Factors affecting southern water vole (Arvicola sapidus) detection and occupancy probabilities in Mediterranean farmland

Failure to detect a species at sites where it is present (i.e. imperfect detection) is known to occur frequently, but this is often disregarded in monitoring programs and metapopulation studies. Here we modelled for the first time the probability of patch occupancy by a threatened small mammal, the southern water vole (Arvicola sapidus, while accounting for the probability of detection given occupancy. Based on replicated presence sign surveys conducted in autumn (November–December 2013) and winter (February–March 2014) in a farmland landscape, we used occupancy detection modelling to test the effects of vegetation, sampling effort, observer experience, and rainfall on detection probability. We then assessed whether occupancy was related to patch size, isolation, vegetation, or presence of water, after correcting for imperfect detection. The mean detection probabilities of water vole signs in autumn (0.71) and winter (0.81) indicated that false absences may be generated in about 20–30% of occupied patches surveyed by a single observer on a single occasion. There was no statistical support for the effects of covariates on detectability. After controlling for imperfect detection, the mean probabilities of occupancy in autumn (0.31) and winter (0.29) were positively related to patch size and presence of water, and negatively so, albeit weakly, to patch isolation. Overall, our study underlined the importance of accounting for imperfect detection in sign surveys of small mammals such as water voles, pointing out the need to use occupancy detection modelling together with replicate surveys for accurately estimating occupancy and the factors affecting it.

Detection and molecular characterization of Grapevine yellow speckle viroid 1 isolates infecting grapevines in Brazil.

Abstract Presently, Hop stunt viroid(HSVd) and Citrus exocortis viroid (CEVd) are the only viroids reported to infect grapevines (Vitis spp.) in Brazil, among the seven viroid species already reported infecting this host in other countries. All grapevine viroid diseases are graft-transmissible and can induce losses especiallywhenassociatedwithviruses.Theaimofthisworkwas to confirm infection by Grapevine yellow speckle viroid 1(GYSVd-1) in grapevine samples exhibiting yellow speckle symptoms in the leaves and in asymptomatic samples sequenced by next generation sequencing (NGS). The occurrence of this viroid in Brazil was further investigated in a second study. Total RNAs and dsRNAs were extracted from five symptomatic plants and 16 asymptomatic samples, respectively. Specific primers were used for RT-PCR and amplified DNA fragments were cloned and sequenced by the Sanger method. Eleven complete nucleotide sequences of GYSVd-1 isolates (366 ?367 nt) were obtained from NGS and from RT-PCR amplicons. Comparisons showed high identities (95.9 ?100 %) among ten isolates and an identity of 87.2 ?90.4 % with a divergent isolate (RM-BR). Phylogenetic analyses placed GYSVd-1 isolates in four clusters (types 1, 2, 3 and 4). All GYSVd-1 infections were confirmed by conventional RT-PCR and RT-qPCR using specific oligonucleo-tides and a labeled probe. This is the first report and molecular characterization of GYSVd-1 infecting grapevines in Brazil, and our survey indicates that this viroid could be widespread in the major grape producing regions of Brazil. Keywords GYSVd-1 . Incidence . Next generation sequencing. Secondary structure. Vine.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.

AGV Safety Areas Obstacle Detection and Interaction with Point Cloud Derived Map

This thesis project aims to the development of an algorithm for the obstacle detection and the interaction between the safety areas of an Automated Guided Vehicles (AGV) and a Point Cloud derived map inside the context of a CAD software. The first part of the project focuses on the implementation of an algorithm for the clipping of general polygons, with which has been possible to: construct the safety areas polygon, derive the sweep of this areas along the navigation path performing a union and detect the intersections with line or polygon representing the obstacles. The second part is about the construction of a map in terms of geometric entities (lines and polygons) starting from a point cloud given by the 3D scan of the environment. The point cloud is processed using: filters, clustering algorithms and concave/convex hull derived algorithms in order to extract line and polygon entities representing obstacles. Finally, the last part aims to use the a priori knowledge of possible obstacle detections on a given segment, to predict the behavior of the AGV and use this prediction to optimize the choice of the vehicle's assigned velocity in that segment, minimizing the travel time.

A 2D/3D image analysis system to track fluorescently labeled structures in rod-shaped cells: application to measure spindle pole asymmetry during mitosis.

