Exposure models in Switzerland: an overview of the present situation

A survey was undertaken among Swiss occupational health and safety specialists in 2004 to identify uses, difficulties, and possible developments of exposure models. Occupational hygienists (121), occupational physicians (169), and safety specialists (95) were surveyed with an in depth questionnaire. Results obtained indicate that models are not used very much in practice in Switzerland and are reserved to research groups focusing on specific topics. However, various determinants of exposure are often considered important by professionals (emission rate, work activity), and in some cases recorded and used (room parameters, operator activity). These parameters cannot be directly included in present models. Nevertheless, more than half of the occupational hygienists think that it is important to develop quantitative exposure models. Looking at research institutions, there is, however, a big interest in the use of models to solve problems which are difficult to address with direct measurements; i. e. retrospective exposure assessment for specific clinical cases and prospective evaluation for new situations or estimation of the effect of selected parameters. In a recent study about cases of acutepulmonary toxicity following water proofing spray exposure, exposure models have been used to reconstruct exposure of a group of patients. Finally, in the context of exposure prediction, it is also important to report about a measurement database existing in Switzerland since 1991. [Authors]

Symmetry-breaking in cumulative measures of shapes of polymer models.

Using numerical simulations we investigate shapes of random equilateral open and closed chains, one of the simplest models of freely fluctuating polymers in a solution. We are interested in the 3D density distribution of the modeled polymers where the polymers have been aligned with respect to their three principal axes of inertia. This type of approach was pioneered by Theodorou and Suter in 1985. While individual configurations of the modeled polymers are almost always nonsymmetric, the approach of Theodorou and Suter results in cumulative shapes that are highly symmetric. By taking advantage of asymmetries within the individual configurations, we modify the procedure of aligning independent configurations in a way that shows their asymmetry. This approach reveals, for example, that the 3D density distribution for linear polymers has a bean shape predicted theoretically by Kuhn. The symmetry-breaking approach reveals complementary information to the traditional, symmetrical, 3D density distributions originally introduced by Theodorou and Suter.

Extremal properties of certain risk models

Cette thèse s'intéresse à étudier les propriétés extrémales de certains modèles de risque d'intérêt dans diverses applications de l'assurance, de la finance et des statistiques. Cette thèse se développe selon deux axes principaux, à savoir: Dans la première partie, nous nous concentrons sur deux modèles de risques univariés, c'est-à- dire, un modèle de risque de déflation et un modèle de risque de réassurance. Nous étudions le développement des queues de distribution sous certaines conditions des risques commun¬s. Les principaux résultats sont ainsi illustrés par des exemples typiques et des simulations numériques. Enfin, les résultats sont appliqués aux domaines des assurances, par exemple, les approximations de Value-at-Risk, d'espérance conditionnelle unilatérale etc. La deuxième partie de cette thèse est consacrée à trois modèles à deux variables: Le premier modèle concerne la censure à deux variables des événements extrême. Pour ce modèle, nous proposons tout d'abord une classe d'estimateurs pour les coefficients de dépendance et la probabilité des queues de distributions. Ces estimateurs sont flexibles en raison d'un paramètre de réglage. Leurs distributions asymptotiques sont obtenues sous certaines condi¬tions lentes bivariées de second ordre. Ensuite, nous donnons quelques exemples et présentons une petite étude de simulations de Monte Carlo, suivie par une application sur un ensemble de données réelles d'assurance. L'objectif de notre deuxième modèle de risque à deux variables est l'étude de coefficients de dépendance des queues de distributions obliques et asymétriques à deux variables. Ces distri¬butions obliques et asymétriques sont largement utiles dans les applications statistiques. Elles sont générées principalement par le mélange moyenne-variance de lois normales et le mélange de lois normales asymétriques d'échelles, qui distinguent la structure de dépendance de queue comme indiqué par nos principaux résultats. Le troisième modèle de risque à deux variables concerne le rapprochement des maxima de séries triangulaires elliptiques obliques. Les résultats théoriques sont fondés sur certaines hypothèses concernant le périmètre aléatoire sous-jacent des queues de distributions. -- This thesis aims to investigate the extremal properties of certain risk models of interest in vari¬ous applications from insurance, finance and statistics. This thesis develops along two principal lines, namely: In the first part, we focus on two univariate risk models, i.e., deflated risk and reinsurance risk models. Therein we investigate their tail expansions under certain tail conditions of the common risks. Our main results are illustrated by some typical examples and numerical simu¬lations as well. Finally, the findings are formulated into some applications in insurance fields, for instance, the approximations of Value-at-Risk, conditional tail expectations etc. The second part of this thesis is devoted to the following three bivariate models: The first model is concerned with bivariate censoring of extreme events. For this model, we first propose a class of estimators for both tail dependence coefficient and tail probability. These estimators are flexible due to a tuning parameter and their asymptotic distributions are obtained under some second order bivariate slowly varying conditions of the model. Then, we give some examples and present a small Monte Carlo simulation study followed by an application on a real-data set from insurance. The objective of our second bivariate risk model is the investigation of tail dependence coefficient of bivariate skew slash distributions. Such skew slash distributions are extensively useful in statistical applications and they are generated mainly by normal mean-variance mixture and scaled skew-normal mixture, which distinguish the tail dependence structure as shown by our principle results. The third bivariate risk model is concerned with the approximation of the component-wise maxima of skew elliptical triangular arrays. The theoretical results are based on certain tail assumptions on the underlying random radius.

