The Learning of the subject Biology in a Master in Biomedical Physics

BIOLOGY is a dynamic and fascinating science. The study of this subject is an amazing trip for all the students that have a first contact with this subject. Here, we present the development of the study and learning experience of this subject belonging to an area of knowledge that is different to the training curriculum of students who have studied Physics during their degree period. We have taken a real example, the “Elements of Biology” subject, which is taught as part of the Official Biomedical Physics Master, at the Physics Faculty, of the Complutense University of Madrid, since the course 2006/07. Its main objective is to give to the student an understanding how the Physics can have numerous applications in the Biomedical Sciences area, giving the basic training to develop a professional, academic or research career. The results obtained when we use new virtual tools combined with the classical learning show that there is a clear increase in the number of persons that take and pass the final exam. On the other hand, this new learning strategy is well received by the students and this is translated to a higher participation and a decrease of the giving the subject u

Infinitesimal moduli for the Strominger system and Killing spinors in generalized geometry

We construct the space of infinitesimal variations for the Strominger system and an obstruction space to integrability, using elliptic operator theory. We initiate the study of the geometry of the moduli space, describing the infinitesimal structure of a natural foliation on this space. The associated leaves are related to generalized geometry and correspond to moduli spaces of solutions of suitable Killing spinor equations on a Courant algebroid. As an application, we propose a unifying framework for metrics with holonomy \SU(3) and solutions of the Strominger system.Comment: 48 pages. Section 5 and Appendix A from previous version have been suppressed and will appear elsewhere. Title slightly changed, references added, presentation improved. To appear in Math. Anna

Effect of elevated inorganic carbon on the cytosolic homeostasis of NO3- in the marine angiosperm Posidonia oceanica (L.) Delile

The marine angiosperm Posidonia oceanica is a mediterranean endemism of great ecological significance. As other marine plants, P. oceanica has adapted secondarily to the marine environment and develop anew different mechanisms to colonize it. Among others, this plant has developed a plasma membrane system for the direct uptake of bicarbonate. In this work we have developed both NO3- and Cl- selective microelectrodes for the continuous monitoring of the intracellular (cytosolic) NO3- and Cl-. In the light, leaf mesophyll cells show a cytosolic NO3- concentration of 5.7±0.2 mM (n=10), while in the dark cytosolic NO3- raises up to 8.7±1.1 mM; these values are in the range of concentrations quoted for Arabidopsis thaliana (Cookson et al., 2005). The enrichment of natural seawater (NSW) with 3 mM NaHCO3 caused a decrease of the cytosolic NO3- concentration of 1 mM and a decrease of the cytosolic concentration of Cl- of 3.5 mM. The saturation of NSW with 1000 µL CO2 L-1 produced a lower diminution of the cytosolic NO3- (0.3 mM). In the presence of 0.1 mM of the plasma membrane permeable inhibitor of the carbonic anhydrase (EZ) the diminution of cytosolic NO3- caused by the same concentration of CO2 was much lower, 0.1 mM. The addition of inorganic carbon, either HCO3- or CO2, has an effect on the cytosolic mechanisms for anionic homeostasis, one of which is the opening of the slow anion channels. These channels are permeable to NO3- and Cl- and could elicit the efflux of these ions. In P. oceanica, the response in the presence of EZ points out that the inorganic carbon species that cause the NO3-/Cl- efflux is HCO3-. This effect could contribute to plant biomass N dilution observed in elevated CO2. References: Cookson et al. 2005. Plant Physiology 138, 1097–1105.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Estimating Macroeconomic Models: A Likelihood Approach

This paper shows how particle filtering allows us to undertake likelihood-based inference in dynamic macroeconomic models. The models can be nonlinear and/or non-normal. We describe how to use the output from the particle filter to estimate the structural parameters of the model, those characterizing preferences and technology, and to compare different economies. Both tasks can be implemented from either a classical or a Bayesian perspective. We illustrate the technique by estimating a business cycle model with investment-specific technological change, preference shocks, and stochastic volatility.

