Density, climate and varying return points: an analysis of long-term population fluctuations in the threatened European tree frog.

Experimental research has identified many putative agents of amphibian decline, yet the population-level consequences of these agents remain unknown, owing to lack of information on compensatory density dependence in natural populations. Here, we investigate the relative importance of intrinsic (density-dependent) and extrinsic (climatic) factors impacting the dynamics of a tree frog (Hyla arborea) population over 22 years. A combination of log-linear density dependence and rainfall (with a 2-year time lag corresponding to development time) explain 75% of the variance in the rate of increase. Such fluctuations around a variable return point might be responsible for the seemingly erratic demography and disequilibrium dynamics of many amphibian populations.

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.

The nuclear hormone receptor PPARγ counteracts vascular calcification by inhibiting Wnt5a signalling in vascular smooth muscle cells.

Vascular calcification is a hallmark of advanced atherosclerosis. Here we show that deletion of the nuclear receptor PPARγ in vascular smooth muscle cells of low density lipoprotein receptor (LDLr)-deficient mice fed an atherogenic diet high in cholesterol, accelerates vascular calcification with chondrogenic metaplasia within the lesions. Vascular calcification in the absence of PPARγ requires expression of the transmembrane receptor LDLr-related protein-1 in vascular smooth muscle cells. LDLr-related protein-1 promotes a previously unknown Wnt5a-dependent prochondrogenic pathway. We show that PPARγ protects against vascular calcification by inducing the expression of secreted frizzled-related protein-2, which functions as a Wnt5a antagonist. Targeting this signalling pathway may have clinical implications in the context of common complications of atherosclerosis, including coronary artery calcification and valvular sclerosis.

Functions for estimating stem diameter and tree age using tree height, crown width and existing stand database information

Abstract

A consistent extension of the local spin density approximation to account for quantum dot mass and dielectric mismatches

A consistent extension of local spin density approximation (LSDA) to account for mass and dielectric mismatches in nanocrystals is presented. The extension accounting for variable effective mass is exact. Illustrative comparisons with available configuration interaction calculations show that the approach is also very reliable when it comes to account for dielectric mismatches. The modified LSDA is as fast and computationally low demanding as LSDA. Therefore, it is a tool suitable to study large particle systems in inhomogeneous media without much effort.

Endogenous growth with capital in R&D production functions

[eng] In this paper we claim that capital is as important in the production of ideas as in the production of final goods. Hence, we introduce capital in the production of knowledge and discuss the associated problems arising from the public good nature of knowledge. We show that although population growth can affect economic growth, it is not necessary for growth to arise. We derive both the social planner and the decentralized economy growth rates and show the optimal subsidy that decentralizes it. We also show numerically that the effects of population growth on the market growth rate, the optimal growth rate and the optimal subsidy are small. Besides, we find that physical capital is more important for the production of knowledge than for the production of goods.

Endogenous growth with capital in R&D production functions

[eng] In this paper we claim that capital is as important in the production of ideas as in the production of final goods. Hence, we introduce capital in the production of knowledge and discuss the associated problems arising from the public good nature of knowledge. We show that although population growth can affect economic growth, it is not necessary for growth to arise. We derive both the social planner and the decentralized economy growth rates and show the optimal subsidy that decentralizes it. We also show numerically that the effects of population growth on the market growth rate, the optimal growth rate and the optimal subsidy are small. Besides, we find that physical capital is more important for the production of knowledge than for the production of goods.

Effects of zoledronate versus placebo on spine bone mineral density and microarchitecture assessed by the trabecular bone score in postmenopausal women with osteoporosis: A three-year study.

The trabecular bone score (TBS) is an index of bone microarchitectural texture calculated from anteroposterior dual-energy X-ray absorptiometry (DXA) scans of the lumbar spine (LS) that predicts fracture risk, independent of bone mineral density (BMD). The aim of this study was to compare the effects of yearly intravenous zoledronate (ZOL) versus placebo (PLB) on LS BMD and TBS in postmenopausal women with osteoporosis. Changes in TBS were assessed in the subset of 107 patients recruited at the Department of Osteoporosis of the University Hospital of Berne, Switzerland, who were included in the HORIZON trial. All subjects received adequate calcium and vitamin D3. In these patients randomly assigned to either ZOL (n = 54) or PLB (n = 53) for 3 years, BMD was measured by DXA and TBS assessed by TBS iNsight (v1.9) at baseline and 6, 12, 24, and 36 months after treatment initiation. Baseline characteristics (mean ± SD) were similar between groups in terms of age, 76.8 ± 5.0 years; body mass index (BMI), 24.5 ± 3.6 kg/m(2) ; TBS, 1.178 ± 0.1 but for LS T-score (ZOL-2.9 ± 1.5 versus PLB-2.1 ± 1.5). Changes in LS BMD were significantly greater with ZOL than with PLB at all time points (p < 0.0001 for all), reaching +9.58% versus +1.38% at month 36. Change in TBS was significantly greater with ZOL than with PLB as of month 24, reaching +1.41 versus-0.49% at month 36; p = 0.031, respectively. LS BMD and TBS were weakly correlated (r = 0.20) and there were no correlations between changes in BMD and TBS from baseline at any visit. In postmenopausal women with osteoporosis, once-yearly intravenous ZOL therapy significantly increased LS BMD relative to PLB over 3 years and TBS as of 2 years. © 2013 American Society for Bone and Mineral Research.

Functions of the peroxisome proliferator-activated receptor (PPAR) alpha and beta in skin homeostasis, epithelial repair, and morphogenesis.

