Person:
Valencia Delfa, José Luis

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First Name
José Luis
Last Name
Valencia Delfa
URI
https://hdl.handle.net/20.500.14352/77459
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Estudios estadísticos
Department
Estadística y Ciencia de los Datos
Area
Estadística e Investigación Operativa
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2020 - 20233
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Start date: 2020 ×

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    Project number: 81
    La Matemática Financiera ante la incertidumbre y globalización de los mercados. Aplicación del MiFID II
    (2020) Elices López, Mercedes; Maestro Muñoz, Mª Luisa; Blanco García, Susana; Blanco Hernández, Mª Teresa; Fernández Ruiz, Antonio José; Fernández-Montes Romero, Antonio; Ricote Gil, Fernando; Segovia Vargas, María Jesús; Sánchez González, Mª Pilar; Espín Gutiérrez, Cristobal; Valencia Delfa, José Luis; Villeta López, María Del Carmen
    El proyecto tiene como objetivo actualizar las guías docentes al nuevo marco legal europeo de los mercados financieros y nuestra asignatura, en particular. Integraremos la perspectiva de estudiantes y profesores, en el proceso de evaluación de calidad de los contenidos.
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    Both HCV Infection and Elevated Liver Stiffness Significantly Impacts on Several Parameters of T-Cells Homeostasis in HIV-Infected Patients
    (Journal of Clinical Medicine, 2020) Restrepo, Clara; Álvarez, Beatriz; Valencia Delfa, José Luis; García, Marcial; Navarrete Muñoz, María A.; Ligos, José M.; Cabello, Alfonso; Prieto, Laura; Nistal, Sara; Montoya, María; Górgolas, Miguel; Rallón, Norma; Benito, José M.
    1) Background: The role of hepatitis C virus (HCV) co-infection on the T-cell homeostasis disturbances in human immunodeficiency virus (HIV)-infected patients as well as its reversion after HCV eradication with direct acting antivirals (DAAs) therapy has not been yet clarified. We extensively analyzed the effect of HCV co-infection on immune parameters of HIV pathogenesis and its evolution after HCV eradication with DAAs. (2) Methods: Seventy individuals were included in the study—25 HIV-monoinfected patients, 25 HIV/HCV-coinfected patients and 20 HIV and HCV seronegative subjects. All patients were on antiretroviral therapy and undetectable HIV-viremia. Immune parameters, such as maturation, activation, apoptosis, senescence and exhaustion of T-cells were assessed by flow cytometry. Cross-sectional and longitudinal (comparing pre- and post-DAAs data in HIV/HCV coinfected patients) analyses were performed. Univariate and multivariate (general linear model and canonical discriminant analysis -CDA-) analyses were used to assess differences between groups. (3) Results—The CDA was able to clearly separate HIV/HCV coinfected from HIV-monoinfected patients, showing a more disturbed T-cells homeostasis in HIV/HCV patients, especially activation and exhaustion of T-cells. Interestingly, those perturbations were more marked in HIV/HCV patients with increased liver stiffness. Eradication of HCV with DAAs restored some but not all the T-cells homeostasis disturbances, with activation and exhaustion of effector CD8 T-cells remaining significantly increased three months after HCV eradication. (4) Conclusions—HCV co-infection significantly impacts on several immune markers of HIV pathogenesis, especially in patients with increased liver stiffness. Eradication of HCV with DAAs ameliorates but does not completely normalize these alterations. It is of utmost relevance to explore other mechanisms underlying the immune damage observed in HIV/HCV coinfected patients with control of both HIV and HCV replication.
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    Time series clustering using trend, seasonal and autoregressive components to identify maximum temperature patterns in the Iberian Peninsula
    (Environmental and Ecological Statistics, 2023) Arnobio Palacios Gutiérrez; María Villeta López; Valencia Delfa, José Luis; Villeta López, María Del Carmen; Jose Luis Valencia Delfa; María Villeta López; Duczmal, Luiz; Cocchi, Daniela
    Time series (TS) clustering is a crucial area of data mining that can be used to identify interesting patterns. This study introduces a novel approach to obtain clusters of TS by representing them with feature vectors that define the trend, seasonality and noise components of each series in order to identify areas of the Iberian Peninsula (IP) that follow the same pattern of change in regards to maximum temperature during 1931–2009. This representation allows for dimensionality reduction, and is obtained based on singular spectrum analysis decomposition in a sequential manner, which is a well-developed methodology of TS analysis and forecasting with applications ranging from the decomposition and filtering of nonparametric TS to parameter estimation and forecasting. In this approach, the trend, seasonality and residual components of each TS corresponding to a specific area in the Iberian region are extracted using the proposed SSA methodology. Afterwards, the feature vectors of the TS are obtained by modelling the extracted components and estimating their parameters. Finally, a clustering algorithm is applied to group the TS into clusters, which are defined according to the centroids. This methodology is applied to a climate database with reasonable results that align with the defined characteristics, enabling a spatial exploration of the IP. The results identified three differentiated zones that can be used to describe how the maximum temperature varied: in the northern and central zones, an increase in temperature was noted over time, whereas in the southern zone, a slight decrease was noted. Moreover, different seasonal variations were observed across the zones.