Microsatellite variations of Quercus aquifolioides populations at varying altitudes in the Wolong Natural Reserve of China

Abstract

Models for vertical wood density of Scots pine, Norway spruce and birch stems, and their application to determine average wood density

Abstract

Evaluación del estándar SQL:1999 respecto a las características orientadas al objeto que soporta y su implementación a los SGBD object-relational

Memoria de TFC en el que se analiza el estándar SQL:1999 y se compara con PostgreeSQL y Oracle.

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.

Unlike PAHs from Exxon Valdez crude oil, PAHs from Gulf of Alaska coals are not readily bioavailable.

In the wake of the 1989 Exxon Valdez oil spill, spatially and temporally spill-correlated biological effects consistent with polycyclic aromatic hydrocarbon (PAH) exposure were observed. Some works have proposed that confounding sources from local source rocks, prominently coals, are the provenance of the PAHs. Representative coal deposits along the southeast Alaskan coast (Kulthieth Formation) were sampled and fully characterized chemically and geologically. The coals have variable but high total organic carbon content technically classifying as coals and coaly shale, and highly varying PAH contents. Even for coals with high PAH content (approximately 4000 ppm total PAHs), a PAH-sensitive bacterial biosensor demonstrates nondetectable bioavailability as quantified, based on naphthalene as a test calibrant. These results are consistent with studies indicating that materials such as coals strongly diminish the bioavailability of hydrophobic organic compounds and support previous work suggesting that hydrocarbons associated with the regional background in northern Gulf of Alaska marine sediments are not appreciably bioavailable.

Collembolan community in broad-leaved forests and in conifer stands of Cryptomeria japonica in Central Japan

Collembolan communities in conifer plantations (Japanese cedar, Cryptomeria japonica) and secondary deciduous broad-leaved forests of varying ages were investigated to determine the extent to which forest conversion (broad-leaved to coniferous) affects the species richness and assemblage composition of Collembola in central Japan. Density and total species richness of Collembola not differed between the broad-leaved and cedar forests except immediately after clear-cutting. The amount of forest-floor organic matter was larger in cedar forests and positively correlated with the species richness of detritus feeders. Species richness of fungal feeders and sucking feeders positively correlated with the species richness of forest-floor plants. There was difference in collembolan species composition between the forest types. The age of the forests seemed to have only small importance for the collembolan community, except during the first four years after clear-cutting. The conversion to artificial cedar stands has not reduced the abundance or species richness of collembolan communities, but has affected community composition. Differences in species composition may be related to the ground floras.

Road Safety Audit for US 6 from the ECL of Iowa City to the WCL of West Liberty in Johnson and Muscatine Counties, Iowa, 2008

Approximately 13.2 miles of US 6 in eastern Iowa extends from the east corporate limits of Iowa City, Iowa, to the west corporate limits of West Liberty, Iowa. This segment of US 6 is a service level B primary highway, with an annual daily traffic volume varying from 3,480 vehicles per day (vpd) to 5,700 vpd. According to 2001–2007 crash density data from the Iowa Department of Transportation (Iowa DOT), the corridor is currently listed among the top 5% of non-freeway Iowa DOT roads in several crash categories, including crashes involving excessive speed, impaired drivers, single-vehicle run-off-road, and multiple-vehicle crossed centerline. A road safety audit of this corridor was deemed appropriate by the Iowa Department of Transportation’s Office of Traffic and Safety. Staff and officials from the Iowa DOT, Iowa State Patrol, Governor’s Traffic Safety Bureau, Federal Highway Administration, Center for Transportation Research and Education, and several local law enforcement and transportation agencies met to review crash data and discuss potential safety improvements to this segment of US 6. This report outlines the findings and recommendations of the road safety audit team to address the safety concerns on this US 6 corridor and explains several selected mitigation strategies.

