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.

Aminoterminal amphipathic α-helix AH1 of hepatitis C virus nonstructural protein 4B possesses a dual role in RNA replication and virus production.

Nonstructural protein 4B (NS4B) is a key organizer of hepatitis C virus (HCV) replication complex formation. In concert with other nonstructural proteins, it induces a specific membrane rearrangement, designated as membranous web, which serves as a scaffold for the HCV replicase. The N-terminal part of NS4B comprises a predicted and a structurally resolved amphipathic α-helix, designated as AH1 and AH2, respectively. Here, we report a detailed structure-function analysis of NS4B AH1. Circular dichroism and nuclear magnetic resonance structural analyses revealed that AH1 folds into an amphipathic α-helix extending from NS4B amino acid 4 to 32, with positively charged residues flanking the helix. These residues are conserved among hepaciviruses. Mutagenesis and selection of pseudorevertants revealed an important role of these residues in RNA replication by affecting the biogenesis of double-membrane vesicles making up the membranous web. Moreover, alanine substitution of conserved acidic residues on the hydrophilic side of the helix reduced infectivity without significantly affecting RNA replication, indicating that AH1 is also involved in virus production. Selective membrane permeabilization and immunofluorescence microscopy analyses of a functional replicon harboring an epitope tag between NS4B AH1 and AH2 revealed a dual membrane topology of the N-terminal part of NS4B during HCV RNA replication. Luminal translocation was unaffected by the mutations introduced into AH1, but was abrogated by mutations introduced into AH2. In conclusion, our study reports the three-dimensional structure of AH1 from HCV NS4B, and highlights the importance of positively charged amino acid residues flanking this amphipathic α-helix in membranous web formation and RNA replication. In addition, we demonstrate that AH1 possesses a dual role in RNA replication and virus production, potentially governed by different topologies of the N-terminal part of NS4B.

Reliable selfing rate estimates from imperfect population genetic data.

Genotypic frequencies at codominant marker loci in population samples convey information on mating systems. A classical way to extract this information is to measure heterozygote deficiencies (FIS) and obtain the selfing rate s from FIS = s/(2 - s), assuming inbreeding equilibrium. A major drawback is that heterozygote deficiencies are often present without selfing, owing largely to technical artefacts such as null alleles or partial dominance. We show here that, in the absence of gametic disequilibrium, the multilocus structure can be used to derive estimates of s independent of FIS and free of technical biases. Their statistical power and precision are comparable to those of FIS, although they are sensitive to certain types of gametic disequilibria, a bias shared with progeny-array methods but not FIS. We analyse four real data sets spanning a range of mating systems. In two examples, we obtain s = 0 despite positive FIS, strongly suggesting that the latter are artefactual. In the remaining examples, all estimates are consistent. All the computations have been implemented in a open-access and user-friendly software called rmes (robust multilocus estimate of selfing) available at http://ftp.cefe.cnrs.fr, and can be used on any multilocus data. Being able to extract the reliable information from imperfect data, our method opens the way to make use of the ever-growing number of published population genetic studies, in addition to the more demanding progeny-array approaches, to investigate selfing rates.

Comparison of the organization and fine structure of a chicken and a Xenopus laevis vitellogenin gene.

The structural organization and the coding nucleotide sequence of the Xenopus laevis A2 and the chicken major vitellogenin genes have been compared. Both genes show the same exon-intron organization. However, the degree of homology between the nucleotide and derived amino acid sequences varies extensively along the genes. Several of the 35 exons are quite similar, and a unique cysteine motif in the lipovitellin II domain is conserved between the two genes. In contrast, one internal region is quite divergent. Part of this region encodes phosvitin, which appears to have evolved rapidly by both point mutations and duplications of serines or short other amino acid stretches. On the basis of these observations, we discuss the possible mechanism of evolution of phosvitin in vertebrates.