BACKGROUND: The yeast Schizosaccharomyces pombe is frequently used as a model for studying the cell cycle. The cells are rod-shaped and divide by medial fission. The process of cell division, or cytokinesis, is controlled by a network of signaling proteins called the Septation Initiation Network (SIN); SIN proteins associate with the SPBs during nuclear division (mitosis). Some SIN proteins associate with both SPBs early in mitosis, and then display strongly asymmetric signal intensity at the SPBs in late mitosis, just before cytokinesis. This asymmetry is thought to be important for correct regulation of SIN signaling, and coordination of cytokinesis and mitosis. In order to study the dynamics of organelles or large protein complexes such as the spindle pole body (SPB), which have been labeled with a fluorescent protein tag in living cells, a number of the image analysis problems must be solved; the cell outline must be detected automatically, and the position and signal intensity associated with the structures of interest within the cell must be determined. RESULTS: We present a new 2D and 3D image analysis system that permits versatile and robust analysis of motile, fluorescently labeled structures in rod-shaped cells. We have designed an image analysis system that we have implemented as a user-friendly software package allowing the fast and robust image-analysis of large numbers of rod-shaped cells. We have developed new robust algorithms, which we combined with existing methodologies to facilitate fast and accurate analysis. Our software permits the detection and segmentation of rod-shaped cells in either static or dynamic (i.e. time lapse) multi-channel images. It enables tracking of two structures (for example SPBs) in two different image channels. For 2D or 3D static images, the locations of the structures are identified, and then intensity values are extracted together with several quantitative parameters, such as length, width, cell orientation, background fluorescence and the distance between the structures of interest. Furthermore, two kinds of kymographs of the tracked structures can be established, one representing the migration with respect to their relative position, the other representing their individual trajectories inside the cell. This software package, called "RodCellJ", allowed us to analyze a large number of S. pombe cells to understand the rules that govern SIN protein asymmetry. CONCLUSIONS: "RodCell" is freely available to the community as a package of several ImageJ plugins to simultaneously analyze the behavior of a large number of rod-shaped cells in an extensive manner. The integration of different image-processing techniques in a single package, as well as the development of novel algorithms does not only allow to speed up the analysis with respect to the usage of existing tools, but also accounts for higher accuracy. Its utility was demonstrated on both 2D and 3D static and dynamic images to study the septation initiation network of the yeast Schizosaccharomyces pombe. More generally, it can be used in any kind of biological context where fluorescent-protein labeled structures need to be analyzed in rod-shaped cells. AVAILABILITY: RodCellJ is freely available under http://bigwww.epfl.ch/algorithms.html, (after acceptance of the publication).

Automatic image-based identification of Saimaa ringed seals

The Saimaa ringed seal is one of the most endangered seals in the world. It is a symbol of Lake Saimaa and a lot of effort have been applied to save it. Traditional methods of seal monitoring include capturing the animals and installing sensors on their bodies. These invasive methods for identifying can be painful and affect the behavior of the animals. Automatic identification of seals using computer vision provides a more humane method for the monitoring. This Master's thesis focuses on automatic image-based identification of the Saimaa ringed seals. This consists of detection and segmentation of a seal in an image, analysis of its ring patterns, and identification of the detected seal based on the features of the ring patterns. The proposed algorithm is evaluated with a dataset of 131 individual seals. Based on the experiments with 363 images, 81\% of the images were successfully segmented automatically. Furthermore, a new approach for interactive identification of Saimaa ringed seals is proposed. The results of this research are a starting point for future research in the topic of seal photo-identification.

L'élastographie ultrasonore dynamique vasculaire : une nouvelle modalité d'imagerie non-invasive pour la caractérisation mécanique de la thrombose veineuse

