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.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

Mult-class differentiation of cannabis seedlings in a forensic context

This article presents an experimental study about the classification ability of several classifiers for multi-classclassification of cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland lawenforcement authorities regularly ask forensic laboratories to determinate the chemotype of a seized cannabisplant and then to conclude if the plantation is legal or not. This classification is mainly performed when theplant is mature as required by the EU official protocol and then the classification of cannabis seedlings is a timeconsuming and costly procedure. A previous study made by the authors has investigated this problematic [1]and showed that it is possible to differentiate between drug type (illegal) and fibre type (legal) cannabis at anearly stage of growth using gas chromatography interfaced with mass spectrometry (GC-MS) based on therelative proportions of eight major leaf compounds. The aims of the present work are on one hand to continueformer work and to optimize the methodology for the discrimination of drug- and fibre type cannabisdeveloped in the previous study and on the other hand to investigate the possibility to predict illegal cannabisvarieties. Seven classifiers for differentiating between cannabis seedlings are evaluated in this paper, namelyLinear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), Nearest NeighbourClassification (NNC), Learning Vector Quantization (LVQ), Radial Basis Function Support Vector Machines(RBF SVMs), Random Forest (RF) and Artificial Neural Networks (ANN). The performance of each method wasassessed using the same analytical dataset that consists of 861 samples split into drug- and fibre type cannabiswith drug type cannabis being made up of 12 varieties (i.e. 12 classes). The results show that linear classifiersare not able to manage the distribution of classes in which some overlap areas exist for both classificationproblems. Unlike linear classifiers, NNC and RBF SVMs best differentiate cannabis samples both for 2-class and12-class classifications with average classification results up to 99% and 98%, respectively. Furthermore, RBFSVMs correctly classified into drug type cannabis the independent validation set, which consists of cannabisplants coming from police seizures. In forensic case work this study shows that the discrimination betweencannabis samples at an early stage of growth is possible with fairly high classification performance fordiscriminating between cannabis chemotypes or between drug type cannabis varieties.

A multilevel multiscale finite volume method

The Multiscale Finite Volume (MsFV) method has been developed to efficiently solve reservoir-scale problems while conserving fine-scale details. The method employs two grid levels: a fine grid and a coarse grid. The latter is used to calculate a coarse solution to the original problem, which is interpolated to the fine mesh. The coarse system is constructed from the fine-scale problem using restriction and prolongation operators that are obtained by introducing appropriate localization assumptions. Through a successive reconstruction step, the MsFV method is able to provide an approximate, but fully conservative fine-scale velocity field. For very large problems (e.g. one billion cell model), a two-level algorithm can remain computational expensive. Depending on the upscaling factor, the computational expense comes either from the costs associated with the solution of the coarse problem or from the construction of the local interpolators (basis functions). To ensure numerical efficiency in the former case, the MsFV concept can be reapplied to the coarse problem, leading to a new, coarser level of discretization. One challenge in the use of a multilevel MsFV technique is to find an efficient reconstruction step to obtain a conservative fine-scale velocity field. In this work, we introduce a three-level Multiscale Finite Volume method (MlMsFV) and give a detailed description of the reconstruction step. Complexity analyses of the original MsFV method and the new MlMsFV method are discussed, and their performances in terms of accuracy and efficiency are compared.

CNDOL: A fast and reliable method for the calculation of electronic properties of very large systems. Applications to retinal binding pocket in rhodopsin and gas phase porphine.

Very large molecular systems can be calculated with the so called CNDOL approximate Hamiltonians that have been developed by avoiding oversimplifications and only using a priori parameters and formulas from the simpler NDO methods. A new diagonal monoelectronic term named CNDOL/21 shows great consistency and easier SCF convergence when used together with an appropriate function for charge repulsion energies that is derived from traditional formulas. It is possible to obtain a priori molecular orbitals and electron excitation properties after the configuration interaction of single excited determinants with reliability, maintaining interpretative possibilities even being a simplified Hamiltonian. Tests with some unequivocal gas phase maxima of simple molecules (benzene, furfural, acetaldehyde, hexyl alcohol, methyl amine, 2,5 dimethyl 2,4 hexadiene, and ethyl sulfide) ratify the general quality of this approach in comparison with other methods. The calculation of large systems as porphine in gas phase and a model of the complete retinal binding pocket in rhodopsin with 622 basis functions on 280 atoms at the quantum mechanical level show reliability leading to a resulting first allowed transition in 483 nm, very similar to the known experimental value of 500 nm of "dark state." In this very important case, our model gives a central role in this excitation to a charge transfer from the neighboring Glu(-) counterion to the retinaldehyde polyene chain. Tests with gas phase maxima of some important molecules corroborate the reliability of CNDOL/2 Hamiltonians.

