Tools and techniques to study ligand-receptor interactions and receptor activation by TNF superfamily members.

Ligands and receptors of the TNF superfamily are therapeutically relevant targets in a wide range of human diseases. This chapter describes assays based on ELISA, immunoprecipitation, FACS, and reporter cell lines to monitor interactions of tagged receptors and ligands in both soluble and membrane-bound forms using unified detection techniques. A reporter cell assay that is sensitive to ligand oligomerization can identify ligands with high probability of being active on endogenous receptors. Several assays are also suitable to measure the activity of agonist or antagonist antibodies, or to detect interactions with proteoglycans. Finally, self-interaction of membrane-bound receptors can be evidenced using a FRET-based assay. This panel of methods provides a large degree of flexibility to address questions related to the specificity, activation, or inhibition of TNF-TNF receptor interactions in independent assay systems, but does not substitute for further tests in physiologically relevant conditions.

Emotional intelligence and enterprise handbook : tools and techniques to help students succeed in life and work.

Manual diseñado para que profesores de enseñanza secundaria ayuden y apoyen el desarrollo de la inteligencia emocional, que abarca la conciencia y la responsabilidad, la actitud positiva y apreciativa, la empatía y el respeto, la motivación y la persistencia. Las actividades para nueve sesiones desarrollan la inteligencia con respecto a uno mismo, otras personas, el empleo y el futuro.

Local economic development tools and techniques : a guidebook for local government /

September 1979.

Selection and application of risk management tools and techniques for build-operate-transfer projects

Construction projects are risky. However, the characteristics of the risk highly depend on the type of procurement being adopted for managing the project. A build-operate-transfer (BOT) project is recognized as one of the most risky project schemes. There are instances of project failure where a BOT scheme was employed. Ineffective rts are increasingly being managed using various risk management tools and techniques. However, application of those tools depends on the nature of the project, organization's policy, project management strategy, risk attitude of the project team members, and availability of the resources. Understanding of the contents and contexts of BOT projects, together with a thorough understanding of risk management tools and techniques, helps select processes of risk management for effective project implementation in a BOT scheme. This paper studies application of risk management tools and techniques in BOT projects through reviews of relevant literatures and develops a model for selecting risk management process for BOT projects. The application to BOT projects is considered from the viewpoints of the major project participants. Discussion is also made with regard to political risks. This study would contribute to the establishment of a framework for systematic risk management in BOT projects.

Geo-enabling repositories - techniques, tools, and challenges

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Implementation of IPM programs on European greenhouse tomato production areas: Tools and constraints

Whiteflies and whitefly-transmitted viruses are some of the major constraints on European tomato production. The main objectives of this study were to: identify where and why whiteflies are a major limitation on tomato crops; collect information about whiteflies and associated viruses; determine the available management tools; and identify key knowledge gaps and research priorities. This study was conducted within the framework of ENDURE (European Network for Durable Exploitation of Crop Protection Strategies). Two whitefly species are the main pests of tomato in Europe: Bemisia tabaci and Trialeurodes vaporariorum. Trialeurodes vaporariorum is widespread to all areas where greenhouse industry is present, and B. tabaci has invaded, since the early 1990’s, all the subtropical and tropical areas. Biotypes B and Q of B. tabaci are widespread and especially problematic. Other key tomato pests are Aculops lycopersici, Helicoverpa armigera, Frankliniella occidentalis, and leaf miners. Tomato crops are particularly susceptible to viruses causingTomato yellow leaf curl disease (TYLCD). High incidences of this disease are associated to high pressure of its vector, B. tabaci. The ranked importance of B. tabaci established in this study correlates with the levels of insecticide use, showing B. tabaci as one of the principal drivers behind chemical control. Confirmed cases of resistance to almost all insecticides have been reported. Integrated Pest Management based on biological control (IPM-BC) is applied in all the surveyed regions and identified as the strategy using fewer insecticides. Other IPM components include greenhouse netting and TYLCD-tolerant tomato cultivars. Sampling techniques differ between regions, where decisions are generally based upon whitefly densities and do not relate to control strategies or growing cycles. For population monitoring and control, whitefly species are always identified. In Europe IPM-BC is the recommended strategy for a sustainable tomato production. The IPM-BC approach is mainly based on inoculative releases of the parasitoids Eretmocerus mundus and Encarsia formosa and/or the polyphagous predators Macrolophus caliginosus and Nesidiocoris tenuis. However, some limitations for a wider implementation have been identified: lack of biological solutions for some pests, costs of beneficials, low farmer confidence, costs of technical advice, and low pest injury thresholds. Research priorities to promote and improve IPM-BC are proposed on the following domains: (i) emergence and invasion of new whitefly-transmitted viruses; (ii) relevance of B. tabaci biotypes regarding insecticide resistance; (iii) biochemistry and genetics of plant resistance; (iv) economic thresholds and sampling techniques of whiteflies for decision making; and (v) conservation and management of native whitefly natural enemies and improvement of biological control of other tomato pests.