L’accident thromboembolique veineux, tel que la thrombose veineuse profonde (TVP) ou thrombophlébite des membres inférieurs, est une pathologie vasculaire caractérisée par la formation d’un caillot sanguin causant une obstruction partielle ou totale de la lumière sanguine. Les embolies pulmonaires sont une complication mortelle des TVP qui surviennent lorsque le caillot se détache, circule dans le sang et produit une obstruction de la ramification artérielle irriguant les poumons. La combinaison d’outils et de techniques d’imagerie cliniques tels que les règles de prédiction cliniques (signes et symptômes) et les tests sanguins (D-dimères) complémentés par un examen ultrasonographique veineux (test de compression, écho-Doppler), permet de diagnostiquer les premiers épisodes de TVP. Cependant, la performance de ces outils diagnostiques reste très faible pour la détection de TVP récurrentes. Afin de diriger le patient vers une thérapie optimale, la problématique n’est plus basée sur la détection de la thrombose mais plutôt sur l’évaluation de la maturité et de l’âge du thrombus, paramètres qui sont directement corrélées à ses propriétés mécaniques (e.g. élasticité, viscosité). L’élastographie dynamique (ED) a récemment été proposée comme une nouvelle modalité d’imagerie non-invasive capable de caractériser quantitativement les propriétés mécaniques de tissus. L’ED est basée sur l’analyse des paramètres acoustiques (i.e. vitesse, atténuation, pattern de distribution) d’ondes de cisaillement basses fréquences (10-7000 Hz) se propageant dans le milieu sondé. Ces ondes de cisaillement générées par vibration externe, ou par source interne à l’aide de la focalisation de faisceaux ultrasonores (force de radiation), sont mesurées par imagerie ultrasonore ultra-rapide ou par résonance magnétique. Une méthode basée sur l’ED adaptée à la caractérisation mécanique de thromboses veineuses permettrait de quantifier la sévérité de cette pathologie à des fins d’amélioration diagnostique. Cette thèse présente un ensemble de travaux reliés au développement et à la validation complète et rigoureuse d’une nouvelle technique d’imagerie non-invasive élastographique pour la mesure quantitative des propriétés mécaniques de thromboses veineuses. L’atteinte de cet objectif principal nécessite une première étape visant à améliorer les connaissances sur le comportement mécanique du caillot sanguin (sang coagulé) soumis à une sollicitation dynamique telle qu’en ED. Les modules de conservation (comportement élastique, G’) et de perte (comportement visqueux, G’’) en cisaillement de caillots sanguins porcins sont mesurés par ED lors de la cascade de coagulation (à 70 Hz), et après coagulation complète (entre 50 Hz et 160 Hz). Ces résultats constituent les toutes premières mesures du comportement dynamique de caillots sanguins dans une gamme fréquentielle aussi étendue. L’étape subséquente consiste à mettre en place un instrument innovant de référence (« gold standard »), appelé RheoSpectris, dédié à la mesure de la viscoélasticité hyper-fréquence (entre 10 Hz et 1000 Hz) des matériaux et biomatériaux. Cet outil est indispensable pour valider et calibrer toute nouvelle technique d’élastographie dynamique. Une étude comparative entre RheoSpectris et la rhéométrie classique est réalisée afin de valider des mesures faites sur différents matériaux (silicone, thermoplastique, biomatériaux, gel). L’excellente concordance entre les deux technologies permet de conclure que RheoSpectris est un instrument fiable pour la mesure mécanique à des fréquences difficilement accessibles par les outils actuels. Les bases théoriques d’une nouvelle modalité d’imagerie élastographique, nommée SWIRE (« shear wave induced resonance dynamic elastography »), sont présentées et validées sur des fantômes vasculaires. Cette approche permet de caractériser les propriétés mécaniques d’une inclusion confinée (e.g. caillot sanguin) à partir de sa résonance (amplification du déplacement) produite par la propagation d’ondes de cisaillement judicieusement orientées. SWIRE a également l’avantage d’amplifier l’amplitude de vibration à l’intérieur de l’hétérogénéité afin de faciliter sa détection et sa segmentation. Finalement, la méthode DVT-SWIRE (« Deep venous thrombosis – SWIRE ») est adaptée à la caractérisation de l’élasticité quantitative de thromboses veineuses pour une utilisation en clinique. Cette méthode exploite la première fréquence de résonance mesurée dans la thrombose lors de la propagation d’ondes de cisaillement planes (vibration d’une plaque externe) ou cylindriques (simulation de la force de radiation par génération supersonique). DVT-SWIRE est appliquée sur des fantômes simulant une TVP et les résultats sont comparés à ceux donnés par l’instrument de référence RheoSpectris. Cette méthode est également utilisée avec succès dans une étude ex vivo pour l’évaluation de l’élasticité de thromboses porcines explantées après avoir été induites in vivo par chirurgie.

Driving saccade to pursuit using image motion

Within the context of active vision, scant attention has been paid to the execution of motion saccades—rapid re-adjustments of the direction of gaze to attend to moving objects. In this paper we first develop a methodology for, and give real-time demonstrations of, the use of motion detection and segmentation processes to initiate capture saccades towards a moving object. The saccade is driven by both position and velocity of the moving target under the assumption of constant target velocity, using prediction to overcome the delay introduced by visual processing. We next demonstrate the use of a first order approximation to the segmented motion field to compute bounds on the time-to-contact in the presence of looming motion. If the bound falls below a safe limit, a panic saccade is fired, moving the camera away from the approaching object. We then describe the use of image motion to realize smooth pursuit, tracking using velocity information alone, where the camera is moved so as to null a single constant image motion fitted within a central image region. Finally, we glue together capture saccades with smooth pursuit, thus effecting changes in both what is being attended to and how it is being attended to. To couple the different visual activities of waiting, saccading, pursuing and panicking, we use a finite state machine which provides inherent robustness outside of visual processing and provides a means of making repeated exploration. We demonstrate in repeated trials that the transition from saccadic motion to tracking is more likely to succeed using position and velocity control, than when using position alone.

