Pròtesi invertida d’espatlla: estudi retrospectiu del conflicte escapular en 2 models diferents de pròtesis

Aquest és un estudi retrospectiu que compara la mobilitat i el conflicto escàpulo-humeral entre 2 models diferents de pròtesi invertida d’espatlla. Aquestes pròtesis s’han implantat en pacients amb ruptures del manegot dels rotadors irreparables. Aquesta cirugía no està exenta de complicacions, i una de les més habituals és el conflicto escàpulo-humeral o notch.

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.

Mammalian genes are transcribed with widely different bursting kinetics.

In prokaryotes and eukaryotes, most genes appear to be transcribed during short periods called transcriptional bursts, interspersed by silent intervals. We describe how such bursts generate gene-specific temporal patterns of messenger RNA (mRNA) synthesis in mammalian cells. To monitor transcription at high temporal resolution, we established various gene trap cell lines and transgenic cell lines expressing a short-lived luciferase protein from an unstable mRNA, and recorded bioluminescence in real time in single cells. Mathematical modeling identified gene-specific on- and off-switching rates in transcriptional activity and mean numbers of mRNAs produced during the bursts. Transcriptional kinetics were markedly altered by cis-regulatory DNA elements. Our analysis demonstrated that bursting kinetics are highly gene-specific, reflecting refractory periods during which genes stay inactive for a certain time before switching on again.

Therapeutic switching: from antidermatophytic essential oils to new leishmanicidal products

This study examined whether the antidermatophytic activity of essential oils (EOs) can be used as an indicator for the discovery of active natural products against Leishmania amazonensis. The aerial parts of seven plants were hydrodistilled. Using broth microdilution techniques, the obtained EOs were tested against three strains of dermatophytes (Trichophyton mentagrophytes, Microsporum gypseum and Microsporum canis). To compare the EOs antifungal and antiparasitic effects, the EOs activities against axenic amastigotes of L. amazonensis were concurrently evaluated. For the most promising EOs, their antileishmanial activities against parasites infecting peritoneal macrophages of BALB/c mice were measured. The most interesting antifungal candidates were the EOs from Cymbopogon citratus, Otacanthus azureus and Protium heptaphyllum, whereas O. azureus, Piper hispidum and P. heptaphyllum EOs exhibited the lowest 50% inhibitory concentration (IC50) values against axenic amastigotes, thus revealing a certain correspondence between both activities. The P. hispidum EO was identified as the most promising product in the results from the infected macrophages model (IC50: 4.7 µg/mL, safety index: 8). The most abundant compounds found in this EO were sesquiterpenes, notably curzerene and furanodiene. Eventually, the evaluation of the antidermatophytic activity of EOs appears to be an efficient method for identifying new potential drugs for the treatment of L. amazonensis.

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.

CNS safety at 48-week of switching to ATV/r plus 3TC or two nucleos(t)ides in HIV-suppressed patients on stable ART: the SALT neurocognitive sub-study.

