Dimensionality of the Rosenberg self-esteem scale and its relationships with the three- and five-factor personality models

The authors investigated the dimensionality of the French version of the Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1965) using confirmatory factor analysis. We tested models of 1 or 2 factors. Results suggest the RSES is a 1-dimensional scale with 3 highly correlated items. Comparison with the Revised NEO-Personality Inventory (NEO-PI-R; Costa, McCrae, & Rolland, 1998) demonstrated that Neuroticism correlated strongly and Extraversion and Conscientiousness moderately with the RSES. Depression accounted for 47% of the variance of the RSES. Other NEO-PI-R facets were also moderately related with self-esteem.

Measuring the Economic Significance of Structural Exchange Rate Models

This paper examines both the in-sample and out-of-sample performance of three monetary fundamental models of exchange rates and compares their out-of-sample performance to that of a simple Random Walk model. Using a data-set consisting of five currencies at monthly frequency over the period January 1980 to December 2009 and a battery of newly developed performance measures, the paper shows that monetary models do better (in-sample and out-of-sample forecasting) than a simple Random Walk model.

Stability of Growth Models with Generalised Lag Structures

This paper considers the lag structures of dynamic models in economics, arguing that the standard approach is too simple to capture the complexity of actual lag structures arising, for example, from production and investment decisions. It is argued that recent (1990s) developments in the the theory of functional differential equations provide a means to analyse models with generalised lag structures. The stability and asymptotic stability of two growth models with generalised lag structures are analysed. The paper concludes with some speculative discussion of time-varying parameters.

Forward Looking and Myopic Regional Computable General Equilibrium Models. How Significant is the Distinction?

We present a stylized intertemporal forward-looking model able that accommodates key regional economic features, an area where the literature is not well developed. The main difference, from the standard applications, is the role of saving and its implication for the balance of payments. Though maintaining dynamic forward-looking behaviour for agents, the rate of private saving is exogenously determined and so no neoclassical financial adjustment is needed. Also, we focus on the similarities and the differences between myopic and forward-looking models, highlighting the divergences among the main adjustment equations and the resulting simulation outcomes.

Nano-electro-mechanical in dynamical cellular processes

Report for the scientific sojourn carried out at the Department of Structure and Constituents of Matter during 2007.The main focus of the work was on phenomena related to nano-electromechanical processes that take place on a cellular level. Additionally, it has also been performed independent work related to charge and energy transfer in bio molecules, energy transfer in coupled spin systems as well as electrodynamics of nonlinear metamaterials.

No Good Deals - No Bad Models

Faced with the problem of pricing complex contingent claims, an investor seeks to make his valuations robust to model uncertainty. We construct a notion of a model- uncertainty-induced utility function and show that model uncertainty increases the investor's eff ective risk aversion. Using the model-uncertainty-induced utility function, we extend the \No Good Deals" methodology of Cochrane and Sa a-Requejo [2000] to compute lower and upper good deal bounds in the presence of model uncertainty. We illustrate the methodology using some numerical examples.

Cardiac expression of ms1/STARS, a novel gene involved in cardiac development and disease, is regulated by GATA4.

Ms1/STARS is a novel muscle-specific actin-binding protein that specifically modulates the myocardin-related transcription factor (MRTF)-serum response factor (SRF) regulatory axis within striated muscle. This ms1/STARS-dependent regulatory axis is of central importance within the cardiac gene regulatory network and has been implicated in cardiac development and postnatal cardiac function/homeostasis. The dysregulation of ms1/STARS is associated with and causative of pathological cardiac phenotypes, including cardiac hypertrophy and cardiomyopathy. In order to gain an understanding of the mechanisms governing ms1/STARS expression in the heart, we have coupled a comparative genomic in silico analysis with reporter, gain-of-function, and loss-of-function approaches. Through this integrated analysis, we have identified three evolutionarily conserved regions (ECRs), α, SINA, and DINA, that act as cis-regulatory modules and confer differential cardiac cell-specific activity. Two of these ECRs, α and DINA, displayed distinct regulatory sensitivity to the core cardiac transcription factor GATA4. Overall, our results demonstrate that within embryonic, neonatal, and adult hearts, GATA4 represses ms1/STARS expression with the pathologically associated depletion of GATA4 (type 1/type 2 diabetic models), resulting in ms1/STARS upregulation. This GATA4-dependent repression of ms1/STARS expression has major implications for MRTF-SRF signaling in the context of cardiac development and disease.

Identification of particular groups of microRNAs that positively or negatively impact on beta cell function in obese models of type 2 diabetes.

