<p>Because of the difficulties in concluding whether a person has drowned or not, information that could be relevant for postmortal diagnosis of drowning was studied. With postmortal CT images lung volume, mean attenuation, anterior-posterior difference, lung density profile and amount of water within the lungs were investigated.</p><p>The report also evaluates three examples of software that calculates lung volume from postmortal CT images: Siemens’ Syngo Pulmo CT, Siemens‘ Volume Evaluation and GE Medical Systems’ Volume Viewer. The method used at autopsy was also studied. The repeatability and validity were tested and sources of errors identified.</p><p>Repeatability and validity for the three tested types of software were acceptable, while the method used at autopsy had to be improved. The study also showed that lung volume related to length, anterior-posterior difference and lung density profile seemed to vary between drowned and other deceased. These measures might conclude whether a person has drowned.</p>

Paediatric posterior fossa tumors are often effectively controlled with a combination of radiation, chemotherapy and surgery. However, therapeutic craniospinal radiation has been associated with widespread cognitive late effects. Working memory is one such cognitive ability that has yet to be fully examined in this clinical population. Bilateral tracts connecting the cerebellum with the DLPFC were delineated using DTI tractography in all participants, replicating the cerebrocerebellar pathway outlined in an animal model. There were observable differences in white matter integrity (quantified by DTI measures of anisotropy, and mean, axial, and radial diffusivity) of the cerebellum-DLPFC pathway in patients versus controls. Additionally, working memory deficits that were found in patients were correlated with DTI indices pertaining to the cerebellum-DLPFC pathway. Therefore, this thesis is the first to explore the possible relations between white matter integrity of this pathway following treatment for paediatric posterior fossa tumors and working memory function.

Paediatric posterior fossa tumors are often effectively controlled with a combination of radiation, chemotherapy and surgery. However, therapeutic craniospinal radiation has been associated with widespread cognitive late effects. Working memory is one such cognitive ability that has yet to be fully examined in this clinical population. Bilateral tracts connecting the cerebellum with the DLPFC were delineated using DTI tractography in all participants, replicating the cerebrocerebellar pathway outlined in an animal model. There were observable differences in white matter integrity (quantified by DTI measures of anisotropy, and mean, axial, and radial diffusivity) of the cerebellum-DLPFC pathway in patients versus controls. Additionally, working memory deficits that were found in patients were correlated with DTI indices pertaining to the cerebellum-DLPFC pathway. Therefore, this thesis is the first to explore the possible relations between white matter integrity of this pathway following treatment for paediatric posterior fossa tumors and working memory function.

Se trata de 1147 pacientes (1547 ojos) operados de catarata y observados durante 3 años. En los que 827 ojos fueron operados mediante técnica de extracción extracapsular de catarata (EECC), 358 ojos fuerons operados con facoemulsificación y aspiración exhaustiva de las células epiteliales (AECE). La presencia de opacidad de la cápsula posterior (OPC) fue significativamente menor (p>0.05) en el grupo en el que se realizó AECE, en comparación con las otras dos técnicas. La dilatación pupilar insuficiente (menor de 8 milímetros) durante la cirugía y el antecedente de uveítis mostraron ser factor de riesgo en el desarrollo de OCP. La agudez visual en los 3 grupos de pacientes no mostró una diferencia significativa, debido a que se consideraron también los pacientes a los cuales se les realizó capsulotomía con YAG láser. La diabetes, el glaucoma, la miopía y el síndorme de pseudoexfoliación no mostraron ser factor de riesgo en la OCP. / It is a 3 -year study of 1147 cataract-operated person (1574 eyes), 827 eyes were made an extracapsular cataract surgery, 358 eyes were made a facoemulsification and 389 eyes were treat by epithelial cells exhaustive aspiration (ECEA). The posterior capsule opacification (PCO) was statistically significant low frequent in ECEA gropu (p>0.05). The low pupil dilatation (8 millimeters and less) and the patients with uveitis was a risk group factor for development of PCO. The best visual acuity 3 years after surgery was similar in all the groups, because the patients who was made a YAG laser capsulorhexis were include in the study too.

Because of the difficulties in concluding whether a person has drowned or not, information that could be relevant for postmortal diagnosis of drowning was studied. With postmortal CT images lung volume, mean attenuation, anterior-posterior difference, lung density profile and amount of water within the lungs were investigated. The report also evaluates three examples of software that calculates lung volume from postmortal CT images: Siemens’ Syngo Pulmo CT, Siemens‘ Volume Evaluation and GE Medical Systems’ Volume Viewer. The method used at autopsy was also studied. The repeatability and validity were tested and sources of errors identified. Repeatability and validity for the three tested types of software were acceptable, while the method used at autopsy had to be improved. The study also showed that lung volume related to length, anterior-posterior difference and lung density profile seemed to vary between drowned and other deceased. These measures might conclude whether a person has drowned.

There is extensive evidence that structures in the anterior attentional system (i.e. dorsolateral prefrontal cortex and anterior cingulate) are susceptible to normal aging processes, whereas structural changes in the posterior attentional system are minimal. Using the Stroop priming task, we investigated whether reducing the involvement of the anterior attentional system by pre-cuing the location of the target stimulus would eliminate age differences in interference. Older adults continued to be susceptible to interference when the location cue was ambiguous or invalid, but were less susceptible when the target location of a stimulus was presented with a valid cue.

