Steady-state expression of self-regulated genes.

MOTIVATION: Regulatory gene networks contain generic modules such as feedback loops that are essential for the regulation of many biological functions. The study of the stochastic mechanisms of gene regulation is instrumental for the understanding of how cells maintain their expression at levels commensurate with their biological role, as well as to engineer gene expression switches of appropriate behavior. The lack of precise knowledge on the steady-state distribution of gene expression requires the use of Gillespie algorithms and Monte-Carlo approximations. METHODOLOGY: In this study, we provide new exact formulas and efficient numerical algorithms for computing/modeling the steady-state of a class of self-regulated genes, and we use it to model/compute the stochastic expression of a gene of interest in an engineered network introduced in mammalian cells. The behavior of the genetic network is then analyzed experimentally in living cells. RESULTS: Stochastic models often reveal counter-intuitive experimental behaviors, and we find that this genetic architecture displays a unimodal behavior in mammalian cells, which was unexpected given its known bimodal response in unicellular organisms. We provide a molecular rationale for this behavior, and we implement it in the mathematical picture to explain the experimental results obtained from this network.

Inherently self-calibrating non-Cartesian parallel imaging.

The use of self-calibrating techniques in parallel magnetic resonance imaging eliminates the need for coil sensitivity calibration scans and avoids potential mismatches between calibration scans and subsequent accelerated acquisitions (e.g., as a result of patient motion). Most examples of self-calibrating Cartesian parallel imaging techniques have required the use of modified k-space trajectories that are densely sampled at the center and more sparsely sampled in the periphery. However, spiral and radial trajectories offer inherent self-calibrating characteristics because of their densely sampled center. At no additional cost in acquisition time and with no modification in scanning protocols, in vivo coil sensitivity maps may be extracted from the densely sampled central region of k-space. This work demonstrates the feasibility of self-calibrated spiral and radial parallel imaging using a previously described iterative non-Cartesian sensitivity encoding algorithm.

Self-Expanding Versus Balloon-Expandable Stents in Acute Myocardial Infarction: Results From the APPOSITION II Study: Self-Expanding Stents in ST-Segment Elevation Myocardial Infarction.

OBJECTIVES: This study sought to investigate whether self-expanding stents are more effective than balloon-expandable stents for reducing stent malapposition at 3 days after implantation in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. BACKGROUND: Acute myocardial infarction is associated with vasoconstriction and large thrombus burden. Resolution of vasoconstriction and thrombus load during the first hours to days after primary percutaneous coronary intervention may lead to stent undersizing and malapposition, which may subsequently lead to stent thrombosis or restenosis. In addition, aggressive stent deployment may cause distal embolization. METHODS: Eighty patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention were randomized to receive a self-expanding stent (STENTYS, STENTYS SA, Paris, France) (n = 43) or a balloon-expandable stent (VISION, Abbott Vascular, Santa Clara, California; or Driver, Medtronic, Minneapolis, Minnesota) (n = 37) at 9 European centers. The primary endpoint was the proportion of stent strut malapposition at 3 days after implantation measured by optical coherence tomography. Secondary endpoints included major adverse cardiac events (cardiac death, recurrent myocardial infarction, emergent bypass surgery, or clinically driven target lesion revascularization). RESULTS: At 3 days after implantation, on a per-strut basis, a lower rate of malapposed stent struts was observed by optical coherence tomography in the self-expanding stent group than in the balloon-expandable group (0.58% vs. 5.46%, p < 0.001). On a per-patient basis, none of the patients in the self-expanding stent group versus 28% in the balloon-expandable group presented ≥5% malapposed struts (p < 0.001). At 6 months, major adverse cardiac events were 2.3% versus 0% in the self-expanding and balloon-expandable groups, respectively (p = NS). CONCLUSIONS: Strut malapposition at 3 days is significantly lower in ST-segment elevation myocardial infarction patients allocated to self-expanding stents when than in those allocated to balloon-expandable stents. The impact of this difference on clinical outcome and the risk of late stent thrombosis need to be evaluated further. (Randomized Comparison Between the STENTYS Self-expanding Coronary Stent and a Balloon-expandable Stent in Acute Myocardial Infarction [APPOSITION II]; NCT01008085).

Stochastic models and numerical algorithms for a class of regulatory gene networks.

Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Sexual dimorphism in resource acquisition and deployment: both size and timing matter.

Background and Aims The males and females of many dioecious plant species differ from one another in important life-history traits, such as their size. If male and female reproductive functions draw on different resources, for example, one should expect males and females to display different allocation strategies as they grow. Importantly, these strategies may differ not only between the two sexes, but also between plants of different age and therefore size. Results are presented from an experiment that asks whether males and females of Mercurialis annua, an annual plant with indeterminate growth, differ over time in their allocation of two potentially limiting resources (carbon and nitrogen) to vegetative (below-and above-ground) and reproductive tissues.Methods Comparisons were made of the temporal patterns of biomass allocation to shoots, roots and reproduction and the nitrogen content in the leaves between the sexes of M. annua by harvesting plants of each sex after growth over different periods of time.Key Results and Conclusions Males and females differed in their temporal patterns of allocation. Males allocated more to reproduction than females at early stages, but this trend was reversed at later stages. Importantly, males allocated proportionally more of their biomass towards roots at later stages, but the roots of females were larger in absolute terms. The study points to the important role played by both the timing of resource deployment and the relative versus absolute sizes of the sinks and sources in sexual dimorphism of an annual plant.

