Cyclical Features of Uzawa-Lucas Endogenous Growth Model

This paper analyzes the cyclical properties of a generalized version of Uzawa-Lucas endogenous growth model. We study the dynamic features of different cyclical components of this model characterized by a variety of decomposition methods. The decomposition methods considered can be classified in two groups. On the one hand, we consider three statistical filters: the Hodrick-Prescott filter, the Baxter-King filter and Gonzalo-Granger decomposition. On the other hand, we use four model-based decomposition methods. The latter decomposition procedures share the property that the cyclical components obtained by these methods preserve the log-linear approximation of the Euler-equation restrictions imposed by the agent’s intertemporal optimization problem. The paper shows that both model dynamics and model performance substantially vary across decomposition methods. A parallel exercise is carried out with a standard real business cycle model. The results should help researchers to better understand the performance of Uzawa-Lucas model in relation to standard business cycle models under alternative definitions of the business cycle.

An Estimated New-Keynesian Model with Unemployment as Excess Supply of Labor

Wage stickiness is incorporated to a New-Keynesian model with variable capital to drive endogenous unemployment uctuations de ned as the log di¤erence between aggregate labor supply and aggregate labor demand. We estimated such model using Bayesian econometric techniques and quarterly U.S. data. The second-moment statistics of the unemployment rate in the model give a good t to those observed in U.S. data. Our results also show that wage-push shocks, demand shifts and monetary policy shocks are the three major determinants of unemployment fl uctuations. Compared to an estimated New-Keynesian model without unemployment (Smets and Wouters, 2007): wage stickiness is higher, labor supply elasticity is lower, the slope of the New-Keynesian Phillips curve is flatter, and the importance of technology innovations on output variability increases.

Efecto de la radiación visible sobre la supervivencia de Acinetobacter baumannii sobre soportes inertes

Acinetobacter baumannii es una bacteria de gran importancia clínica debido a las infecciones nosocomiales a las que se asocia. La amenaza que supone en el ámbito hospitalario está directamente relacionada con su capacidad para sobrevivir a condiciones hostiles tales como cambios de temperatura, estrés lumínico y sequedad. En este contexto, se ha estudiado el efecto de la radiación visible sobre poblaciones de A. baumannii (ATCC 19606) mantenidas a temperatura ambiente en medio líquido (condiciones de ayuno) y sobre soporte sólido (condiciones de ayuno y sequedad). Para determinar la posible pérdida de cultivabilidad y la entrada en estado viable no cultivable (VNC), las poblaciones de A. baumannii se inocularon en solución salina estéril o se fijaron a filtros de acetato de celulosa estériles y se incubaron a 20ºC en condiciones de oscuridad (control) o exposición a luz visible. A lo largo de la supervivencia, utilizando microscopía de epifluerescencia, se cuantificaron las células totales, viables y cultivables. Además, se determinó la capacidad de formar biofilms de estas poblaciones. Bajo condiciones de oscuridad, tanto en soportes sólidos como en medio líquido, no se detectó pérdida de cultivabilidad, actividad o integridad celular durante al menos 7 días. Sin embargo, la luz visible tuvo un efecto negativo sobre las poblaciones de A. baumannii expuestas tanto en medio líquido como sobre soporte sólidos. En medio líquido, si bien la radiación luminosa no afectó a la integridad celular, al finalizar el periodo de exposición (7 días) el número de células cultivables descendió 1,5 log y el 27% de la población se encontraba en estado VNC. En condiciones de sequedad, la pérdida de cultivabilidad se detectó ya desde el primer día de exposición, situándose por debajo del límite de detección tras 5 días; la densidad de células viable también disminuyó, de modo que tras 7 días de exposición el 4% de la población era VNC. Además, la capacidad de formar biofilms se vio negativamente afectada a lo largo de la permanencia tanto en luz como en oscuridad. El efecto negativo de la luz fue especialmente relevante en poblaciones mantenidas en soportes sólidos. Bajo condiciones de ayuno, A. baumannii es capaz de persistir durante periodos de tiempo de al menos una semana incluso en ausencia de humedad. Sin embargo, la exposición de radiación luminosa induce la entrada en estado VNC en estas mismas condiciones, siendo este efecto negativo más acusado en condiciones de ayuno y sequedad.

A High Degree of LINE-1 Hypomethylation Is a Unique Feature of Early-Onset Colorectal Cancer

12 p.

Prognostic Role of Host Cyclooxygenase and Cytokine Genotypes in a Caucasian Cohort of Patients with Gastric Adenocarcinoma

11 p.