Peroxisomal beta-oxidation--a metabolic pathway with multiple functions.

Fatty acid degradation in most organisms occurs primarily via the beta-oxidation cycle. In mammals, beta-oxidation occurs in both mitochondria and peroxisomes, whereas plants and most fungi harbor the beta-oxidation cycle only in the peroxisomes. Although several of the enzymes participating in this pathway in both organelles are similar, some distinct physiological roles have been uncovered. Recent advances in the structural elucidation of numerous mammalian and yeast enzymes involved in beta-oxidation have shed light on the basis of the substrate specificity for several of them. Of particular interest is the structural organization and function of the type 1 and 2 multifunctional enzyme (MFE-1 and MFE-2), two enzymes evolutionarily distant yet catalyzing the same overall enzymatic reactions but via opposite stereochemistry. New data on the physiological roles of the various enzymes participating in beta-oxidation have been gathered through the analysis of knockout mutants in plants, yeast and animals, as well as by the use of polyhydroxyalkanoate synthesis from beta-oxidation intermediates as a tool to study carbon flux through the pathway. In plants, both forward and reverse genetics performed on the model plant Arabidopsis thaliana have revealed novel roles for beta-oxidation in the germination process that is independent of the generation of carbohydrates for growth, as well as in embryo and flower development, and the generation of the phytohormone indole-3-acetic acid and the signal molecule jasmonic acid.

Water as an essential nutrient: the physiological basis of hydration.

How much water we really need depends on water functions and the mechanisms of daily water balance regulation. The aim of this review is to describe the physiology of water balance and consequently to highlight the new recommendations with regard to water requirements. Water has numerous roles in the human body. It acts as a building material; as a solvent, reaction medium and reactant; as a carrier for nutrients and waste products; in thermoregulation; and as a lubricant and shock absorber. The regulation of water balance is very precise, as a loss of 1% of body water is usually compensated within 24 h. Both water intake and water losses are controlled to reach water balance. Minute changes in plasma osmolarity are the main factors that trigger these homeostatic mechanisms. Healthy adults regulate water balance with precision, but young infants and elderly people are at greater risk of dehydration. Dehydration can affect consciousness and can induce speech incoherence, extremity weakness, hypotonia of ocular globes, orthostatic hypotension and tachycardia. Human water requirements are not based on a minimal intake because it might lead to a water deficit due to numerous factors that modify water needs (climate, physical activity, diet and so on). Water needs are based on experimentally derived intake levels that are expected to meet the nutritional adequacy of a healthy population. The regulation of water balance is essential for the maintenance of health and life. On an average, a sedentary adult should drink 1.5 l of water per day, as water is the only liquid nutrient that is really essential for body hydration.

Classification of human astrocytic gliomas on the basis of gene expression: a correlated group of genes with angiogenic activity emerges as a strong predictor of subtypes.

The development of targeted treatment strategies adapted to individual patients requires identification of the different tumor classes according to their biology and prognosis. We focus here on the molecular aspects underlying these differences, in terms of sets of genes that control pathogenesis of the different subtypes of astrocytic glioma. By performing cDNA-array analysis of 53 patient biopsies, comprising low-grade astrocytoma, secondary glioblastoma (respective recurrent high-grade tumors), and newly diagnosed primary glioblastoma, we demonstrate that human gliomas can be differentiated according to their gene expression. We found that low-grade astrocytoma have the most specific and similar expression profiles, whereas primary glioblastoma exhibit much larger variation between tumors. Secondary glioblastoma display features of both other groups. We identified several sets of genes with relatively highly correlated expression within groups that: (a). can be associated with specific biological functions; and (b). effectively differentiate tumor class. One prominent gene cluster discriminating primary versus nonprimary glioblastoma comprises mostly genes involved in angiogenesis, including VEGF fms-related tyrosine kinase 1 but also IGFBP2, that has not yet been directly linked to angiogenesis. In situ hybridization demonstrating coexpression of IGFBP2 and VEGF in pseudopalisading cells surrounding tumor necrosis provided further evidence for a possible involvement of IGFBP2 in angiogenesis. The separating groups of genes were found by the unsupervised coupled two-way clustering method, and their classification power was validated by a supervised construction of a nearly perfect glioma classifier.

Molecular basis of interaction between Na,K-ATPase and FXYD proteins.

