Nonlinear analysis of drainage systems to examine surface deformation: an example from Potwar Plateau (Northern Pakistan)

We devise a procedure in order to characterize the relative vulnerability of the Earth's surface to tectonic deformation using the geometrical characteristics of drainage systems. The present study focuses on the nonlinear analysis of drainage networks extracted from Digital Elevation Models in order to localize areas strongly influenced by tectonics. We test this approach on the Potwar Plateau in northern Pakistan. This area is regularly affected by damaging earthquakes. Conventional studies cannot pinpoint the zones at risk, as the whole region is characterized by a sparse and diffuse seismicity. Our approach is based on the fact that rivers tend to linearize under tectonic forcing. Thus, the low fractal dimensions of the Swan, Indus and Jehlum Rivers are attributed to neotectonic activity. A detailed textural analysis is carried out to investigate the linearization, heterogeneity and connectivity of the drainage patterns. These textural aspects are quantified using the fractal dimension, as well as lacunarity and succolarity analysis. These three methods are complimentary in nature, i.e. objects with similar fractal dimensions can be distinguished further with lacunarity and/or succolarity analysis. We generate maps of fractal dimensions, lacunarity and succolarity values using a sliding window of 2.5 arc minutes by 2.5 arc minutes (2.5'×2.5'). These maps are then interpreted in terms of land surface vulnerability to tectonics. This approach allowed us to localize several zones where the drainage system is highly structurally controlled on the Potwar Plateau. The region located between Muree and Muzaffarabad is found to be prone to destructive events whereas the area westward from the Indus seems relatively unaffected. We conclude that a nonlinear analysis of the drainage system is an efficient additional tool to locate areas likely to be affected by massive destructing events affecting the Earth's surface and therefore threaten human activities

Microcomputed tomography: comparison of parameters in image acquisition, reconstruction and processing for the evaluation of the bone repair process and its application to assess the effect of different therapies

CAPES, FAPEMIG, CNPqThe use of microcomputed tomography (microCT) has largely increased. Issues related to its application and parameter definitions still need to be investigated. The objective 1 of this work was to conduct a literature review on the use of microCT in Dentistry for bone tissue investigations and the steps involved in the methodology. The literature review indicated that the characteristics of the sample and the parameters to be evaluated should be considered so that the process of image acquisition, reconstruction and processing are properly carried out. The second objective was to investigate the influence of variables in the microtomographic analysis of bone repair, such as: voxel size and filter thickness used in image acquisition, and the experience of examiners in data analysis. Objective 3 was to determine whether image binning and frame averaging during microCT acquisition affect the morphometric results of bone tissue repair. Objectives 2 and 3 demonstrated that voxel size and image binning affect data analysis, demonstrated by the differences observed in the Tb.Th and BV/TV parameters. Finally, in objective 4, the application of microCT, in association with other methodologies, was used to assess the effect of radiotherapy and lowintensity laser therapy (LLLT) on bone repair of defects grafted with deproteinized mineralized bovine bone (DBBM) and defects filled by clot. It could be concluded, that the amount of neoformed bone tissue observed was smaller in the groups submitted to radiotherapy, and no difference was observed in the results in LLLT groups. The incorporation of graft particles with the newly formed bone was preserved, demonstrating that the osteoconductivity of the biomaterial was maintained even after radiotherapy.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorTese (Doutorado)A utilização da microtomografia computadorizada (microCT) tem crescido enormemente. No entanto, questões relacionadas a sua aplicação e a definições de parâmetros, ainda carecem de investigações. O primeiro objetivo foi realizar uma revisão da literatura sobre o uso da microCT na Odontologia para o estudo do tecido ósseo e as etapas envolvidas na metodologia. A revisão de literatura demonstrou que para cada tipo de análise devem ser consideradas as características da amostra e os parâmetros a serem avaliados para que as etapas de aquisição, reconstrução e processamento da imagem sejam realizadas de forma ideal. O segundo objetivo foi investigar a influência de determinadas variáveis na análise microtomográfica do reparo ósseo, como: o tamanho do voxel e a espessura do filtro utilizado na aquisição de imagem, e a experiência dos examinadores no processamento dos dados. O objetivo 3 foi determinar se o binning da imagem e o número médio das projeções (frame averaging) obtidas durante a aquisição na microCT afetam os resultados morfométricos do reparo do tecido ósseo. Os objetivos 2 e 3 demonstraram que o tamanho do voxel e o binning da imagem afetam a análise dos dados, demonstrado pelas diferenças observadas nos parâmetros Tb.Th e BV/TV. Por último, no objetivo 4, a aplicação da microCT, em associação a outras metodologias, foi utilizada para avaliar o efeito da radioterapia e da laserterapia de baixa intensidade (LLLT) no reparo ósseo de defeitos enxertados com osso bovino mineralizado deproteinizado (DBBM) e não enxertados (coágulo). Pode– se concluir no objetivo 4, por meio da análise microtomográfica e histomorfométrica, que a quantidade de tecido ósseo neoformado observada foi menor nos grupos submetidos à radioterapia, sendo que não foi observada diferença nos resultados pela aplicaçãp da LLLT. A incorporação das partículas do enxerto com o osso neoformado foi preservada demonstrando que a osteocondutividade do biomaterial foi mantida mesmo após a radioterapia.2023-08-2