Estimating Nonlinear Dynamic Equilibrium economies: A Likelihood Approach

This paper presents a framework to undertake likelihood-based inference in nonlinear dynamic equilibrium economies. We develop a Sequential Monte Carlo algorithm that delivers an estimate of the likelihood function of the model using simulation methods. This likelihood can be used for parameter estimation and for model comparison. The algorithm can deal both with nonlinearities of the economy and with the presence of non-normal shocks. We show consistency of the estimate and its good performance in finite simulations. This new algorithm is important because the existing empirical literature that wanted to follow a likelihood approach was limited to the estimation of linear models with Gaussian innovations. We apply our procedure to estimate the structural parameters of the neoclassical growth model.Likelihood-Based Inference, Dynamic Equilibrium Economies, Nonlinear Filtering, Sequential Monte Carlo)

Convergence Properties of the Likelihood of Computed Dynamic Models

This paper studies the econometrics of computed dynamic models. Since these models generally lack a closed-form solution, their policy functions are approximated by numerical methods. Hence, the researcher can only evaluate an approximated likelihood associated with the approximated policy function rather than the exact likelihood implied by the exact policy function. What are the consequences for inference of the use of approximated likelihoods? First, we find conditions under which, as the approximated policy function converges to the exact policy, the approximated likelihood also converges to the exact likelihood. Second, we show that second order approximation errors in the policy function, which almost always are ignored by researchers, have first order effects on the likelihood function. Third, we discuss convergence of Bayesian and classical estimates. Finally, we propose to use a likelihood ratio test as a diagnostic device for problems derived from the use of approximated likelihoods.

Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood

This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a Sequential Monte Carlo filter proposed by Fernández-Villaverde and Rubio-Ramírez (2004) and the Kalman filter. The Sequential Monte Carlo filter exploits the nonlinear structure of the economy and evaluates the likelihood function of the model by simulation methods. The Kalman filter estimates a linearization of the economy around the steady state. We report two main results. First, both for simulated and for real data, the Sequential Monte Carlo filter delivers a substantially better fit of the model to the data as measured by the marginal likelihood. This is true even for a nearly linear case. Second, the differences in terms of point estimates, even if relatively small in absolute values, have important effects on the moments of the model. We conclude that the nonlinear filter is a superior procedure for taking models to the data.Likelihood-Based Inference, Dynamic Equilibrium Economies, Nonlinear Filtering, Kalman Filter, Sequential Monte Carlo

MEDEA: A DSGE Model for the Spanish Economy

In this paper, we provide a brief introduction to a new macroeconometric model of the Spanish economy named MEDEA (Modelo de Equilibrio Dinámico de la Economía EspañolA). MEDEA is a dynamic stochastic general equilibrium (DSGE) model that aims to describe the main features of the Spanish economy for policy analysis, counterfactual exercises, and forecasting. MEDEA is built in the tradition of New Keynesian models with real and nominal rigidities, but it also incorporates aspects such as a small open economy framework, an outside monetary authority such as the ECB, and population growth, factors that are important in accounting for aggregate fluctuations in Spain. The model is estimated with Bayesian techniques and data from the last two decades. Beyond describing the properties of the model, we perform different exercises to illustrate the potential of MEDEA, including historical decompositions, long-run and short-run simulations, and counterfactual experiments.DSGE Models, Likelihood Estimation, Bayesian Methods

Convergence Properties of the Likelihood of Computed Dynamic Models

This paper studies the econometrics of computed dynamic models. Since these models generally lack a closed-form solution, economists approximate the policy functions of the agents in the model with numerical methods. But this implies that, instead of the exact likelihood function, the researcher can evaluate only an approximated likelihood associated with the approximated policy function. What are the consequences for inference of the use of approximated likelihoods? First, we show that as the approximated policy function converges to the exact policy, the approximated likelihood also converges to the exact likelihood. Second, we prove that the approximated likelihood converges at the same rate as the approximated policy function. Third, we find that the error in the approximated likelihood gets compounded with the size of the sample. Fourth, we discuss convergence of Bayesian and classical estimates. We complete the paper with three applications to document the quantitative importance of our results.computed dynamic models, likelihood inference, asymptotic properties

A, B, C's (and D)'s for Understanding VARs

The dynamics of a linear (or linearized) dynamic stochastic economic model can be expressed in terms of matrices (A,B,C,D) that define a state space system. An associated state space system (A,K,C,Sigma) determines a vector autoregression for observables available to an econometrician. We review circumstances under which the impulse response of the VAR resembles the impulse response associated with the economic model. We give four examples that illustrate a simple condition for checking whether the mapping from VAR shocks to economic shocks is invertible. The condition applies when there are equal numbers of VAR and economic shocks.