Decomposing differences in total factor productivity across firm size

This paper investigates the extent to which the gap in total factor productivity between small and large firms is due to differences in the endowment of factors determining productivity and to the returns associated with these factors. We place particular emphasis on the contribution of differences in the propensity to innovate and in the use of skilled labor across firms of different size. Empirical evidence from a representative sample of Spanish manufacturing firms corroborates that both differences in endowments and returns to innovation and skilled labor significantly contribute to the productivity gap between small and large firms. In addition, it is observed that the contribution of innovation to this gap is caused only by differences in quantity, while differences in returns have no effect; in the case of human capital, however, most of the effect can be attributed to increasing differences in returns between small and large firms.

Flagellin promotes myeloid differentiation factor 88-dependent development of Th2-type response.

Activation of dendritic cells (DC) by microbial products via Toll-like receptors (TLR) is instrumental in the induction of immunity. In particular, TLR signaling plays a major role in the instruction of Th1 responses. The development of Th2 responses has been proposed to be independent of the adapter molecule myeloid differentiation factor 88 (MyD88) involved in signal transduction by TLRs. In this study we show that flagellin, the bacterial stimulus for TLR5, drives MyD88-dependent Th2-type immunity in mice. Flagellin promotes the secretion of IL-4 and IL-13 by Ag-specific CD4(+) T cells as well as IgG1 responses. The Th2-biased responses are associated with the maturation of DCs, which are shown to express TLR5. Flagellin-mediated DC activation requires MyD88 and induces NF-kappaB-dependent transcription and the production of low levels of proinflammatory cytokines. In addition, the flagellin-specific response is characterized by the lack of secretion of the Th1-promoting cytokine IL-12 p70. In conclusion, this study suggests that flagellin and, more generally, TLR ligands can control Th2 responses in a MyD88-dependent manner.

Estudio de las capas de acoplamiento y alternativas al reflector de Bragg en estructuras BAW-CRF

Las capas de acoplamiento son un elemento clave en los dispositivos BAW CRF. El factor de acoplamiento K de estas capas permite el diseño de una determinada respuesta. Debido al limitado número de materiales con las que implementarlas, las soluciones que utilizan capas de λ/4 ofrecen un rango discreto de K. Por otra parte, el reflector de Bragg es un mecanismo de aislamiento mecánico entre el sustrato y la estructura BAW que está formado por capas alternas de alta y baja impedancia acústica de λ/4. El problema que presenta es la reducción del factor de calidad asociado a las pérdidas producida por las ondas shear. Este proyecto presenta un método para la obtención de un rango continuo de factores de acoplamiento y estudia la mejora del factor de calidad de las estructuras BAW con reflector de Bragg partiendo de dos materiales con alta y baja impedancia acústica.

Wound infection after excision and primary midline closure for pilonidal disease: risk factor analysis to improve patient selection.

BACKGROUND: Excision and primary midline closure for pilonidal disease (PD) is a simple procedure; however, it is frequently complicated by infection and prolonged healing. The aim of this study was to analyze risk factors for surgical site infection (SSI) in this context. METHODS: All consecutive patients undergoing excision and primary closure for PD from January 2002 through October 2008 were retrospectively assessed. The end points were SSI, as defined by the Center for Disease Control, and time to healing. Univariable and multivariable risk factor analyses were performed. RESULTS: One hundred thirty-one patients were included [97 men (74%), median age = 24 (range 15-66) years]. SSI occurred in 41 (31%) patients. Median time to healing was 20 days (range 12-76) in patients without SSI and 62 days (range 20-176) in patients with SSI (P < 0.0001). In univariable and multivariable analyses, smoking [OR = 2.6 (95% CI 1.02, 6.8), P = 0.046] and lack of antibiotic prophylaxis [OR = 5.6 (95% CI 2.5, 14.3), P = 0.001] were significant predictors for SSI. Adjusted for SSI, age over 25 was a significant predictor of prolonged healing. CONCLUSION: This study suggests that the rate of SSI after excision and primary closure of PD is higher in smokers and could be reduced by antibiotic prophylaxis. SSI significantly prolongs healing time, particularly in patients over 25 years.

Brain-derived neurotrophic factor enhances the hippocampal expression of key postsynaptic proteins in vivo including the monocarboxylate transporter MCT2.

Brain-derived neurotrophic factor (BDNF) promotes synaptic plasticity via an enhancement in expression of specific synaptic proteins. Recent results suggest that the neuronal monocarboxylate transporter MCT2 is a postsynaptic protein critically involved in synaptic plasticity and long-term memory. To investigate in vivo whether BDNF can modulate the expression of MCT2 as well as other proteins involved in synaptic plasticity, acute injection of BDNF was performed in mouse dorsal hippocampal CA1 area. Using immunohistochemistry, it was found that MCT2 expression was enhanced in part of the CA1 area and in the dentate gyrus 6 h after a single intrahippocampal injection of BDNF. Similarly, expression of the immediate early genes Arc and Zif268 was enhanced in the same hippocampal areas, in accordance with their role in synaptic plasticity. Immunoblot analysis confirmed the significant enhancement in MCT2 protein expression. In contrast, no changes were observed for the glial monocarboxylate transporters MCT1 and MCT4. When other synaptic proteins were investigated, it was found that postsynaptic density 95 (PSD95) and glutamate receptor 2 (GluR2) protein levels were significantly enhanced while no effect could be detected for synaptophysin, synaptosomal-associated protein 25 (SNAP25), αCaMKII and GluR1. These results demonstrate that MCT2 expression can be upregulated together with other key postsynaptic proteins in vivo under conditions related to synaptic plasticity, further suggesting the importance of energetics for memory formation.