Structural macro factors and the affine term structure of interest rates

This work consists of three essays investigating the ability of structural macroeconomic models to price zero coupon U.S. government bonds. 1. A small scale 3 factor DSGE model implying constant term premium is able to provide reasonable a fit for the term structure only at the expense of the persistence parameters of the structural shocks. The test of the structural model against one that has constant but unrestricted prices of risk parameters shows that the exogenous prices of risk-model is only weakly preferred. We provide an MLE based variance-covariance matrix of the Metropolis Proposal Density that improves convergence speeds in MCMC chains. 2. Affine in observable macro-variables, prices of risk specification is excessively flexible and provides term-structure fit without significantly altering the structural parameters. The exogenous component of the SDF is separating the macro part of the model from the term structure and the good term structure fit has as a driving force an extremely volatile SDF and an implied average short rate that is inexplicable. We conclude that the no arbitrage restrictions do not suffice to temper the SDF, thus there is need for more restrictions. We introduce a penalty-function methodology that proves useful in showing that affine prices of risk specifications are able to reconcile stable macro-dynamics with good term structure fit and a plausible SDF. 3. The level factor is reproduced most importantly by the preference shock to which it is strongly and positively related but technology and monetary shocks, with negative loadings, are also contributing to its replication. The slope factor is only related to the monetary policy shocks and it is poorly explained. We find that there are gains in in- and out-of-sample forecast of consumption and inflation if term structure information is used in a time varying hybrid prices of risk setting. In-sample yield forecast are better in models with non-stationary shocks for the period 1982-1988. After this period, time varying market price of risk models provide better in-sample forecasts. For the period 2005-2008, out of sample forecast of consumption and inflation are better if term structure information is incorporated in the DSGE model but yields are better forecasted by a pure macro DSGE model.

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.

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.

Culture in infancy : an account of a way the object "sculpts" early development.

The
 aim
 of
 this
 paper
 is
 to
 bring
 into
 consideration
 a
 way
 of
 studying
 culture
 in
 infancy.
 An
 emphasis 
is 
put 
on
 the
 role 
that 
the 
material 
object 
plays 
in 
early 
interactive
 processes. 
Accounted
 as
 a
 cultural
 artefact,
 the
 object
 is
 seen
 as
 a
 fundamental
 element
 within
 triadic
 mother‐object‐ infant
 interactions
 and
 is
 believed
 to
 be
 a
 driving
 force
 both
 for
 communicative
 and
 cognitive
 development.
 In
 order
 to
 reconsider
 the
 importance
 of
 the
 object
 in
 child
 development
 and
 to
 present
 an 
approach
 of 
studying
object
 construction,
accounts 
in 
literature 
on 
early 
communication
 development
 and
 the
 importance
 of
 the
 object
 are
 reviewed
 and
 discussed
 under
 the
 light
 of
 the
 cultural 
specificity 
of 
the 
material 
object.


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.

Septal lesions impair the acquisition of a cued place navigation task: attentional or memory deficit?

These experiments were designed to analyze how medial septal lesions reducing the cholinergic innervation in the hippocampus might affect place learning. Rats with quisqualic lesions of the medial septal area (MS) were trained in a water maze and on a homing table where the escape position was located at a spatially fixed position and further indicated by a salient cue suspended above it. The lesioned rats were significantly impaired in reaching the cued escape platform during training. In addition rats, did not show any discrimination of the training sector during a probe trial in which no platform or cue was present. This impairment remained significant during further training in the absence of the cue. When the cued escape platform was located at an unpredictable spatial location, the MS-lesioned rats showed no deficit and spent more time under the cue than control rats during the probe trial. On the homing board, with a salient object in close proximity to the escape hole, the MS rats showed no deficit in escape latencies, although a significant reduction in spatial memory was observed. However, this was overcome by additional training in the absence of the cue. Under these conditions, rats with septal lesions were prone to develop a pure guidance strategy, whereas normal rats combined a guidance strategy with a memory of the escape position relative to more distant landmarks. The presence of a salient cue appeared to decrease attention to environmental landmarks, thus reducing spatial memory. These data confirm the general hypothesis that MS lesions reduce the capacity to rely on a representation of the relation between several landmarks with different salience.

Modelling variation of needle density of Scots pine at high latitudes

[Abstract]

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.