Caracterización de caminantes en cúmulos de percolación con lagunaridad diversa

En este trabajo de tesis se construyeron redes numéricas correlacionadas mediante el Modelo Dual de Sitios y Enlaces, el cual ha sido ampliamente utilizado en el área de Fisicoquímica de Superficies para estudiar medios porosos. Las redes numéricas con distinta longitud de correlación son manejadas mediante una rutina de percolación de sitios, para obtener el cúmulo infinito. El cúmulo infinito de percolación que luego es caracterizado mediante su dimensión fractal y su lagunaridad. Se observó que la dimensión fractal no es suficiente para caracterizar cúmulos infinitos de percolación construidos sobre sistemas espacialmente correlacionados. Se implementan caminatas aleatorias, se considera el caminante auto-evitante y paseos aleatorios sin regreso. Se contrastan los resultados, se discuten las diferencias observadas y los valores reportados y la posibilidad de su empleo como medios de evolución. Entre los principales resultados se tiene: 1. La longitud de correlación de las redes y la lagunaridad de los cúmulos son características particulares de cada sistema. 2. La conexidad del cúmulo infinito de percolación, construido sobre el medio, es una función de la longitud de correlación de la red casi de manera lineal. 3. Los exponentes de los tres tipos de caminantes son función de la longitud de correlación y difieren entre ellos. 4. El caminante auto-evitante incrementa su desplazamiento a mayor longitud de correlación y su coeficiente extremo a extremo concuerda con la teoría de Flory para cualquier longitud de correlación. Las redes correlacionadas se construyen con el lenguaje de programación C, utilizando un método de Monte Carlo clásico. La longitud de correlación se mide con una ecuación establecida en trabajos previos. El umbral de percolación se obtiene utilizando el algoritmo de HoshenKopelman, implementado en el software Mathematica 8.0.4, la dimensión fractal y la lagunaridad se obtienen por el método de conteo de cajas y desplazamiento de cajas, 2 respectivamente, utilizando el software ImageJ versión 1.47i. Por otra parte, para medir las propiedades dinámicas sobre los cúmulos infinitos de percolación, se desarrollaron programas propios en lenguaje C. Finalmente se concluye de nuestros resultados y medidas que; los cúmulos de percolación construidos sobre las redes correlacionadas pueden ser modelos útiles como medios de evolución heterogéneos y/o no homogéneos. En ellos se puede analizar el comportamiento del ensamblaje de cadenas de polímeros, el fenómeno de plegamiento de proteínas o procesos de difusión, por mencionar algunos. De manera que, la principal aportación de este trabajo consiste en la elaboración de estructuras geométricas conocidas como cúmulos de percolación sobre redes numéricas correlacionadas. Tales modelos han sido caracterizados mediante sus propiedades estáticas: longitud de correlación, umbral de percolación, dimensión fractal y lagunaridad; y propiedades dinámicas como la dimensión espectral (conexidad del medio) empleando la dimensión del caminante aleatorio, la dimensión del caminante auto-evitante y la dimensión del caminante sin regres

Integrated Graph Theoretic, Radiomics, and Deep Learning Framework for Personalized Clinical Diagnosis, Prognosis, and Treatment Response Assessment of Body Tumors

Purpose: A new paradigm is beginning to emerge in radiology with the advent of increased computational capabilities and algorithms. The future of radiological reading rooms is heading towards a unique collaboration between computer scientists and radiologists. The goal of computational radiology is to probe the underlying tissue using advanced algorithms and imaging parameters and produce a personalized diagnosis that can be correlated to pathology. This thesis presents a complete computational radiology framework (I GRAD) for personalized clinical diagnosis, prognosis and treatment planning using an integration of graph theory, radiomics, and deep learning. Methods: There are three major components of the I GRAD framework–image segmentation, feature extraction, and clinical decision support. Image Segmentation: I developed the multiparametric deep learning (MPDL) tissue signature model for segmentation of normal and abnormal tissue from multiparametric (mp) radiological images. The segmentation MPDL network was constructed from stacked sparse autoencoders (SSAE) with five hidden layers. The MPDL network parameters were optimized using k-fold cross-validation. In addition, the MPDL segmentation network was tested on an independent dataset. Feature Extraction: I developed the radiomic feature mapping (RFM) and contribution scattergram (CSg) methods for characterization of spatial and inter-parametric relationships in multiparametric imaging datasets. The radiomic feature maps were created by filtering radiological images with first and second order statistical texture filters followed by the development of standardized features for radiological correlation to biology and clinical decision support. The contribution scattergram was constructed to visualize and understand the inter-parametric relationships of the breast MRI as a complex network. This multiparametric imaging complex network was modeled using manifold learning and evaluated using graph theoretic analysis. Feature Integration: The different clinical and radiological features extracted from multiparametric radiological images and clinical records were integrated using a hybrid multiview manifold learning technique termed the Informatics Radiomics Integration System (IRIS). IRIS uses hierarchical clustering in combination with manifold learning to visualize the high-dimensional patient space on a two-dimensional heatmap. The heatmap highlights the similarity and dissimilarity between different patients and variables. Results: All the algorithms and techniques presented in this dissertation were developed and validated using breast cancer as a model for diagnosis and prognosis using multiparametric breast magnetic resonance imaging (MRI). The deep learning MPDL method demonstrated excellent dice similarity of 0.87±0.05 and 0.84±0.07 for segmentation of lesions on malignant and benign breast patients, respectively. Furthermore, each of the methods, MPDL, RFM, and CSg demonstrated excellent results for breast cancer diagnosis with area under the receiver (AUC) operating characteristic (ROC) curve of 0.85, 0.91, and 0.87, respectively. Furthermore, IRIS classified patients with low risk of breast cancer recurrence from patients with medium and high risk with an AUC of 0.93 compared to OncotypeDX, a 21 gene assay for breast cancer recurrence. Conclusion: By integrating advanced computer science methods into the radiological setting, the I-GRAD framework presented in this thesis can be used to model radiological imaging data in combination with clinical and histopathological data and produce new tools for personalized diagnosis, prognosis or treatment planning by physicians