Melanoma patients respond to a cytotoxic T lymphocyte-defined self-peptide with diverse and nonoverlapping T-cell receptor repertoires.

HLA-A2+ melanoma patients develop naturally a strong CD8+ T cell response to a self-peptide derived from Melan-A. Here, we have used HLA-A2/peptide tetramers to isolate Melan-A-specific T cells from tumor-infiltrated lymph nodes of two HLA-A2+ melanoma patients and analyzed their TCR beta chain V segment and complementarity determining region 3 length and sequence. We found a broad diversity in Melan-A-specific immune T-cell receptor (TCR) repertoires in terms of both TCR beta chain variable gene segment usage and clonal composition. In addition, immune TCR repertoires selected in the patients were not overlapping. In contrast to previously characterized CD8+ T-cell responses to viral infections, this study provides evidence against usage of highly restricted TCR repertoire in the natural response to a self-differentiation tumor antigen.

Business research, self-fulfilling prophecy, and the inherent responsibility of scholars.

Business research and teaching institutions play an important role in shaping the way businesses perceive their relations to the broader society and its moral expectations. Hence, as ethical scandals recently arose in the business world, questions related to the civic responsibilities of business scholars and to the role business schools play in society have gained wider interest. In this article, I argue that these ethical shortcomings are at least partly resulting from the mainstream business model with its taken-for granted basic assumptions such as specialization or the value-neutrality of business research. Redefining the roles and civic responsibilities of business scholars for business practice implies therefore a thorough analysis of these assumptions if not their redefinition. The takenforgrantedness of the mainstream business model is questioned by the transformation of the societal context in which business activities are embedded. Its value-neutrality in turn is challenged by self-fulfilling prophecy effects, which highlight the normative influence of business schools. In order to critically discuss some basic assumptions of mainstream business theory, I propose to draw parallels with the corporate citizenship concept and the stakeholder theory. Their integrated approach of the relation between business practice and the broader society provides interesting insights for the social reembedding of business research and teaching.

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.

Regulation of Nedd4-2 self-ubiquitination and stability by a PY motif located within its HECT-domain.

Ubiquitin ligases play a pivotal role in substrate recognition and ubiquitin transfer, yet little is known about the regulation of their catalytic activity. Nedd4 (neural-precursor-cell-expressed, developmentally down-regulated 4)-2 is an E3 ubiquitin ligase composed of a C2 domain, four WW domains (protein-protein interaction domains containing two conserved tryptophan residues) that bind PY motifs (L/PPXY) and a ubiquitin ligase HECT (homologous with E6-associated protein C-terminus) domain. In the present paper we show that the WW domains of Nedd4-2 bind (weakly) to a PY motif (LPXY) located within its own HECT domain and inhibit auto-ubiquitination. Pulse-chase experiments demonstrated that mutation of the HECT PY-motif decreases the stability of Nedd4-2, suggesting that it is involved in stabilization of this E3 ligase. Interestingly, the HECT PY-motif mutation does not affect ubiquitination or down-regulation of a known Nedd4-2 substrate, ENaC (epithelial sodium channel). ENaC ubiquitination, in turn, appears to promote Nedd4-2 self-ubiquitination. These results support a model in which the inter- or intra-molecular WW-domain-HECT PY-motif interaction stabilizes Nedd4-2 by preventing self-ubiquitination. Substrate binding disrupts this interaction, allowing self-ubiquitination of Nedd4-2 and subsequent degradation, resulting in down-regulation of Nedd4-2 once it has ubiquitinated its target. These findings also point to a novel mechanism employed by a ubiquitin ligase to regulate itself differentially compared with substrate ubiquitination and stability.

Optimized venous return with a self-expanding cannula: from computational fluid dynamics to clinical application.

The Smart canula concept allows for collapsed cannula insertion, and self-expansion within a vein of the body. (A) Computational fluid dynamics, and (B) bovine experiments (76+/-3.8 kg) were performed for comparative analyses, prior to (C) the first clinical application. For an 18F access, a given flow of 4 l/min (A) resulted in a pressure drop of 49 mmHg for smart cannula versus 140 mmHg for control. The corresponding Reynolds numbers are 680 versus 1170, respectively. (B) For an access of 28F, the maximal flow for smart cannula was 5.8+/-0.5 l/min versus 4.0+/-0.1 l/min for standard (P<0.0001), for 24F 5.5+/-0.6 l/min versus 3.2+/-0.4 l/min (P<0.0001), and for 20F 4.1+/-0.3 l/min versus 1.6+/-0.3 l/min (P<0.0001). The flow obtained with the smart cannula was 270+/-45% (20F), 172+/-26% (24F), and 134+/-13% (28F) of standard (one-way ANOVA, P=0.014). (C) First clinical application (1.42 m2) with a smart cannula showed 3.55 l/min (100% predicted) without additional fluids. All three assessment steps confirm the superior performance of the smart cannula design.