Estimation of the normal contact stiffness for frictional interface in sticking and sliding conditions

Modeling of frictional contact systems with high accuracy needs the knowledge of several contact parameters, which are mainly related to the local phenomena at the contact interfaces and affect the complex dynamics of mechanical systems in a prominent way. This work presents a newer approach for identifying reliable values of the normal contact stiffness between surfaces in contact, in both sliding and sticking conditions. The combination of experimental tests, on a dedicated set-up, with finite element modeling, allowed for an indirect determination of the normal contact stiffness. The stiffness was found to increase with increasing contact pressure and decreasing roughness, while the evolution of surface topography and third-body rheology affected the contact stiffness when sliding

Infinite index extensions of local nets and defects

Subfactor theory provides a tool to analyze and construct extensions of Quantum Field Theories, once the latter are formulated as local nets of von Neumann algebras. We generalize some of the results of [LR95] to the case of extensions with infinite Jones index. This case naturally arises in physics, the canonical examples are given by global gauge theories with respect to a compact (non-finite) group of internal symmetries. Building on the works of Izumi, Longo, Popa [ILP98] and Fidaleo, Isola [FI99], we consider generalized Q-systems (of intertwiners) for a semidiscrete inclusion of properly infinite von Neumann algebras, which generalize ordinary Q-systems introduced by Longo [Lon94] to the infinite index case. We characterize inclusions which admit generalized Q-systems of intertwiners and define a braided product among the latter, hence we construct examples of QFTs with defects (phase boundaries) of infinite index, extending the family of boundaries in the grasp of [BKLR16].Comment: 50 page

Competitive segmentation of the hippocampus and the amygdala from MRI scans

The hippocampus and the amygdala are two brain structures which play a central role in several fundamental cognitive processes. Their segmentation from Magnetic Resonance Imaging (MRI) scans is a unique way to measure their atrophy in some neurological diseases, but it is made difficult by their complex geometry. Their simultaneous segmentation is considered here through a competitive homotopic region growing method. It is driven by relational anatomical knowledge, which enables to consider the segmentation of atrophic structures in a straightforward way. For both structures, this fast algorithm gives results which are comparable to manual segmentation with a better reproducibility. Its performances regarding segmentation quality, automation and computation time, are amongst the best published data.L’hippocampe et l’amygdale sont deux structures cérébrales intervenant dans plusieurs fonctions cognitives fondamentales. Leur segmentation, à partir de volumes d’imagerie par résonance magnétique (IRM), est un outil essentiel pour mesurer leur atteinte dans certaines pathologies neurologiques, mais elle est rendue difficile par leur géométrie complexe. Nous considérons leur segmentation simultanée par une méthode de déformation homotopique compétitive de régions. Celle-ci est guidée par des connaissances anatomiques relationnelles ; ceci permet de considérer directement des structures atrophiées. Rapide, l’algorithme donne, pour les deux structures, des résultats comparables à la segmentation manuelle avec une meilleure reproductibilité. Ses performances, concernant la qualité de la segmentation, le degré d’automatisation et le temps de calcul, sont parmi les meilleures de la littérature

Inference algorithms for gene networks: a statistical mechanics analysis

The inference of gene regulatory networks from high throughput gene expression data is one of the major challenges in systems biology. This paper aims at analysing and comparing two different algorithmic approaches. The first approach uses pairwise correlations between regulated and regulating genes; the second one uses message-passing techniques for inferring activating and inhibiting regulatory interactions. The performance of these two algorithms can be analysed theoretically on well-defined test sets, using tools from the statistical physics of disordered systems like the replica method. We find that the second algorithm outperforms the first one since it takes into account collective effects of multiple regulators

Segmentation compétitive de l'hippocampe et de l'amygdale à partir de volumes IRM

L'hippocampe et l'amygdale sont deux structures cérébrales intervenant dans plusieurs fonctions cognitives fondamentales. Leur segmentation est un outil essentiel pour mesurer leur atteinte dans certaines pathologies neurologiques, mais elle est rendue difficile par leur complexité. Nous considérons leur segmentation simultanée par une méthode de déformation homotopique compétitive de régions. celle-ci est guidée par des connaissances anatomiques relationnelles, et non des a priori statistiques, pour pouvoir considérer des structures atrophiées. Rapide, l'algorithme donne des résultats satisfaisants pour les deux structures par rapport à la segmentation manuelle et à la littérature