Reactions to peripheral image motion using a head/eye platform

The authors demonstrate four real-time reactive responses to movement in everyday scenes using an active head/eye platform. They first describe the design and realization of a high-bandwidth four-degree-of-freedom head/eye platform and visual feedback loop for the exploration of motion processing within active vision. The vision system divides processing into two scales and two broad functions. At a coarse, quasi-peripheral scale, detection and segmentation of new motion occurs across the whole image, and at fine scale, tracking of already detected motion takes place within a foveal region. Several simple coarse scale motion sensors which run concurrently at 25 Hz with latencies around 100 ms are detailed. The use of these sensors are discussed to drive the following real-time responses: (1) head/eye saccades to moving regions of interest; (2) a panic response to looming motion; (3) an opto-kinetic response to continuous motion across the image and (4) smooth pursuit of a moving target using motion alone.

Prototipo de herramienta para el inventariado automático de piezas de Lego basado en visión artificial

Permitida la difusión del código bajo los términos de la licencia BSD de tres cláusulas.

Unsupervised segmentation using Gabor wavelets and statistical features in LIDAR data analysis

In this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. The LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. Our aim in this paper is to segment ground (flat field)from non-ground (houses and high vegetation) in hilly urban areas. By projecting the 2.5D data onto a surface, we obtain a texture map as a grey-level image. Based on the image, Gabor wavelet filters are applied to generate Gabor wavelet features. These features are then grouped into various windows. Among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. The test results have shown that ground areas can successfully be segmented from LIDAR data. Most buildings and high vegetation can be detected. In addition, Gabor wavelet transform can partially remove hill or slope effects in the original data by tuning Gabor parameters.

Evaluation of Red Colour Segmentation Algorithms in Traffic Signs Detection

Colour segmentation is the most commonly used method in road signs detection. Road sign contains several basic colours such as red, yellow, blue and white which depends on countries.The objective of this thesis is to do an evaluation of the four colour segmentation algorithms. Dynamic Threshold Algorithm, A Modification of de la Escalera’s Algorithm, the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm. The processing time and segmentation success rate as criteria are used to compare the performance of the four algorithms. And red colour is selected as the target colour to complete the comparison. All the testing images are selected from the Traffic Signs Database of Dalarna University [1] randomly according to the category. These road sign images are taken from a digital camera mounted in a moving car in Sweden.Experiments show that the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm are more accurate and stable to detect red colour of road signs. And the method could also be used in other colours analysis research. The yellow colour which is chosen to evaluate the performance of the four algorithms can reference Master Thesis of Yumei Liu.

Software tools and efficient algorithms for the feature detection, feature tracking, event localization, and visualization of large sets of atmospheric data

Data sets describing the state of the earth's atmosphere are of great importance in the atmospheric sciences. Over the last decades, the quality and sheer amount of the available data increased significantly, resulting in a rising demand for new tools capable of handling and analysing these large, multidimensional sets of atmospheric data. The interdisciplinary work presented in this thesis covers the development and the application of practical software tools and efficient algorithms from the field of computer science, aiming at the goal of enabling atmospheric scientists to analyse and to gain new insights from these large data sets. For this purpose, our tools combine novel techniques with well-established methods from different areas such as scientific visualization and data segmentation. In this thesis, three practical tools are presented. Two of these tools are software systems (Insight and IWAL) for different types of processing and interactive visualization of data, the third tool is an efficient algorithm for data segmentation implemented as part of Insight.Insight is a toolkit for the interactive, three-dimensional visualization and processing of large sets of atmospheric data, originally developed as a testing environment for the novel segmentation algorithm. It provides a dynamic system for combining at runtime data from different sources, a variety of different data processing algorithms, and several visualization techniques. Its modular architecture and flexible scripting support led to additional applications of the software, from which two examples are presented: the usage of Insight as a WMS (web map service) server, and the automatic production of a sequence of images for the visualization of cyclone simulations. The core application of Insight is the provision of the novel segmentation algorithm for the efficient detection and tracking of 3D features in large sets of atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. Data segmentation usually leads to a significant reduction of the size of the considered data. This enables a practical visualization of the data, statistical analyses of the features and their events, and the manual or automatic detection of interesting situations for subsequent detailed investigation. The concepts of the novel algorithm, its technical realization, and several extensions for avoiding under- and over-segmentation are discussed. As example applications, this thesis covers the setup and the results of the segmentation of upper-tropospheric jet streams and cyclones as full 3D objects. Finally, IWAL is presented, which is a web application for providing an easy interactive access to meteorological data visualizations, primarily aimed at students. As a web application, the needs to retrieve all input data sets and to install and handle complex visualization tools on a local machine are avoided. The main challenge in the provision of customizable visualizations to large numbers of simultaneous users was to find an acceptable trade-off between the available visualization options and the performance of the application. Besides the implementational details, benchmarks and the results of a user survey are presented.

Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.