Computational Methods for Parameter Estimation in Climate Models

Intensive computational methods have been used by Earth scientists in a wide range of problems in data inversion and uncertainty quantification such as earthquake epicenter location and climate projections. To quantify the uncertainties resulting from a range of plausible model configurations it is necessary to estimate a multidimensional probability distribution. The computational cost of estimating these distributions for geoscience applications is impractical using traditional methods such as Metropolis/Gibbs algorithms as simulation costs limit the number of experiments that can be obtained reasonably. Several alternate sampling strategies have been proposed that could improve on the sampling efficiency including Multiple Very Fast Simulated Annealing (MVFSA) and Adaptive Metropolis algorithms. The performance of these proposed sampling strategies are evaluated with a surrogate climate model that is able to approximate the noise and response behavior of a realistic atmospheric general circulation model (AGCM). The surrogate model is fast enough that its evaluation can be embedded in these Monte Carlo algorithms. We show that adaptive methods can be superior to MVFSA to approximate the known posterior distribution with fewer forward evaluations. However the adaptive methods can also be limited by inadequate sample mixing. The Single Component and Delayed Rejection Adaptive Metropolis algorithms were found to resolve these limitations, although challenges remain to approximating multi-modal distributions. The results show that these advanced methods of statistical inference can provide practical solutions to the climate model calibration problem and challenges in quantifying climate projection uncertainties. The computational methods would also be useful to problems outside climate prediction, particularly those where sampling is limited by availability of computational resources.National Science Foundation OCE-0415251CONACyT-Mexico 159764Institute for Geophysic

Bayesian time-varying autoregressions: Theory, methods and applications

We review the class of time-varying autoregressive (TVAR) models and a range of related recent developments of Bayesian time series modelling

Estimation of

Bayesian model calibration has become a powerful tool for the analysis of experimental data coupled with a physics-based mathematical model. The forward problem of prediction, especially within the range of data, is generally well-posed. There are many well-known issues with the approach when solving the inverse problem of parameter estimation, especially when the calibration parameters have physical interpretations. In this poster, we explore several techniques to identify and overcome these challenges. First, we consider regularization, which refers to the process of constraining the solution space in a meaningful and reasonable way. This is accomplished via the Moment Penalization prior distribution and the associated probability of prior coherency. Secondly, we consider a pseudo-Bayesian approach which we refer to as modularization. By focusing on a small number of parameters which are considered of-interest and forfeiting the ability to learn about the remaining parameters, robust inferential procedures can sometimes be obtained. These ideas are illustrated using several simple examples and a dynamic material property application where material properties of Tantalum are estimated

Gestor de carga de baterías (BMS)

La función principal de los Gestores de Carga de Batería (BMS) es prolongar la vida útil de la batería así como mejorar el rendimiento y seguridad que esta ofrece a los usuarios. En proyecto consta de dos partes. En la primera parte se realiza un estudio sobre la situación de las baterías de ión-litio, su problemática y las funciones del Gestor de Carga de Baterías. En la segunda parte se diseña un sistema de equilibrado de baterías formadas por series de siete células. Este sistema consiste en un circuito ecualizador de múltiples transformadores y un sistema de control.The main function of the Battery Management System (BMS) is to extend the useful life of the batteries and also improve their performance and security they offer to users. This project has two parts. In the first one a study on the status of the Li-ion batteries is made, their problems and the main BMS´s functions are analyzed. In the second part a system of balancing batteries formed by a series of seven cells is designed. This system consists of an equalizer circuit with multiple transformers and a control system

Pathogen-dependent role of turbot (Scophthalmus maximus) interferon-gamma

11 páginas, 7 figurasInterferon-gamma has been typically described as a pro-inflammatory cytokine playing an important role in the resolution of both viral and bacterial diseases. Nevertheless, some anti-inflammatory functions are also attributed to this molecule. In this work we have characterized for the first time the turbot (Scophthalmus maximus) interferon-gamma gene (ifng) and its expression pattern under basal conditions, after type I IFNs administration, and viral and bacterial infection. The intramuscular injection of an expression plasmid encoding turbot Ifng (pMCV1.4-ifng) was not able to affect the transcription of numerous immune genes directly related to the activity of IFN-gamma, with the exception of macrophage-colony stimulating factor (csf1). It was also unable to reduce the mortality caused by a Viral Hemorrhagic Septicemia Virus (VHSV) or Aeromonas salmonicida challenge. Interestingly, at 24 h post-infection, turbot previously inoculated with pMCV1.4-ifng and infected with VHSV showed an increase in the expression of pro-inflammatory cytokines and type I IFNs compared to those fish not receiving expression plasmid, indicating a synergic effect of Ifng and VHSV. On the other hand, some macrophage markers, such as the macrophage receptor with collagenous structure (marco), were down-regulated by Ifng during the viral infection. Ifng had the opposite effect in those turbot infected with the bacteria, showing a reduction in the transcription of pro-inflammatory and type I IFNs genes, and an increase in the expression of genes related to the activity of macrophagesThis work has been funded by the projects CSD2007-00002 “Aquagenomics”, 201230E057 (CSIC) and AGL2014-51773-C3 from the Spanish Ministerio de Economía y Competitividad. P. Pereiro received a predoctoral grant from the gs3:Ministerio de Educación [F.P.U. fellowship AP2010-2408]Peer reviewe