Progress in noncommutative function theory

In this expository paper we describe the study of certain non-self-adjoint operator algebras, the Hardy algebras, and their representation theory. We view these algebras as algebras of (operator valued) functions on their spaces of representations. We will show that these spaces of representations can be parameterized as unit balls of certain $W^{*}$-correspondences and the functions can be viewed as Schur class operator functions on these balls. We will provide evidence to show that the elements in these (non commutative) Hardy algebras behave very much like bounded analytic functions and the study of these algebras should be viewed as noncommutative function theory

Transcriptional and Functional Studies of a Cd(II)/Pb(II)-Responsive Transcriptional Regulator(CmtR) from Acidithiobacillus ferrooxidans ATCC 23270

The acidophilic Acidithiobacillusferrooxidans can resist exceptionally high cadmium (Cd) concentrations. This property is important for its use in biomining processes, where Cd and other metal levels range usually between 15 and 100 mM. To learn about the mechanisms that allow A. ferrooxidans cells to survive in this environment, a bioinformatic search of its genome showed the presence of that a Cd(II)/Pb(II)-responsive transcriptional regulator (CmtR) was possibly related to Cd homeostasis. The expression of the CmtR was studied by real-time reverse transcriptase PCR using A. ferrooxidans cells adapted for growth in the presence of high concentrations of Cd. The putative A. ferrooxidans Cd resistance determinant was found to be upregulated when this bacterium was exposed to Cd in the range of 15–30 mM. The CmtR from A. ferrooxidans was cloned and expressed in Escherichiacoli, the soluble protein was purified by one-step affinity chromatography to apparent homogeneity. UV–Vis spectroscopic measurements showed that the reconstruction CmtR was able to bind Cd(II) forming Cd(II)–CmtR complex in vitro. The sequence alignment and molecular modeling showed that the crucial residues for CmtR binding were likely to be Cys77, Cys112, and Cys121. The results reported here strongly suggest that the high resistance of the extremophilic A. ferrooxidans to Cd including the Cd(II)/Pb(II)-responsive transcriptional regulator

Rapidly re-computable EEG (electroencephalography) forward models for realistic head shapes

Solution of the EEG source localization (inverse) problem utilizing model-based methods typically requires a significant number of forward model evaluations. For subspace based inverse methods like MUSIC [6], the total number of forward model evaluations can often approach an order of 10{sup 3} or 10{sup 4}. Techniques based on least-squares minimization may require significantly more evaluations. The observed set of measurements over an M-sensor array is often expressed as a linear forward spatio-temporal model of the form: F = GQ + N (1) where the observed forward field F (M-sensors x N-time samples) can be expressed in terms of the forward model G, a set of dipole moment(s) Q (3xP-dipoles x N-time samples) and additive noise N. Because of their simplicity, ease of computation, and relatively good accuracy, multi-layer spherical models [7] (or fast approximations described in [1], [7]) have traditionally been the 'forward model of choice' for approximating the human head. However, approximation of the human head via a spherical model does have several key drawbacks. By its very shape, the use of a spherical model distorts the true distribution of passive currents in the skull cavity. Spherical models also require that the sensor positions be projected onto the fitted sphere (Fig. 1), resulting in a distortion of the true sensor-dipole spatial geometry (and ultimately the computed surface potential). The use of a single 'best-fitted' sphere has the added drawback of incomplete coverage of the inner skull region, often ignoring areas such as the frontal cortex. In practice, this problem is typically countered by fitting additional sphere(s) to those region(s) not covered by the primary sphere. The use of these additional spheres results in added complication to the forward model. Using high-resolution spatial information obtained via X-ray CT or MR imaging, a realistic head model can be formed by tessellating the head into a set of contiguous regions (typically the scalp, outer skull, and inner skull surfaces). Since accurate in vivo determination of internal conductivities is currently not currently possible, the head is typically assumed to consist of a set of contiguous isotropic regions, each with constant conductivity