Formulation and characterization of a 0.1% rapamycin cream for the treatment of Tuberous Sclerosis Complex-related angiofibromas

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GRMHD formulation of highly super-Chandrasekhar magnetized white dwarfs: stable configurations of non-spherical white dwarfs

The topic of magnetized super-Chandrasekhar white dwarfs is in the limelight, particularly in the last few years, since our proposal of their existence. By full-scale general relativistic magnetohydrodynamic (GRMHD) numerical analysis, we confirm in this work the existence of stable, highly magnetized, significantly super-Chandrasekhar white dwarfs with mass more than 3 solar mass. While a poloidal field geometry renders the white dwarfs oblate, a toroidal field makes them prolate retaining an overall quasi-spherical shape, as speculated in our earlier work. These white dwarfs are expected to serve as the progenitors of over-luminous type Ia supernovae.

THE REPETITION AS A FORMULATION STRATEGY OF ORAL DISCOURSE AND THE TOPICAL ORGANIZATION

The purpose of this paper is to discuss the repetition as construction strategy and textual-interactive formulation, in order to see how this trick is directly related to the topic of discourse, through its concentration and organicity properties. In this way, the use of a word or syntactic structure is classified as a repetition if it operates for the construction of a topical concentration, establishing a relation of reference with the matrix. The main interest of this research is to analyze to what extent the strategy of repetition establishes the maintenance function and the topical conduction, as well as other strategies of textual-interactive constructions, such as parentheses, paraphrases and corrections. We also intend to observe how a topic, being an analytic unit, can particularize the functions of repetition.  

Simultaneous kinetic spectrophotometric determination of norfloxacin and rifampicin in pharmaceutical formulation and human urine samples by use of chemometrics approaches

A kinetic spectrophotometric method with aid of chemometrics is proposed for the simultaneous determination of norfloxacin and rifampicin in mixtures. The proposed method was applied for the simultaneous determination of these two compounds in pharmaceutical formulation and human urine samples, and the results obtained are similar to those obtained by high performance liquid chromatography.

A distributed knowledge management infrastructure based on a Topic Map grid

Modern enterprise knowledge management systems typically require distributed approaches and the integration of numerous heterogeneous sources of information. A powerful foundation for these tasks can be Topic Maps, which not only provide a semantic net-like knowledge representation means and the possibility to use ontologies for modelling knowledge structures, but also offer concepts to link these knowledge structures with unstructured data stored in files, external documents etc. In this paper, we present the architecture and prototypical implementation of a Topic Map application infrastructure, the ‘Topic Grid’, which enables transparent, node-spanning access to different Topic Maps distributed in a network.

Addressing issues in sparseness, ecological bias and formulation of the adjacency matrix in Bayesian spatio-temporal analysis of disease counts

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when analyzing a large birth defect registry in New South Wales. The specific components and related research objectives of the thesis were developed from gaps in the literature on current formulations of the CAR model, and health service planning requirements. Data from a large probabilistically-linked database from 1990 to 2004, consisting of fields from two separate registries: the Birth Defect Registry (BDR) and Midwives Data Collection (MDC) were used in the analyses in this thesis. The main objective was split into smaller goals. The first goal was to determine how the specification of the neighbourhood weight matrix will affect the smoothing properties of the CAR model, and this is the focus of chapter 6. Secondly, I hoped to evaluate the usefulness of incorporating a zero-inflated Poisson (ZIP) component as well as a shared-component model in terms of modeling a sparse outcome, and this is carried out in chapter 7. The third goal was to identify optimal sampling and sample size schemes designed to select individual level data for a hybrid ecological spatial model, and this is done in chapter 8. Finally, I wanted to put together the earlier improvements to the CAR model, and along with demographic projections, provide forecasts for birth defects at the SLA level. Chapter 9 describes how this is done. For the first objective, I examined a series of neighbourhood weight matrices, and showed how smoothing the relative risk estimates according to similarity by an important covariate (i.e. maternal age) helped improve the model’s ability to recover the underlying risk, as compared to the traditional adjacency (specifically the Queen) method of applying weights. Next, to address the sparseness and excess zeros commonly encountered in the analysis of rare outcomes such as birth defects, I compared a few models, including an extension of the usual Poisson model to encompass excess zeros in the data. This was achieved via a mixture model, which also encompassed the shared component model to improve on the estimation of sparse counts through borrowing strength across a shared component (e.g. latent risk factor/s) with the referent outcome (caesarean section was used in this example). Using the Deviance Information Criteria (DIC), I showed how the proposed model performed better than the usual models, but only when both outcomes shared a strong spatial correlation. The next objective involved identifying the optimal sampling and sample size strategy for incorporating individual-level data with areal covariates in a hybrid study design. I performed extensive simulation studies, evaluating thirteen different sampling schemes along with variations in sample size. This was done in the context of an ecological regression model that incorporated spatial correlation in the outcomes, as well as accommodating both individual and areal measures of covariates. Using the Average Mean Squared Error (AMSE), I showed how a simple random sample of 20% of the SLAs, followed by selecting all cases in the SLAs chosen, along with an equal number of controls, provided the lowest AMSE. The final objective involved combining the improved spatio-temporal CAR model with population (i.e. women) forecasts, to provide 30-year annual estimates of birth defects at the Statistical Local Area (SLA) level in New South Wales, Australia. The projections were illustrated using sixteen different SLAs, representing the various areal measures of socio-economic status and remoteness. A sensitivity analysis of the assumptions used in the projection was also undertaken. By the end of the thesis, I will show how challenges in the spatial analysis of rare diseases such as birth defects can be addressed, by specifically formulating the neighbourhood weight matrix to smooth according to a key covariate (i.e. maternal age), incorporating a ZIP component to model excess zeros in outcomes and borrowing strength from a referent outcome (i.e. caesarean counts). An efficient strategy to sample individual-level data and sample size considerations for rare disease will also be presented. Finally, projections in birth defect categories at the SLA level will be made.