Structure priors for multivariate time series

Abstract A class of prior distributions for multivariate autoregressive models is presented. This class of priors is built taking into account the latent component structure that characterizes a collection of autoregressive processes. In particular, the state-space representation of a vector autoregressive process leads to the decomposition of each time series in the multivariate process into simple underlying components. These components may have a common structure across the series. A key feature of the proposed priors is that they allow the modeling of such common structure. This approach also takes into account the uncertainty in the number of latent processes, consequently handling model order uncertainty in the multivariate autoregressive framework. Posterior inference is achieved via standard Markov chain Monte Carlo (MCMC) methods. Issues related to inference and exploration of the posterior distribution are discussed. We illustrate the methodology analyzing two data sets: a synthetic data set with quasi-periodic * Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico 87131-1141, USA. E-mail: [email protected]. † Department of Applied Mathematics and Statistics. Baskin School of Engineering. University of California, Santa Cruz 1156 High Street, Santa Cruz CA 95064, USA. E-mail: [email protected]. 1 latent structure, and seasonally adjusted US monthly housing data consisting of housing starts and housing sold over the period 1965 through 1974

Análisis de depresión, ansiedad y estrés en estudiantes de medicina posterior al confinamiento por covid-19

Introduction. Evidence points to alarming levels of depression, anxiety and stress in this student population, which justifies the implementation of studies to observe their post-pandemic situation. Method. Observational, cross-sectional and analytical study. A Google Form digital survey was used for the application of the scales DASS-21, GHQ-28, ISEL, IES-COVID19 to observe the presence of depression, anxiety and stress and their relationship with perceived social support and general health status. Result. The sample was of 60 medical undergraduate students of INUMEDH, A 60% prevalence of depression, anxiety and stress was found, as well as a high correlation with perceived social support on the part of the students, moderate correlations (p <.001) with the presence of somatic symptoms, insomnia and social dysfunction. The correlation was high for the presence of the variables in relation to the situation by COVID19 (p <.001). Conclusion. The presence of depression, anxiety and stress was high in this student population. A significantly high relationship of depression and stress was found due to the COVID19 situation.Introducción. La evidencia señala alarmantes niveles de depresión, ansiedad y estrés en esta población estudiantil, lo que justifica la implementación de estudios para observar la situación de estos posterior a la pandemia. Método. Estudio Observacional, transversal y analítico. Se utilizó encuestas digitales de Google Form para la aplicación de las escalas DASS-21, GHQ-28, ISEL, IES-COVID19 para observar la presencia de depresión, ansiedad y estrés y su relación con el apoyo social percibido y el estado de salud general. Resultado. La muestra fue de 60 alumnos de la licenciatura en medicina de INUMEDH, Se encontró una prevalencia del 60% de depresión, ansiedad y estrés, así como una correlación alta con el apoyo social percibido de parte de los estudiantes, correlaciones moderadas (p <.001) con la presencia de síntomas somáticos, insomnio y disfunción social. La correlación fue alta para la presencia de las variables en relación con la situación por COVID19 (p <.001) Conclusión. La presencia de depresión, ansiedad y estrés fue elevada en esta población estudiantil. Se encontro una relación significativamente alta de depresión y estrés debido a la situación por COVID19

Conserved gene regulation during acute inflammation between zebrafish and mammals

9 páginas, 4 figuras, 1 tabla.-- This work is licensed under a Creative Commons Attribution 4.0 International LicenseZebrafish (Danio rerio), largely used as a model for studying developmental processes, has also emerged as a valuable system for modelling human inflammatory diseases. However, in a context where even mice have been questioned as a valid model for these analysis, a systematic study evaluating the reproducibility of human and mammalian inflammatory diseases in zebrafish is still lacking. In this report, we characterize the transcriptomic regulation to lipopolysaccharide in adult zebrafish kidney, liver, and muscle tissues using microarrays and demonstrate how the zebrafish genomic responses can effectively reproduce the mammalian inflammatory process induced by acute endotoxin stress. We provide evidence that immune signaling pathways and single gene expression is well conserved throughout evolution and that the zebrafish and mammal acute genomic responses after lipopolysaccharide stimulation are highly correlated despite the differential susceptibility between species to that compound. Therefore, we formally confirm that zebrafish inflammatory models are suited to study the basic mechanisms of inflammation in human inflammatory diseases, with great translational impact potentialThis work was funded by the projects CSD2007–00002 “Aquagenomics” and AGL2014-51773-C3 from the Spanish Ministerio de Economía y Competitividad, and 201230E057 “Proyecto Intramural Especial, PIE”, Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC). P. Pereiro and M. Varela received predoctoral grants from the Ministerio de Educación (F.P.U. fellowship AP2010-2408) and the JAE Program (funded though the CSIC and European Social Funds), respectivelyPeer reviewe