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.

Stability of cascade networks via fluid models

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Study and implementation of some quantitative trading models

Quantitative or algorithmic trading is the automatization of investments decisions obeying a fixed or dynamic sets of rules to determine trading orders. It has increasingly made its way up to 70% of the trading volume of one of the biggest financial markets such as the New York Stock Exchange (NYSE). However, there is not a signi cant amount of academic literature devoted to it due to the private nature of investment banks and hedge funds. This projects aims to review the literature and discuss the models available in a subject that publications are scarce and infrequently. We review the basic and fundamental mathematical concepts needed for modeling financial markets such as: stochastic processes, stochastic integration and basic models for prices and spreads dynamics necessary for building quantitative strategies. We also contrast these models with real market data with minutely sampling frequency from the Dow Jones Industrial Average (DJIA). Quantitative strategies try to exploit two types of behavior: trend following or mean reversion. The former is grouped in the so-called technical models and the later in the so-called pairs trading. Technical models have been discarded by financial theoreticians but we show that they can be properly cast into a well defined scientific predictor if the signal generated by them pass the test of being a Markov time. That is, we can tell if the signal has occurred or not by examining the information up to the current time; or more technically, if the event is F_t-measurable. On the other hand the concept of pairs trading or market neutral strategy is fairly simple. However it can be cast in a variety of mathematical models ranging from a method based on a simple euclidean distance, in a co-integration framework or involving stochastic differential equations such as the well-known Ornstein-Uhlenbeck mean reversal ODE and its variations. A model for forecasting any economic or financial magnitude could be properly defined with scientific rigor but it could also lack of any economical value and be considered useless from a practical point of view. This is why this project could not be complete without a backtesting of the mentioned strategies. Conducting a useful and realistic backtesting is by no means a trivial exercise since the \laws" that govern financial markets are constantly evolving in time. This is the reason because we make emphasis in the calibration process of the strategies' parameters to adapt the given market conditions. We find out that the parameters from technical models are more volatile than their counterpart form market neutral strategies and calibration must be done in a high-frequency sampling manner to constantly track the currently market situation. As a whole, the goal of this project is to provide an overview of a quantitative approach to investment reviewing basic strategies and illustrating them by means of a back-testing with real financial market data. The sources of the data used in this project are Bloomberg for intraday time series and Yahoo! for daily prices. All numeric computations and graphics used and shown in this project were implemented in MATLAB^R scratch from scratch as a part of this thesis. No other mathematical or statistical software was used.

Modelling world investment markets using threshold conditional correlation models

In this paper we propose a parsimonious regime-switching approach to model the correlations between assets, the threshold conditional correlation (TCC) model. This method allows the dynamics of the correlations to change from one state (or regime) to another as a function of observable transition variables. Our model is similar in spirit to Silvennoinen and Teräsvirta (2009) and Pelletier (2006) but with the appealing feature that it does not suffer from the course of dimensionality. In particular, estimation of the parameters of the TCC involves a simple grid search procedure. In addition, it is easy to guarantee a positive definite correlation matrix because the TCC estimator is given by the sample correlation matrix, which is positive definite by construction. The methodology is illustrated by evaluating the behaviour of international equities, govenrment bonds and major exchange rates, first separately and then jointly. We also test and allow for different parts in the correlation matrix to be governed by different transition variables. For this, we estimate a multi-threshold TCC specification. Further, we evaluate the economic performance of the TCC model against a constant conditional correlation (CCC) estimator using a Diebold-Mariano type test. We conclude that threshold correlation modelling gives rise to a significant reduction in portfolio´s variance.

Large-scale analysis of orthologs and paralogs under covarion-like and constant-but-different models of amino acid evolution.

Functional divergence between homologous proteins is expected to affect amino acid sequences in two main ways, which can be considered as proxies of biochemical divergence: a "covarion-like" pattern of correlated changes in evolutionary rates, and switches in conserved residues ("conserved but different"). Although these patterns have been used in case studies, a large-scale analysis is needed to estimate their frequency and distribution. We use a phylogenomic framework of animal genes to answer three questions: 1) What is the prevalence of such patterns? 2) Can we link such patterns at the amino acid level with selection inferred at the codon level? 3) Are patterns different between paralogs and orthologs? We find that covarion-like patterns are more frequently detected than "constant but different," but that only the latter are correlated with signal for positive selection. Finally, there is no obvious difference in patterns between orthologs and paralogs.

