Lossless compression of hyperspectral images

Technological progress has made a huge amount of data available at increasing spatial and spectral resolutions. Therefore, the compression of hyperspectral data is an area of active research. In somefields, the original quality of a hyperspectral image cannot be compromised andin these cases, lossless compression is mandatory. The main goal of this thesisis to provide improved methods for the lossless compression of hyperspectral images. Both prediction- and transform-based methods are studied. Two kinds of prediction based methods are being studied. In the first method the spectra of a hyperspectral image are first clustered and and an optimized linear predictor is calculated for each cluster. In the second prediction method linear prediction coefficients are not fixed but are recalculated for each pixel. A parallel implementation of the above-mentioned linear prediction method is also presented. Also,two transform-based methods are being presented. Vector Quantization (VQ) was used together with a new coding of the residual image. In addition we have developed a new back end for a compression method utilizing Principal Component Analysis (PCA) and Integer Wavelet Transform (IWT). The performance of the compressionmethods are compared to that of other compression methods. The results show that the proposed linear prediction methods outperform the previous methods. In addition, a novel fast exact nearest-neighbor search method is developed. The search method is used to speed up the Linde-Buzo-Gray (LBG) clustering method.

Efeito do ácido giberélico, do bioestimulante crop set e do anelamento na produção e na qualidade da uva 'Thompson Seedless' no Vale do São Francisco

O objetivo do presente trabalho foi avaliar os efeitos do ácido giberélico, do bioestimulante Crop Set e do anelamento sobre o aumento do tamanho de bagas e a produtividade da uva 'Thompson Seedless' no Vale do São Francisco. O experimento foi conduzido durante dois ciclos de produção (2001 e 2002), no Campo Experimental de Bebedouro, pertencente à Embrapa Semi-Árido, em Petrolina-PE. Utilizou-se do delineamento experimental de blocos ao acaso, com 12 tratamentos e 3 repetições. Os tratamentos corresponderam à aplicação de ácido giberélico em cinco fases de desenvolvimento da videira, nas doses de 10 + 15 + 15 +50 + 50 mg.L-1, do bioestimulante Crop Set em duas doses de 0,1 e 0,2% e do anelamento no caule, isolados e combinados entre si. Os tratamentos combinados de anelamento + ácido giberélico e anelamento + ácido giberélico + Crop Set destacaram-se como aqueles que promoveram os maiores peso e tamanho de cachos e de bagas, com diferenças significativas em relação à testemunha. Entretanto, o anelamento não cicatrizou completamente, causando a morte de plantas, recomendando-se cautela na sua realização. Apesar de não se observar efeito significativo dos tratamentos sobre a produtividade, pode-se notar um aumento de 63% para o tratamento anelamento + ácido giberélico em relação ao ciclo de produção de 2001.

Level Set Methods in Image Processing

This thesis gives an overview of the use of the level set methods in the field of image science. The similar fast marching method is discussed for comparison, also the narrow band and the particle level set methods are introduced. The level set method is a numerical scheme for representing, deforming and recovering structures in an arbitrary dimensions. It approximates and tracks the moving interfaces, dynamic curves and surfaces. The level set method does not define how and why some boundary is advancing the way it is but simply represents and tracks the boundary. The principal idea of the level set method is to represent the N dimensional boundary in the N+l dimensions. This gives the generality to represent even the complex boundaries. The level set methods can be powerful tools to represent dynamic boundaries, but they can require lot of computing power. Specially the basic level set method have considerable computational burden. This burden can be alleviated with more sophisticated versions of the level set algorithm like the narrow band level set method or with the programmable hardware implementation. Also the parallel approach can be used in suitable applications. It is concluded that these methods can be used in a quite broad range of image applications, like computer vision and graphics, scientific visualization and also to solve problems in computational physics. Level set methods and methods derived and inspired by it will be in the front line of image processing also in the future.