Résumé au large public Notre corps est constitué de différents types de cellules. La condition minimale ou primordiale pour la survie des cellules est d'avoir de l'énergie. Cette tâche est assumée en partie par une protéine qui se situe dans la membrane de chaque cellule. Nommé Na, K¬ATPase ou pompe à sodium, c'est une protéine pressente dans toutes les cellules chez les mammifères est composée de deux sous-unités, α et β. En transportant 3 ions de sodium hors de la cellule et 2 ions de potassium à l'intérieur de la cellule, elle transforme l'énergie chimique sous forme de l'ATP en énergie motrice, qui permet aux cellules par la suite d'échanger des matériaux entre l'espace intracellulaire et extracellulaire ainsi que d'ingérer des nutriments provenant de son environnement. Le manque de cette protéine chez la souris entraîne la mort de l'embryon. Des défauts fonctionnels de cette protéine sont responsables de plusieurs maladies humaines comme par exemple, un type de migraine. En dehors de sa fonction vitale, cette protéine est également engagée dans diverses activités physiologiques comme la contractilité musculaire, l'activité nerveuse et la régulation du volume sanguin. Vue l'importance de cette protéine, sa découverte par Jens C. Skou en 1957 a été honorée d'un Prix Noble de chimie quarante ans plus tard. Depuis lors, nous connaissons de mieux en mieux les mécanismes de fonctionnement de la Na, K-ATPase. Entre autre, sa régulation par une famille de protéines appelées protéines FXYD. Cette famille contient 7 membres (FXYD 1-7). L'un d'entre eux nommé FXYD 2 est lié à une maladie héréditaire connue sous le nom de hypomagnesemia. Nous disposons actuellement d'informations concernant les conséquences de la régulation par les protéines FXYD sur activité de la Na, K-ATPase, mais nous savons très peu sur le mode d'interaction entre les protéines FXYD et la Na, K-ATPase. Dans ce travail de thèse, nous avons réussi à localiser des zones d'interaction dans la sous- unité a de la Na, K-ATPase et dans FXYD 7. En même temps, nous avons déterminé un 3ème site de liaison spécifique au sodium de la Na, K-ATPase. Une partie de ce site se situe à l'intérieur d'un domaine protéique qui interagit avec les protéines FXYD. De plus, ce site a été démontré comme responsable d'un mécanisme de transport de la Na, K-ATPase caractérisé par un influx ionique. En conclusion, les résultats de ce travail de thèse fournissent de nouvelles preuves sur les régions d'interaction entre la Na, K-ATPase et les protéines FXYD. La détermination d'un 3ème site spécifique au sodium et sa relation avec un influx ionique offrent la possibilité 1) d'explorer les mécanismes avec lesquels les protéines FXYD régulent l'activité de la Na, ATPase et 2) de localiser un site à sodium qui est essentielle pour mieux comprendre l'organisation et le fonctionnement de la Na, K-ATPase. Résumé Les gradients de concentration de Na+ et de K+ à travers la membrane plasmatique des cellules animales sont cruciaux pour la survie et l'homéostasie de cellules. De plus, des fonctions cellulaires spécifiques telles que la reabsorption de Na dans le rein et le côlon, la contraction musculaire et l'excitabilité nerveuse dépendent de ces gradients. La Na, K¬ATPase ou pompe à sodium est une protéine membranaire ubiquitaire. Elle crée et maintient ces gradients en utilisant l'énergie obtenu par l'hydrolyse de l'adénosine triphosphate. L'unité fonctionnelle minimale de cette protéine se compose d'une sous-unité catalytique α et d'une sous-unité régulatrice β. Récemment, il a été montré que des membres de la famille FXYD, sont des régulateurs tissu-spécifiques de la Na, K-ATPase qui influencent ses propriétés de transport. Cependant, on connaît peu de chose au sujet de la nature moléculaire de l'interaction entre les protéines FXYD et la Na, K-ATPase. Dans cette étude, nous fournissons, pour la première fois, l'évidence directe que des résidus du domaine transmembranaire (TM) 9 de la sous-unité α de la Na, K-ATPase sont impliqués dans l'interaction fonctionnelle et structurale avec les protéines FXYD. De plus nous avons identifié des régions dans le domaine transmembranaire de FXYD 7 qui sont importantes pour l'association stable avec la Na, K-ATPase et une série de résidus responsables des régulations fonctionnelles. Nous avons aussi montré les contributions fonctionnelles du TM 9 de la Na, K-ATPase à la translocation de Na + en déterminant un 3ème site spécifique au Na+. Ce site se situe probablement dans un espace entre TM 9, TM 6 et TM 5 de la sous-unité α de la pompe à sodium. De plus, nous avons constaté que le 3ème site de Na + est fonctionnellement lié à un courant entrant de la pompe sensible à l'ouabaïne et activé par le pH acide. En conclusion, ce travail donne de nouvelles perspectives de l'interaction structurale et fonctionnelle entre les protéines FXYD et la Na, K-ATPase. En outre, les contributions fonctionnelles de TM 9 offrent de nouvelles possibilités pour explorer le mécanisme par lequel les protéines FXYD régulent les propriétés fonctionnelles de la Na, K-ATPase. La détermination du 3ème site au Na + fournit une compréhension avancée du site spécifique au Na + de la Na, K-ATPase et du mécanisme de transport de la Na, K-ATPase. Summary The Na+ and K+ gradients across the plasma membrane of animal cells are crucial for cell survival and homeostasis. Moreover, specific tissue functions such as Na+ reabsorption in kidney and colon, muscle contraction and nerve excitability depend on the maintenance of these gradients. Na, K-ATPase or sodium pump, an ubiquitous membrane protein, creates and maintains these gradients by using the energy from the hydrolysis of ATP. The minimal functional unit of this protein is composed of a catalytic α subunit and a regulatory β subunit. Recently, members of the FXYD family, have been reported to be tissue-specific regulators of Na, K-ATPase by influencing its transport properties. However, little is known about the molecular nature of the interaction between FXYD proteins and Na, K-ATPase. In this study, we provide, for the first time, direct evidence that residues from the transmembrane (TM) domain 9 of the α subunit of Na, K-ATPase are implicated in the functional and structural interaction with FXYD proteins. Moreover, we have identified regions in the TM domain of FXYD 7 important for the stable association with Na, K-ATPase and a stretch of residues responsible for the functional regulations. We have further revealed the functional contributions of TM 9 of the Na, K-ATPase α subunit to the Na+ translocation by determining a 3rd Na+-specific cation binding site. This site is likely in a space between TM 9, TM 6 and TM 5 of the a subunit of the sodium pump. Moreover, we have found that the 3rd Na+ binding site is functionally linked to an acidic pH- activated ouabain-sensitive inward pump current. In conclusion, this work gives new insights into the structural and functional interaction between FXYD proteins and Na, K-ATPase. Functional contributions of TM 9 offer new possibilities to explore the mechanism by which FXYD proteins regulate functional properties of Na, K-ATPase. The determination of the 3rd Na+ binding site provides an advanced understanding concerning the Na+ -specific binding site of Na, K-ATPase and the 3rd Na+ site related transport mechanism.