Structure and kinematics of the Siviez-Mischabel nappe in the Mattertal (western Alps of Switzerland) = Structure et cinématique de la nappe de Siviez-Mischabel le long de la vallée de Zermatt (Alpes de Suisse occidentale)

Résumé Cette étude porte sur le flanc inverse de la nappe de Siviez-Mischabel et sur les unités tectoniques sous jacentes (zone de Stalden supérieur et zone Houillère) dans la vallée menant à Zermatt. L'étude structurale du granite permien de Randa (orthogneiss oeillé) permet de mieux comprendre les effets de la déformation alpine sur les roches de socle. La cartographie détaillée de l'orthogneiss et de son encaissant, ainsi que l'étude lithostratigraphique des terrains sédimentaires associés permettent de proposer un schéma structural et cinématique du flanc inverse de la nappe de Siviez-Mischabel et de mieux comprendre ses relations avec les unités tectoniques sous-jacentes. L'analyse structurale de l'orthogneiss de Randa et de son encaissant révèle la superposition de plusieurs phases de déformation ductile. Cet orthogneiss formé sous des conditions métamorphiques du faciès schiste vert possède une forte schistosité alpine avec au moins deux linéations d'extension. La première, L1, orientée NW-SE est associée à la mise en place de la nappe. La seconde, L2, orientée SW-NE, se corrèle au cisaillement ductile du Simplon. La quantification de la déformation au moyen de la méthode de Fry sur les faciès porphyriques donne des ellipses à rapports axiaux compris entre 1.9 et 5.3, en accord avec les valeurs obtenues par d'autres marqueurs {tourmalines étirées, fibres). Les valeurs mesurées parallèlement à L1 ou L2 sont très semblables. La méthode de Fry a nécessité une étude théorique préalable afin de vérifier son applicabilité aux orthogneiss oeillés. La méthode requiert une distribution spatiale homogène et isotrope des marqueurs utilisés. Les tests statistiques effectués ont révélé que les phénocristaux de feldspath alcalin satisfont à cette condition et qu'ils peuvent être utilisés comme marqueur de la déformation au moyen de la méthode de Fry. Les valeurs obtenues révèlent l'importance du cisaillement ductile du Simplon sur la géométrie de la nappe dans la région d'étude. Le levé cartographique a permis d'améliorer la lithostratigraphie de la base de la nappe de Siviez-Mischabel. Trois formations en position renversée peuvent être observées sous les gneiss formant le coeur de la nappe. Ces trois formations forment le coeur du synclinal de St-Niklaus qui connecte la nappe de Siviez-Mischabel à la zone de Stalden supérieur. La datation par U-Pb de zircons détritiques et magmatiques par LA-ICP-MS permet de contraindre l'âge des formations observées (probablement Carbonifère à Trias précoce). Ces données ont des répercussions importantes sur la structure de la nappe dans la région, prouvant l'existence de plusieurs plis avec des séries normales et renversées bien préservées. La définition et la datation de ces formations, ainsi que leur identification dans la-Zone- Houillère avoisinante permettent de mieux comprendre la géométrie initiale et les relations tectoniques des nappes du Pennique moyen dans la vallée de Zermatt. Summary This study investigates the overturned limb of the Siviez-Mischabel nappe and underlying tectonic units (Upper Stalden zone and Houillère zone) in the Mattertal area. Detailed structural analysis in the Permian Randa granite (augen orthogneiss) allows a better understanding of the Alpine deformation effects on basement rocks. Detailed mapping of this orthogneiss and surrounding rocks, and the study of the lithostratigraphy in the related sedimentary horizons allow the proposition of a structural and kinematic model for the overturned limb of the Siviez-Mischabel and to better understand the relations with the underlying tectonic units. The structural analysis of the Randa orthogneiss and surrounding rocks revealed the superposition of several phases of ductile deformation. This orthogneiss formed under greenschist facies metamorphic conditions displays a strong Alpine foliation with at least two stretching lineations. The first lineation, L1, is oriented NW-SE and is related to the nappe emplacement northward. The second one, L2, is related to the Simplon ductile shear zone. Strain estimation using the Fry method has been performed on porphyritic facies of the Randa orthogneiss. The obtained ellipses have axial ratios varying between 1.9 and 5.3, in agreement with strain estimation obtained from other markers (stretched turmalines, fringes). The strain values are very similar if measured parallel to L1 or to L2. A theoretical approach was necessary to verify the relevant application of the Fry method to augen orthogneiss. This method requires that the distribution of the used markers has to be homogeneous and isotropic. Statistical tests have been done and revealed that K-feldspar phenocrysts satisfy these conditions and can be used as strain markers with the Fry method. The obtained strain measurements revealed the importance of the Simplon ductile shear zone on the geometry of the nappe in the studied area. Mapping has improved the lithostratigraphy at the base of the Siviez-Mischabel nappe. Three overturned formations can be observed below the gneisses forming the core of the nappe. These three formations form the St-Niklaus syncline, which connects the Siviez-Mischabel nappe to the underlying Upper Stalden zone. U-Pb dating of detrital and magmatic zircons by LA-ICPMS allowed the age of the observed formations to be constrained (presumably Carboniferous to Early Triassic). This data has critical implications for nappe structure in the region, composed of few recumbent folds with well preserved normal and overturned limbs. The definition and dating of these formations, as well as their identification in the adjacent "Houillère Zone" improve the understanding of the geometry and tectonic relations of the Middle Penninic nappes in the Mattertal.

