“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.

classification and content based retrieval of digital mammograms and placental sonograms

Image processing has been a challenging and multidisciplinary research area since decades with continuing improvements in its various branches especially Medical Imaging. The healthcare industry was very much benefited with the advances in Image Processing techniques for the efficient management of large volumes of clinical data. The popularity and growth of Image Processing field attracts researchers from many disciplines including Computer Science and Medical Science due to its applicability to the real world. In the meantime, Computer Science is becoming an important driving force for the further development of Medical Sciences. The objective of this study is to make use of the basic concepts in Medical Image Processing and develop methods and tools for clinicians’ assistance. This work is motivated from clinical applications of digital mammograms and placental sonograms, and uses real medical images for proposing a method intended to assist radiologists in the diagnostic process. The study consists of two domains of Pattern recognition, Classification and Content Based Retrieval. Mammogram images of breast cancer patients and placental images are used for this study. Cancer is a disaster to human race. The accuracy in characterizing images using simplified user friendly Computer Aided Diagnosis techniques helps radiologists in detecting cancers at an early stage. Breast cancer which accounts for the major cause of cancer death in women can be fully cured if detected at an early stage. Studies relating to placental characteristics and abnormalities are important in foetal monitoring. The diagnostic variability in sonographic examination of placenta can be overlooked by detailed placental texture analysis by focusing on placental grading. The work aims on early breast cancer detection and placental maturity analysis. This dissertation is a stepping stone in combing various application domains of healthcare and technology.

Anti-lipopolysaccharide factor and crustin-III, the anti-white spot virus peptides in Penaeus monodon: Control of viral infection by up-regulation

White Spot Syndrome Virus (WSSV) is the most devastating disease affecting shrimp culture around the world. Though, considerable progress has been made in the detection and molecular characterization of WSSV in recent years, information pertaining to immune gene expression in shrimps with respect to WSSV infection remains limited. In this context, the present study was undertaken to understand the differential expression of antimicrobial peptide (AMP) genes in the haemocytes of Penaeus monodon in response to WSSV infection on a time-course basis employing semi-quantitative RT-PCR. The present work analyzes the expression profile of six AMP genes (ALF, crustin-1, crustin-2, crustin-3, penaeidin-3 and penaeidin-5), eight WSSV genes (DNA polymerase, endonuclease, immediate early gene, latency related gene, protein kinase, ribonucleotide reductase, thymidine kinase and VP28) and three control genes (18S rRNA, β-actin and ELF) in P. monodon in response to WSSV challenge. Penaeidins were found to be up-regulated during early hours of infection and crustin-3 during late period of infection. However, ALF was found to be up-regulated early to late period of WSSV infection. The present study suggests that AMPs viz. ALF and crustin-3 play an important role in antiviral defense in shrimps. WSSV gene transcripts were detected post-challenge day 1 itself and increased considerably day 5 onwards. Evaluation of the control genes confirmed ELF as the most reliable control gene followed by 18S rRNA and β-actin for gene expression studies in shrimps. This study indicated the role of AMPs in the protection of shrimps against viral infection and their possible control through the up-regulation of AMPs

Stereo-Based Head Pose Tracking Using Iterative Closest Point and Normal Flow Constraint

In this text, we present two stereo-based head tracking techniques along with a fast 3D model acquisition system. The first tracking technique is a robust implementation of stereo-based head tracking designed for interactive environments with uncontrolled lighting. We integrate fast face detection and drift reduction algorithms with a gradient-based stereo rigid motion tracking technique. Our system can automatically segment and track a user's head under large rotation and illumination variations. Precision and usability of this approach are compared with previous tracking methods for cursor control and target selection in both desktop and interactive room environments. The second tracking technique is designed to improve the robustness of head pose tracking for fast movements. Our iterative hybrid tracker combines constraints from the ICP (Iterative Closest Point) algorithm and normal flow constraint. This new technique is more precise for small movements and noisy depth than ICP alone, and more robust for large movements than the normal flow constraint alone. We present experiments which test the accuracy of our approach on sequences of real and synthetic stereo images. The 3D model acquisition system we present quickly aligns intensity and depth images, and reconstructs a textured 3D mesh. 3D views are registered with shape alignment based on our iterative hybrid tracker. We reconstruct the 3D model using a new Cubic Ray Projection merging algorithm which takes advantage of a novel data structure: the linked voxel space. We present experiments to test the accuracy of our approach on 3D face modelling using real-time stereo images.