AIMS/HYPOTHESIS: MicroRNAs are key regulators of gene expression involved in health and disease. The goal of our study was to investigate the global changes in beta cell microRNA expression occurring in two models of obesity-associated type 2 diabetes and to assess their potential contribution to the development of the disease. METHODS: MicroRNA profiling of pancreatic islets isolated from prediabetic and diabetic db/db mice and from mice fed a high-fat diet was performed by microarray. The functional impact of the changes in microRNA expression was assessed by reproducing them in vitro in primary rat and human beta cells. RESULTS: MicroRNAs differentially expressed in both models of obesity-associated type 2 diabetes fall into two distinct categories. A group including miR-132, miR-184 and miR-338-3p displays expression changes occurring long before the onset of diabetes. Functional studies indicate that these expression changes have positive effects on beta cell activities and mass. In contrast, modifications in the levels of miR-34a, miR-146a, miR-199a-3p, miR-203, miR-210 and miR-383 primarily occur in diabetic mice and result in increased beta cell apoptosis. These results indicate that obesity and insulin resistance trigger adaptations in the levels of particular microRNAs to allow sustained beta cell function, and that additional microRNA deregulation negatively impacting on insulin-secreting cells may cause beta cell demise and diabetes manifestation. CONCLUSIONS/INTERPRETATION: We propose that maintenance of blood glucose homeostasis or progression toward glucose intolerance and type 2 diabetes may be determined by the balance between expression changes of particular microRNAs.

Model Switching and Model Averaging in Time- Varying Parameter Regression Models

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

Identificació de noves dianes terapèutiques en neoplàsies limfoides

El limfoma de cèl•lules de mantell (LCM) és un limfoma de cèl•lules B incurable que presenta sobreexpressió de ciclina D1. Això fa necessari el desenvolupament de noves teràpies. Els gens supressors de tumors estan alterats en càncer pel silenciament epigenètic aberrant, com a conseqüència de la desacetilació de les histones dels seus promotors. Els inhibidors de les desacetilases d'histones (HDACi) són nous compostos amb resultats prometedors per al tractament de tumors. L'objectiu principal, i que ha durat 7 mesos, va ser analitzar l'activitat antitumoral de l'àcid hidroxàmic suberoilanílid (SAHA, vorinostat), un HDACi en fase d'assajos clínics per al tractament de varis tumors, en cèl•lules de LCM. Es va analitzar la sensibilitat al SAHA (Merck Pharmaceuticals) en nou línies cel•lulars humanes de LCM, que es diferenciaven en les alteracions genètiques, les característiques replicatives i la sensibilitat als fàrmacs; i cèl•lules primàries de 6 pacients. El SAHA va presentar un efecte citotòxic heterogeni amb DL50 (Dosi Letal 50) de 3.25 μM a &25 μM amb 24 d'incubació. Aquest efecte citotòxic s'incrementava notablement després de 48 hores d'incubació assolint una DL50 de 0.34 a 5.69 μM. Cal destacar que 5 dels 6 casos de les mostres primàries de LCM van mostrar una elevada sensibilitat (DL50 & 8.07 μM). A nivell mecanistic, el SAHA va augmentar l'acetilació de les histones H3 i H4, i va disminuir els nivells de proteïna de la ciclina D1 i c-Flip. La citometria de flux i els anàlisis per Western Blot van posar de manifest que l'efecte citotòxic del SAHA es dóna a través de l'activació de la via mitocondrial de mort cel•lular i la cascada de caspases. El SAHA indueix l'expressió transcripcional de la proteïna proapoptòtica Bmf. Aquests resultats suggereixen que el SAHA podria ser una nova teràpia prometedora per al tractament del LCM.

Model Uncertainty in Panel Vector Autoregressive Models.

We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities across cross-sectional units. The resulting BMA framework can find a parsimonious PVAR specification, thus dealing with overparameterization concerns. We use these methods in an application involving the euro area sovereign debt crisis and show that our methods perform better than alternatives. Our findings contradict a simple view of the sovereign debt crisis which divides the euro zone into groups of core and peripheral countries and worries about financial contagion within the latter group.

Model Uncertainty in Panel Vector Autoregressive Models.

We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities across cross-sectional units. The resulting BMA framework can find a parsimonious PVAR specification, thus dealing with overparameterization concerns. We use these methods in an application involving the euro area sovereign debt crisis and show that our methods perform better than alternatives. Our findings contradict a simple view of the sovereign debt crisis which divides the euro zone into groups of core and peripheral countries and worries about financial contagion within the latter group.

The convenient calculation of some test statistics in models of discrete choice

The paper considers the use of artificial regression in calculating different types of score test when the log

On hyperbolic once-punctured-torus bundles IV: automata for lightning curves

"Vegeu el resum a l'inici del document del fitxer adjunt."

Profile of a serial killer: cellular and molecular approaches to study individual cytotoxic T-cells following therapeutic vaccination.

T-cell vaccination may prevent or treat cancer and infectious diseases, but further progress is required to increase clinical efficacy. Step-by-step improvements of T-cell vaccination in phase I/II clinical studies combined with very detailed analysis of T-cell responses at the single cell level are the strategy of choice for the identification of the most promising vaccine candidates for testing in subsequent large-scale phase III clinical trials. Major aims are to fully identify the most efficient T-cells in anticancer therapy, to characterize their TCRs, and to pinpoint the mechanisms of T-cell recruitment and function in well-defined clinical situations. Here we discuss novel strategies for the assessment of human T-cell responses, revealing in part unprecedented insight into T-cell biology and novel structural principles that govern TCR-pMHC recognition. Together, the described approaches advance our knowledge of T-cell mediated-protection from human diseases.