A Prospective Study on Algorithms Adapted to the Spatial Frequency in Tomography

The use of iterative algorithms in tomographic reconstruction always leads to a frequency adapted rate of convergence in that low frequencies are accurately reconstructed after a few iterations, while high frequencies sometimes require many more computations. In this paper, we propose to build frequency adapted (FA) algorithms based on a condition of incomplete backprojection and propose an FA simultaneous algebraic reconstruction technique (FA-SART) algorithm as an example. The results obtained with the FA-SART algorithm demonstrate a very fast convergence on a highly detailed phantom when compared to the original SART algorithm. Though the use of such an FA algorithm may seem difficult, we specify in which case it is relevant and propose several ways to improve the reconstruction process with FA algorithms

Algorithmic aspects of sparse approximations

Typical tasks in signal processing may be done in simpler ways or more efficiently if the signals to analyze are represented in a proper way. This thesis deals with some algorithmic problems related to signal approximation, more precisely, in the novel field of sparse approximation using redundant dictionaries of functions. Orthogonal bases permit to approximate signals by just taking the N waveforms whose associated projections have maximal amplitudes. This nice property is no longer valid if the used base is redundant. In fact, finding the best decomposition becomes a NP Hard problem in the general case. Thus, suboptimal heuristics have been developed; the best known ones are Matching Pursuit and Basis Pursuit. Both remain highly complex which prevent them from being used in practice in many situations. The first part of the thesis is concerned with this computational bottleneck. We propose to create a tree structure endowing the dictionary and grouping similar atoms in the same branches. An approximation algorithm, called Tree-Based Pursuit, exploiting this structure is presented. It considerably lowers the cost of finding good approximations with redundant dictionaries. The quality of the representation does not only depend on the approximation algorithm but also on the dictionary used. One of the main advantages of these techniques is that the atoms can be tailored to match the features present in the signal. It might happen that some knowledge about the class of signals to approximate directly leads to the dictionary. For most natural signals, however, the underlying structures are not clearly known and may be obfuscated. Learning dictionaries based on examples is an alternative to manual design and is gaining in interest. Most natural signals exhibit behaviors invariant to translations in space or in time. Thus, we propose an algorithm to learn redundant dictionaries under the translation invariance constraint. In the case of images, the proposed solution is able to recover atoms similar to Gabor functions, line edge detectors and curved edge detectors. The two first categories were already observed and the third one completes the range of natural features and is a major contribution of this algorithm. Sparsity is used to define the efficiency of approximation algorithms as well as to characterize good dictionaries. It directly comes from the fact that these techniques aim at approximating signals with few significant terms. This property was successfully exploited as a dimension reduction method for different signal processing tasks as analysis, de-noising or compression. In the last chapter, we tackle the problem of finding the nearest neighbor to a query signal in a set of signals that have a sparse representation. We take advantage of sparsity to approximate quickly the distance between the query and all elements of the database. In this way, we are able to prune recursively all elements that do not match the query, while providing bounds on the true distance. Validation of this technique on synthetic and real data sets confirms that it could be very well suited to process queries over large databases of compressed signals, avoiding most of the burden of decoding

Mobiliser en partenariat les principes de l'agroécologie pour reconcevoir des systèmes ovins lait productifs plus autonomes

L’intensification de l’élevage et la spécialisation ont conduit à une diminution des surfaces en prairies naturelles (Kristensen et al. 2005), y compris dans les zones défavorisées (Quetier et al., 2005). Pourtant, face aux changements globaux en cours, les éleveurs doivent renforcer la durabilité de leur exploitation. Plusieurs auteurs évoquent le recours à l’agroécologie pour favoriser la conception de systèmes agricoles durables (Gliesman, 1998; Altieri, 2002). Pour y parvenir, des principes de l’agroécologie ont été énoncés mais ils restent cependant relativement généraux et appliqués aux systèmes de productions végétales (Altieri, 2002). Leur application au domaine de l’élevage est récent (Dumont et al., 2013 ; Bonaudo et al., 2013) et repose sur des travaux et réflexions portés par des chercheurs. Selon ces auteurs, d’une part, l’agroécologie implique de concevoir les systèmes d’élevage en s’appuyant sur les régulations biologiques et les interactions entre les composantes du système pour valoriser les ressources fourragères locales et être autonomes en intrants. D’autre part, elle implique aussi de travailler avec les acteurs du territoire qui gèrent ces élevages et ces ressources locales (éleveurs, conseillers...). Dans ce contexte, la question est : comment traduire les principes de l’agroécologie en leviers d’action pour les éleveurs et leurs conseillers pour aider au développement de systèmes d’élevage durables et adaptés aux changements globaux ? Pour y répondre, une étude a été menée dans le cadre d’un partenariat entre des chercheurs zootechniciens, des éleveurs ovins lait du bassin de Roquefort et des conseillers agricoles. L’objectif était de caractériser les systèmes ovins lait engagés dans une quête d’autonomie et d’évaluer leurs performances au regard des principes agroécologiques