Efficient learning of decomposable models with a bounded clique size

The learning of probability distributions from data is a ubiquitous problem in the fields of Statistics and Artificial Intelligence. During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models due to their advantageous theoretical properties. Some of these algorithms can be used to search for a maximum likelihood decomposable model with a given maximum clique size, k, which controls the complexity of the model. Unfortunately, the problem of learning a maximum likelihood decomposable model given a maximum clique size is NP-hard for k > 2. In this work, we propose a family of algorithms which approximates this problem with a computational complexity of O(k · n^2 log n) in the worst case, where n is the number of implied random variables. The structures of the decomposable models that solve the maximum likelihood problem are called maximal k-order decomposable graphs. Our proposals, called fractal trees, construct a sequence of maximal i-order decomposable graphs, for i = 2, ..., k, in k − 1 steps. At each step, the algorithms follow a divide-and-conquer strategy based on the particular features of this type of structures. Additionally, we propose a prune-and-graft procedure which transforms a maximal k-order decomposable graph into another one, increasing its likelihood. We have implemented two particular fractal tree algorithms called parallel fractal tree and sequential fractal tree. These algorithms can be considered a natural extension of Chow and Liu’s algorithm, from k = 2 to arbitrary values of k. Both algorithms have been compared against other efficient approaches in artificial and real domains, and they have shown a competitive behavior to deal with the maximum likelihood problem. Due to their low computational complexity they are especially recommended to deal with high dimensional domains.

Frame-level features conveying phonetic information for language and speaker recognition

150 p.

Postantifungal Effect of Micafungin against the Species Complexes of Candida albicans and Candida parapsilosis

Micafungin is an effective antifungal agent useful for the therapy of invasive candidiasis. Candida albicans is the most common cause of invasive candidiasis; however, infections due to non-C. albicans species, such as Candida parapsilosis, are rising. Killing and postantifungal effects (PAFE) are important factors in both dose interval choice and infection outcome. The aim of this study was to determinate the micafungin PAFE against 7 C. albicans strains, 5 Candida dubliniensis, 2 Candida Africana, 3 C. parapsilosis, 2 Candida metapsilosis and 2 Candida orthopsilosis. For PAFE studies, cells were exposed to micafungin for 1 h at concentrations ranging from 0.12 to 8 mu g/ml. Time-kill experiments (TK) were conducted at the same concentrations. Samples were removed at each time point (0-48 h) and viable counts determined. Micafungin (2 mu g/ml) was fungicidal (>= 3 log(10) reduction) in TK against 5 out of 14 (36%) strains of C. albicans complex. In PAFE experiments, fungicidal endpoint was achieved against 2 out of 14 strains (14%). In TK against C. parapsilosis, 8 mu g/ml of micafungin turned out to be fungicidal against 4 out 7 (57%) strains. Conversely, fungicidal endpoint was not achieved in PAFE studies. PAFE results for C. albicans complex (41.83 +/- 2.18 h) differed from C. parapsilosis complex (8.07 +/- 4.2 h) at the highest tested concentration of micafungin. In conclusion, micafungin showed significant differences in PAFE against C. albicans and C. parapsilosis complexes, being PAFE for the C. albicans complex longer than for the C. parapsilosis complex.

Dipeptidyl-Peptidase IV Activity Is Correlated with Colorectal Cancer Prognosis

Background Dipeptidyl-peptidase IV (EC 3.4.14.5) (DPPIV) is a serine peptidase involved in cell differentiation, adhesion, immune modulation and apoptosis, functions that control neoplastic transformation. Previous studies have demonstrated altered expression and activity of tissue and circulating DPPIV in several cancers and proposed its potential usefulness for early diagnosis in colorectal cancer (CRC). Methods and principal findings The activity and mRNA and protein expression of DPPIV was prospectively analyzed in adenocarcinomas, adenomas, uninvolved colorectal mucosa and plasma from 116 CRC patients by fluorimetric, quantitative RT-PCR and immunohistochemical methods. Results were correlated with the most important classic pathological data related to aggressiveness and with 5-year survival rates. Results showed that: 1) mRNA levels and activity of DPPIV increased in colorectal neoplasms (Kruskal-Wallis test, p<0.01); 2) Both adenomas and CRCs displayed positive cytoplasmic immunostaining with luminal membrane reinforcement; 3) Plasmatic DPPIV activity was lower in CRC patients than in healthy subjects (Mann-U test, p<0.01); 4) Plasmatic DPPIV activity was associated with worse overall and disease-free survivals (log-rank p<0.01, Cox analysis p<0.01). Conclusion/significance 1) Up-regulation of DPPIV in colorectal tumors suggests a role for this enzyme in the neoplastic transformation of colorectal tissues. This finding opens the possibility for new therapeutic targets in these patients. 2) Plasmatic DPPIV is an independent prognostic factor in survival of CRC patients. The determination of DPPIV activity levels in the plasma may be a safe, minimally invasive and inexpensive way to define the aggressiveness of CRC in daily practice.