BIO-INSPIRED COMPUTATIONAL TECHNIQUES APPLIED TO THE CLUSTERING AND VISUALIZATION OF SPATIO-TEMPORAL GEOSPATIAL DATA

The coverage and volume of geo-referenced datasets are extensive and incessantly¦growing. The systematic capture of geo-referenced information generates large volumes¦of spatio-temporal data to be analyzed. Clustering and visualization play a key¦role in the exploratory data analysis and the extraction of knowledge embedded in¦these data. However, new challenges in visualization and clustering are posed when¦dealing with the special characteristics of this data. For instance, its complex structures,¦large quantity of samples, variables involved in a temporal context, high dimensionality¦and large variability in cluster shapes.¦The central aim of my thesis is to propose new algorithms and methodologies for¦clustering and visualization, in order to assist the knowledge extraction from spatiotemporal¦geo-referenced data, thus improving making decision processes.¦I present two original algorithms, one for clustering: the Fuzzy Growing Hierarchical¦Self-Organizing Networks (FGHSON), and the second for exploratory visual data analysis:¦the Tree-structured Self-organizing Maps Component Planes. In addition, I present¦methodologies that combined with FGHSON and the Tree-structured SOM Component¦Planes allow the integration of space and time seamlessly and simultaneously in¦order to extract knowledge embedded in a temporal context.¦The originality of the FGHSON lies in its capability to reflect the underlying structure¦of a dataset in a hierarchical fuzzy way. A hierarchical fuzzy representation of¦clusters is crucial when data include complex structures with large variability of cluster¦shapes, variances, densities and number of clusters. The most important characteristics¦of the FGHSON include: (1) It does not require an a-priori setup of the number¦of clusters. (2) The algorithm executes several self-organizing processes in parallel.¦Hence, when dealing with large datasets the processes can be distributed reducing the¦computational cost. (3) Only three parameters are necessary to set up the algorithm.¦In the case of the Tree-structured SOM Component Planes, the novelty of this algorithm¦lies in its ability to create a structure that allows the visual exploratory data analysis¦of large high-dimensional datasets. This algorithm creates a hierarchical structure¦of Self-Organizing Map Component Planes, arranging similar variables' projections in¦the same branches of the tree. Hence, similarities on variables' behavior can be easily¦detected (e.g. local correlations, maximal and minimal values and outliers).¦Both FGHSON and the Tree-structured SOM Component Planes were applied in¦several agroecological problems proving to be very efficient in the exploratory analysis¦and clustering of spatio-temporal datasets.¦In this thesis I also tested three soft competitive learning algorithms. Two of them¦well-known non supervised soft competitive algorithms, namely the Self-Organizing¦Maps (SOMs) and the Growing Hierarchical Self-Organizing Maps (GHSOMs); and the¦third was our original contribution, the FGHSON. Although the algorithms presented¦here have been used in several areas, to my knowledge there is not any work applying¦and comparing the performance of those techniques when dealing with spatiotemporal¦geospatial data, as it is presented in this thesis.¦I propose original methodologies to explore spatio-temporal geo-referenced datasets¦through time. Our approach uses time windows to capture temporal similarities and¦variations by using the FGHSON clustering algorithm. The developed methodologies¦are used in two case studies. In the first, the objective was to find similar agroecozones¦through time and in the second one it was to find similar environmental patterns¦shifted in time.¦Several results presented in this thesis have led to new contributions to agroecological¦knowledge, for instance, in sugar cane, and blackberry production.¦Finally, in the framework of this thesis we developed several software tools: (1)¦a Matlab toolbox that implements the FGHSON algorithm, and (2) a program called¦BIS (Bio-inspired Identification of Similar agroecozones) an interactive graphical user¦interface tool which integrates the FGHSON algorithm with Google Earth in order to¦show zones with similar agroecological characteristics.

Spatiotemporal analysis of forensic case data: a visualisation approach

Whether for investigative or intelligence aims, crime analysts often face up the necessity to analyse the spatiotemporal distribution of crimes or traces left by suspects. This article presents a visualisation methodology supporting recurrent practical analytical tasks such as the detection of crime series or the analysis of traces left by digital devices like mobile phone or GPS devices. The proposed approach has led to the development of a dedicated tool that has proven its effectiveness in real inquiries and intelligence practices. It supports a more fluent visual analysis of the collected data and may provide critical clues to support police operations as exemplified by the presented case studies.

Design, data management, and population baseline characteristics of the PERFORM magnetic resonance imaging project.