From First Contact to Close Encounters: A Developmentally Deep Perceptual System for a Humanoid Robot

This thesis presents a perceptual system for a humanoid robot that integrates abilities such as object localization and recognition with the deeper developmental machinery required to forge those competences out of raw physical experiences. It shows that a robotic platform can build up and maintain a system for object localization, segmentation, and recognition, starting from very little. What the robot starts with is a direct solution to achieving figure/ground separation: it simply 'pokes around' in a region of visual ambiguity and watches what happens. If the arm passes through an area, that area is recognized as free space. If the arm collides with an object, causing it to move, the robot can use that motion to segment the object from the background. Once the robot can acquire reliable segmented views of objects, it learns from them, and from then on recognizes and segments those objects without further contact. Both low-level and high-level visual features can also be learned in this way, and examples are presented for both: orientation detection and affordance recognition, respectively. The motivation for this work is simple. Training on large corpora of annotated real-world data has proven crucial for creating robust solutions to perceptual problems such as speech recognition and face detection. But the powerful tools used during training of such systems are typically stripped away at deployment. Ideally they should remain, particularly for unstable tasks such as object detection, where the set of objects needed in a task tomorrow might be different from the set of objects needed today. The key limiting factor is access to training data, but as this thesis shows, that need not be a problem on a robotic platform that can actively probe its environment, and carry out experiments to resolve ambiguity. This work is an instance of a general approach to learning a new perceptual judgment: find special situations in which the perceptual judgment is easy and study these situations to find correlated features that can be observed more generally.

Robust fault detection using consistency techniques for uncertainty handling

Often practical performance of analytical redundancy for fault detection and diagnosis is decreased by uncertainties prevailing not only in the system model, but also in the measurements. In this paper, the problem of fault detection is stated as a constraint satisfaction problem over continuous domains with a big number of variables and constraints. This problem can be solved using modal interval analysis and consistency techniques. Consistency techniques are then shown to be particularly efficient to check the consistency of the analytical redundancy relations (ARRs), dealing with uncertain measurements and parameters. Through the work presented in this paper, it can be observed that consistency techniques can be used to increase the performance of a robust fault detection tool, which is based on interval arithmetic. The proposed method is illustrated using a nonlinear dynamic model of a hydraulic system

Microcalcification evaluation in computer assisted diagnosis for digital mammography

In order to develop applications for z;isual interpretation of medical images, the early detection and evaluation of microcalcifications in digital mammograms is verg important since their presence is often associated with a high incidence of breast cancers. Accurate classification into benign and malignant groups would help improve diagnostic sensitivity as well as reduce the number of unnecessa y biopsies. The challenge here is the selection of the useful features to distinguish benign from malignant micro calcifications. Our purpose in this work is to analyse a microcalcification evaluation method based on a set of shapebased features extracted from the digitised mammography. The segmentation of the microcalcifications is performed using a fixed-tolerance region growing method to extract boundaries of calcifications with manually selected seed pixels. Taking into account that shapes and sizes of clustered microcalcifications have been associated with a high risk of carcinoma based on digerent subjective measures, such as whether or not the calcifications are irregular, linear, vermiform, branched, rounded or ring like, our efforts were addressed to obtain a feature set related to the shape. The identification of the pammeters concerning the malignant character of the microcalcifications was performed on a set of 146 mammograms with their real diagnosis known in advance from biopsies. This allowed identifying the following shape-based parameters as the relevant ones: Number of clusters, Number of holes, Area, Feret elongation, Roughness, and Elongation. Further experiments on a set of 70 new mammogmms showed that the performance of the classification scheme is close to the mean performance of three expert radiologists, which allows to consider the proposed method for assisting the diagnosis and encourages to continue the investigation in the sense of adding new features not only related to the shape