<p>The dissertation focuses on solving some important theoretical and methodological problems associated with Bayesian modeling of infinite dimensional `objects', popularly called nonparametric Bayes. The term `infinite dimensional object' can refer to a density, a conditional density, a regression surface or even a manifold. Although Bayesian density estimation as well as function estimation are well-justified in the existing literature, there has been little or no theory justifying the estimation of more complex objects (e.g. conditional density, manifold, etc.). Part of this dissertation focuses on exploring the structure of the spaces on which the priors for conditional densities and manifolds are supported while studying how the posterior concentrates as increasing amounts of data are collected.</p><p>With the advent of new acquisition devices, there has been a need to model complex objects associated with complex data-types e.g. millions of genes affecting a bio-marker, 2D pixelated images, a cloud of points in the 3D space, etc. A significant portion of this dissertation has been devoted to developing adaptive nonparametric Bayes approaches for learning low-dimensional structures underlying higher-dimensional objects e.g. a high-dimensional regression function supported on a lower dimensional space, closed curves representing the boundaries of shapes in 2D images and closed surfaces located on or near the point cloud data. Characterizing the distribution of these objects has a tremendous impact in several application areas ranging from tumor tracking for targeted radiation therapy, to classifying cells in the brain, to model based methods for 3D animation and so on. </p><p> </p><p> The first three chapters are devoted to Bayesian nonparametric theory and modeling in unconstrained Euclidean spaces e.g. mean regression and density regression, the next two focus on Bayesian modeling of manifolds e.g. closed curves and surfaces, and the final one on nonparametric Bayes spatial point pattern data modeling when the sampling locations are informative of the outcomes.</p> / Dissertation

The aim of this study is to develop a set-up that can be used by patients performing posterior pelvic tilt exercises to assess and improve the effectiveness of the exercise by visual feedbacks. Lifetime of low back pain prevalence is between %60 and %90. In other words almost everyone encounters the problem of low back pain sometime during their life. Therapeutic and protective exercises are the most important components of treatment for the low back pain. People who have mechanical based low back pain due to postural disorders, have weakness of abdominal and back muscles. Posterior pelvic tilt exercises are one of the effective types of exercises to solve this problem. These can be done standing against a wall or lying on a surface. These exercises are advised to patients generally as home exercise programs. However most patients cannot do their exercises effectively due to lack of training and control. In posterior pelvic tilt exercise, the patient is asked to straighten his/her lumbar lordosis and exert as much pressure as possible to the surface he/she is lying on. It is believed that the efficiency of the exercise is correlated with the amount of this pressure. Entertaining visual feedback may increase patient&amp / #8217 / s motivation and consequently quality of the exercise. In this experimental set-up, pressure variations were determined by three receivers which were placed under the back of the patient to provide feedback for proper posterior pelvic tilt exercises. By means of this experimental set-up training for these exercises was achieved easily and the quality of exercises was improved.

Estimation of location and scale parameters from a random sample of size n is of paramount importance in Statistics. An estimator is called fully efficient if it attains the Cramer-Rao minimum variance bound besides being unbiased. The method that yields such estimators, at any rate for large n, is the method of modified maximum likelihood estimation. Apparently, such estimators cannot be made more efficient by using sample based classical methods. That makes room for Bayesian method of estimation which engages prior distributions and likelihood functions. A formal combination of the prior knowledge and the sample information is called posterior distribution. The posterior distribution is maximized with respect to the unknown parameter(s). That gives HPD (highest probability density) estimator(s). Locating the maximum of the posterior distribution is, however, enormously difficult (computationally and analytically) in most situations. To alleviate these difficulties, we use modified likelihood function in the posterior distribution instead of the likelihood function. We derived the HPD estimators of location and scale parameters of distributions in the family of Generalized Logistic. We have extended the work to experimental design, one way ANOVA. We have obtained the HPD estimators of the block effects and the scale parameter (in the distribution of errors) / they have beautiful algebraic forms. We have shown that they are highly efficient. We have given real life examples to illustrate the usefulness of our results. Thus, the enormous computational and analytical difficulties with the traditional Bayesian method of estimation are circumvented at any rate in the context of experimental design.

Univariate hierarchical Bayes models are being vigorously researched for use in disease mapping, engineering, geology, and ecology. This dissertation shows how the models can also be used to build modelbased risk maps for areabased roadway tra&#64259;c crashes. Countylevel vehicle crash records and roadway data from Texas are used to illustrate the method. A potential extension that uses univariate hierarchical models to develop networkbased risk maps is also discussed. Several Bayesian multivariate spatial models for estimating the tra&#64259;c crash rates from di&#64256;erent types of crashes simultaneously are then developed. The speci&#64257;c class of spatial models considered is conditional autoregressive (CAR) model. The univariate CAR model is generalized for several multivariate cases. A general theorem for each case is provided to ensure that the posterior distribution is proper under improper and &#64258;at prior. The performance of various multivariate spatial models is compared using a Bayesian information criterion. The Markov chain Monte Carlo (MCMC) computational techniques are used for the model parameter estimation and statistical inference. These models are illustrated and compared again with the Texas crash data. There are many directions in which this study can be extended. This dissertation concludes with a short summary of this research and recommends several promising extensions.