INTRODUCTION Due to their low CNS penetrance, there are concerns about the capacity of non-conventional PI-based ART (monotherapy and dual therapies) to preserve neurocognitive performance (NP). METHODS We evaluated the NP change of aviremic participants of the SALT clinical trial (1) switching therapy to dual therapy (DT: ATV/r+3TC) or triple therapy (TT: ATV/r+2NRTI) who agreed to perform an NP assessment (NPZ-5) at baseline and W48. Neurocognitive impairment and NP were assessed using AAN-2007 criteria (2) and global deficit scores (GDS) (3). Neurocognitive change (GDS change: W48 - baseline) and the effect of DT on NP evolution crude and adjusted by significant confounders were determined using ANCOVA. RESULTS A total of 158 patients were included (Table 1). They had shorter times because HIV diagnosis, ART initiation and HIV-suppression and their virologic outcome at W48 by snapshot was higher (79.1% vs 72.7%; p=0.04) compared to the 128 patients not included in the sub-study. By AAN-2007 criteria, 51 patients in each ART group (68% vs 63%) were neurocognitively impaired at baseline (p=0.61). Forty-seven patients were not reassessed at W48: 30 lost of follow-up (16 DT-14 TT) and 17 had non-evaluable data (6 DT-11 TT). Patients retested were more likely to be men (78.9% vs 61.4%) and had neurological cofounders (9.6% vs 0%) than patients non-retested. At W48, 3 out of 16 (5.7%) patients on DT and 6 out of 21 (10.5%) on TT who were non-impaired at baseline became impaired (p=0.49) while 10 out of 37 (18.9%) on DT and 7 out of 36 (12.3%) on TT who were neurocognitively impaired at baseline became non-impaired (p=0.44). Mean GDS changes (95% CI) were: Overall -0.2 (-0.3 to -0.04): DT -0.26 (-0.4 to -0.07) and TT -0.08 (-0.2 to 0.07). NP was similar between DT and TT (0.15). This absence of differences was also observed in all cognitive tests. Effect of DT: -0.16 [-0.38 to 0.06]) (r(2)=0.16) on NP evolution was similar to TT (reference), even after adjusting (DT: -0.11 [-0.33 to 0.1], TT: reference) by significant confounders (geographical origin, previous ATV use and CD4 cell count) (r(2)=0.25). CONCLUSIONS NP stability was observed after 48 weeks of follow up in the majority of patients whether DT or TT was used to maintain HIV-suppression. Incidence rates of NP impairment or NP impairment recovery were also similar between DT and TT.

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.

Lipid changes in HIV-patients switching to the coformulated single tablet FTC/RPV/TDF (Eviplera®). Efficacy and safety analysis. GeSida Study 8114.

INTRODUCTION Rilpivirine (RPV) has a better lipid profile than efavirenz (EFV) in naïve patients (1). Switching to RPV may be convenient for many patients, while maintaining a good immunovirological control (2). The aim of this study was to analyze lipid changes in HIV-patients at 24 weeks after switching to Eviplera® (emtricitabine/RPV/tenofovir disoproxil fumarate [FTC/RPV/TDF]). MATERIALS AND METHODS Retrospective, multicentre study of a cohort of asymptomatic HIV-patients who switched from a regimen based on 2 nucleoside reverse transcriptase inhibitors (NRTI)+protease inhibitor (PI)/non nucleoside reverse transcriptase inhibitor (NNRTI) or ritonavir boosted PI monotherapy to Eviplera® during February-December, 2013; all had undetectable HIV viral load for ≥3 months prior to switching. Patients with previous failures on antiretroviral therapy (ART) including TDF and/or FTC/3TC, with genotype tests showing resistance to components of Eviplera®, or who had changed the third drug of the ART during the study period were excluded. Changes in lipid profile and cardiovascular risk (CVR), and efficacy and safety at 24 weeks were analyzed. RESULTS Among 305 patients included in the study, 298 were analyzed (7 cases were excluded due to lack of data). Men 81.2%, mean age 44.5 years, 75.8% of HIV sexually transmitted. 233 (78.2%) patients switched from a regimen based on 2 NRTI+NNRTI (90.5% EFV/FTC/TDF). The most frequent reasons for switching were central nervous system (CNS) adverse events (31.0%), convenience (27.6%) and metabolic disorders (23.2%). At this time, 293 patients have reached 24 weeks: 281 (95.9%) have continued Eviplera®, 6 stopped it (3 adverse events, 2 virologic failures, 1 discontinuation) and 6 have been lost to follow up. Lipid profiles of 283 cases were available at 24 weeks and mean (mg/dL) baseline vs 24 weeks are: total cholesterol (193 vs 169; p=0.0001), HDL-c (49 vs 45; p=0.0001), LDL-c (114 vs 103; p=0.001), tryglycerides (158 vs 115; p=0.0001), total cholesterol to HDL-c ratio (4.2 vs 4.1; p=0.3). CVR decreased (8.7 vs 7.5%; p= 0.0001). CD4 counts were similar to baseline (653 vs 674 cells/µL; p=0.08), and 274 (96.8%) patients maintained viral suppression. CONCLUSIONS At 24 weeks after switching to Eviplera®, lipid profile and CVR improved while maintaining a good immunovirological control. Most subjects switched to Eviplera® from a regimen based on NNRTI, mainly EFV/FTC/TDF. CNS adverse events, convenience and metabolic disorders were the most frequent reasons for switching.

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.

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