Bacterial peptidoglycan biosynthesis and recognition by the innate immune system

Dissertation presented to obtain the Ph.D degree in Biology

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Multi-scale label interaction to improve object detection and segmentation

The project aims at advancing the state of the art in the use of context information for classification of image and video data. The use of context in the classification of images has been showed of great importance to improve the performance of actual object recognition systems. In our project we proposed the concept of Multi-scale Feature Labels as a general and compact method to exploit the local and global context. The feature extraction from the discriminative probability or classification confidence label field is of great novelty. Moreover the use of a multi-scale representation of the feature labels lead to a compact and efficient description of the context. The goal of the project has been also to provide a general-purpose method and prove its suitability in different image/video analysis problem. The two-year project generated 5 journal publications (plus 2 under submission), 10 conference publications (plus 2 under submission) and one patent (plus 1 pending). Of these publications, a relevant number make use of the main result of this project to improve the results in detection and/or segmentation of objects.

Neuropathies diabétiques: tableaux cliniques, détection précoce et appel au spécialiste [Diabetic neuropathies: clinical sub-types, early detection and asking help from the specialist].

In diabetes mellitus, it is expected to see a common, mainly sensitive, distal symmetrical polyneuropathy (DPN) involving a large proportion of diabetic patients according to known risk factors. Several other diabetic peripheral neuropathies are recognized, such as dysautonomia and multifocal neuropathies including lumbosacral radiculoplexus and oculomotor palsies. In this review, general aspects of diabetic neuropathies are examined, and it is discussed why and how the general practionner has to perform a yearly examination. At the present time, some consensuses emerge to ask help from the specialist when faced to other forms of peripheral neuropathies than distal symmetrical DPN.

Molecular characterization of HLA-C incompatibilities in HLA-ABDR-matched unrelated bone marrow donor-recipient pairs. Sequence of two new Cw alleles (Cw*02023 and Cw*0707) and recognition by cytotoxic T lymphocytes.

While the influence of HLA-AB and -DRB1 matching on the outcome of bone marrow transplantation (BMT) with unrelated donors is clear, the evaluation of HLA-C has been hampered by its poor serological definition. Because the low resolution of standard HLA-C typing could explain the significant number of positive cytotoxic T lymphocyte precursor frequency (CTLpf) tests found among HLA-AB-subtype, DRB1/B3/B5-subtype matched patient/donor pairs, we have identified by sequencing the incompatibilities recognized by CD8+ CTL clones obtained from such positive CTLpf tests. In most cases the target molecules were HLA-C antigens that had escaped detection by serology (e.g. Cw*1601, 1502 or 0702). Direct recognition of HLA-C by a CTL clone was demonstrated by lysis of the HLA class I-negative 721.221 cell line transfected with Cw*1601 cDNA. Because of the functional importance of Cw polymorphism, a PCR-SSO oligotyping procedure was set up allowing the resolution of 29 Cw alleles. Oligotyping of a panel of 382 individuals (including 101 patients and their 272 potential unrelated donors, 5 related donors and 4 platelet donors) allowed to determine HLA-C and HLA A-B-Cw-DRB1 allelic frequencies, as well as a number of A-Cw, B-Cw, and DRB1-Cw associations. Two new HLA-Cw alleles (Cw*02023 and Cw*0707) were identified by DNA sequencing of PCR-amplified exon 2-intron 2-exon 3 amplicons. Furthermore, we determined the degree of HLA-C compatibility in 287 matched pairs that could be formed from 73 patients and their 184 potential unrelated donors compatible for HLA-AB by serology and for HLA-DRB1/ B3/B5 by oligotyping. Cw mismatches were identified in 42.1% of these pairs, and AB-subtype oligotyping showed that 30% of these Cw-incompatible pairs were also mismatched for A or B-locus subtype. The degree of HLA-C incompatibility was strongly influenced by the linkage with B alleles and by the ABDR haplotypes. Cw alleles linked with B*4403, B*5101, B18, and B62 haplotypes were frequently mismatched. Apparently high resolution DNA typing for HLA-AB does not result in full matching at locus C. Since HLA-C polymorphism is recognized by alloreactive CTLs, such incompatibilities might be as relevant as AB-subtype mismatches in clinical transplantation.