A pilot study identifying a set of microRNAs as precise diagnostic biomarkers of acute kidney injury

In the last decade, Acute Kidney Injury (AKI) diagnosis and therapy have not notably improved probably due to delay in the diagnosis, among other issues. Precocity and accuracy should be critical parameters in novel AKI biomarker discovery. microRNAs are key regulators of cell responses to many stimuli and they can be secreted to the extracellular environment. Therefore, they can be detected in body fluids and are emerging as novel disease biomarkers. We aimed to identify and validate serum miRNAs useful for AKI diagnosis and management. Using qRT-PCR arrays in serum samples, we determined miRNAs differentially expressed between AKI patients and healthy controls. Statistical and target prediction analysis allowed us to identify a panel of 10 serum miRNAs. This set was further validated, by qRT-PCR, in two independent cohorts of patients with relevant morbi-mortality related to AKI: Intensive Care Units (ICU) and Cardiac Surgery (CS). Statistical correlations with patient clinical parameter were performed. Our results demonstrated that the 10 selected miRNAs (miR-101-3p, miR-127-3p, miR-210-3p, miR-126-3p, miR-26b-5p, miR-29a-3p, miR-146a-5p, miR-27a-3p, miR-93-3p and miR-10a-5p) were diagnostic biomarkers of AKI in ICU patients, exhibiting areas under the curve close to 1 in ROC analysis. Outstandingly, serum miRNAs estimated before CS predicted AKI development later on, thus becoming biomarkers to predict AKI predisposition. Moreover, after surgery, the expression of the miRNAs was modulated days before serum creatinine increased, demonstrating early diagnostic value. In summary, we have identified a set of serum miRNAs as AKI biomarkers useful in clinical practice, since they demonstrate early detection and high diagnostic value and they recognize patients at risk.

Classification-Based compression of Multispectral Images

The purpose of this thesis is to present a new approach to the lossy compression of multispectral images. Proposed algorithm is based on combination of quantization and clustering. Clustering was investigated for compression of the spatial dimension and the vector quantization was applied for spectral dimension compression. Presenting algo¬rithms proposes to compress multispectral images in two stages. During the first stage we define the classes' etalons, another words to each uniform areas are located inside the image the number of class is given. And if there are the pixels are not yet assigned to some of the clusters then it doing during the second; pass and assign to the closest eta¬lons. Finally a compressed image is represented with a flat index image pointing to a codebook with etalons. The decompression stage is instant too. The proposed method described in this paper has been tested on different satellite multispectral images from different resources. The numerical results and illustrative examples of the method are represented too.

Integrin-Specific Mechanoresponses to Compression and Extension Probed by Cylindrical Flat-Ended AFM Tips in Lung Cells

Cells from lung and other tissues are subjected to forces of opposing directions that are largely transmitted through integrin-mediated adhesions. How cells respond to force bidirectionality remains ill defined. To address this question, we nanofabricated flat-ended cylindrical Atomic Force Microscopy (AFM) tips with ~1 µm2 cross-section area. Tips were uncoated or coated with either integrin-specific (RGD) or non-specific (RGE/BSA) molecules, brought into contact with lung epithelial cells or fibroblasts for 30 s to form focal adhesion precursors, and used to probe cell resistance to deformation in compression and extension. We found that cell resistance to compression was globally higher than to extension regardless of the tip coating. In contrast, both tip-cell adhesion strength and resistance to compression and extension were the highest when probed at integrin-specific adhesions. These integrin-specific mechanoresponses required an intact actin cytoskeleton, and were dependent on tyrosine phosphatases and Ca2+ signaling. Cell asymmetric mechanoresponse to compression and extension remained after 5 minutes of tip-cell adhesion, revealing that asymmetric resistance to force directionality is an intrinsic property of lung cells, as in most soft tissues. Our findings provide new insights on how lung cells probe the mechanochemical properties of the microenvironment, an important process for migration, repair and tissue homeostasis.

Gene Set Analysis for improving genetic association studies

Introduction. Genetic epidemiology is focused on the study of the genetic causes that determine health and diseases in populations. To achieve this goal a common strategy is to explore differences in genetic variability between diseased and nondiseased individuals. Usual markers of genetic variability are single nucleotide polymorphisms (SNPs) which are changes in just one base in the genome. The usual statistical approach in genetic epidemiology study is a marginal analysis, where each SNP is analyzed separately for association with the phenotype. Motivation. It has been observed, that for common diseases the single-SNP analysis is not very powerful for detecting genetic causing variants. In this work, we consider Gene Set Analysis (GSA) as an alternative to standard marginal association approaches. GSA aims to assess the overall association of a set of genetic variants with a phenotype and has the potential to detect subtle effects of variants in a gene or a pathway that might be missed when assessed individually. Objective. We present a new optimized implementation of a pair of gene set analysis methodologies for analyze the individual evidence of SNPs in biological pathways. We perform a simulation study for exploring the power of the proposed methodologies in a set of scenarios with different number of causal SNPs under different effect sizes. In addition, we compare the results with the usual single-SNP analysis method. Moreover, we show the advantage of using the proposed gene set approaches in the context of an Alzheimer disease case-control study where we explore the Reelin signal pathway.