Detection and Tracking of Dynamic Objects. A MultiRobot approach to Critical Infrastructures Surveillance.

Despite that Critical Infrastructures (CIs) security and surveillance are a growing concern for many countries and companies, Multi Robot Systems (MRSs) have not been yet broadly used in this type of facilities. This dissertation presents a novel study of the challenges arisen by the implementation of this type of systems and proposes solutions to specific problems. First, a comprehensive analysis of different types of CIs has been carried out, emphasizing the influence of the different characteristics of the facilities in the design of a security and surveillance MRS. One of the most important needs for the surveillance of a CI is the detection of intruders. From a technical point of view this problem can be abstracted as equivalent to the Detection and Tracking of Mobile Objects (DATMO). This dissertation proposes algorithms to solve this specific problem in a CI environment. Using 3D range images of the environment as input data, two detection algorithms for ground robots have been developed. These detection algorithms provide a list of moving objects in the robot detection area. Direct image differentiation and computer vision techniques are used when the robot is static. Alternatively, multi-layer ground reconstructions are compared to detect the dynamic objects when the robot is moving. Since CIs usually spread over large areas, it is very useful to incorporate aerial vehicles in the surveillance MRS. Therefore, a moving object detection algorithm for aerial vehicles has been also developed. This algorithm compares the real optical flow obtained from a down-face oriented camera with an artificial optical flow computed using a RANSAC based homography matrix. Two tracking algorithms have been developed to follow the moving objects trajectories. These algorithms can efficiently handle occlusions and crossings, as well as exchange information among robots. The multirobot tracking can be applied to any type of communication structure: centralized, decentralized or a combination of both. Even more, the developed tracking algorithms are independent of the detection algorithms and could be potentially used with other detection procedures or even with static sensors, such as cameras. In addition, using the 3D point clouds available to the robots, a relative localization algorithm has been developed to improve the position estimation of a given robot with observations from other robots. All the developed algorithms have been extensively tested in different simulated CIs using the Webots robotics simulator. Furthermore, the algorithms have also been validated with real robots operating in real scenarios. In conclusion, this dissertation presents a multirobot approach to Critical Infrastructure Surveillance, mainly focusing on Detecting and Tracking Dynamic Objects.

Fluoro-sensing applied to detection and identification of hydrocarbons in inland waters. Study of the impact of different UV light sources

This paper presents the detection and identification of hydrocarbons through flu oro-sensing by developing a simple and inexpensive detector for inland water, in contrast to current systems, designed to be used for marine waters at large distances and being extremely costly. To validate the proposed system, three test-benches have been mounted, with various UV-Iight sources. Main application of this system would be detect hydrocarbons pollution in rivers, lakes or dams, which in fact, is of growing interest by administrations.

Detection and quantification of peanut traces in wheat flour by near infrared hyperspectral imaging spectroscopy using principal-component analysis

The use of a common environment for processing different powder foods in the industry has increased the risk of finding peanut traces in powder foods. The analytical methods commonly used for detection of peanut such as enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR) represent high specificity and sensitivity but are destructive and time-consuming, and require highly skilled experimenters. The feasibility of NIR hyperspectral imaging (HSI) is studied for the detection of peanut traces down to 0.01% by weight. A principal-component analysis (PCA) was carried out on a dataset of peanut and flour spectra. The obtained loadings were applied to the HSI images of adulterated wheat flour samples with peanut traces. As a result, HSI images were reduced to score images with enhanced contrast between peanut and flour particles. Finally, a threshold was fixed in score images to obtain a binary classification image, and the percentage of peanut adulteration was compared with the percentage of pixels identified as peanut particles. This study allowed the detection of traces of peanut down to 0.01% and quantification of peanut adulteration from 10% to 0.1% with a coefficient of determination (r2) of 0.946. These results show the feasibility of using HSI systems for the detection of peanut traces in conjunction with chemical procedures, such as RT-PCR and ELISA to facilitate enhanced quality-control surveillance on food-product processing lines.

NDT for the detection and characterization of superficial treatments on stone material from archaeological sites of Merida (Spain)

Different treatments (consolidation and water-repellent) were applied on samples of marble and granite from the Front stage of the Roman Theatre of Merida (Spain). The main goal is to study the effects of these treatments on archaeological stone material, by analyzing the surface changes. X-Ray Fluorescence and Laser-Induced Breakdown Spectroscopy techniques, as well as Nuclear Magnetic Resonance have been used in order to study changes in the surface properties of the material, comparing treated and untreated specimens. The results confirm that silicon (Si) marker tracking allows the detection of applied treatments, increasing the peak signal in treated specimens. Furthermore, it is also possible to prove changes both within the pore system of the materialand in the distribution of surface water, resulting from the application of these products

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.