Countercyclical Fiscal Policy and Cyclical Factor Utilization

In a neoclassical growth model with monopolistic competition in the product market, the presence of cyclical factor utilization enhances the stabilization role of countercyclical taxes. The costs of varying capital utilization take the form of varying rates of depreciation, which in turn have amplifying effect on investment decisions as well as the volatility of most aggregate variables. This creates an additional channel through which taxes affect the economy, a channel that enhances the stabilization role of countercyclical taxes, with particularly strong effects in the labor market. However, in terms of welfare, countercyclical taxes are welfare inferior due to reduced precautionary saving motives.

Common and Idiosyncratic Factors of the Exchange Risk Premium in Emerging European Markets

Existing empirical evidence suggests that the Uncovered Interest Rate Parity (UIRP) condition may not hold due to an exchange risk premium. For a panel data set of eleven emerging European economies we decompose this exchange risk premium into an idiosyncratic (country-specific) elements and a common factor using a principal components approach. We present evidence of a stationary idiosyncratic component and nonstationary common factor. This result leads to the conclusion of a nonstationary risk premium for these countries and a violation of the UIRP in the long-run, which is in contrast to previous studies often documenting a stationary premium in developed countries. Furthermore, we report that the variation in the premium is largely attributable to a common factor influenced by economic developments in the United States.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

IInflation and Globalisation: A Dynamic Factor Model with Stochastic Volatility

National inflation rates reflect domestic and international (regional and global) influences. The relative importance of these components remains a controversial empirical issue. We extend the literature on inflation co-movement by utilising a dynamic factor model with stochastic volatility to account for shifts in the variance of inflation and endogenously determined regional groupings. We find that most of inflation variability is explained by the country specific disturbance term. Nevertheless, the contribution of the global component in explaining industrialised countries’ inflation rates has increased over time.

Tumor necrosis factor (TNF) signaling, but not TWEAK (TNF-like weak inducer of apoptosis)-triggered cIAP1 (cellular inhibitor of apoptosis protein 1) degradation, requires cIAP1 RING dimerization and E2 binding.

Cellular inhibitor of apoptosis (cIAP) proteins, cIAP1 and cIAP2, are important regulators of tumor necrosis factor (TNF) superfamily (SF) signaling and are amplified in a number of tumor types. They are targeted by IAP antagonist compounds that are undergoing clinical trials. IAP antagonist compounds trigger cIAP autoubiquitylation and degradation. The TNFSF member TWEAK induces lysosomal degradation of TRAF2 and cIAPs, leading to elevated NIK levels and activation of non-canonical NF-kappaB. To investigate the role of the ubiquitin ligase RING domain of cIAP1 in these pathways, we used cIAP-deleted cells reconstituted with cIAP1 point mutants designed to interfere with the ability of the RING to dimerize or to interact with E2 enzymes. We show that RING dimerization and E2 binding are required for IAP antagonists to induce cIAP1 degradation and protect cells from TNF-induced cell death. The RING functions of cIAP1 are required for full TNF-induced activation of NF-kappaB, however, delayed activation of NF-kappaB still occurs in cIAP1 and -2 double knock-out cells. The RING functions of cIAP1 are also required to prevent constitutive activation of non-canonical NF-kappaB by targeting NIK for proteasomal degradation. However, in cIAP double knock-out cells TWEAK was still able to increase NIK levels demonstrating that NIK can be regulated by cIAP-independent pathways. Finally we show that, unlike IAP antagonists, TWEAK was able to induce degradation of cIAP1 RING mutants. These results emphasize the critical importance of the RING of cIAP1 in many signaling scenarios, but also demonstrate that in some pathways RING functions are not required.

Implicacions del factor tissular en l'activació plaquetària: mecanismes d'acció

La formació de trombus està mediada per dos sistemes paral•lels: el sistema de la coagulació i agregació plaquetària. El prinicipal inciador de la cascada de la coagulació in vivo és el factor tissular (FT). La hipòtesi del present treball és que el FT és capaç d’induir agregació i activació plaquetària independentment del seu paper en la coagulació. Mitjançant estudis d’agregació hem vist que el FT indueix activació i agregació plaquetària. A més també hem pogut veure que en un sistema que simula el flux sanguini, les plaquetes són capaces d’adherir-se a una superfície coberta per FT, de manera similar al que s’espera en una placa ateroscleròtica trencada. Hem trobat que el FT indueix activació plaquetària per vies de senyalització que involucren la PKC, PI3K, tirosin quinases i Ser/Tre fosfatases. Per últim hem generat una línia cel•lular que expressa FT a la membrana. Aquesta línia cel•lular permetrà futurs estudis de la funció del FT en la seva forma nativa, és a dir, com a proteïna transmembrana enlloc de proteïna soluble purificada. En resum, aquests estudis han permès veure com el FT és capaç d’activar directament les plaquetes i induir la seva agregació jugant així un doble paper en els mecanismes de trombosi i hemostàsia.

Forecasting with Medium and Large Bayesian VARs

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We nd that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting model as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.