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.

Dynamin-SNARE interactions control trans-SNARE formation in intracellular membrane fusion.

The fundamental processes of membrane fission and fusion determine size and copy numbers of intracellular organelles. Although SNARE proteins and tethering complexes mediate intracellular membrane fusion, fission requires the presence of dynamin or dynamin-related proteins. Here we study these reactions in native yeast vacuoles and find that the yeast dynamin homologue Vps1 is not only an essential part of the fission machinery, but also controls membrane fusion by generating an active Qa SNARE-tethering complex pool, which is essential for trans-SNARE formation. Our findings provide new insight into the role of dynamins in membrane fusion by directly acting on SNARE proteins.

Peptide substrate specificity of the membrane-bound metalloprotease of Leishmania.

The promastigote surface protease (PSP) of Leishmania is a neutral membrane-bound zinc enzyme. The protease has no exopeptidase activity and does not cleave a large selection of substrates with chromogenic and fluorogenic leaving groups at the P1' site. The substrate specificity of the enzyme was studied by using natural and synthetic peptides of known amino acid sequence. The identification of 11 cleavage sites indicates that the enzyme preferentially cleaves peptides at the amino side when hydrophobic residues are in the P1' site and basic amino acid residues in the P2' and P3' sites. In addition, tyrosine residues are commonly found at the P1 site. Hydrolysis is not, however, restricted to these residues. These results have allowed the synthesis of a model peptide, H2N-L-I-A-Y-L-K-K-A-T-COOH, which is cleaved by PSP between the tyrosine and leucine residues with a kcat/Km ratio of 1.8 X 10(6) M-1 s-1. Furthermore, a synthetic nonapeptide overlapping the last four amino acids of the prosequence and the first five residues of mature PSP was found to be cleaved by the protease at the expected site to release the mature enzyme. This result suggests a possible autocatalytic mechanism for the activation of the protease. Finally, the hydroxamate-derivatized dipeptide Cbz-Tyr-Leu-NHOH was shown to inhibit PSP competitively with a KI of 17 microM.

Strain specific variation of outer membrane proteins of wild Yersinia pestis strains subjected to different growth temperatures

Three Yersinia pestis strains isolated from humans and one laboratory strain (EV76) were grown in rich media at 28§C and 37§C and their outer membrane protein composition compared by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-Page). Several proteins with molecular weights ranging from 34 kDa to 7 kDa were observed to change in relative abundance in samples grown at different temperatures. At least seven Y. pestis outer membrane proteins showed a temperature-dependent and strain-specific behaviour. Some differences between the outer membrane proteins of full-pathogenic wild isolates and the EV76 strain could aldso be detected and the relevance of this finding on the use of laboratory strains as a reference to the study of Y. pestis biological properties is discuted.

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.

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.

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

Identification and characterization of novel molecular mechanisms involved in initiation and progression of myelination