Detection and characterization of carcinoma cells in the blood

A highly sensitive assay combining immunomagnetic enrichment with multiparameter flow cytometric and immunocytochemical analysis has been developed to detect, enumerate, and characterize carcinoma cells in the blood. The assay can detect one epithelial cell or less in 1 ml of blood. Peripheral blood (10–20 ml) from 30 patients with carcinoma of the breast, from 3 patients with prostate cancer, and from 13 controls was examined by flow cytometry for the presence of circulating epithelial cells defined as nucleic acid+, CD45−, and cytokeratin+. Highly significant differences in the number of circulating epithelial cells were found between normal controls and patients with cancer including 17 with organ-confined disease. To determine whether the circulating epithelial cells in the cancer patients were neoplastic cells, cytospin preparations were made after immunomagnetic enrichment and were analyzed. Epithelial cells from patients with breast cancer generally stained with mAbs against cytokeratin and 3 of 5 for mucin-1. In contrast, no cells that stained for these antigens were observed in the blood from normal controls. The morphology of the stained cells was consistent with that of neoplastic cells. Of 8 patients with breast cancer followed for 1–10 months, there was a good correlation between changes in the level of tumor cells in the blood with both treatment with chemotherapy and clinical status. The present assay may be helpful in early detection, in monitoring disease, and in prognostication.

In vivo detection and imaging of phosphatidylserine expression during programmed cell death

One of the earliest events in programmed cell death is the externalization of phosphatidylserine, a membrane phospholipid normally restricted to the inner leaflet of the lipid bilayer. Annexin V, an endogenous human protein with a high affinity for membrane bound phosphatidylserine, can be used in vitro to detect apoptosis before other well described morphologic or nuclear changes associated with programmed cell death. We tested the ability of exogenously administered radiolabeled annexin V to concentrate at sites of apoptotic cell death in vivo. After derivatization with hydrazinonicotinamide, annexin V was radiolabeled with technetium 99m. In vivo localization of technetium 99m hydrazinonicotinamide-annexin V was tested in three models: fuminant hepatic apoptosis induced by anti-Fas antibody injection in BALB/c mice; acute rejection in ACI rats with transplanted heterotopic PVG cardiac allografts; and cyclophosphamide treatment of transplanted 38C13 murine B cell lymphomas. External radionuclide imaging showed a two- to sixfold increase in the uptake of radiolabeled annexin V at sites of apoptosis in all three models. Immunohistochemical staining of cardiac allografts for exogenously administered annexin V revealed intense staining of numerous myocytes at the periphery of mononuclear infiltrates of which only a few demonstrated positive apoptotic nuclei by the terminal deoxynucleotidyltransferase-mediated UTP end labeling method. These results suggest that radiolabeled annexin V can be used in vivo as a noninvasive means to detect and serially image tissues and organs undergoing programmed cell death.

Detection and determination of oligonucleotide triplex formation-mediated transcription-coupled DNA repair in HeLa nuclear extracts

Transcription-coupled repair (TCR) plays an important role in removing DNA damage from actively transcribed genes. It has been speculated that TCR is the most important mechanism for repairing DNA damage in non-dividing cells such as neurons. Therefore, abnormal TCR may contribute to the development of many age-related and neurodegenerative diseases. However, the molecular mechanism of TCR is not well understood. Oligonucleotide DNA triplex formation provides an ideal system to dissect the molecular mechanism of TCR since triplexes can be formed in a sequence-specific manner to inhibit transcription of target genes. We have recently studied the molecular mechanism of triplex-forming oligonucleotide (TFO)-mediated TCR in HeLa nuclear extracts. Using plasmid constructs we demonstrate that the level of TFO-mediated DNA repair activity is directly correlated with the level of transcription of the plasmid in HeLa nuclear extracts. TFO-mediated DNA repair activity was further linked with transcription since the presence of rNTPs in the reaction was essential for AG30-mediated DNA repair activity in HeLa nuclear extracts. The involvement of individual components, including TFIID, TFIIH, RNA polymerase II and xeroderma pigmentosum group A (XPA), in the triplex-mediated TCR process was demonstrated in HeLa nuclear extracts using immunodepletion assays. Importantly, our studies also demonstrated that XPC, a component involved in global genome DNA repair, is involved in the AG30-mediated DNA repair process. The results obtained in this study provide an important new understanding of the molecular mechanisms involved in the TCR process in mammalian cells.