Beclin 1 mitigates motor and neuropathological deficits in genetic mouse models of Machado-Joseph disease.

Machado-Joseph disease or spinocerebellar ataxia type 3, the most common dominantly-inherited spinocerebellar ataxia, results from translation of the polyglutamine-expanded and aggregation prone ataxin 3 protein. Clinical manifestations include cerebellar ataxia and pyramidal signs and there is no therapy to delay disease progression. Beclin 1, an autophagy-related protein and essential gene for cell survival, is decreased in several neurodegenerative disorders. This study aimed at evaluating if lentiviral-mediated beclin 1 overexpression would rescue motor and neuropathological impairments when administered to pre- and post-symptomatic lentiviral-based and transgenic mouse models of Machado-Joseph disease. Beclin 1-mediated significant improvements in motor coordination, balance and gait with beclin 1-treated mice equilibrating longer periods in the Rotarod and presenting longer and narrower footprints. Furthermore, in agreement with the improvements observed in motor function beclin 1 overexpression prevented neuronal dysfunction and neurodegeneration, decreasing formation of polyglutamine-expanded aggregates, preserving Purkinje cell arborization and immunoreactivity for neuronal markers. These data show that overexpression of beclin 1 in the mouse cerebellum is able to rescue and hinder the progression of motor deficits when administered to pre- and post-symptomatic stages of the disease.

Validation of Logistic Regression Models for Landslide Susceptibility Maps

A wide range of numerical models and tools have been developed over the last decades to support the decision making process in environmental applications, ranging from physical models to a variety of statistically-based methods. In this study, a landslide susceptibility map of a part of Three Gorges Reservoir region of China was produced, employing binary logistic regression analyses. The available information includes the digital elevation model of the region, geological map and different GIS layers including land cover data obtained from satellite imagery. The landslides were observed and documented during the field studies. The validation analysis is exploited to investigate the quality of mapping.

New genetic models and mesenchymal cells as tools to ameliorate anti-tumor immunity

