Industrial nanoparticle application : representative survey among Swiss companies shows low usage and basic protection means

Manufactured nanoparticles are introduced into industrial processes, but they are suspected to cause similar negative health effects as ambient particles. The poor knowledge about the scale of this introduction did not allow global risk analysis so far. In 2006 a targeted telephone survey among Swiss companies (1) showed the usage of nanoparticles in a few selected companies but did not provide data to extrapolate on the totality of the Swiss workforce. To gain this kind of information a layered representative questionnaire survey among 1'626 Swiss companies was conducted in 2007. Data was collected about the number of potentially exposed persons in the companies and their protection strategy. The response rate was 58.3%. An expected number of 586 companies (95%−confidence interval 145 to 1'027) was shown by this study to use nanoparticles in Switzerland. Estimated 1'309 (1'073 to 1'545) workers do their job in the same room as a nanoparticle application. Personal protection was shown to be the predominant type of protection means. Companies starting productions with nanomaterials need to consider incorporating protection measures into the plans. This will not only benefit the workers' health, but will also likely increase the competitiveness of the companies. Technical and organisational protection means are not only more cost−effective on the long term, but are also easier to control. Guidelines may have to be designed specifically for different industrial applications, including fields outside nanotechnology, and adapted to all sizes of companies.

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.

Basic Skill Assessment Requirements for Placement into Courses and Programs at Iowa Community Colleges, September 26, 2007

Iowa Department of Education surveyed Iowa’s 15 community colleges to gain information about each institution’s basic skill assessment requirements for placement into courses and programs. The survey asked what basic skill assessment(s) each institution uses, whether developmental course placement was mandatory, and what scores students needed to obtain to avoid being required or urged to take developmental courses in math, science, and reading. Additionally, staff members at each college were asked what the testing requirements are for students’ enrolled full time in high school that are taking community college classes.

The effects of polycyclic aromatic hydrocarbons and heavy metals on terrestrial annelids in urban soils

The effect of soil contamination by polycyclic aromatic hydrocarbons (PAH) and heavy metals on earthworms and enchytraeids was studied in urban parks, in Brno, Czech Republic. In spring and autumn 2007, annelids were collected and soil samples taken in lawns along transects, at three different distances (1, 5 and 30 m) from streets with heavy traffic. In both seasons, two parks with two transects each were sampled. Earthworms were collected using the electrical octet method. Enchytraeids were extracted by the wet funnel method from soil cores. All collected annelids were counted and identified. Basic chemical parameters and concentrations of 16 PAH, Cd, Cu, Pb, and Zn were analysed from soil from each sampling point. PAH concentrations were rather low, decreasing with the distance from the street in spring but not in autumn. Heavy metal concentrations did not decrease significantly with increasing distance. Annelid densities did not significantly differ between distances, although there was a trend of increase in the number of earthworms with increasing distance. There were no significant correlations between soil content of PAH or heavy metals and earthworm or enchytraeid densities. Earthworm density and biomass were negatively correlated with soil pH; and enchytraeid density was positively correlated with soil phosphorus.

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.

ACID-SENSING ION CHANNELS: functional and molecular characterization in putative nociceptors, and modulation by nerve injury and serine protease