Functional studies of the deubiquitinating enzymes USP19, USP4 and UCH-L1

El marcaje de proteínas con ubiquitina, conocido como ubiquitinación, cumple diferentes funciones que incluyen la regulación de varios procesos celulares, tales como: la degradación de proteínas por medio del proteosoma, la reparación del ADN, la señalización mediada por receptores de membrana, y la endocitosis, entre otras (1). Las moléculas de ubiquitina pueden ser removidas de sus sustratos gracias a la acción de un gran grupo de proteasas, llamadas enzimas deubiquitinizantes (DUBs) (2). Las DUBs son esenciales para la manutención de la homeostasis de la ubiquitina y para la regulación del estado de ubiquitinación de diferentes sustratos. El gran número y la diversidad de DUBs descritas refleja tanto su especificidad como su utilización para regular un amplio espectro de sustratos y vías celulares. Aunque muchas DUBs han sido estudiadas a profundidad, actualmente se desconocen los sustratos y las funciones biológicas de la mayoría de ellas. En este trabajo se investigaron las funciones de las DUBs: USP19, USP4 y UCH-L1. Utilizando varias técnicas de biología molecular y celular se encontró que: i) USP19 es regulada por las ubiquitin ligasas SIAH1 y SIAH2 ii) USP19 es importante para regular HIF-1α, un factor de transcripción clave en la respuesta celular a hipoxia, iii) USP4 interactúa con el proteosoma, iv) La quimera mCherry-UCH-L1 reproduce parcialmente los fenotipos que nuestro grupo ha descrito previamente al usar otros constructos de la misma enzima, y v) UCH-L1 promueve la internalización de la bacteria Yersinia pseudotuberculosis.

Heterogeneity and the formation of risk - sharing coalitions

We offer a new explanation of partial risk sharing based on coalition formation and segmentation of society in a risky environment, without assuming limited commitment and imperfect information. Heterogenous individuals in a society freely choose with whom they will share risk. A partition belonging to the core of the membership game obtains. Perfect risk sharing does not necessarily arise. Focusing on mutual insurance rule and assuming that individuals only differ with respect to risk, we show that the core partition is homophily-based. The distribution of risk affects the number and size of these coalitions. Individuals may pay a lower risk premium in riskier societies. A higher heterogeneity in risk leads to a lower degree of risk sharing. We discuss how the endogenous partition of society into risk-sharing coalitions may shed light on empirical evidence on partial risk sharing. The case of heterogenous risk aversion leads to similar results.

A human ribonuclease induces apoptosis associated with p21WAF1/CIP1 induction and JNK inactivation

Ribonucleases are promising agents for use in anticancer therapy. Among the different ribonucleases described to be cytotoxic, a paradigmatic example is onconase which manifests cytotoxic and cytostatic effects, presents synergism with several kinds of anticancer drugs and is currently in phase II/III of its clinical trial as an anticancer drug against different types of cancer. The mechanism of cytotoxicity of PE5, a variant of human pancreatic ribonuclease carrying a nuclear localization signal, has been investigated and compared to that of onconase. Methods: Cytotoxicity was measured by the MTT method and by the tripan blue exclusion assay. Apoptosis was assessed by flow cytometry, caspase enzymatic detection and confocal microscopy. Cell cycle phase analysis was performed by flow cytometry. The expression of different proteins was analyzed by western blot.n Results: We show that the cytotoxicity of PE5 is produced through apoptosis, that it does not require the proapoptotic activity of p53 and is not prevented by the multiple drug resistance phenotype. We also show that PE5 and onconase induce cell death at the same extent although the latter is also able to arrest the cell growth. We have compared the cytotoxic effects of both ribonucleases in the NCI/ADR-RES cell line by measuring their effects on the cell cycle, on the activation of different caspases and on the expression of different apoptosis- and cell cycle-related proteins. PE5 increases the number of cells in S and G2/M cell cycle phases, which is accompanied by the increased expression of cyclin E and p21WAF1/CIP1 together with the underphosphorylation of p46 forms of JNK. Citotoxicity of onconase in this cell line does not alter the cell cycle phase distribution and it is accompanied by a decreased expression of XIAP. Conclusions: We conclude that PE5 kills the cells through apoptosis associated with the p21WAF1/CIP1 induction and the inactivation of JNK. This mechanism is significantly different from that found for onconase