Résumé Le but final de ce projet est d'utiliser des cellules T ou des cellules souches mésenchymateuses modifiées génétiquement afin de surexprimer localement les deux chémokines CXCL13 et CCL2 ensemble ou chacune séparément à l'intérieur d'une tumeur solide. CXCL13 est supposé induire des structures lymphoïdes ectopiques. Un niveau élevé de CCL2 est présumé initier une inflammation aiguë. La combinaison des deux effets amène à un nouveau modèle d'étude des mécanismes régulateur de la tolérance périphérique et de l'immunité tumorale. Les connaissances acquises grâce à ce modèle pourraient permettre le développement ou l'amélioration des thérapies immunes du cancer. Le but premier de ce travail a été l'établissement d'un modèle génétique de la souris permettant d'exprimer spécifiquement dans la tumeur les deux chémokines d'intérêt à des niveaux élevés. Pour accomplir cette tâche, qui est en fait une thérapie génétique de tumeurs solides, deux types de cellules porteuses potentielles ont été évaluées. Des cellules CD8+ T et des cellules mésenchymateuses de la moelle osseuse transférées dans des receveurs portant une tumeur. Si on pouvait répondre aux besoins de la thérapie génétique, indépendamment de la thérapie immune envisagée, on posséderait là un outil précieux pour bien d'autres approches thérapeutiques. Plusieurs lignées de souris transgéniques ont été générées comme source de cellules CD8+ T modifiées afin d'exprimer les chémokines d'intérêt. Dans une approche doublement transgénique les propriétés de deux promoteurs spécifiques de cellules T ont été combinées en utilisant la technologie Cre-loxP. Le promoteur de granzyme B confère une dépendance d'activation et le promoteur distal de lck assure une forte expression constitutive dès que les cellules CD8+ T ont été activées. Les transgènes construits ont montré une bonne performance in vivo et des souris qui expriment CCL2 dans des cellules CD8+ T activées ont été obtenues. Ces cellules peuvent maintenant être utilisées avec différents protocoles pour transférer des cellules T cytotoxiques (CTL) dans des receveurs porteur d'une tumeur, permettant ainsi d'évaluer leur capacité en tant que porteuse de chémokine d'infiltrer la tumeur. L'établissement de souris transgéniques, qui expriment pareillement CXCL13 est prévu dans un avenir proche. L'évaluation de cellules mésenchymateuses de la moelle osseuse a démontré que ces cellules se greffent efficacement dans le stroma tumoral suite à la co-injection avec des cellules tumorales. Cela représente un outil précieux pour la recherche, vu qu'il permet d'introduire des cellules manipulées dans un modèle tumoral. Les résultats confirment partiellement d'autres résultats rapportés dans un modèle amélioré. Cependant, l'efficacité et la spécificité suggérées de la migration systémique de cellules mésenchymateuses de la moelle osseuse dans une tumeur n'ont pas été observées dans notre modèle, ce qui indique, que ces cellules ne se prêtent pas à une utilisation thérapeutique. Un autre résultat majeur de ce travail est l'établissement de cultures de cellules mésenchymateuses de la moelle osseuse in vitro conditionnées par des tumeurs, ce qui a permis à ces cellules de s'étendre plus rapidement en gardant leur capacité de migration et de greffe. Cela offre un autre outil précieux, vu que la culture in vitro est un pas nécessaire pour une manipulation thérapeutique. Abstract The ultimate aim of the presented project is to use genetically modified T cells or mesenchymal stem cells to locally overexpress the two chemokines CXCL13 and CCL2 together or each one alone inside a solid tumor. CXCL13 is supposed to induce ectopic lymphoid structures and a high level of CCL2 is intended to trigger acute inflamation. The combination of these two effects represents a new model for studying mechanisms that regulate peripheral tolerance and tumor immunity. Gained insights may help developing or improving immunotherapy of cancer. The primary goal of the executed work was the establishment of a genetic mouse model that allows tumor-specific expression of high levels of the two chemokines of interest. For accomplishing this task, which represents gene therapy of solid tumors, two types of potentially useful carrier cells were evaluated. CD8+ T cells and mesenchymal bone marrow cells to be used in adoptive cell transfers into tumor-bearing mice. Irrespectively of the envisaged immunotherapy, satisfaction of so far unmet needs of gene therapy would be a highly valuable tool that may be employed by many other therapeutic approaches, too. Several transgenic mouse lines were generated as a source of CD8+ T cells modified to express the chemokines of interest. In a double transgenic approach the properties of two T cell-specific promoters were combined using Cre-loxP technology. The granzyme B promoter confers activation-dependency and the lck distal promoter assures strong constitutive expression once the CD8+ T cell has been activated. The constructed transgenes showed a good performance in vivo and mice expressing CCL2 in activated CD8+ T cells were obtained. These cells can now be used with different protocols for adoptively transferring cytotoxic T cells (CTL) into tumor-bearing recipients, thus allowing to study their capacity as tumor-infiltrating chemokine carrier. The establishment of transgenic mice likewisely expressing CXCL13 is expected in the near future. In addition, T cells from generated single transgenic mice that have high expression of an EGFP reporter in both CD4+ and CD8+ cells can be easily traced in vivo when setting up adoptive transfer conditions. The evaluation of mesenchymal bone marrow cells demonstrated that these cells can efficiently engraft into tumor stroma upon local coinjection with tumor cells. This represents a valuable tool for research purposes as it allows to introduce manipulated stromal cells into a tumor model. Therefore, the established engraftment model is suited for studying the envisaged immunotherapy. These results confirm to some extend previously reported results in an improved model, however, the suggested systemic tumor homing efficiency and specificity of mesenchymal bone marrow cells was not observed in our model indicating that these cells may not be suited for therapeutic use. Another major result of the presented work is the establishment oftumor-conditioned in vitro culture of mesenchymal bone marrow cells, which allowed to more rapidly expand these cells while maintaining their tumor homing and engrafting capacities. This offers another valuable tool as in vitro culture is a necessary step for therapeutic manipulations.