Résumé Les canaux ioniques ASICs (acid-sensing ion channels) appartiennent à la famille des canaux ENaC/Degenerin. Pour l'instant, quatre gènes (1 à 4) ont été clonés dont certains présentent des variants d'épissage. Leur activation par une acidification rapide du milieu extracellulaire génère un courant entrant transitoire essentiellement sodique accompagné pour certains types d'ASICs d'une phase soutenue. Les ASICs sont exprimés dans le système nerveux, central (SNC) et périphérique (SNP). On leur attribue un rôle dans l'apprentissage, la mémoire et l'ischémie cérébrale au niveau central ainsi que dans la nociception (douleur aiguë et inflammatoire) et la méchanotransduction au niveau périphérique. Toutefois, les données sont parfois contradictoires. Certaines études suggèrent qu'ils sont des senseurs primordiaux impliqués dans la détection de l'acidification et la douleur. D'autres études suggèrent plutôt qu'ils ont un rôle modulateur inhibiteur dans la douleur. De plus, le fait que leur activation génère majoritairement un courant transitoire alors que les fibres nerveuses impliquées dans la douleur répondent à un stimulus nocif avec une adaptation lente suggère que leurs propriétés doivent être modulés par des molécules endogènes. Dans une première partie de ma thèse, nous avons abordé la question de l'expression fonctionnelle des ASICs dans les neurones sensoriels primaires afférents du rat adulte pour clarifier le rôle des ASICs dans les neurones sensoriels. Nous avons caractérisé leurs propriétés biophysiques et pharmacologiques par la technique du patch-clamp en configuration « whole-cell ». Nous avons pu démontrer que près de 60% des neurones sensoriels de petit diamètre expriment des courants ASICs. Nous avons mis en évidence trois types de courant ASIC dans ces neurones. Les types 1 et 3 ont des propriétés compatibles avec un rôle de senseur du pH alors que le type 2 est majoritairement activé par des pH inférieurs à pH6. Le type 1 est médié par des homomers de la sous-unité ASIC1 a qui sont perméables aux Ca2+. Nous avons étudié leur co-expression avec des marqueurs des nocicepteurs ainsi que la possibilité d'induire une activité neuronale suite à une acidification qui soit dépendante des ASICs. Le but était d'associer un type de courant ASIC avec une fonction potentielle dans les neurones sensoriels. Une majorité des neurones exprimant les courants ASIC co-expriment des marqueurs des nocicepteurs. Toutefois, une plus grande proportion des neurones exprimant le type 1 n'est pas associée à la nociception par rapport aux types 2 et 3. Nous avons montré qu'il est possible d'induire des potentiels d'actions suite à une acidification. La probabilité d'induction est proportionnelle à la densité des courants ASIC et à l'acidité de la stimulation. Puis, nous avons utilisé cette classification comme un outil pour appréhender les potentielles modulations fonctionnelles des ASICs dans un model de neuropathie (spared nerve injury). Cette approche fut complétée par des expériences de «quantitative RT-PCR ». En situation de neuropathie, les courants ASIC sont dramatiquement changés au niveau de leur expression fonctionnelle et transcriptionnelle dans les neurones lésés ainsi que non-lésés. Dans une deuxième partie de ma thèse, suite au test de différentes substances sécrétées lors de l'inflammation et l'ischémie sur les propriétés des ASICs, nous avons caractérisé en détail la modulation des propriétés des courants ASICs notamment ASIC1 par les sérines protéases dans des systèmes d'expression recombinants ainsi que dans des neurones d'hippocampe. Nous avons montré que l'exposition aux sérine-protéases décale la dépendance au pH de l'activation ainsi que la « steady-state inactivation »des ASICs -1a et -1b vers des valeurs plus acidiques. Ainsi, l'exposition aux serine protéases conduit à une diminution du courant quand l'acidification a lieu à partir d'un pH7.4 et conduit à une augmentation du courant quand l'acidification alleu à partir d'un pH7. Nous avons aussi montré que cette régulation a lieu des les neurones d'hippocampe. Nos résultats dans les neurones sensoriels suggèrent que certains courants ASICs sont impliqués dans la transduction de l'acidification et de la douleur ainsi que dans une des phases du processus conduisant à la neuropathie. Une partie des courants de type 1 perméables au Ca 2+ peuvent être impliqués dans la neurosécrétion. La modulation par les sérines protéases pourrait expliquer qu'en situation d'acidose les canaux ASICs soient toujours activables. Résumé grand publique Les neurones sont les principales cellules du système nerveux. Le système nerveux est formé par le système nerveux central - principalement le cerveau, le cervelet et la moelle épinière - et le système nerveux périphérique -principalement les nerfs et les neurones sensoriels. Grâce à leur nombreux "bras" (les neurites), les neurones sont connectés entre eux, formant un véritable réseau de communication qui s'étend dans tout le corps. L'information se propage sous forme d'un phénomène électrique, l'influx nerveux (ou potentiels d'actions). A la base des phénomènes électriques dans les neurones il y a ce que l'on appelle les canaux ioniques. Un canal ionique est une sorte de tunnel qui traverse l'enveloppe qui entoure les cellules (la membrane) et par lequel passent les ions. La plupart de ces canaux sont normalement fermés et nécessitent d'être activés pour s'ouvrire et générer un influx nerveux. Les canaux ASICs sont activés par