Quantitative information from magnetic resonance imaging (MRI) may substantiate clinical findings and provide additional insight into the mechanism of clinical interventions in therapeutic stroke trials. The PERFORM study is exploring the efficacy of terutroban versus aspirin for secondary prevention in patients with a history of ischemic stroke. We report on the design of an exploratory longitudinal MRI follow-up study that was performed in a subgroup of the PERFORM trial. An international multi-centre longitudinal follow-up MRI study was designed for different MR systems employing safety and efficacy readouts: new T2 lesions, new DWI lesions, whole brain volume change, hippocampal volume change, changes in tissue microstructure as depicted by mean diffusivity and fractional anisotropy, vessel patency on MR angiography, and the presence of and development of new microbleeds. A total of 1,056 patients (men and women ≥ 55 years) were included. The data analysis included 3D reformation, image registration of different contrasts, tissue segmentation, and automated lesion detection. This large international multi-centre study demonstrates how new MRI readouts can be used to provide key information on the evolution of cerebral tissue lesions and within the macrovasculature after atherothrombotic stroke in a large sample of patients.

Quantitative integration of high-resolution hydrogeophysical data through Monte-Carlo-type conditional simulations

Geophysical techniques can help to bridge the inherent gap with regard to spatial resolution and the range of coverage that plagues classical hydrological methods. This has lead to the emergence of the new and rapidly growing field of hydrogeophysics. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters and their inherent trade-off between resolution and range the fundamental usefulness of multi-method hydrogeophysical surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative integration of the resulting vast and diverse database in order to obtain a unified model of the probed subsurface region that is internally consistent with all available data. To address this problem, we have developed a strategy towards hydrogeophysical data integration based on Monte-Carlo-type conditional stochastic simulation that we consider to be particularly suitable for local-scale studies characterized by high-resolution and high-quality datasets. Monte-Carlo-based optimization techniques are flexible and versatile, allow for accounting for a wide variety of data and constraints of differing resolution and hardness and thus have the potential of providing, in a geostatistical sense, highly detailed and realistic models of the pertinent target parameter distributions. Compared to more conventional approaches of this kind, our approach provides significant advancements in the way that the larger-scale deterministic information resolved by the hydrogeophysical data can be accounted for, which represents an inherently problematic, and as of yet unresolved, aspect of Monte-Carlo-type conditional simulation techniques. We present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure. Our procedure is first tested on pertinent synthetic data and then applied to corresponding field data collected at the Boise Hydrogeophysical Research Site near Boise, Idaho, USA.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Qualité des données fournies par la statistique médicale VESKA: le cas de la fracture du fémur proximal. [Quality of data provided by VESKA medical statistics: the case of the fractured proximal femur].

Within the framework of a retrospective study of the incidence of hip fractures in the canton of Vaud (Switzerland), all cases of hip fracture occurring among the resident population in 1986 and treated in the hospitals of the canton were identified from among five different information sources. Relevant data were then extracted from the medical records. At least two sources of information were used to identify cases in each hospital, among them the statistics of the Swiss Hospital Association (VESKA). These statistics were available for 9 of the 18 hospitals in the canton that participated in the study. The number of cases identified from the VESKA statistics was compared to the total number of cases for each hospital. For the 9 hospitals the number of cases in the VESKA statistics was 407, whereas, after having excluded diagnoses that were actually "status after fracture" and double entries, the total for these hospitals was 392, that is 4% less than the VESKA statistics indicate. It is concluded that the VESKA statistics provide a good approximation of the actual number of cases treated in these hospitals, with a tendency to overestimate this number. In order to use these statistics for calculating incidence figures, however, it is imperative that a greater proportion of all hospitals (50% presently in the canton, 35% nationwide) participate in these statistics.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

The Complete Chromosomal Organization of the Reference Strain of the Leishmania Genome Project, L. major ;Friedlin'.

In recent years, analysis of the genomes of many organisms has received increasing international attention. The bulk of the effort to date has centred on the Human Genome Project and analysis of model organisms such as yeast, Drosophila and Caenorhabditis elegans. More recently, the revolution in genome sequencing and gene identification has begun to impact on infectious disease organisms. Initially, much of the effort was concentrated on prokaryotes, but small eukaryotic genomes, including the protozoan parasites Plasmodium, Toxoplasma and trypanosomatids (Leishmania, Trypanosoma brucei and T. cruzi), as well as some multicellular organisms, such as Brugia and Schistosoma, are benefiting from the technological advances of the genome era. These advances promise a radical new approach to the development of novel diagnostic tools, chemotherapeutic targets and vaccines for infectious disease organisms, as well as to the more detailed analysis of cell biology and function.Several networks or consortia linking laboratories around the world have been established to support these parasite genome projects[1] (for more information, see http://www.ebi.ac.uk/ parasites/paratable.html). Five of these networks were supported by an initiative launched in 1994 by the Specific Programme for Research and Tropical Diseases (TDR) of the WHO[2, 3, 4, 5, 6]. The Leishmania Genome Network (LGN) is one of these[3]. Its activities are reported at http://www.ebi.ac.uk/parasites/leish.html, and its current aim is to map and sequence the genome of Leishmania by the year 2002. All the mapping, hybridization and sequence data are also publicly available from LeishDB, an AceDB-based genome database (http://www.ebi.ac.uk/parasites/LGN/leissssoft.html).