Modern statistical models for forensic fingerprint examinations : a critical review

Over the last decade, the development of statistical models in support of forensic fingerprint identification has been the subject of increasing research attention, spurned on recently by commentators who claim that the scientific basis for fingerprint identification has not been adequately demonstrated. Such models are increasingly seen as useful tools in support of the fingerprint identification process within or in addition to the ACE-V framework. This paper provides a critical review of recent statistical models from both a practical and theoretical perspective. This includes analysis of models of two different methodologies: Probability of Random Correspondence (PRC) models that focus on calculating probabilities of the occurrence of fingerprint configurations for a given population, and Likelihood Ratio (LR) models which use analysis of corresponding features of fingerprints to derive a likelihood value representing the evidential weighting for a potential source.

Using 3D surface datasets to understand landslide evolution: From analogue models to real case study

Early detection of landslide surface deformation with 3D remote sensing techniques, as TLS, has become a great challenge during last decade. To improve our understanding of landslide deformation, a series of analogue simulation have been carried out on non-rigid bodies coupled with 3D digitizer. All these experiments have been carried out under controlled conditions, as water level and slope angle inclination. We were able to follow 3D surface deformation suffered by complex landslide bodies from precursory deformation still larger failures. These experiments were the basis for the development of a new algorithm for the quantification of surface deformation using automatic tracking method on discrete points of the slope surface. To validate the algorithm, comparisons were made between manually obtained results and algorithm surface displacement results. Outputs will help in understanding 3D deformation during pre-failure stages and failure mechanisms, which are fundamental aspects for future implementation of 3D remote sensing techniques in early warning systems.

Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data

Background Multiple logistic regression is precluded from many practical applications in ecology that aim to predict the geographic distributions of species because it requires absence data, which are rarely available or are unreliable. In order to use multiple logistic regression, many studies have simulated "pseudo-absences" through a number of strategies, but it is unknown how the choice of strategy influences models and their geographic predictions of species. In this paper we evaluate the effect of several prevailing pseudo-absence strategies on the predictions of the geographic distribution of a virtual species whose "true" distribution and relationship to three environmental predictors was predefined. We evaluated the effect of using a) real absences b) pseudo-absences selected randomly from the background and c) two-step approaches: pseudo-absences selected from low suitability areas predicted by either Ecological Niche Factor Analysis: (ENFA) or BIOCLIM. We compared how the choice of pseudo-absence strategy affected model fit, predictive power, and information-theoretic model selection results. Results Models built with true absences had the best predictive power, best discriminatory power, and the "true" model (the one that contained the correct predictors) was supported by the data according to AIC, as expected. Models based on random pseudo-absences had among the lowest fit, but yielded the second highest AUC value (0.97), and the "true" model was also supported by the data. Models based on two-step approaches had intermediate fit, the lowest predictive power, and the "true" model was not supported by the data. Conclusion If ecologists wish to build parsimonious GLM models that will allow them to make robust predictions, a reasonable approach is to use a large number of randomly selected pseudo-absences, and perform model selection based on an information theoretic approach. However, the resulting models can be expected to have limited fit.

Designing business models of cloud platforms

Cloud computing and its three facets (Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS)) are terms that denote new developments in the software industry. In particular, PaaS solutions, also referred to as cloud platforms, are changing the way software is being produced, distributed, consumed, and priced. Software vendors have started considering cloud platforms as a strategic option but are battling to redefine their offerings to embrace PaaS. In contrast to SaaS and IaaS, PaaS allows for value co-creation with partners to develop complementary components and applications. It thus requires multisided business models that bring together two or more distinct customer segments. Understanding how to design PaaS business models to establish a flourishing ecosystem is crucial for software vendors. This doctoral thesis aims to address this issue in three interrelated research parts. First, based on case study research, the thesis provides a deeper understanding of current PaaS business models and their evolution. Second, it analyses and simulates consumers' preferences regarding PaaS business models, using a conjoint approach to find out what determines the choice of cloud platforms. Finally, building on the previous research outcomes, the third part introduces a design theory for the emerging class of PaaS business models, which is grounded on an extensive action design research study with a large European software vendor. Understanding PaaS business models from a market as well as a consumer perspective will, together with the design theory, inform and guide decision makers in their business model innovation plans. It also closes gaps in the research related to PaaS business model design and more generally related to platform business models.

Additive functions in boolean models of gene regulatory network modules.

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.