Radial transport of nutrients: the plant root as a polarized epithelium.

In higher plants, roots acquire water and soil nutrients and transport them upward to their aerial parts. These functions are closely related to their anatomical structure; water and nutrients entering the root first move radially through several concentric layers of the epidermis, cortex, and endodermis before entering the central cylinder. The endodermis is the innermost cortical cell layer that features rings of hydrophobic cell wall material called the Casparian strips, which functionally resemble tight junctions in animal epithelia. Nutrient uptake from the soil can occur through three different routes that can be interconnected in various ways: the apoplastic route (through the cell wall), the symplastic route (through cellular connections), and a coupled trans-cellular route (involving polarized influx and efflux carriers). This Update presents recent advances in the radial transport of nutrients highlighting the coupled trans-cellular pathway and the roles played by the endodermis as a barrier.

Accurate calculations of intermolecular interaction energies using explicitly correlated coupled cluster wave functions and a dispersion-weighted MP2 method.

Explicitly correlated coupled-cluster calculations of intermolecular interaction energies for the S22 benchmark set of Jurecka, Sponer, Cerny, and Hobza (Chem. Phys. Phys. Chem. 2006, 8, 1985) are presented. Results obtained with the recently proposed CCSD(T)-F12a method and augmented double-zeta basis sets are found to be in very close agreement with basis set extrapolated conventional CCSD(T) results. Furthermore, we propose a dispersion-weighted MP2 (DW-MP2) approximation that combines the good accuracy of MP2 for complexes with predominately electrostatic bonding and SCS-MP2 for dispersion-dominated ones. The MP2-F12 and SCS-MP2-F12 correlation energies are weighted by a switching function that depends on the relative HF and correlation contributions to the interaction energy. For the S22 set, this yields a mean absolute deviation of 0.2 kcal/mol from the CCSD(T)-F12a results. The method, which allows obtaining accurate results at low cost, is also tested for a number of dimers that are not in the training set.

Estimation of jump-diffusion processes via empirical characteristic functions

Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.

