decreased statistical power compared to individually randomized trials. The intracluster correlation coefficient (ICC) is crucial in the design and analysis of CRTs, and measures the proportion of total variance due to clustering. Missing data is a common problem in CRTs and should be accommodated with appropriate statistical techniques because they can compromise the advantages created by randomization and are a potential source of bias. In three papers, I investigate statistical approaches for handling missing data in CRTs. In the first paper, I carry out a systematic review evaluating current practice of handling missing data in CRTs. The results show high rates of missing data in the majority of CRTs, yet handling of missing data remains suboptimal. Fourteen (16%) of the 86 reviewed trials reported carrying out a sensitivity analysis for missing data. Despite suggestions to weaken the missing data assumption from the primary analysis, only five of the trials weakened the assumption. None of the trials reported using missing not at random (MNAR) models. Due to the low proportion of CRTs reporting an appropriate sensitivity analysis for missing data, the second paper aims to facilitate performing a sensitivity analysis for missing data in CRTs by extending the pattern mixture approach for missing clustered data under the MNAR assumption. I implement multilevel multiple imputation (MI) in order to account for the hierarchical structure found in CRTs, and multiply imputed values by a sensitivity parameter, k, to examine parameters of interest under different missing data assumptions. The simulation results show that estimates of parameters of interest in CRTs can vary widely under different missing data assumptions. A high proportion of missing data can occur among CRTs because missing data can be found at the individual level as well as the cluster level. In the third paper, I use a simulation study to compare missing data strategies to handle missing cluster level covariates, including the linear mixed effects model, single imputation, single level MI ignoring clustering, MI incorporating clusters as fixed effects, and MI at the cluster level using aggregated data. The results show that when the ICC is small (ICC ≤ 0.1) and the proportion of missing data is low (≤ 25\%), the mixed model generates unbiased estimates of regression coefficients and ICC. When the ICC is higher (ICC > 0.1), MI at the cluster level using aggregated data performs well for missing cluster level covariates, though caution should be taken if the percentage of missing data is high.