Structural components in functional data

Analyzing functional data often leads to finding common factors, for which functional principal component analysis proves to be a useful tool to summarize and characterize the random variation in a function space. The representation in terms of eigenfunctions is optimal in the sense of L-2 approximation. However, the eigenfunctions are not always directed towards an interesting and interpretable direction in the context of functional data and thus could obscure the underlying structure. To overcome such difficulty, an alternative to functional principal component analysis is proposed that produces directed components which may be more informative and easier to interpret. These structural components are similar to principal components, but are adapted to situations in which the domain of the function may be decomposed into disjoint intervals such that there is effectively independence between intervals and positive correlation within intervals. The approach is demonstrated with synthetic examples as well as real data. Properties for special cases are also studied.

Post-detection analysis for grating-based ultra-small angle X-ray scattering.

Until recently, the hard X-ray, phase-sensitive imaging technique called grating interferometry was thought to provide information only in real space. However, by utilizing an alternative approach to data analysis we demonstrated that the angular resolved ultra-small angle X-ray scattering distribution can be retrieved from experimental data. Thus, reciprocal space information is accessible by grating interferometry in addition to real space. Naturally, the quality of the retrieved data strongly depends on the performance of the employed analysis procedure, which involves deconvolution of periodic and noisy data in this context. The aim of this article is to compare several deconvolution algorithms to retrieve the ultra-small angle X-ray scattering distribution in grating interferometry. We quantitatively compare the performance of three deconvolution procedures (i.e., Wiener, iterative Wiener and Lucy-Richardson) in case of realistically modeled, noisy and periodic input data. The simulations showed that the algorithm of Lucy-Richardson is the more reliable and more efficient as a function of the characteristics of the signals in the given context. The availability of a reliable data analysis procedure is essential for future developments in grating interferometry.

Normalisation informatique et marchés de services : le cas du XML

Extensible Markup Language (XML) is a generic computing language that provides an outstanding case study of commodification of service standards. The development of this language in the late 1990s marked a shift in computer science as its extensibility let store and share any kind of data. Many office suites software rely on it. The chapter highlights how the largest multinational firms pay special attention to gain a recognised international standard for such a major technological innovation. It argues that standardisation processes affects market structures and can lead to market capture. By examining how a strategic use of standardisation arenas can generate profits, it shows that Microsoft succeeded in making its own technical solution a recognised ISO standard in 2008, while the same arena already adopted two years earlier the open source standard set by IBM and Sun Microsystems. Yet XML standardisation also helped to establish a distinct model of information technology services at the expense of Microsoft monopoly on proprietary software

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.

Reliable selfing rate estimates from imperfect population genetic data.