Multivariate Statistical Process Control and Case-Based Reasoning for situation assessment of Sequencing Batch Reactors

ABSRACT This thesis focuses on the monitoring, fault detection and diagnosis of Wastewater Treatment Plants (WWTP), which are important fields of research for a wide range of engineering disciplines. The main objective is to evaluate and apply a novel artificial intelligent methodology based on situation assessment for monitoring and diagnosis of Sequencing Batch Reactor (SBR) operation. To this end, Multivariate Statistical Process Control (MSPC) in combination with Case-Based Reasoning (CBR) methodology was developed, which was evaluated on three different SBR (pilot and lab-scales) plants and validated on BSM1 plant layout.

A study on the phylogeny and the ecology of ammonia-oxidizing bacteria using a new molecular marker based on the gene amoB

L'agricultura i la industrialització han causat un augment significatiu del nombre d'ambients rics en amoni. La presència de compostos nitrogenats redueix la qualitat de l'aigua, causant problemes de toxicitat, deteriorant el medi ambient i fins i tot afectant la salut humana. En conseqüència, la nitrificació s'ha convertit en un procés global que afecta al cicle del nitrogen a la biosfera. Els bacteris oxidadors d'amoni (AOB) són els responsables de l'oxidació de l'amoni a nitrit, i juguen un paper essencial en el cicle del nitrogen. Els primers oxidadors d'amoni foren aïllats a finals del segle XIX, però la lentitud del seu creixement i les dificultats per cultivar-los feren que fins als anys 80, amb els primers estudis emprant el gen 16SrDNA, no s'assolís un coneixement complert d'aquest grup bacterià. Actualment les bases de dades contenen multitud d'entrades amb seqüències corresponents a AOB. L'objectiu d'aquest treball era trobar, desenvolupar i avaluar eines útils i fiables per a l'estudi dels AOB en mostres ambientals. En aquest treball primer descrivim la utilització de la hibridació in situ amb fluorescència (FISH), mitjançant l'aplicació de sondes amb diana en el 16SrRNA dels AOB. La FISH ens va permetre detectar i recomptar aquest grup bacterià; no obstant, aquest mètode no permetia la detecció de noves seqüències, pel que es necessitava una nova eina. Amb aquesta intenció vam aplicar la seqüència de la sonda Nso1225 en una PCR. El fet d'amplificar específicament un fragment del 16SrDNA dels AOB va suposar el desenvolupament d'una nova eina molecular que permetia detectar la presència i diversitat d'aquests bacteris en ambients naturals. Malgrat tot, algunes seqüències pertanyents a bacteris no oxidadors d'amoni del subgrup β dels proteobacteris, eren també obtingudes amb aquesta tècnica. Així mateix, un dels inconvenients de l'ús del 16SrDNA com a marcador és la impossibilitat de detectar simultàniament els AOB que pertanyen als subgrups β i γ dels proteobacteris. El gen amoA, que codifica per la subunitat A de l'enzim amoni monooxigenasa (AMO), era aleshores àmpliament utilitzat com a marcador per a la detecció dels AOB. En aquest treball també descrivim la utilització d'aquest marcador en mostres procedents d'un reactor SBR. Aquest marcador ens va permetre identificar seqüències de AOB en la mostra, però la necessitat de detectar amoA mitjançant clonatge fa que l'ús d'aquest marcador requereixi massa temps per a la seva utilització com a eina en estudis d'ecologia microbiana amb moltes mostres. Per altra banda, alguns autors han assenyalat l'obtenció de seqüències de no AOB en utilitzar amoA en un protocol de PCR-DGGE. Amb la finalitat d'obtenir una eina ràpida i rigorosa per detectar i identificar els AOB, vam desenvolupar un joc nou d'oligonucleòtids amb diana en el gen amoB, que codifica per a la subunitat transmembrana de l'enzim AMO. Aquest gen ha demostrat ser un bon marcador molecular pels AOB, oferint, sense tenir en compte afiliacions filogenètiques, una elevada especificitat, sensibilitat i fiabilitat. En aquest treball també presentem una anàlisi de RT-PCR basada en la detecció del gen amoB per a la quantificació del gènere Nitrosococcus. El nou joc d'oligonucleòtids dissenyat permet una enumeració altament específica i sensible de tots els γ-Nitrosococcus coneguts. Finalment, vam realitzar un estudi poligènic, comparant i avaluant els marcadors amoA, amoB i 16SrDNA, i vàrem construir un arbre filogenètic combinat. Com a resultat concloem que amoB és un marcador adequat per a la detecció i identificació dels AOB en mostres ambientals, proporcionant alhora agrupacions consistents en fer inferències filogenètiques. Per altra banda, la seqüència sencera del gen 16S rDNA és indicada com a marcador en estudis amb finalitats taxonòmiques i filogenètiques en treballar amb cultius purs de AOB.

