Browsing by Author "Sepulveda, M."

Now showing 1 - 4 of 4
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    Diffusion tensor imaging metrics associated with future disability in multiple sclerosis
    (2023) Lopez-Soley, E.; Martinez-Heras, E.; Solana, E.; Solanes, A.; Radua, J.; Vivo, F.; Prados, F.; Sepulveda, M.; Cabrera-Maqueda, J. M.; Fonseca, E.; Blanco, Y.; Alba-Arbalat, S.; Martinez-Lapiscina, E. H.; Villoslada, P.; Saiz, A.; Llufriu, S.
    The relationship between brain diffusion microstructural changes and disability in multiple sclerosis (MS) remains poorly understood. We aimed to explore the predictive value of microstructural properties in white (WM) and grey matter (GM), and identify areas associated with mid-term disability in MS patients. We studied 185 patients (71% female; 86% RRMS) with the Expanded Disability Status Scale (EDSS), timed 25-foot walk (T25FW), nine-hole peg test (9HPT), and Symbol Digit Modalities Test (SDMT) at two time-points. We used Lasso regression to analyse the predictive value of baseline WM fractional anisotropy and GM mean diffusivity, and to identify areas related to each outcome at 4.1 years follow-up. Motor performance was associated with WM (T25FW: RMSE=0.524, R-2=0.304; 9HPT dominant hand: RMSE=0.662, R-2=0.062; 9HPT non-dominant hand: RMSE=0.649, R-2=0.139), and SDMT with GM diffusion metrics (RMSE=0.772, R-2=0.186). Cingulum, longitudinal fasciculus, optic radiation, forceps minor and frontal aslant were the WM tracts most closely linked to motor dysfunction, and temporal and frontal cortex were relevant for cognition. Regional specificity related to clinical outcomes provide valuable information that can be used to develop more accurate predictive models that could improve therapeutic strategies.
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    Geographical variation in the use of intertidal rocky shores by the lizard Microlophus atacamensis in relation to changes in terrestrial productivity along the Atacama Desert coast
    (2008) Farina, J. M.; Sepulveda, M.; Reyna, M. V.; Wallem, K. P.; Ossa-Zazzali, P. G.
    The movement of materials and organisms between ecosystems is a common process in nature.
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    Hunter-Gatherer Mobility Strategies in the High Andes of Northern Chile during the Late Pleistocene-Early Holocene Transition (ca. 11,500-9500 CAL BP)
    (2017) Osorio, D.; Capriles, J.; Ugalde, P.; Herrera, K.; Sepulveda, M.; Gayo Hernández, Eugenia Monserrat; Latorre H., Claudio; Jackson, D.; De Pol-Holz, R.; Santoro, C.
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    Searching for Changing-state AGNs in Massive Data Sets. I. Applying Deep Learning and Anomaly-detection Techniques to Find AGNs with Anomalous Variability Behaviors
    (2021) Sanchez-Saez, P.; Lira, H.; Marti, L.; Sanchez-Pi, N.; Arredondo, J.; Bauer, F. E.; Bayo, A.; Cabrera-Vives, G.; Donoso-Oliva, C.; Estevez, P. A.; Eyheramendy, S.; Forster, F.; Hernandez-Garcia, L.; Arancibia, A. M. Munoz; Perez-Carrasco, M.; Sepulveda, M.; Vergara, J. R.
    The classic classification scheme for active galactic nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs. The physical mechanism behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods that are able to detect AGNs right in the act of changing state. Here we present an anomaly-detection technique designed to identify AGN light curves with anomalous behaviors in massive data sets. The main aim of this technique is to identify CSAGN at different stages of the transition, but it can also be used for more general purposes, such as cleaning massive data sets for AGN variability analyses. We used light curves from the Zwicky Transient Facility data release 5 (ZTF DR5), containing a sample of 230,451 AGNs of different classes. The ZTF DR5 light curves were modeled with a Variational Recurrent Autoencoder (VRAE) architecture, that allowed us to obtain a set of attributes from the VRAE latent space that describes the general behavior of our sample. These attributes were then used as features for an Isolation Forest (IF) algorithm that is an anomaly detector for a "one class" kind of problem. We used the VRAE reconstruction errors and the IF anomaly score to select a sample of 8809 anomalies. These anomalies are dominated by bogus candidates, but we were able to identify 75 promising CSAGN candidates.