Reaching for the Holy Grail: insights from infection/cure models on the prospects for vaccines for Trypanosoma cruziinfection

Prevention of Trypanosoma cruzi infection in mammals likely depends on either prevention of the invading trypomastigotes from infecting host cells or the rapid recognition and killing of the newly infected cells byT. cruzi-specific T cells. We show here that multiple rounds of infection and cure (by drug therapy) fails to protect mice from reinfection, despite the generation of potent T cell responses. This disappointing result is similar to that obtained with many other vaccine protocols used in attempts to protect animals from T. cruziinfection. We have previously shown that immune recognition ofT. cruziinfection is significantly delayed both at the systemic level and at the level of the infected host cell. The systemic delay appears to be the result of a stealth infection process that fails to trigger substantial innate recognition mechanisms while the delay at the cellular level is related to the immunodominance of highly variable gene family proteins, in particular those of the trans-sialidase family. Here we discuss how these previous studies and the new findings herein impact our thoughts on the potential of prophylactic vaccination to serve a productive role in the prevention of T. cruziinfection and Chagas disease.

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Neuronal in vitro models for the estimation of acute systemic toxicity.

The objective of the EU funded integrated project "ACuteTox" is to develop a strategy in which general cytotoxicity, together with organ-specific endpoints and biokinetic features, are taken into consideration in the in vitro prediction of oral acute systemic toxicity. With regard to the nervous system, the effects of 23 reference chemicals were tested with approximately 50 endpoints, using a neuronal cell line, primary neuronal cell cultures, brain slices and aggregated brain cell cultures. Comparison of the in vitro neurotoxicity data with general cytotoxicity data generated in a non-neuronal cell line and with in vivo data such as acute human lethal blood concentration, revealed that GABA(A) receptor function, acetylcholine esterase activity, cell membrane potential, glucose uptake, total RNA expression and altered gene expression of NF-H, GFAP, MBP, HSP32 and caspase-3 were the best endpoints to use for further testing with 36 additional chemicals. The results of the second analysis showed that no single neuronal endpoint could give a perfect improvement in the in vitro-in vivo correlation, indicating that several specific endpoints need to be analysed and combined with biokinetic data to obtain the best correlation with in vivo acute toxicity.

Soissons, vue d'ensemble de la cathédrale [Saint-Gervais-et-Saint-Protais, dégâts dus à la guerre] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 55115

The role of dentritic cells in leishmania infection : use of novel DC lines for the development of monoclonal antibodies