Seismic detection and characterization of landslides and other mass movements

Seismic methods used in the study of snow avalanches may be employed to detect and characterize landslides and other mass movements, using standard spectrogram/sonogram analysis. For snow avalanches, the spectrogram for a station that is approached by a sliding mass exhibits a triangular time/frequency signature due to an increase over time in the higher-frequency constituents. Recognition of this characteristic footprint in a spectrogram suggests a useful metric for identifying other mass-movement events such as landslides. The 1 June 2005 slide at Laguna Beach, California is examined using data obtained from the Caltech/USGS Regional Seismic Network. This event exhibits the same general spectrogram features observed in studies of Alpine snow avalanches. We propose that these features are due to the systematic relative increase in high-frequency energy transmitted to a seismometer in the path of a mass slide owing to a reduction of distance from the source signal. This phenomenon is related to the path of the waves whose high frequencies are less attenuated as they traverse shorter source-receiver paths. Entrainment of material in the course of the slide may also contribute to the triangular time/frequency signature as a consequence of the increase in the energy involved in the process; in this case the contribution would be a source effect. By applying this commonly observed characteristic to routine monitoring algorithms, along with custom adjustments for local site effects, we seek to contribute to the improvement in automatic detection and monitoring methods of landslides and other mass movements.

Detection and dynamics of porcine circovirus 2 shedding in semen using conventional and real-time PCR

The dynamics of porcine circovirus type 2 (PCV2) shedding in semen of naturally infected boars was studied. Semen was collected serially each 15 or 20 days during 62 days from 5 boars from a herd and from 11 boars from an artificial insemination center. All boars were positive for PCV2 DNA by nested polymerase chain reaction of raw semen in at least two sampling dates, and most of them had detectable shedding in all sampling dates. Real-time quantitative PCR was performed in 23 samples. All samples showed low amounts of PCV2 DNA, ranging from 98 to 652 PCV2 copies/mL. No differences between the frequencies of PCV2 DNA shed in semen were found considering herds and age of boars. PCV2 shedding in the semen can occur continuously or intermittently up to 60 days in naturally infected boars at 12 to 42 months old in absence of PCV2 clinical signs. These results demonstrate sporadic and long-term shedding patterns of low amounts of PCV2 DNA in semen from naturally infected boars.

Doppler vortography : detection and quantification of the vortices in the left ventricle

Nous proposons une nouvelle méthode pour quantifier la vorticité intracardiaque (vortographie Doppler), basée sur l’imagerie Doppler conventionnelle. Afin de caractériser les vortex, nous utilisons un indice dénommé « Blood Vortex Signature (BVS) » (Signature Tourbillonnaire Sanguine) obtenu par l’application d’un filtre par noyau basé sur la covariance. La validation de l’indice BVS mesuré par vortographie Doppler a été réalisée à partir de champs Doppler issus de simulations et d’expériences in vitro. Des résultats préliminaires obtenus chez des sujets sains et des patients atteints de complications cardiaques sont également présentés dans ce mémoire. Des corrélations significatives ont été observées entre la vorticité estimée par vortographie Doppler et la méthode de référence (in silico: r2 = 0.98, in vitro: r2 = 0.86). Nos résultats suggèrent que la vortographie Doppler est une technique d’échographie cardiaque prometteuse pour quantifier les vortex intracardiaques. Cet outil d’évaluation pourrait être aisément appliqué en routine clinique pour détecter la présence d’une insuffisance ventriculaire et évaluer la fonction diastolique par échocardiographie Doppler.

Biologically motivated model for object detection and identification in real-world scenes

The classical computer vision methods can only weakly emulate some of the multi-level parallelisms in signal processing and information sharing that takes place in different parts of the primates’ visual system thus enabling it to accomplish many diverse functions of visual perception. One of the main functions of the primates’ vision is to detect and recognise objects in natural scenes despite all the linear and non-linear variations of the objects and their environment. The superior performance of the primates’ visual system compared to what machine vision systems have been able to achieve to date, motivates scientists and researchers to further explore this area in pursuit of more efficient vision systems inspired by natural models. In this paper building blocks for a hierarchical efficient object recognition model are proposed. Incorporating the attention-based processing would lead to a system that will process the visual data in a non-linear way focusing only on the regions of interest and hence reducing the time to achieve real-time performance. Further, it is suggested to modify the visual cortex model for recognizing objects by adding non-linearities in the ventral path consistent with earlier discoveries as reported by researchers in the neuro-physiology of vision.