The three peroxisome proliferator-activated receptors (PPAR alpha, PPAR beta, and PPAR gamma) are ligand-activated transcription factors belonging to the nuclear hormone receptor superfamily. They are regarded as being sensors of physiological levels of fatty acids and fatty acid derivatives. In the adult mouse skin, they are found in hair follicle keratinocytes but not in interfollicular epidermis keratinocytes. Skin injury stimulates the expression of PPAR alpha and PPAR beta at the site of the wound. Here, we review the spatiotemporal program that triggers PPAR beta expression immediately after an injury, and then gradually represses it during epithelial repair. The opposing effects of the tumor necrosis factor-alpha and transforming growth factor-beta-1 signalling pathways on the activity of the PPAR beta promoter are the key elements of this regulation. We then compare the involvement of PPAR beta in the skin in response to an injury and during hair morphogenesis, and underscore the similarity of its action on cell survival in both situations.

Spine bone texture assessed by trabecular bone score (TBS) predicts osteoporotic fractures in men: The Manitoba Bone Density Program.

INTRODUCTION: One quarter of osteoporotic fractures occur in men. TBS, a gray-level measurement derived from lumbar spine DXA image texture, is related to microarchitecture and fracture risk independently of BMD. Previous studies reported the ability of spine TBS to predict osteoporotic fractures in women. Our aim was to evaluate the ability of TBS to predict clinical osteoporotic fractures in men. METHODS: 3620 men aged ≥50 (mean 67.6years) at the time of baseline DXA (femoral neck, spine) were identified from a database (Province of Manitoba, Canada). Health service records were assessed for the presence of non-traumatic osteoporotic fracture after BMD testing. Lumbar spine TBS was derived from spine DXA blinded to clinical parameters and outcomes. We used Cox proportional hazard regression to analyze time to first fracture adjusted for clinical risk factors (FRAX without BMD), osteoporosis treatment and BMD (hip or spine). RESULTS: Mean followup was 4.5years. 183 (5.1%) men sustain major osteoporotic fractures (MOF), 91 (2.5%) clinical vertebral fractures (CVF), and 46 (1.3%) hip fractures (HF). Correlation between spine BMD and spine TBS was modest (r=0.31), less than correlation between spine and hip BMD (r=0.63). Significantly lower spine TBS were found in fracture versus non-fracture men for MOF (p<0.001), HF (p<0.001) and CVF (p=0.003). Area under the receiver operating characteristic curve (AUC) for incident fracture discrimination with TBS was significantly better than chance (MOF AUC=0.59, p<0.001; HF AUC=0.67, p<0.001; CVF AUC=0.57, p=0.032). TBS predicted MOF and HF (but not CVF) in models adjusted for FRAX without BMD and osteoporosis treatment. TBS remained a predictor of HF (but not MOF) after further adjustment for hip BMD or spine BMD. CONCLUSION: We observed that spine TBS predicted MOF and HF independently of the clinical FRAX score, HF independently of FRAX and BMD in men. Studies with more incident fractures are needed to confirm these findings.

Modelling variation of needle density of Scots pine at high latitudes

[Abstract]

Access Management Research and Awareness Program : Phase IV, 1999

Access management involves balancing the dual roles that roadways must play - through travel and access to property and economic activity. When these roles are not in proper balance, the result is a roadway system that functions sub-optimally. Arterial routes that have a too high driveway density and provide overly extensive access to property have high crash rates and begin to suffer in terms of traffic operations. Such routes become congested, delays increase, and mean travel speeds decline. The Iowa access management research and awareness project has had four distinct phases. Phase I involved a detailed review of the extensive national access management literature so lessons learned elsewhere could be applied in Iowa. In Phase II original case study research was conducted in Iowa. Phase III of the project concentrated on outreach and education about access management. Phase IV of the Iowa access management project extended the work conducted during Phases II and III. The main work products for Phase IV were as follows: 1) three additional before and after case studies, illustrating the impacts of various access management treatments on traffic safety, traffic operations, and business vitality; 2) an access management handbook aimed primarily at local governments in Iowa; 3) a modular access management toolkit with brief descriptions of various access management treatments and considerations; and 4) an extensive outreach plan aimed at getting the results of Phases I through IV of the project out to diverse audiences in Iowa and elsewhere.

Spine trabecular bone score subsequent to bone mineral density improves fracture discrimination in women.

Bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is used to diagnose osteoporosis and assess fracture risk. However, DXA cannot evaluate trabecular microarchitecture. This study used a novel software program (TBS iNsight; Med-Imaps, Geneva, Switzerland) to estimate bone texture (trabecular bone score [TBS]) from standard spine DXA images. We hypothesized that TBS assessment would differentiate women with low trauma fracture from those without. In this study, TBS was performed blinded to fracture status on existing research DXA lumbar spine (LS) images from 429 women. Mean participant age was 71.3 yr, and 158 had prior fractures. The correlation between LS BMD and TBS was low (r = 0.28), suggesting these parameters reflect different bone properties. Age- and body mass index-adjusted odds ratios (ORs) ranged from 1.36 to 1.63 for LS or hip BMD in discriminating women with low trauma nonvertebral and vertebral fractures. TBS demonstrated ORs from 2.46 to 2.49 for these respective fractures; these remained significant after lowest BMD T-score adjustment (OR = 2.38 and 2.44). Seventy-three percent of all fractures occurred in women without osteoporosis (BMD T-score > -2.5); 72% of these women had a TBS score below the median, thereby appropriately classified them as being at increased risk. In conclusion, TBS assessment enhances DXA by evaluating trabecular pattern and identifying individuals with vertebral or low trauma fracture. TBS identifies 66-70% of women with fracture who were not classified with osteoporosis by BMD alone.

Effect of compression wood on surface roughness and surface absorption of medium density fiberboard

Abstract