Les cellules dendritiques (DCs) sont des cellules multifonctionnelles qui font le lien entre le sytème immunitaire inné et adaptatif chez les mammifères. Il existe plusieurs sous-types de DCs basés sur leurs fonctions et l'endroit où elles se situent dans le corps. Dans le cadre de cette thèse, nous avons étudié le rôle de ces cellules face à une infection parasitaire. La Leishmania est un parasite causant une maladie appelée Leishmaniose, maladie endémique de l'Afrique, de l'Asie et de certaines régions de l'Amérique du Sud. Certaines espèces causent des lésions cutanées, alors que d'autres causent des lésions dans les muqueuses ou dans les organes internes. Le système immunitaire répond en générant une réponse inflammatoire qui élimine l'infection. Lors d'une réponse non-inflammatoire (de type cytokines, chemokines), cela va amener à une persistance du parasite sur le long terme. Les DC s'activant en présence du parasite dans la peau, vont le transporter vers un ganglion. A cet endroit, se trouvent différents sous-types de DC qui ont la particularité de présenter l'antigène (spécifique à la Leishmaniose) aux lymphocytes T, ce qui va alors amener à une réponse immunitaire puissante contre le parasite. Nous avons comparé différentes espèces de Leishmaniose dans leur façon d'activer les DC et différents modèles de souris ont été utilisé dans ce but-là. Les souris du type C57BL/6 sont connues pour être résistantes à L. major et sensibles à L. mexicana, alors qu'au contraire, les souris Balb/c sont connues pour être sensibles à ces deux espèces. En utilisant des parasites fluorescents transgéniques, nous avons comparé ces deux espèces de parasites (L. major et L. mexicana) en recherchant quelles cellules elles sont capables d'infecter in-vivo dans un modèle murin. Le rôle général des DC dans une infection à L. major a déjà été décrit. Dans notre étude, nous avons étudié le besoin en DC CD8a+ dans les ganglions afin d'engendrer une réponse face à une infection à L. major. Les souris qui n'ont pas ce sous-type de DC sont beaucoup plus sensibles à l'infection : elles ont des marqueurs inflammatoires plus bas et des lésions plus grandes. Nous avons également remarqué que les DC CD8a+ jouent un rôle crucial dans une phase plus avancée de l'infection. Dans notre laboratoire, nous avons la chance d'avoir une source illimitée de DCs de sous-type CD8a+ provenant d'une souris génétiquement modifiée par nos soin. Grâce à cela, nous avons utilisé ces cellules CD8a+ pour immuniser des rats afin de produire des anticorps monoclonaux ayant des propriétés spécifiques comme l'identification de protéines uniques présentes à la surface des DC et qui ensuite, modulent une réponse immunitaire in-vivo. Nous sommes actuellement en phase de caractérisation de plus de 750 hybridomes générés dans notre laboratoire. - Les cellules dendritiques (DCs) constituent le lien entre le système inné et adaptatif de la réponse immunitaire, car elles sont capables de présenter l'antigène, de donner la co- stimulation et de relâcher des cytokines et chimokines. Au cours de cette thèse, nous avons exploré différentes familles de DC lors d'infections parasitaires, telles que la Leishmaniose, parasite intracellulaire qui infecte les mammifères. La plupart des lésions cutanées résistantes sont caractérisées par une réponse pro-inflammatoire générée par l'IL-12. A l'inverse, pour la forme non résistante, la réponse est générée par l'IL-4 et l'IL-10, dans les modèles murins vulnérables. L'infection avec Lmajor a été caractérisée chez la souris C57BL/6 (Thl) et chez la souris Balb/c (Th2). Chez la souris C57BL/6 la lésion guérit, alors que chez la souris Balb/c, la lésion est au contraire non-cicatrisante. Nous avons comparé l'activation causée dans l'ensemble des DC par différentes espéces de Leishmania, et plus spécifiquement dans les DC CD8a+ présentes dans les ganglions lymphatiques et leur rôle dans la vulnérabilité à L. major. Ces cellules sont spécialisées dans la présentation croisée d'antigènes exogènes par le CMH-I et le haut taux de production d'IL-12 après activation. En utilisant des DC dérivées de moelle osseuse, nous avons constaté que L. guyanensis V+ (transportant un retrovirus) était le plus efficace pour l'activation des DC in-vitro comparé à L. major, L. mexicana et L. guyanensis (V-). Toutefois, in-vivo, les souris infectées avec L. major ont vu la taille de leur ganglions lymphatiques drainants augmentée, 3-6 semaines après l'infection dans les deux espèces de souris (les C57BL/6 résistantes et les Balb/c sensibles). En utilisant un parasite fluorescent transgénique, nous avons trouvé que les souris C57BL/6 sensibles à Lmexicana ont un nombre plus important de cellules Β infectées et un plus petit nombre de DC dérivées des monocytes inflammatoires, comparé au souris infectées avec L. major. Les conséquences de ces observations sont encore à l'étude. Des souris déficientes en CD8ct+DC et CD103+ sont plus sensibles à L. major que les souris WT: leurs lésions sont plus grandes et la charge parasitaire est plus importante. Nous avons généré une chimère de moelles osseuse CD11-DTR et Batf3-/- en mélangeant les moelles de ces deux souris, afin de déterminer le temps après infection où le manque de DC's CD8a+ contribue le plus à l'augmentation de la vulnérabilité chez la souris KO. Ces souris produisent plus d'IgG1 et IgE, font une réponse Th2 plus forte et Thl moins forte. Nous avons constaté que les souris déficientes en DC CD8a+ au début de la réponse immunitaire adaptive (trois semaines après injection) maintiennent un haut taux de lésions de grande taille, semblable à celui des souris chez qui les cellules ont été déplétées avant l'injection. Cela indique que les DC CD8a+ sont nécessaires pour l'efficacité de l'immunité dans la phase chronique de l'infection à L. major. Parallèlement à cela, nous avons aussi commencé une génération d'anticorps monoclonaux dirigés contre les DC CD8a+ activés en utilisant des souches établies dans notre laboratoire. En partant d'une librairie de 763 hybridomes, nous avons identifié plusieurs clones dignes d'intérêt avec une capacité fonctionnelle à moduler la prolifération et la sécrétion de cytokines des cellules T, ainsi que les molécules de co-stimulation présentes à la surface des DC activées elle-même. - Dendritic cells (DCs) are the bridge between the innate and the adaptive arms of the immune systems. They are professional antigen presentation cells and have important cytokine/chemokine release functions. In this dissertation we have focussed on the study of the different subsets of DCs in parasitic infection immunity. Leishmania are intra-cellular parasites of many different species that infect mammals. Most cutaneous lesions that are self- healing are characterized with a pro-inflammatory response with IL-12 while high levels of cytokines such as IL-4 and IL-10 characterized in susceptible mouse models. In mice L. major infection has been well characterized in C57BL/6 mice (Thl) that form healing lesions while Balb/c mice (Th2) form non-healing lesions. This thesis is focussed on comparing DC activation at large by different strains of Leishmania and more specifically, dLN resident CD8a+ DCs and their role in L. major susceptibility. This subset is specialized in cross- presentation of exogenous antigens in the MHC-I pathway and produce high levels of EL-12. Using bone marrow derived DCs we found that L. guyanensis V+ (carrying a retro-virus) was the most efficient at activating DCs in-vitro. In-vivo however L. major infected mice had the largest dLNs 3-6 weeks after infection in both genetically resistant C57BL/6 and susceptible Balb/c mice. Using transgenic fluorescent parasites, we found that C57BL/6 mice which are susceptible to L. mexicana had more number of infected Β cells and fewer number of infected inflammatory monocyte derived DCs in contrast to L. major infection. Using mice deficient in CD8a+ DCs, we found that these mice were more susceptible to L. major than their WT counterparts. They made larger lesions, had higher parasite burdens, higher levels of Th2 indicating immunolgloblins as measured by higher serie IgE levels and lower CD4+ IFNy+ cells. A mixed bone marrow chimera system of CDllc-DTR and Batf3~'~ was generated to determine the time point at which the lack of CD8a+ DCs most contributes to the increased susceptibility in KO mice. We found that mice depleted of CD8a+ DCs at the advent of the adaptive response (3 weeks after infection) maintained the significantly higher lesion size similar to mice whose cells were depleted from the onset of infection. This indicates that CD8a+ DCs are required for effective immunity in the chronic phase of L. major infection. We also began the generation of a valuable tool of monoclonal antibodies against activated CD8a+ DCs using our in-house DC line. From a library of 763 hybridomas we have identified several interesting clones with a functional ability to modulate Τ cell proliferation and cytokine secretion as well as down-modulating co-stimulatory molecules on activated DC cells themselves.