l'acidification et sont exprimés dans tout le système nerveux. Cette acidification a lieu notamment lors d'une attaque cérébrale (ischémie cérébrale) ou lors de l'inflammation. Les expériences sur les animaux ont montré que les canaux ASICs avaient entre autre un rôle dans la mort des neurones lors d'une attaque cérébrale et dans la douleur inflammatoire. Lors de ma thèse je me suis intéressé au rôle des ASICs dans la douleur et à l'influence des substances produites pendant l'inflammation sur leur activation par l'acidification. J'ai ainsi pu montrer chez le rat que la majorité des neurones sensoriels impliqués dans la douleur ont des canaux ASICs et que l'activation de ces canaux induit des potentiels d'action. Nous avons opéré des rats pour qu'ils présentent les symptômes d'une maladie chronique appelée neuropathie. La neuropathie se caractérise par une plus grande sensibilité à la douleur. Les rats neuropathiques présentent des changements de leurs canaux ASICs suggérant que ces canaux ont une peut-être un rôle dans la genèse ou les symptômes de cette maladie. J'ai aussi montré in vitro qu'un type d'enryme produit lors de l'inflammation et l'ischémie change les propriétés des ASICs. Ces résultats confirment un rôle des ASICs dans la douleur suggérant notamment un rôle jusque là encore non étudié dans la douleur neuropathique. De plus, ces résultats mettent en évidence une régulation des ASICs qui pourrait être importante si elle se confirmait in vivo de part les différents rôles des ASICs. Abstract Acid-sensing ion channels (ASICs) are members of the ENaC/Degenerin superfamily of ion channels. Their activation by a rapid extracellular acidification generates a transient and for some ASIC types also a sustained current mainly mediated by Na+. ASICs are expressed in the central (CNS) and in the peripheral (PNS) nervous system. In the CNS, ASICs have a putative role in learning, memory and in neuronal death after cerebral ischemia. In the PNS, ASICs have a putative role in nociception (acute and inflammatory pain) and in mechanotransduction. However, studies on ASIC function are somewhat controversial. Some studies suggest a crucial role of ASICs in transduction of acidification and in pain whereas other studies suggest rather a modulatory inhibitory role of ASICs in pain. Moreover, the basic property of ASICs, that they are activated only transiently is irreconcilable with the well-known property of nociception that the firing of nociceptive fibers demonstrated very little adaptation. Endogenous molecules may exist that can modulate ASIC properties. In a first part of my thesis, we addressed the question of the functional expression of ASICs in adult rat dorsal root ganglion (DRG) neurons. Our goal was to elucidate ASIC roles in DRG neurons. We characterized biophysical and pharmacological properties of ASIC currents using the patch-clamp technique in the whole-cell configuration. We observed that around 60% of small-diameter sensory neurons express ASICs currents. We described in these neurons three ASIC current types. Types 1 and 3 have properties compatible with a role of pH-sensor whereas type 2 is mainly activated by pH lower than pH6. Type 1 is mediated by ASIC1a homomultimers which are permeable to Ca 2+. We studied ASIC co-expression with nociceptor markers. The goal was to associate an ASIC current type with a potential function in sensory neurons. Most neurons expressing ASIC currents co-expressed nociceptor markers. However, a higher proportion of the neurons expressing type 1 was not associated with nociception compared to type 2 and -3. We completed this approach with current-clamp measurements of acidification-induced action potentials (APs). We showed that activation of ASICs in small-diameter neurons can induce APs. The probability of AP induction is positively correlated with the ASIC current density and the acidity of stimulation. Then, we used this classification as a tool to characterize the potential functional modulation of ASICs in the spared nerve injury model of neuropathy. This approach was completed by quantitative RT-PCR experiments. ASICs current expression was dramatically changed at the functional and transcriptional level in injured and non-injured small-diameter DRG neurons. In a second part of my thesis, following an initial screening of the effect of various substances secreted during inflammation and ischemia on ASIC current properties, we characterized in detail the modulation of ASICs, in particular of ASIC1 by serine proteases in a recombinant expression system as well as in hippocampal neurons. We showed that protease exposure shifts the pH dependence of ASIC1 activation and steady-state inactivation to more acidic pH. As a consequence, protease exposure leads to a decrease in the current response if ASIC1 is activated by a pH drop from pH 7.4. If, however, acidification occurs from a basal pH of 7, protease-exposed ASIC1a shows higher activity than untreated ASIC1a. We provided evidence that this bi-directional regulation of ASIC1a function also occurs in hippocampal neurons. Our results in DRG neurons suggest that some ASIC currents are involved in the transduction of peripheral acidification and pain. Furthermore, ASICs may participate to the processes leading to neuropathy. Some Ca 2+-permeable type 1 currents may be involved in neurosecretion. ASIC modulation by serine proteases may be physiologically relevant, allowing ASIC activation under sustained slightly acidic conditions.