Development of molecular monitoring methods and assessment of the environmental fate of the biological control agent of fire blight Pseudomonas fluorescens EPS62e

Pseudomonas fluorescens EPS62e es va seleccionar com a agent de biocontrol del foc bacterià per la seva eficàcia en el control de Erwinia amylovora. En aquest treball es van desenvolupar mètodes de traçabilitat que van permetre la seva detecció específica i quantificació. Mitjançant les tècniques RAPD i U-PCR es van obtenir fragments d'amplificació diferencial per EPS62e que es van seqüenciar i caracteritzar com marcadors SCAR per dissenyar una PCR en temps real. La PCR a temps real es va utilitzar simultàniament amb mètodes microbiològics per estudiar l'adaptabilitat epifítica de EPS62e en pomera i perera. L'ús combinat de mètodes microbiològics i moleculars va permetre la identificació de tres estats fisiològics de EPS62e: la colonització activa, l'entrada en un estat de viable però no cultivable, i la mort cel·lular. Aquest treball mostra que EPS62e està ben adaptada a la colonització de flors a camp, encoratjant la seva utilització dins d'una estratègia de control biològic contra el foc bacterià.

Integration of knowledge-based, qualitative and numeric tools for real time dynamic systems supervision

The proposal presented in this thesis is to provide designers of knowledge based supervisory systems of dynamic systems with a framework to facilitate their tasks avoiding interface problems among tools, data flow and management. The approach is thought to be useful to both control and process engineers in assisting their tasks. The use of AI technologies to diagnose and perform control loops and, of course, assist process supervisory tasks such as fault detection and diagnose, are in the scope of this work. Special effort has been put in integration of tools for assisting expert supervisory systems design. With this aim the experience of Computer Aided Control Systems Design (CACSD) frameworks have been analysed and used to design a Computer Aided Supervisory Systems (CASSD) framework. In this sense, some basic facilities are required to be available in this proposed framework: ·

Artificial Cavities and Nest Site Selection by Puerto Rican Parrots: a Multiscale Assessment

We examined nest site selection by Puerto Rican Parrots, a secondary cavity nester, at several spatial scales using the nest entrance as the central focal point relative to 20 habitat and spatial variables. The Puerto Rican Parrot is unique in that, since 2001, all known nesting in the wild has occurred in artificial cavities, which also provided us with an opportunity to evaluate nest site selection without confounding effects of the actual nest cavity characteristics. Because of the data limitations imposed by the small population size of this critically endangered endemic species, we employed a distribution-free statistical simulation approach to assess site selection relative to characteristics of used and unused nesting sites. Nest sites selected by Puerto Rican Parrots were characterized by greater horizontal and vertical visibility from the nest entrance, greater density of mature sierra palms, and a more westerly and leeward orientation of nest entrances than unused sites. Our results suggest that nest site selection in this species is an adaptive response to predation pressure, to which the parrots respond by selecting nest sites offering advantages in predator detection and avoidance at all stages of the nesting cycle. We conclude that identifying and replicating the “nest gestalt” of successful nesting sites may facilitate conservation efforts for this and other endangered avian species.