Allergie aux venins d'hyménopteres: nouveautés diagnostiques et prise en charge [Hymenoptera venom allergy: new diagnostic tools and management].

Anaphylactic reactions to hymenoptera venoms are common and, in our latitudes, mainly concern wasps and bees. Recently, molecular biology techniques have contributed to identifying and to sequencing the major allergens of insect venoms and led to the production of recombinant allergens. Assays for specific IgE directed against these recombinant allergens have recently been made available in clinical practice. They provide considerable assistance in identifying the insect responsible for an anaphylactic reaction, in particular when standard tests are positive for both wasp and bee. This article focuses on these new laboratory tests and also reviews the management of patients experiencing an anaphylactic reaction after hymenoptera sting.

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.

Free tools and strategies for the generation of 3D finite element meshes: Modeling of the cardiac structures

The Finite Element Method is a well-known technique, being extensively applied in different areas. Studies using the Finite Element Method (FEM) are targeted to improve cardiac ablation procedures. For such simulations, the finite element meshes should consider the size and histological features of the target structures. However, it is possible to verify that some methods or tools used to generate meshes of human body structures are still limited, due to nondetailed models, nontrivial preprocessing, or mainly limitation in the use condition. In this paper, alternatives are demonstrated to solid modeling and automatic generation of highly refined tetrahedral meshes, with quality compatible with other studies focused on mesh generation. The innovations presented here are strategies to integrate Open Source Software (OSS). The chosen techniques and strategies are presented and discussed, considering cardiac structures as a first application context. © 2013 E. Pavarino et al.

Tools and methods for the quality assurance of force platforms in human movement analysis

The human movement analysis (HMA) aims to measure the abilities of a subject to stand or to walk. In the field of HMA, tests are daily performed in research laboratories, hospitals and clinics, aiming to diagnose a disease, distinguish between disease entities, monitor the progress of a treatment and predict the outcome of an intervention [Brand and Crowninshield, 1981; Brand, 1987; Baker, 2006]. To achieve these purposes, clinicians and researchers use measurement devices, like force platforms, stereophotogrammetric systems, accelerometers, baropodometric insoles, etc. This thesis focus on the force platform (FP) and in particular on the quality assessment of the FP data. The principal objective of our work was the design and the experimental validation of a portable system for the in situ calibration of FPs. The thesis is structured as follows: Chapter 1. Description of the physical principles used for the functioning of a FP: how these principles are used to create force transducers, such as strain gauges and piezoelectrics transducers. Then, description of the two category of FPs, three- and six-component, the signals acquisition (hardware structure), and the signals calibration. Finally, a brief description of the use of FPs in HMA, for balance or gait analysis. Chapter 2. Description of the inverse dynamics, the most common method used in the field of HMA. This method uses the signals measured by a FP to estimate kinetic quantities, such as joint forces and moments. The measures of these variables can not be taken directly, unless very invasive techniques; consequently these variables can only be estimated using indirect techniques, as the inverse dynamics. Finally, a brief description of the sources of error, present in the gait analysis. Chapter 3. State of the art in the FP calibration. The selected literature is divided in sections, each section describes: systems for the periodic control of the FP accuracy; systems for the error reduction in the FP signals; systems and procedures for the construction of a FP. In particular is detailed described a calibration system designed by our group, based on the theoretical method proposed by ?. This system was the “starting point” for the new system presented in this thesis. Chapter 4. Description of the new system, divided in its parts: 1) the algorithm; 2) the device; and 3) the calibration procedure, for the correct performing of the calibration process. The algorithm characteristics were optimized by a simulation approach, the results are here presented. In addiction, the different versions of the device are described. Chapter 5. Experimental validation of the new system, achieved by testing it on 4 commercial FPs. The effectiveness of the calibration was verified by measuring, before and after calibration, the accuracy of the FPs in measuring the center of pressure of an applied force. The new system can estimate local and global calibration matrices; by local and global calibration matrices, the non–linearity of the FPs was quantified and locally compensated. Further, a non–linear calibration is proposed. This calibration compensates the non– linear effect in the FP functioning, due to the bending of its upper plate. The experimental results are presented. Chapter 6. Influence of the FP calibration on the estimation of kinetic quantities, with the inverse dynamics approach. Chapter 7. The conclusions of this thesis are presented: need of a calibration of FPs and consequential enhancement in the kinetic data quality. Appendix: Calibration of the LC used in the presented system. Different calibration set–up of a 3D force transducer are presented, and is proposed the optimal set–up, with particular attention to the compensation of non–linearities. The optimal set–up is verified by experimental results.