Epithelial sodium channel/degenerin family of ion channels: a variety of functions for a shared structure.

The recently discovered epithelial sodium channel (ENaC)/degenerin (DEG) gene family encodes sodium channels involved in various cell functions in metazoans. Subfamilies found in invertebrates or mammals are functionally distinct. The degenerins in Caenorhabditis elegans participate in mechanotransduction in neuronal cells, FaNaC in snails is a ligand-gated channel activated by neuropeptides, and the Drosophila subfamily is expressed in gonads and neurons. In mammals, ENaC mediates Na+ transport in epithelia and is essential for sodium homeostasis. The ASIC genes encode proton-gated cation channels in both the central and peripheral nervous system that could be involved in pain transduction. This review summarizes the physiological roles of the different channels belonging to this family, their biophysical and pharmacological characteristics, and the emerging knowledge of their molecular structure. Although functionally different, the ENaC/DEG family members share functional domains that are involved in the control of channel activity and in the formation of the pore. The functional heterogeneity among the members of the ENaC/DEG channel family provides a unique opportunity to address the molecular basis of basic channel functions such as activation by ligands, mechanotransduction, ionic selectivity, or block by pharmacological ligands.

Stem Cell Transplantation for Retinal Degenerations

Within the last few years, several reports have revealed that cell transplantation can be an effective way to replace lost neurons in the central nervous system (CNS) of patients affected with neurodegenerative diseases. Concerning the retina, the concept that newborn photoreceptors can integrate the retina and restore some visual functions was univocally demonstrated recently in the mouse eye (MacLaren et al. 2006) and remains to be achieved in human. These results pave the way to a standard approach in regenerative medicine aiming to replace lost photoreceptors. With the discovery of stem cells a great hope has appeared towards elaborating protocols to generate adequate cells to restore visual function in different retinal degeneration processes. Retinal stem cells (RSCs) are good candidates to repair the retina and are present throughout the retina development, including adulthood. However, neonatal mouse RSCs derived from the radial glia population have a different potential to proliferate and differentiate in comparison to adult RSCs. Moreover, we observed that adult mouse RSCs, depending on the culture conditions, have a marked tendency to transform, whereas neonatal RSCs show subtle chromosome abnormalities only after extensive expansion. These characteristics should help to identify the optimal cell source and culture conditions for cell transplantation studies. These results will be discussed in light of other studies using RSCs as well as embryonic stem cells. Another important factor to consider is the host environment, which plays a crucial role for cell integration and which was poorly studied in the normal and the diseased retina. Nonetheless, important results were recently generated to reconsider cell transplantation strategy. Perspectives to enhance cell integration by manipulating the environment will also be presented.

Immunocytochemical localisation of microtubule-associated proteins 1b and 2 in the developing rat spinal cord.

The straightforward anatomical organisation of the developing and mature rat spinal cord was used to determine and interpret the time of appearance and expression patterns of microtubule-associated proteins (MAP) 1b and 2. Immunoblots revealed the presence of MAP1b and 2 in the early embryonic rat spinal cord and confirmed the specificity of the used anti-MAP mouse monoclonal antibodies. The immunocytochemical data demonstrated a rostral-to-caudal and ventral-to-dorsal gradient in the expression of MAP1b/2 within the developing spinal cord. In the matrix layer, MAP1b was found in a distinct radial pattern distributed between the membrana limitans interna and externa between embryonal day (E)12 and E15. Immunostaining for vimentin revealed that this MAP1b pattern was morphologically and topographically different from the radial glial pattern which was present in the matrix layer between E13 and E19. The ventral-to-dorsal developmental gradient of the MAP1b staining in the spinal cord matrix layer indicates a close involvement of MAP1b either in the organisation of the microtubules in the cytoplasmatic extensions of the proliferating neuroblasts or neuroblast mitosis. MAP2 could not be detected in the developing matrix layer. In the mantle and marginal layer, MAP1b was abundantly present between E12 and postnatal day (P)0. After birth, the staining intensity for MAP1b gradually decreased in both layers towards a faint appearance at maturity. The distribution patterns suggest an involvement of MAP1b in the maturation of the motor neurons, the contralaterally and ipsilaterally projecting axons and the ascending and descending long axons of the rat spinal cord. MAP2 was present in the spinal cord grey matter between E12 and maturity, which reflects a role for MAP2 in the development as well as in the maintenance of microtubules. The present description of the expression patterns of MAP1b and 2 in the developing spinal cord suggests important roles of the two proteins in various morphogenetic events. The findings may serve as the basis for future studies on the function of MAP1b and 2 in the development of the central nervous system.