Ultrafast detection and control of molecular dynamics

Many elementary chemical and physical processes such as the breaking of a chemical bond or the vibrational motion of atoms within a molecule take place on a femtosecond (fs = 10−15 s) or picosecond (ps = 10−12 s) time scale. It is now possible to monitor these events as a function of time with temporal resolution well below 100 fs. This capability is based on the pump-probe technique where one optical pulse triggers a reaction and a second delayed optical pulse probes the changes that ensue. To illustrate this capability, the dynamics of ligand motion within a protein are presented. Moving beyond casual observation of a reaction to active control of its outcome requires additional experimental and theoretical effort. To illustrate the concept of control, the effect of optical pulse duration on the vibrational dynamics of a tri-atomic molecule are discussed. The experimental and theoretical resources currently available are poised to make the dream of reaction control a reality for certain molecular systems.

Detection and localization of individual antibody-antigen recognition events by atomic force microscopy.

A methodology has been developed for the study of molecular recognition at the level of single events and for the localization of sites on biosurfaces, in combining force microscopy with molecular recognition by specific ligands. For this goal, a sensor was designed by covalently linking an antibody (anti-human serum albumin, polyclonal) via a flexible spacer to the tip of a force microscope. This sensor permitted detection of single antibody-antigen recognition events by force signals of unique shape with an unbinding force of 244 +/- 22 pN. Analysis revealed that observed unbinding forces originate from the dissociation of individual Fab fragments from a human serum albumin molecule. The two Fab fragments of the antibody were found to bind independently and with equal probability. The flexible linkage provided the antibody with a 6-nm dynamical reach for binding, rendering binding probability high, 0.5 for encounter times of 60 ms. This permitted fast and reliable detection of antigenic sites during lateral scans with a positional accuracy of 1.5 nm. It is indicated that this methodology has promise for characterizing rate constants and kinetics of molecular recognition complexes and for molecular mapping of biosurfaces such as membranes.

The causes and prevention of cancer.

Epidemiological evidence indicates that avoidance of smoking, increased consumption of fruits and vegetables, and control of infections will have a major effect on reducing rates of cancer. Other factors include avoidance of intense sun exposure, increases in physical activity, and reduction of alcohol consumption and possibly red meat. A substantial reduction in breast cancer is likely to require modification of sex hormone levels, and development of practical methods for doing so is a high research priority. Resolution of the potential protective roles of specific antioxidants and other constituents of fruits and vegetables deserves major attention. Mechanistic studies of carcinogenesis indicate an important role of endogenous oxidative damage to DNA that is balanced by elaborate defense and repair processes. Also key is the rate of cell division, which is influenced by hormones, growth, cytotoxicity, and inflammation, as this determines the probability of converting DNA lesions to mutations. These mechanisms may underlie many epidemiologic observations.

Direct detection and isolation of restriction landmark genomic scanning (RLGS) spot DNA markers tightly linked to a specific trait by using the RLGS spot-bombing method.

We have developed a technique for isolating DNA markers tightly linked to a target region that is based on RLGS, named RLGS spot-bombing (RLGS-SB). RLGS-SB allows us to scan the genome of higher organisms quickly and efficiently to identify loci that are linked to either a target region or gene of interest. The method was initially tested by analyzing a C57BL/6-GusS mouse congenic strain. We identified 33 variant markers out of 10,565 total loci in a 4.2-centimorgan (cM) interval surrounding the Gus locus in 4 days of laboratory work. The validity of RLGS-SB to find DNA markers linked to a target locus was also tested on pooled DNA from segregating backcross progeny by analyzing the spot intensity of already mapped RLGS loci. Finally, we used RLGS-SB to identify DNA markers closely linked to the mouse reeler (rl) locus on chromosome 5 by phenotypic pooling. A total of 31 RLGS loci were identified and mapped to the target region after screening 8856 loci. These 31 loci were mapped within 11.7 cM surrounding rl. The average density of RLGS loci located in the rl region was 0.38 cM. Three loci were closely linked to rl showing a recombination frequency of 0/340, which is < 1 cM from rl. Thus, RLGS-SB provides an efficient and rapid method for the detection and isolation of polymorphic DNA markers linked to a trait or gene of interest.