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

Tapping the innovation potential of corporate engagement with stakeholders

Summary This dissertation explores how stakeholder dialogue influences corporate processes, and speculates about the potential of this phenomenon - particularly with actors, like non-governmental organizations (NGOs) and other representatives of civil society, which have received growing attention against a backdrop of increasing globalisation and which have often been cast in an adversarial light by firms - as a source of teaming and a spark for innovation in the firm. The study is set within the context of the introduction of genetically-modified organisms (GMOs) in Europe. Its significance lies in the fact that scientific developments and new technologies are being generated at an unprecedented rate in an era where civil society is becoming more informed, more reflexive, and more active in facilitating or blocking such new developments, which could have the potential to trigger widespread changes in economies, attitudes, and lifestyles, and address global problems like poverty, hunger, climate change, and environmental degradation. In the 1990s, companies using biotechnology to develop and offer novel products began to experience increasing pressure from civil society to disclose information about the risks associated with the use of biotechnology and GMOs, in particular. Although no harmful effects for humans or the environment have been factually demonstrated even to date (2008), this technology remains highly-contested and its introduction in Europe catalysed major companies to invest significant financial and human resources in stakeholder dialogue. A relatively new phenomenon at the time, with little theoretical backing, dialogue was seen to reflect a move towards greater engagement with stakeholders, commonly defined as those "individuals or groups with which. business interacts who have a 'stake', or vested interest in the firm" (Carroll, 1993:22) with whom firms are seen to be inextricably embedded (Andriof & Waddock, 2002). Regarding the organisation of this dissertation, Chapter 1 (Introduction) describes the context of the study, elaborates its significance for academics and business practitioners as an empirical work embedded in a sector at the heart of the debate on corporate social responsibility (CSR). Chapter 2 (Literature Review) traces the roots and evolution of CSR, drawing on Stakeholder Theory, Institutional Theory, Resource Dependence Theory, and Organisational Learning to establish what has already been developed in the literature regarding the stakeholder concept, motivations for engagement with stakeholders, the corporate response to external constituencies, and outcomes for the firm in terms of organisational learning and change. I used this review of the literature to guide my inquiry and to develop the key constructs through which I viewed the empirical data that was gathered. In this respect, concepts related to how the firm views itself (as a victim, follower, leader), how stakeholders are viewed (as a source of pressure and/or threat; as an asset: current and future), corporate responses (in the form of buffering, bridging, boundary redefinition), and types of organisational teaming (single-loop, double-loop, triple-loop) and change (first order, second order, third order) were particularly important in building the key constructs of the conceptual model that emerged from the analysis of the data. Chapter 3 (Methodology) describes the methodology that was used to conduct the study, affirms the appropriateness of the case study method in addressing the research question, and describes the procedures for collecting and analysing the data. Data collection took place in two phases -extending from August 1999 to October 2000, and from May to December 2001, which functioned as `snapshots' in time of the three companies under study. The data was systematically analysed and coded using ATLAS/ti, a qualitative data analysis tool, which enabled me to sort, organise, and reduce the data into a manageable form. Chapter 4 (Data Analysis) contains the three cases that were developed (anonymised as Pioneer, Helvetica, and Viking). Each case is presented in its entirety (constituting a `within case' analysis), followed by a 'cross-case' analysis, backed up by extensive verbatim evidence. Chapter 5 presents the research findings, outlines the study's limitations, describes managerial implications, and offers suggestions for where more research could elaborate the conceptual model developed through this study, as well as suggestions for additional research in areas where managerial implications were outlined. References and Appendices are included at the end. This dissertation results in the construction and description of a conceptual model, grounded in the empirical data and tied to existing literature, which portrays a set of elements and relationships deemed important for understanding the impact of stakeholder engagement for firms in terms of organisational learning and change. This model suggests that corporate perceptions about the nature of stakeholder influence the perceived value of stakeholder contributions. When stakeholders are primarily viewed as a source of pressure or threat, firms tend to adopt a reactive/defensive posture in an effort to manage stakeholders and protect the firm from sources of outside pressure -behaviour consistent with Resource Dependence Theory, which suggests that firms try to get control over extemal threats by focussing on the relevant stakeholders on whom they depend for critical resources, and try to reverse the control potentially exerted by extemal constituencies by trying to influence and manipulate these valuable stakeholders. In situations where stakeholders are viewed as a current strategic asset, firms tend to adopt a proactive/offensive posture in an effort to tap stakeholder contributions and connect the organisation to its environment - behaviour consistent with Institutional Theory, which suggests that firms try to ensure the continuing license to operate by internalising external expectations. In instances where stakeholders are viewed as a source of future value, firms tend to adopt an interactive/innovative posture in an effort to reduce or widen the embedded system and bring stakeholders into systems of innovation and feedback -behaviour consistent with the literature on Organisational Learning, which suggests that firms can learn how to optimize their performance as they develop systems and structures that are more adaptable and responsive to change The conceptual model moreover suggests that the perceived value of stakeholder contribution drives corporate aims for engagement, which can be usefully categorised as dialogue intentions spanning a continuum running from low-level to high-level to very-high level. This study suggests that activities aimed at disarming critical stakeholders (`manipulation') providing guidance and correcting misinformation (`education'), being transparent about corporate activities and policies (`information'), alleviating stakeholder concerns (`placation'), and accessing stakeholder opinion ('consultation') represent low-level dialogue intentions and are experienced by stakeholders as asymmetrical, persuasive, compliance-gaining activities that are not in line with `true' dialogue. This study also finds evidence that activities aimed at redistributing power ('partnership'), involving stakeholders in internal corporate processes (`participation'), and demonstrating corporate responsibility (`stewardship') reflect high-level dialogue intentions. This study additionally finds evidence that building and sustaining high-quality, trusted relationships which can meaningfully influence organisational policies incline a firm towards the type of interactive, proactive processes that underpin the development of sustainable corporate strategies. Dialogue intentions are related to type of corporate response: low-level intentions can lead to buffering strategies; high-level intentions can underpin bridging strategies; very high-level intentions can incline a firm towards boundary redefinition. The nature of corporate response (which encapsulates a firm's posture towards stakeholders, demonstrated by the level of dialogue intention and the firm's strategy for dealing with stakeholders) favours the type of learning and change experienced by the organisation. This study indicates that buffering strategies, where the firm attempts to protect itself against external influences and cant' out its existing strategy, typically lead to single-loop learning, whereby the firm teams how to perform better within its existing paradigm and at most, improves the performance of the established system - an outcome associated with first-order change. Bridging responses, where the firm adapts organisational activities to meet external expectations, typically leads a firm to acquire new behavioural capacities characteristic of double-loop learning, whereby insights and understanding are uncovered that are fundamentally different from existing knowledge and where stakeholders are brought into problem-solving conversations that enable them to influence corporate decision-making to address shortcomings in the system - an outcome associated with second-order change. Boundary redefinition suggests that the firm engages in triple-loop learning, where the firm changes relations with stakeholders in profound ways, considers problems from a whole-system perspective, examining the deep structures that sustain the system, producing innovation to address chronic problems and develop new opportunities - an outcome associated with third-order change. This study supports earlier theoretical and empirical studies {e.g. Weick's (1979, 1985) work on self-enactment; Maitlis & Lawrence's (2007) and Maitlis' (2005) work and Weick et al's (2005) work on sensegiving and sensemaking in organisations; Brickson's (2005, 2007) and Scott & Lane's (2000) work on organisational identity orientation}, which indicate that corporate self-perception is a key underlying factor driving the dynamics of organisational teaming and change. Such theorizing has important implications for managerial practice; namely, that a company which perceives itself as a 'victim' may be highly inclined to view stakeholders as a source of negative influence, and would therefore be potentially unable to benefit from the positive influence of engagement. Such a selfperception can blind the firm from seeing stakeholders in a more positive, contributing light, which suggests that such firms may not be inclined to embrace external sources of innovation and teaming, as they are focussed on protecting the firm against disturbing environmental influences (through buffering), and remain more likely to perform better within an existing paradigm (single-loop teaming). By contrast, a company that perceives itself as a 'leader' may be highly inclined to view stakeholders as a source of positive influence. On the downside, such a firm might have difficulty distinguishing when stakeholder contributions are less pertinent as it is deliberately more open to elements in operating environment (including stakeholders) as potential sources of learning and change, as the firm is oriented towards creating space for fundamental change (through boundary redefinition), opening issues to entirely new ways of thinking and addressing issues from whole-system perspective. A significant implication of this study is that potentially only those companies who see themselves as a leader are ultimately able to tap the innovation potential of stakeholder dialogue.