A camera based digit location and recognition system for garment tracking

Garment information tracking is required for clean room garment management. In this paper, we present a camera-based robust system with implementation of Optical Character Reconition (OCR) techniques to fulfill garment label recognition. In the system, a camera is used for image capturing; an adaptive thresholding algorithm is employed to generate binary images; Connected Component Labelling (CCL) is then adopted for object detection in the binary image as a part of finding the ROI (Region of Interest); Artificial Neural Networks (ANNs) with the BP (Back Propagation) learning algorithm are used for digit recognition; and finally the system is verified by a system database. The system has been tested. The results show that it is capable of coping with variance of lighting, digit twisting, background complexity, and font orientations. The system performance with association to the digit recognition rate has met the design requirement. It has achieved real-time and error-free garment information tracking during the testing.

Benchmark datasets for detection and tracking

There is a rising demand for the quantitative performance evaluation of automated video surveillance. To advance research in this area, it is essential that comparisons in detection and tracking approaches may be drawn and improvements in existing methods can be measured. There are a number of challenges related to the proper evaluation of motion segmentation, tracking, event recognition, and other components of a video surveillance system that are unique to the video surveillance community. These include the volume of data that must be evaluated, the difficulty in obtaining ground truth data, the definition of appropriate metrics, and achieving meaningful comparison of diverse systems. This chapter provides descriptions of useful benchmark datasets and their availability to the computer vision community. It outlines some ground truth and evaluation techniques, and provides links to useful resources. It concludes by discussing the future direction for benchmark datasets and their associated processes.

IMILAST: a community effort to intercompare extratropical cyclone detection and tracking algorithms

The variability of results from different automated methods of detection and tracking of extratropical cyclones is assessed in order to identify uncertainties related to the choice of method. Fifteen international teams applied their own algorithms to the same dataset—the period 1989–2009 of interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERAInterim) data. This experiment is part of the community project Intercomparison of Mid Latitude Storm Diagnostics (IMILAST; see www.proclim.ch/imilast/index.html). The spread of results for cyclone frequency, intensity, life cycle, and track location is presented to illustrate the impact of using different methods. Globally, methods agree well for geographical distribution in large oceanic regions, interannual variability of cyclone numbers, geographical patterns of strong trends, and distribution shape for many life cycle characteristics. In contrast, the largest disparities exist for the total numbers of cyclones, the detection of weak cyclones, and distribution in some densely populated regions. Consistency between methods is better for strong cyclones than for shallow ones. Two case studies of relatively large, intense cyclones reveal that the identification of the most intense part of the life cycle of these events is robust between methods, but considerable differences exist during the development and the dissolution phases.

Detection and attribution of human influence on regional precipitation

Understanding how human influence on climate is affecting precipitation around the world is immensely important for defining mitigation policies, and for adaptation planning. Yet despite increasing evidence for the influence of climate change on global patterns of precipitation, and expectations that significant changes in regional precipitation should have already occurred as a result of human influence on climate, compelling evidence of anthropogenic fingerprints on regional precipitation is obscured by observational and modelling uncertainties and is likely to remain so using current methods for years to come. This is in spite of substantial ongoing improvements in models, new reanalyses and a satellite record that spans over thirty years. If we are to quantify how human-induced climate change is affecting the regional water cycle, we need to consider novel ways of identifying the effects of natural and anthropogenic influences on precipitation that take full advantage of our physical expectations.

Multiple camera people detection and tracking using support integration

This paper proposes a method to locate and track people by combining evidence from multiple cameras using the homography constraint. The proposed method use foreground pixels from simple background subtraction to compute evidence of the location of people on a reference ground plane. The algorithm computes the amount of support that basically corresponds to the ""foreground mass"" above each pixel. Therefore, pixels that correspond to ground points have more support. The support is normalized to compensate for perspective effects and accumulated on the reference plane for all camera views. The detection of people on the reference plane becomes a search for regions of local maxima in the accumulator. Many false positives are filtered by checking the visibility consistency of the detected candidates against all camera views. The remaining candidates are tracked using Kalman filters and appearance models. Experimental results using challenging data from PETS`06 show good performance of the method in the presence of severe occlusion. Ground truth data also confirms the robustness of the method. (C) 2010 Elsevier B.V. All rights reserved.

Road and Traffic Signs Recognition using Vector Machines

Intelligent Transportation System (ITS) is a system that builds a safe, effective and integrated transportation environment based on advanced technologies. Road signs detection and recognition is an important part of ITS, which offer ways to collect the real time traffic data for processing at a central facility.This project is to implement a road sign recognition model based on AI and image analysis technologies, which applies a machine learning method, Support Vector Machines, to recognize road signs. We focus on recognizing seven categories of road sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, are used for representing the data to the SVM for training and test. We compared and analyzed the performances of SVM recognition model using different features and different kernels. Moreover, the performances using different recognition models, SVM and Fuzzy ARTMAP, are observed.