Adapting global conservation strategies to climate change at the European scale: the otter as a flagship species

Climate change has created the need for new strategies in conservation planning that account for the dynamics of factors threatening endangered species. Here we assessed climate change threat to the European otter, a flagship species for freshwater ecosystems, considering how current conservation areas will perform in preserving the species in a climatically changed future. We used an ensemble forecasting approach considering six modelling techniques applied to eleven subsets of otter occurrences across Europe. We performed a pseudo-independent and an internal evaluation of predictions. Future projections of species distribution were made considering the A2 and B2 scenarios for 2080 across three climate models: CCCMA-CGCM2, CSIRO-MK2 and HCCPR HAD-CM3. The current and the predicted otter distributions were used to identify priority areas for the conservation of the species, and overlapped to existing network of protected areas. Our projections show that climate change may profoundly reshuffle the otter's potential distribution in Europe, with important differences between the two scenarios we considered. Overall, the priority areas for conservation of the otter in Europe appear to be unevenly covered by the existing network of protected areas, with the current conservation efforts being insufficient in most cases. For a better conservation, the existing protected areas should be integrated within a more general conservation and management strategy incorporating climate change projections. Due to the important role that the otter plays for freshwater habitats, our study further highlights the potential sensitivity of freshwater habitats in Europe to climate change.

Two-sided learning in new keynesian models: dynamics, (lack of) convergence and the value of information

This paper investigates the role of learning by private agents and the central bank (two-sided learning) in a New Keynesian framework in which both sides of the economy have asymmetric and imperfect knowledge about the true data generating process. We assume that all agents employ the data that they observe (which may be distinct for different sets of agents) to form beliefs about unknown aspects of the true model of the economy, use their beliefs to decide on actions, and revise these beliefs through a statistical learning algorithm as new information becomes available. We study the short-run dynamics of our model and derive its policy recommendations, particularly with respect to central bank communications. We demonstrate that two-sided learning can generate substantial increases in volatility and persistence, and alter the behavior of the variables in the model in a signifficant way. Our simulations do not converge to a symmetric rational expectations equilibrium and we highlight one source that invalidates the convergence results of Marcet and Sargent (1989). Finally, we identify a novel aspect of central bank communication in models of learning: communication can be harmful if the central bank's model is substantially mis-specified