Safety assessment of the commensal strain Bacteroides xylanisolvens DSM 23964

AbstractWe recently isolated and characterized the new strain Bacteroides xylanisolvens DSM 23964 and presented it as potential candidate for the first natural probiotic strain of the genus Bacteroides. In order to evaluate the safety of this strain for use in food, the following standard toxicity assays were conducted with this strain in both viable and pasteurized form: in vitro bacterial reverse mutation assay, in vitro chromosomal aberration assay, and 90day subchronic repeated oral toxicity studies in mice. No mutagenic, clastogenic, or toxic effects were detected even at extremely high doses. In addition, no clinical, hematological, ophthalmological, or histopathological abnormality could be observed after necropsy at any of the doses tested. Hence, the NOAEL could be estimated to be greater than 2.3×1011 CFUs, and 2.3×1014 for pasteurized bacteria calculated as equivalent for an average 70kg human being. In addition, the absence of any in vivo pathogenic properties of viable B. xylanisolvens DSM 23964 cells was confirmed by means of an intraperitoneal abscess formation model in mice which also demonstrated that the bacteria are easily eradicated by the host’s immune system. The obtained results support the assumed safety of B. xylanisolvens DSM 23964 for use in food

Focal adhesion size controls tension-dependent recruitment of α-smooth muscle actin to stress fibers

Expression of α-smooth muscle actin (α-SMA) renders fibroblasts highly contractile and hallmarks myofibroblast differentiation. We identify α-SMA as a mechanosensitive protein that is recruited to stress fibers under high tension. Generation of this threshold tension requires the anchoring of stress fibers at sites of 8–30-μm-long “supermature” focal adhesions (suFAs), which exert a stress approximately fourfold higher (∼12 nN/μm2) on micropatterned deformable substrates than 2–6-μm-long classical FAs. Inhibition of suFA formation by growing myofibroblasts on substrates with a compliance of ≤11 kPa and on rigid micropatterns of 6-μm-long classical FA islets confines α-SMA to the cytosol. Reincorporation of α-SMA into stress fibers is established by stretching 6-μm-long classical FAs to 8.1-μm-long suFA islets on extendable membranes; the same stretch producing 5.4-μm-long classical FAs from initially 4-μm-long islets is without effect. We propose that the different molecular composition and higher phosphorylation of FAs on supermature islets, compared with FAs on classical islets, accounts for higher stress resistance

Ultrafast carrier dynamics in single-wall carbon nanotubes

Time-resolved carrier dynamics in single wall carbon nanotubes is investigated by means of two-color pump-probe experiments. The recombination dynamics is monitored by probing the transient photo-bleaching observed on the first interband transition of the semi-conducting tubes. The carrier dynamics takes place on a one picosecond time scale which is one order of magnitude slower than in graphite. Transient photo-induced absorption is observed with exactly the same dynamics for non-resonant probe conditions and is interpreted as a global red shift of the $\pi-$plasmon resonance. We show that the opening of the band gap in semi-conducting carbon nanotubes determines the non-linear response dynamics over the whole visible and near-infrared spectrum

Learning redundant dictionaries with translation invariance property: the MoTIF algorithm

International audienceSparse approximation using redundant dictionaries is an efficient tool for many applications in the field of signal processing. The performances largely depend on the adaptation of the dictionary to the signal to decompose. As the statistical dependencies are most of the time not obvious in natural high- dimensional data, learning fundamental patterns is an alternative to analytical design of bases and has become a field of acute research. Most of the time, the underlying patterns of a class of signals can be found at any time, and in the design of a dictionary, this translation invariance property should be present. We present a new algorithm for learning short generating functions, each of them building a set of atoms corresponding to all its translations. The resulting dictionary is highly redundant and translation invariant

Face Authentication using Client-specific Matching Pursuit

In this paper, we address the problem of finding image decompositions that allow good compression performance, and that are also efficient for face authentication. We propose to decompose the face image using Matching Pursuit and to perform the face authentication in the compressed domain using a MLP (Multi-Layer Perceptron) classifier. We provide experimental results and comparisons with PCA and LDA systems on the multi-modal benchmark database BANCA using its associated protocol

Mobiliser les principes de l'agroécologie pour redéfinir le cadre d'analyse et d'évaluation des systèmes ovins en zone Roquefort

Dans le Rayon Roquefort, l'intensification de la production laitière avec l'utilisation du progrès génétique de la race Lacaune a entrainé une augmentation des achats d'aliments et d'intrants dans les élevages. Les résultats économiques des élevages ovins lait se retrouvent directement dépendants des prix des céréales, des engrais et du fuel, prix en constante augmentation et de plus en plus fluctuants compte tenu d'évènements climatiques extrêmes de plus en plus fréquents

NK cell education: Physiological and pathological influences

Natural killer (NK) cells represent a critical defense against viral infections and cancers. NK cells require integration of activating and inhibitory NK cell receptors to detect target cells and the balance of these NK cell inputs defines the global NK cell response. The sensitivity of the response is largely defined by interactions between self-major histocompatibility complex class I (MHC-I) molecules and specific inhibitory NK cell receptors, so-called NK cell education. Thus, NK cell education is a crucial process to generate tuned effector NK cell responses in different diseases. In this review, we discuss the relationship between NK cell education and physiologic factors (type of self-MHC-I, self-MHC-I allelic variants, variant of the self-MHC-I-binding peptides, cytokine effects and inhibitory KIR expression) underlying NK cell education profiles (effector function or metabolism). Additionally, we describe the broad-spectrum of effector educated NK cell functions on different pathologies (such as HIV-1, CMV and tumors, among others)