Efficacy, safety and tolerability of linezolid containing regimens in treating MDR-TB and XDR-TB: systematic review and meta-analysis.

Linezolid is used off-label to treat multidrug-resistant tuberculosis (MDR-TB) in absence of systematic evidence. We performed a systematic review and meta-analysis on efficacy, safety and tolerability of linezolid-containing regimes based on individual data analysis. 12 studies (11 countries from three continents) reporting complete information on safety, tolerability, efficacy of linezolid-containing regimes in treating MDR-TB cases were identified based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Meta-analysis was performed using the individual data of 121 patients with a definite treatment outcome (cure, completion, death or failure). Most MDR-TB cases achieved sputum smear (86 (92.5%) out of 93) and culture (100 (93.5%) out of 107) conversion after treatment with individualised regimens containing linezolid (median (inter-quartile range) times for smear and culture conversions were 43.5 (21-90) and 61 (29-119) days, respectively) and 99 (81.8%) out of 121 patients were successfully treated. No significant differences were detected in the subgroup efficacy analysis (daily linezolid dosage ≤600 mg versus >600 mg). Adverse events were observed in 63 (58.9%) out of 107 patients, of which 54 (68.4%) out of 79 were major adverse events that included anaemia (38.1%), peripheral neuropathy (47.1%), gastro-intestinal disorders (16.7%), optic neuritis (13.2%) and thrombocytopenia (11.8%). The proportion of adverse events was significantly higher when the linezolid daily dosage exceeded 600 mg. The study results suggest an excellent efficacy but also the necessity of caution in the prescription of linezolid.