Development of advanced tools and methods for the assessment and management of risk due to atypical major accident scenarios

Proper hazard identification has become progressively more difficult to achieve, as witnessed by several major accidents that took place in Europe, such as the Ammonium Nitrate explosion at Toulouse (2001) and the vapour cloud explosion at Buncefield (2005), whose accident scenarios were not considered by their site safety case. Furthermore, the rapid renewal in the industrial technology has brought about the need to upgrade hazard identification methodologies. Accident scenarios of emerging technologies, which are not still properly identified, may remain unidentified until they take place for the first time. The consideration of atypical scenarios deviating from normal expectations of unwanted events or worst case reference scenarios is thus extremely challenging. A specific method named Dynamic Procedure for Atypical Scenarios Identification (DyPASI) was developed as a complementary tool to bow-tie identification techniques. The main aim of the methodology is to provide an easier but comprehensive hazard identification of the industrial process analysed, by systematizing information from early signals of risk related to past events, near misses and inherent studies. DyPASI was validated on the two examples of new and emerging technologies: Liquefied Natural Gas regasification and Carbon Capture and Storage. The study broadened the knowledge on the related emerging risks and, at the same time, demonstrated that DyPASI is a valuable tool to obtain a complete and updated overview of potential hazards. Moreover, in order to tackle underlying accident causes of atypical events, three methods for the development of early warning indicators were assessed: the Resilience-based Early Warning Indicator (REWI) method, the Dual Assurance method and the Emerging Risk Key Performance Indicator method. REWI was found to be the most complementary and effective of the three, demonstrating that its synergy with DyPASI would be an adequate strategy to improve hazard identification methodologies towards the capture of atypical accident scenarios.

Software tools and efficient algorithms for the feature detection, feature tracking, event localization, and visualization of large sets of atmospheric data

Data sets describing the state of the earth's atmosphere are of great importance in the atmospheric sciences. Over the last decades, the quality and sheer amount of the available data increased significantly, resulting in a rising demand for new tools capable of handling and analysing these large, multidimensional sets of atmospheric data. The interdisciplinary work presented in this thesis covers the development and the application of practical software tools and efficient algorithms from the field of computer science, aiming at the goal of enabling atmospheric scientists to analyse and to gain new insights from these large data sets. For this purpose, our tools combine novel techniques with well-established methods from different areas such as scientific visualization and data segmentation. In this thesis, three practical tools are presented. Two of these tools are software systems (Insight and IWAL) for different types of processing and interactive visualization of data, the third tool is an efficient algorithm for data segmentation implemented as part of Insight.Insight is a toolkit for the interactive, three-dimensional visualization and processing of large sets of atmospheric data, originally developed as a testing environment for the novel segmentation algorithm. It provides a dynamic system for combining at runtime data from different sources, a variety of different data processing algorithms, and several visualization techniques. Its modular architecture and flexible scripting support led to additional applications of the software, from which two examples are presented: the usage of Insight as a WMS (web map service) server, and the automatic production of a sequence of images for the visualization of cyclone simulations. The core application of Insight is the provision of the novel segmentation algorithm for the efficient detection and tracking of 3D features in large sets of atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. Data segmentation usually leads to a significant reduction of the size of the considered data. This enables a practical visualization of the data, statistical analyses of the features and their events, and the manual or automatic detection of interesting situations for subsequent detailed investigation. The concepts of the novel algorithm, its technical realization, and several extensions for avoiding under- and over-segmentation are discussed. As example applications, this thesis covers the setup and the results of the segmentation of upper-tropospheric jet streams and cyclones as full 3D objects. Finally, IWAL is presented, which is a web application for providing an easy interactive access to meteorological data visualizations, primarily aimed at students. As a web application, the needs to retrieve all input data sets and to install and handle complex visualization tools on a local machine are avoided. The main challenge in the provision of customizable visualizations to large numbers of simultaneous users was to find an acceptable trade-off between the available visualization options and the performance of the application. Besides the implementational details, benchmarks and the results of a user survey are presented.

Magnetic Devices and Techniques for the Study of Viscoelasticity of Biomaterials and Myocardial Forces

Thesis (Ph.D.)--University of Washington, 2016-06