Priors about observables in vector autoregressions

Standard practice in Bayesian VARs is to formulate priors on the autoregressive parameters, but economists and policy makers actually have priors about the behavior of observable variables. We show how this kind of prior can be used in a VAR under strict probability theory principles. We state the inverse problem to be solved and we propose a numerical algorithm that works well in practical situations with a very large number of parameters. We prove various convergence theorems for the algorithm. As an application, we first show that the results in Christiano et al. (1999) are very sensitive to the introduction of various priors that are widely used. These priors turn out to be associated with undesirable priors on observables. But an empirical prior on observables helps clarify the relevance of these estimates: we find much higher persistence of output responses to monetary policy shocks than the one reported in Christiano et al. (1999) and a significantly larger total effect.

CAV2 vector gene transfer into central nervious System

Estudi realitzat a partir d’una estada al Institut de Génétique Moléculaire de Montpellier, França, entre 2010 i 2012. En aquest projecte s’ha avaluat les avantatges dels vectors adenovirals canins tipus 2 (CAV2) com a vectors de transferència gènica al sistema nerviós central (SNC) en un model primat no-humà i en un model caní del síndrome de Sly (mucopolisacaridosis tipus 7, MPS VII), malaltia monogènica que cursa amb neurodegeneració. En una primera part del projecte s’ha avaluat la biodistribució, l’eficàcia i la durada de l’expressió del transgen en un model primat no humà, (Microcebus murinus). Com ha vector s’ha utilitzat un CAV2 de primera generació que expressa la proteïna verda fluorescent (CAVGFP). Els resultats aportats en aquesta memòria demostren que en primats no humans, com en d’altres espècies testades anteriorment per l’equip de l’EJ Kremer, la injecció intracerebral de CAV2 resulta en una extensa transducció del SNC, siguent les neurones i els precursors neuronals les cèl•lules preferencialment transduïdes. Els vectors canins, servint-se de vesícules intracel•lulars són transportats, majoritàriament, des de les sinapsis cap al soma neuronal, aquest transport intracel•lular permet una extensa transducció del SNC a partir d’una única injecció intracerebral dels vectors virals. En una segona part d’aquest projecte s’ha avaluat l’ús terapèutic dels CAV2. S’ha injectat un vector helper-dependent que expressa el gen la b-glucuronidasa i el gen de la proteïna verda fluorescent (HD-RIGIE), en el SNC del model caní del síndrome de Sly (MPS VII). La biodistribució i la eficàcia terapèutica han estat avaluades. Els nivells d’activitat enzimàtica en animals malalts injectats amb el vector terapèutic va arribar a valors similars als dels animals no afectes. A més a més s’ha observat una reducció en la quantitat dels GAGs acumulats en les cèl•lules dels animals malalts tractats amb el vector terapèutic, demostrant la potencialitat terapèutica dels CAV2 per a malalties que afecten al SNC. Els resultats aportats en aquest treball ens permeten dir que els CAV2 són unes bones eines terapèutiques per al tractament de malalties que afecten al SNC.

Development and validation of AMANDA, a new algorithm for selecting highly relevant regions in Molecular Interaction Fields

Descriptors based on Molecular Interaction Fields (MIF) are highly suitable for drug discovery, but their size (thousands of variables) often limits their application in practice. Here we describe a simple and fast computational method that extracts from a MIF a handful of highly informative points (hot spots) which summarize the most relevant information. The method was specifically developed for drug discovery, is fast, and does not require human supervision, being suitable for its application on very large series of compounds. The quality of the results has been tested by running the method on the ligand structure of a large number of ligand-receptor complexes and then comparing the position of the selected hot spots with actual atoms of the receptor. As an additional test, the hot spots obtained with the novel method were used to obtain GRIND-like molecular descriptors which were compared with the original GRIND. In both cases the results show that the novel method is highly suitable for describing ligand-receptor interactions and compares favorably with other state-of-the-art methods.

Generation of mouse-induced pluripotent stem cells by transient expression of a single nonviral polycistronic vector

Induced pluripotent stem (iPS) cells have generated keen interestdue to their potential use in regenerative medicine. They havebeen obtained from various cell types of both mice and humans byexogenous delivery of different combinations of Oct4, Sox2, Klf4,c-Myc, Nanog, and Lin28. The delivery of these transcription factorshas mostly entailed the use of integrating viral vectors (retrovirusesor lentiviruses), carrying the risk of both insertional mutagenesisand oncogenesis due to misexpression of these exogenousfactors. Therefore, obtaining iPS cells that do not carry integratedtransgene sequences is an important prerequisite for their eventualtherapeutic use. Here we report the generation of iPS cell linesfrom mouse embryonic fibroblasts with no evidence of integrationof the reprogramming vector in their genome, achieved by nucleofectionof a polycistronic construct coexpressing Oct4, Sox2, Klf4,and c-Myc

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

A new hybrid summarizer based on vector Space model, statistical physics and linguistics

In this article we present a hybrid approach for automatic summarization of Spanish medical texts. There are a lot of systems for automatic summarization using statistics or linguistics, but only a few of them combining both techniques. Our idea is that to reach a good summary we need to use linguistic aspects of texts, but as well we should benefit of the advantages of statistical techniques. We have integrated the Cortex (Vector Space Model) and Enertex (statistical physics) systems coupled with the Yate term extractor, and the Disicosum system (linguistics). We have compared these systems and afterwards we have integrated them in a hybrid approach. Finally, we have applied this hybrid system over a corpora of medical articles and we have evaluated their performances obtaining good results.

Detection of Optimal Models in Parameter Space with Support Vector Machines

The paper proposes an approach aimed at detecting optimal model parameter combinations to achieve the most representative description of uncertainty in the model performance. A classification problem is posed to find the regions of good fitting models according to the values of a cost function. Support Vector Machine (SVM) classification in the parameter space is applied to decide if a forward model simulation is to be computed for a particular generated model. SVM is particularly designed to tackle classification problems in high-dimensional space in a non-parametric and non-linear way. SVM decision boundaries determine the regions that are subject to the largest uncertainty in the cost function classification, and, therefore, provide guidelines for further iterative exploration of the model space. The proposed approach is illustrated by a synthetic example of fluid flow through porous media, which features highly variable response due to the parameter values' combination.

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI.

BACKGROUND AND PURPOSE: MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI. MATERIALS AND METHODS: Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs. RESULTS: The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI. CONCLUSIONS: Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.