Schwann cells synthesize a large amount of membrane that form a specialized structure called myelin that surrounds axons and facilitate the transmission of electrical signal along neurons in peripheral nervous system (PNS). Previous studies demonstrated that both Schwann cell differentiation and de-differentiation (in the situation of a nerve injury or demyelinating disease) are regulated by cell-intrinsic regulators including several transcription factors. In particular, the de-differentiation of mature Schwann cells is driven by the activation of multiple negative regulators of myelination including Sox2, c-Jun, Notch and Pax3, all usually expressed in immature Schwann cells and suppressed at the onset of myelination. In order to identify new regulators of myelination involved in the development of the PNS, we analyzed the gene-expression profiling data from developing PNS and from three models of demyelinating neuropathies. This analysis led to the identification of Sox4, a member of the Sox family of transcription factors, as a potential candidate. To characterize the molecular function of Sox4 in PNS, we generated two transgenic lines of mice, which overexpress Sox4 specifically in Schwann cells. Detailed analysis of these mice showed that the overexpression of Sox4 in Schwann cells causes a delay in progression of myelination between post-natal day 2 (P2) and P5. Our in vitro analysis suggested that Sox4 cDNA can be overexpressed while the protein translation is tightly regulated. Interestingly, we observed that Sox4 protein is stabilized in nerves of the CMT4C mouse, a model of the human neuropathy. We therefore crossed Sox4 transgenic mice with CMT4C mice and we observed that Sox4 overexpression exacerbated the neuropathy phenotype in these mice. While recognized as being crucial for the normal function of both neurons and myelinating glial cells, the processes that regulate the beginning of myelination and the nature of the neuro-glial cross-talk remains mostly unknown. In order to gain insight into the molecular pathways involved in the interactions between neurons and associated glial cells, we developed a neuron-glia co-culture system based on microfluidic chambers and successfully induced myelination in this system by ascorbic acid. Importantly, we observed that in addition to acting on Schwann cells, ascorbic acid also modulate neuronal/axonal NRG1/ErbB2-B3 signalling. The experimental setting used in our study thus allowed us to discover a novel phenomena of propagation for myelination in vitro. The further characterization of this event brought us to identify other compounds able to induce myelination: ADAMs secretases inhibitor GM6001 and cyclic-AMP. The results generated during my thesis project are therefore not only important for the advancement of our understanding of how the PNS works, but may also potentially help to develop new therapies aiming at improvement of PNS myelination under disease conditions. - Les cellules de Schwann synthétisent une grande quantité de membrane formant une structure spécialisée appelée myéline qui entoure les axones et facilite la transmission du signal électrique le long des neurones du système nerveux périphérique (SNP). Des études antérieures ont démontré que la différenciation et la dédifférenciation des cellules de Schwann (dans la situation d'une lésion nerveuse ou d'une maladie démyélinisante) sont régulées par des régulateurs cellulaires intrinsèques, incluant plusieurs facteurs de transcription. En particulier, la dédifférenciation des cellules de Schwann matures est contrôlée par l'activation de plusieurs régulateurs négatifs de la myélinisation dont Sox2, c-Jun, Notch et Pax3, tous habituellement exprimés dans des cellules de Schwann immatures et supprimés au début de la myélinisation. Afin d'identifier de nouveaux régulateurs de myélinisation impliqués dans le développement du SNP, nous avons analysé le profil d'expression génique durant le développement du SNP ainsi que dans trois modèles de neuropathies démyélinisantes. Cette analyse a mené à l'identification de Sox4, un membre de la famille des facteurs de transcription Sox, comme étant un candidat potentiel. Dans le but de caractériser la fonction moléculaire de Sox4 dans le SNP, nous avons généré deux lignées transgéniques de souris qui surexpriment Sox4 spécifiquement dans les cellules de Schwann. L'analyse détaillée de ces souris a montré que la surexpression de Sox4 dans les cellules de Schwann provoque un retard dans la progression de la myélinisation entre le jour postnatal 2 (P2) et P5. Notre analyse in vitro a suggéré que l'ADNc de Sox4 peut être surexprimé alors que la traduction des protéines est quand à elle étroitement régulée. De façon intéressante, nous avons observé que la protéine Sox4 est stabilisée dans les nerfs des souris CMT4C, un modèle de neuropathie humaine. Nous avons donc croisé les souris transgéniques Sox4 avec des souris CMT4C et avons observé que la surexpression de Sox4 exacerbe le phénotype de neuropathie chez ces souris. Bien que reconnus comme étant cruciaux pour le fonctionnement normal des neurones et des cellules gliales myélinisantes, les processus qui régulent le début de la myélinisation ainsi que la nature des interactions neurone-glie restent largement méconnus. Afin de mieux comprendre les mécanismes moléculaires impliqués dans les interactions entre les neurones et les cellules gliales leur étant associés, nous avons développé un système de co-culture neurone-glie basé sur des chambres microfluidiques et y avons induit avec succès la myélinisation avec de l'acide ascorbique. Étonnamment, nous avons remarqué que, en plus d'agir sur les cellules de Schwann, l'acide ascorbique module également la voie de signalisation neuronale/axonale NRG1/ErbB2-B3. Le protocole expérimental utilisé dans notre étude a ainsi permis de découvrir un nouveau phénomène de propagation de la myélinisation in vitro. La caractérisation plus poussée de ce phénomène nous a menés à identifier d'autres composés capables d'induire la myélinisation: L'inhibiteur de sécrétases ADAMs GM6001 et l'AMP cyclique. Les résultats obtenus au cours de mon projet de thèse ne sont donc pas seulement importants pour l'avancement de notre compréhension sur la façon dont le SNP fonctionne, mais peuvent aussi potentiellement aider à développer de nouvelles thérapies visant à l'amélioration de la myélinisation du SNP dans des conditions pathologiques.

Time Varying Dimension Models

Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fitting unless the dimension of the model is small. Motivated by this worry, this paper proposes several Time Varying dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different parsimonious representations. Our TVD models all fall in the category of dynamic mixture models. We discuss the properties of these models and present methods for Bayesian inference. An application involving US inflation forecasting illustrates and compares the different TVD models. We find our TVD approaches exhibit better forecasting performance than several standard benchmarks and shrink towards parsimonious specifications.

Hierarchical Shrinkage in Time-Varying Parameter Models

In this paper, we forecast EU-area inflation with many predictors using time-varying parameter models. The facts that time-varying parameter models are parameter-rich and the time span of our data is relatively short motivate a desire for shrinkage. In constant coefficient regression models, the Bayesian Lasso is gaining increasing popularity as an effective tool for achieving such shrinkage. In this paper, we develop econometric methods for using the Bayesian Lasso with time-varying parameter models. Our approach allows for the coefficient on each predictor to be: i) time varying, ii) constant over time or iii) shrunk to zero. The econometric methodology decides automatically which category each coefficient belongs in. Our empirical results indicate the benefits of such an approach.

Computing Markov-Perfect Optimal Policies in Business-Cycle Models

Time-inconsistency is an essential feature of many policy problems (Kydland and Prescott, 1977). This paper presents and compares three methods for computing Markov-perfect optimal policies in stochastic nonlinear business cycle models. The methods considered include value function iteration, generalized Euler-equations, and parameterized shadow prices. In the context of a business cycle model in which a scal authority chooses government spending and income taxation optimally, while lacking the ability to commit, we show that the solutions obtained using value function iteration and generalized Euler equations are somewhat more accurate than that obtained using parameterized shadow prices. Among these three methods, we show that value function iteration can be applied easily, even to environments that include a risk-sensitive scal authority and/or inequality constraints on government spending. We show that the risk-sensitive scal authority lowers government spending and income-taxation, reducing the disincentive households face to accumulate wealth.