Trace element distribution among rock-forming minerals from metamorphosed to partially molten basic igneous rocks in a contact aureole (Fuerteventura, Canaries)

Low pressure partial melting of basanitic and ankaramitic dykes gave rise to unusual, zebra-like migmatites, in the contact aureole of a layered pyroxenite-gabbro intrusion, in the root zone of an ocean island (Basal Complex, Fuerteventura, Canary Islands). These migmatites are characterised by a dense network of closely spaced, millimetre-wide leucocratic segregations. Their mineralogy consists of plagioclase (An(32-36)), diopside, biotite, oxides (magnetite, ilmenite), +/-amphibole, dominated by plagioclase in the leucosome and diopside in the melanosome. The melanosome is almost completely recrystallised, with the preservation of large, relict igneous diopside phenocrysts in dyke centres. Comparison of whole-rock and mineral major- and trace-element data allowed us to assess the redistribution of elements between different mineral phases and generations during contact metamorphism and partial melting. Dykes within and outside the thermal aureole behaved like closed chemical systems. Nevertheless, Zr, Hf, Y and REEs were internally redistributed, as deduced by comparing the trace element contents of the various diopside generations. Neocrystallised diopside - in the melanosome, leucosome and as epitaxial phenocryst rims - from the migmatite zone, are all enriched in Zr, Hf, Y and REEs compared to relict phenocrysts. This has been assigned to the liberation of trace elements on the breakdown of enriched primary minerals, kaersutite and sphene, on entering the thermal aureole. Major and trace element compositions of minerals in migmatite melanosomes and leucosomes are almost identical, pointing to a syn- or post-solidus reequilibration on the cooling of the migmatite terrain i.e. mineral-melt equilibria were reset to mineral-mineral equilibria. (C) 2007 Elsevier B.V. All rights reserved.

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.

Validation des SCORES cliniques: notions théoriques et pratiques de base. [Validation of clinical scores: theoretical and practical basic notions].

Chaque jour, le médecin utilise dans sa pratique des scores cliniques. Ces scores sont souvent des aides à la décision médicale. Les étapes de validation des scores cliniques sont par contre souvent méconnues du médecin. Cette revue rappelle les bases théoriques de la validation d'un score clinique et propose des exercices pratiques. [Abstract] Physicians are using clinical scores on a regular basis. These scores are generally helpful in making medical decisions. However, the process of validation of clinical scores is often unknown to the physicians. This paper reviews the theory of validation of clinical scores and proposes practical exercises.

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]

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

Seismic Wave Velocity as a Means of In-Place Density Measurement, Part 1, HR-114

Velocity-density tests conducted in the laboratory involved small 4-inch diameter by 4.58-inch-long compacted soil cylinders made up of 3 differing soil types and for varying degrees of density and moisture content, the latter being varied well beyond optimum moisture values. Seventeen specimens were tested, 9 with velocity determinations made along two elements of the cylinder, 180 degrees apart, and 8 along three elements, 120 degrees apart. Seismic energy was developed by blows of a small tack hammer on a 5/8-inch diameter steel ball placed at the center of the top of the cylinder, with the detector placed successively at four points spaced 1/2-inch apart on the side of the specimen involving wave travel paths varying from 3.36 inches to 4.66 inches in length. Time intervals were measured using a model 217 micro-seismic timer in both laboratory and field measurements. Forty blows of the hammer were required for each velocity determination, which amounted to 80 blows on 9 laboratory specimens and 120 blows on the remaining 8 cylinders. Thirty-five field tests were made over the three selected soil types, all fine-grained, using a 2-foot seismic line with hammer-impact points at 6-inch intervals. The small tack hammer and 5/8-inch steel ball was, again, used to develop seismic wave energy. Generally, the densities obtained from the velocity measurements were lower than those measured in the conventional field testing. Conclusions were reached that: (1) the method does not appear to be usable for measurement of density of essentially fine-grained soils when the moisture content greatly exceeds the optimum for compaction, and (2) due to a gradual reduction in velocity upon aging, apparently because of gradual absorption of pore water into the expandable interlayer region of the clay, the seismic test should be conducted immediately after soil compaction to obtain a meaningful velocity value.