Structure of the extracellular domains of human and Xenopus Fn14: implications in the evolution of TWEAK and Fn14 interactions.

TWEAK (TNF homologue with weak apoptosis-inducing activity) and Fn14 (fibroblast growth factor-inducible protein 14) are members of the tumor necrosis factor (TNF) ligand and receptor super-families. Having observed that Xenopus Fn14 cross-reacts with human TWEAK, despite its relatively low sequence homology to human Fn14, we examined the conservation in tertiary fold and binding interfaces between the two species. Our results, combining NMR solution structure determination, binding assays, extensive site-directed mutagenesis and molecular modeling, reveal that, in addition to the known and previously characterized β-hairpin motif, the helix-loop-helix motif makes an essential contribution to the receptor/ligand binding interface. We further discuss the insight provided by the structural analyses regarding how the cysteine-rich domains of the TNF receptor super-family may have evolved over time. DATABASE: Structural data are available in the Protein Data Bank/BioMagResBank databases under the accession codes 2KMZ, 2KN0 and 2KN1 and 17237, 17247 and 17252. STRUCTURED DIGITAL ABSTRACT: TWEAK binds to hFn14 by surface plasmon resonance (View interaction) xeFn14 binds to TWEAK by enzyme linked immunosorbent assay (View interaction) TWEAK binds to xeFn14 by surface plasmon resonance (View interaction) hFn14 binds to TWEAK by enzyme linked immunosorbent assay (View interaction).

Cytoskeletal and DNA structure abnormalities result from bypass of requirement for the cdc10 start gene in the fission yeast Schizosaccharomyces pombe.

The cdc10 gene of the fission yeast S. pombe is required for traverse of the start control in late G1 and commitment to the mitotic cell cycle. To increase our understanding of the events which occur at start, a pseudoreversion analysis was undertaken to identify genes whose products may interact with cdc10 or bypass the requirement for it. A single gene, sct1+ (suppressor of cdc ten), has been identified, mutation of which suppresses all conditional alleles and a null allele of cdc10. Bypass of the requirement for cdc10+ function by sct1-1 mutations leads to pleiotropic defects, including microtubule, microfilament and nuclear structural abnormalities. Our data suggest that sct1 encodes a protein that is dependent upon cdc10+ either for its normal function or expression, or is a component of a checkpoint that monitors execution of p85cdc10 function.

Combining Internet Monitoring Processes, Packaging and Isotopic Analyses to Determine The Market Structure: Example of Gamma Butyrolactone

The Internet is becoming more and more popular among drug users. The use of websites and forums to obtain illicit drugs and relevant information about the means of consumption is a growing phenomenon mainly for new synthetic drugs. Gamma Butyrolactone (GBL), a chemical precursor of Gamma Hydroxy Butyric acid (GHB), is used as a "club drug" and also in drug facilitated sexual assaults. Its market takes place mainly on the Internet through online websites but the structure of the market remains unknown. This research aims to combine digital, physical and chemical information to help understand the distribution routes and the structure of the GBL market. Based on an Internet monitoring process, thirty-nine websites selling GBL, mainly in the Netherlands, were detected between January 2010 and December 2011. Seventeen websites were categorized into six groups based on digital traces (e.g. IP addresses and contact information). In parallel, twenty-five bulk GBL specimens were purchased from sixteen websites for packaging comparisons and carbon isotopic measurements. Packaging information showed a high correlation with digital data confirming the links previously established whereas chemical information revealed undetected links and provided complementary information. Indeed, while digital and packaging data give relevant information about the retailers, the supply routes and the distribution close to the consumer, the carbon isotopic data provides upstream information about the production level and in particular the synthesis pathways and the chemical precursors. A three-level structured market has been thereby identified with a production level mainly located in China and in Germany, an online distribution level mainly hosted in the Netherlands and the customers who order on the Internet.

Structure of the glycosyl-phosphatidylinositol membrane anchor of the Leishmania major promastigote surface protease.

In common with many other plasma membrane glycoproteins of eukaryotic origin, the promastigote surface protease (PSP) of the protozoan parasite Leishmania contains a glycosyl-phosphatidylinositol (GPI) membrane anchor. The GPI anchor of Leishmania major PSP was purified following proteolysis of the PSP and analyzed by two-dimensional 1H-1H NMR, compositional and methylation linkage analyses, chemical and enzymatic modifications, and amino acid sequencing. From these results, the structure of the GPI-containing peptide was found to be Asp-Gly-Gly-Asn-ethanolamine-PO4-6Man alpha 1-6Man alpha 1-4GlcN alpha 1-6myo-inositol-1-PO4-(1-alkyl-2-acyl-glycerol). The glycan structure is identical to the conserved glycan core regions of the GPI anchor of Trypanosoma brucei variant surface glycoprotein and rat brain Thy-1 antigen, supporting the notion that this portion of GPIs are highly conserved. The phosphatidylinositol moiety of the PSP anchor is unusual, containing a fully saturated, unbranched 1-O-alkyl chain (mainly C24:0) and a mixture of fully saturated unbranched 2-O-acyl chains (C12:0, C14:0, C16:0, and C18:0). This lipid composition differs significantly from those of the GPIs of T. brucei variant surface glycoprotein and mammalian erythrocyte acetylcholinesterase but is similar to that of a family of glycosylated phosphoinositides found uniquely in Leishmania.

Qualitative structure-metabolism relationships in the hydrolysis of carbamates.

The aims of this review were 1) to compile a large number of reliable literature data on the metabolic hydrolysis of medicinal carbamates and 2) to extract from such data a qualitative relation between molecular structure and lability to metabolic hydrolysis. The compounds were classified according to the nature of their substituents (R³OCONR&supl;R²), and a metabolic lability score was calculated for each class. A trend emerged, such that the metabolic lability of carbamates decreased (i.e., their metabolic stability increased), in the following series: Aryl-OCO-NHAlkyl >> Alkyl-OCO-NHAlkyl ~ Alkyl-OCO-N(Alkyl)? ? Alkyl-OCO-N(endocyclic) ? Aryl-OCO-N(Alkyl)? ~ Aryl-OCO-N(endocyclic) ? Alkyl-OCO-NHAryl ~ Alkyl-OCO-NHAcyl?>> Alkyl-OCO-NH? > Cyclic carbamates. This trend should prove useful in the design of carbamates as drugs or prodrugs.

The use of external mesh reinforcement to reduce intimal hyperplasia and preserve the structure of human saphenous veins.

The saphenous vein is the conduit of choice in bypass graft procedures. Haemodynamic factors play a major role in the development of intimal hyperplasia (IH), and subsequent bypass failure. To evaluate the potential protective effect of external reinforcement on such a failure, we developed an ex vivo model for the perfusion of segments of human saphenous veins under arterial shear stress. In veins submitted to pulsatile high pressure (mean pressure at 100 mmHg) for 3 or 7 days, the use of an external macroporous polyester mesh 1) prevented the dilatation of the vessel, 2) decreased the development of IH, 3) reduced the apoptosis of smooth muscle cells, and the subsequent fibrosis of the media layer, 4) prevented the remodelling of extracellular matrix through the up-regulation of matrix metalloproteinases (MMP-2, MMP-9) and plasminogen activator type I. The data show that, in an experimental ex vivo setting, an external scaffold decreases IH and maintains the integrity of veins exposed to arterial pressure, via increase in shear stress and decrease wall tension, that likely contribute to trigger selective molecular and cellular changes.

Glucose metabolism, gray matter structure, and memory decline in subjective memory impairment.

OBJECTIVE: To identify biological evidence for Alzheimer disease (AD) in individuals with subjective memory impairment (SMI) and unimpaired cognitive performance and to investigate the longitudinal cognitive course in these subjects. METHOD: [¹⁸F]fluoro-2-deoxyglucose PET (FDG-PET) and structural MRI were acquired in 31 subjects with SMI and 56 controls. Cognitive follow-up testing was performed (average follow-up time: 35 months). Differences in baseline brain imaging data and in memory decline were assessed between both groups. Associations of memory decline with brain imaging data were tested. RESULTS: The SMI group showed hypometabolism in the right precuneus and hypermetabolism in the right medial temporal lobe. Gray matter volume was reduced in the right hippocampus in the SMI group. At follow-up, subjects with SMI showed a poorer performance than controls on measures of episodic memory. Longitudinal memory decline in the SMI group was associated with reduced glucose metabolism in the right precuneus at baseline. CONCLUSION: The cross-sectional difference in 2 independent neuroimaging modalities indicates early AD pathology in SMI. The poorer memory performance at follow-up and the association of reduced longitudinal memory performance with hypometabolism in the precuneus at baseline support the concept of SMI as the earliest manifestation of AD.

How to identify and recruit nurses to a survey 14 and 24 years after graduation in a context of scarce data : lessons learnt from the 2012 nurses at work pilot study on nurses' career paths

Nursing workforce data are scarce in Switzerland, with no active national registry of nurses. The worldwide nursing shortage is also affecting Switzerland, so that evidence-based results of the nurses at work project on career paths and retention are needed as part of the health care system stewardship; nurses at work is a retrospective cohort study of nurses who graduated in Swiss nursing schools in the last 30 years. Results of the pilot study are presented here (process and feasibility). The objectives are (1) to determine the size and structure of the potential target population by approaching two test-cohorts of nursing graduates (1988 and 1998); (2) to test methods of identifying and reaching them 14 and 24 years after graduation; (3) to compute participation rates, and identify recruitment and participation biases.

Vulnerability as a heuristic for interdisciplinary research: Assessing the thematic and methodological structure of empirical life-course studies

Changes in human lives are studied in psychology, sociology, and adjacent fields as outcomes of developmental processes, institutional regulations and policies, culturally and normatively structured life courses, or empirical accounts. However, such studies have used a wide range of complementary, but often divergent, concepts. This review has two aims. First, we report on the structure that has emerged from scientific life course research by focusing on abstracts from longitudinal and life course studies beginning with the year 2000. Second, we provide a sense of the disciplinary diversity of the field and assess the value of the concept of 'vulnerability' as a heuristic tool for studying human lives. Applying correspondence analysis to 10,632 scientific abstracts, we find a disciplinary divide between psychology and sociology, and observe indications of both similarities of-and differences between-studies, driven at least partly by the data and methods employed. We also find that vulnerability takes a central position in this scientific field, which leads us to suggest several reasons to see value in pursuing theory development for longitudinal and life course studies in this direction.

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.