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Browsing Artículos de conferencia by browse.metadata.categoriaods "03 Salud y bienestar"
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- ItemAlgebraic Reconstruction of Source and Attenuation in SPECT Using First Scattering Measurements(Springer, 2018) Cueva, Evelyn; Osses Alvarado, Axel Esteban; Quintana Fresno, Juan Carlos; Tejos Núñez, Cristián Andrés; Courdurier Bettancourt Matias Alejandro; Irarrazaval Mena, PabloHere we present an Algebraic Reconstruction Technique (ART) for solving the identification problem in Single Photon Emission Computed Tomography (SPECT). Traditional reconstruction for SPECT is done by finding the radiation source, nevertheless the attenuation of the surrounding tissue affects the data. In this context, ballistic and first scattering information are used to recover source and attenuation simultaneously. Both measurements are related with the Attenuated Radon Transform and a Klein-Nishina angular type dependency is considered for the scattering. The proposed ART algorithm allow us to obtain good reconstructions of both objects in a few number of iterations.
- ItemANTICHOLINERGIC THERAPY IN CYSTIC-FIBROSIS PATIENTS(EDITIONS SCIENTIFIQUES ELSEVIER, 1995) Sánchez Díaz, Ignacio
- ItemArsenic-related cancer mortality in Northern Chile, 1989-98(LIPPINCOTT WILLIAMS & WILKINS, 2004) Bates, M; Marshall, G; Ferreccio, C; Smith, A
- ItemBasic aspects of oviduct function(PARTHENON PUBLISHING GROUP LTD, 1997) Croxatto, HB; Ortiz, ME; Villalon, M; Cardenas, H; Imarai, M; Hermoso, M; Velasquez, L; Orihuela, P; Coutifaris, C; Mastroianni, L
- ItemBayesian Modeling of Censored Partial Linear Models using Scale-Mixtures of Normal Distributions(AMER INST PHYSICS, 2012) Castro, Luis M.; Lachos, Victor H.; Ferreira, Guillermo P.; Arellano Valle, Reinaldo B.; Stern, JM; Lauretto, MD; Polpo, A; Diniz, MARegression models where the dependent variable is censored (limited) are usually considered in statistical analysis. Particularly, the case of a truncation to the left of zero and a normality assumption for the error terms is studied in detail by [1] in the well known Tobit model. In the present article, this typical censored regression model is extended by considering a partial linear model with errors belonging to the class of scale mixture of normal distributions. We achieve a fully Bayesian inference by adopting a Metropolis algorithm within a Gibbs sampler. The likelihood function is utilized to compute not only some Bayesian model selection measures but also to develop Bayesian case-deletion influence diagnostics based on the q-divergence measures. We evaluate the performances of the proposed methods with simulated data. In addition, we present an application in order to know what type of variables affect the income of housewives.
- ItemCyclooxygenase-2/PGE(2) system mediates the effect of platelet activating factor in oviductal ciliated cells.(SOC STUDY REPRODUCTION, 1998) Villalon, M; Morales, B; Barrera, N; Oliva, C
- ItemEffect of prostaglandins F-2 alpha and E-2 on human sperm motility(CAMBRIDGE UNIV PRESS, 2000) Kerr, B; Villalon, MJ
- ItemEffect of sex hormones in the swelling control of human cervical mucus(CAMBRIDGE UNIV PRESS, 2000) Espinosa, M; Villalon, MJ
- ItemEMBRYO SECRETED FACTORS INCREASE THE FREQUENCY OF CILIARY BEAT OF HAMSTER OVIDUCTAL CILIATED CELLS(SOC STUDY REPRODUCTION, 1995) HERMOSO, MA; VILLALON, MJ
- ItemExamining the effect of freezing on starch gelatinization during heating at high rates using online in situ hot-stage video-microscopy and differential scanning calorimetry(INST CHEMICAL ENGINEERS, 2016) Molina Maydl, María Teresa; Leiva Maturana, Ana María; Bouchon Aguirre, Pedro Alejandro
- ItemHEALTH-RELATED QUALITY OF LIFE OF EATING DISORDERS IN CHILEAN ADOLESCENTS(ELSEVIER SCIENCE INC, 2019) Inostroza, Carolina; Urrejola, Pascuala; Zubarew, Tamara; Loreto Correa, Maria; Gil, Aurora A.; Bedregal, Paula; Vogel, Melina
- ItemHepatic fatty acid profile in mice with nonalcoholic fatty liver disease using magnetic resonance spectroscopy(2019) Xavier, Aline Carvalho da Silva; Zacconi, Flavia C. M.; Cabrera García, Daniel Alejandro; Fuenzalida, Karen; Andía Kohnenkampf, Marcelo EdgardoNonalcoholic fatty liver disease (NAFLD) is characterized by the accumulation of intracellular fatty acids in the liver. The only method to confirm the stage of this disease is the biopsy, but it is invasive and risky to patients. The idea of defining a classifier using magnetic resonance spectroscopy (MRS) emerges due to the need to find a way to replace biopsy with a non-invasive method that can classify NAFLD based on the chemical structure of fatty acids stored in the liver. The purpose of this study is to investigate and compare the composition of fatty acids to the metabolites signals in MRS in NAFLD mice liver at 2 time-point during the progression of the disease. A group of C57BL/6 mice was fed with high-fat diet for one month (N = 8) and for three months (N = 6). First, we made a histological analysis to the liver. Then, we analysed the fatty acids with gas chromatography (GC) and MRS. As a result, the histological analysis showed the progression of fat content, and the GC analysis detected a different fatty acid liver composition during the progression of NAFLD along with an increase of the total fat storage in the liver. The differences in the composition fatty acids are also reflected in the MR Spectrum, which could have clinical potential for monitoring the progression of this disease with a non-invasive technique.
- ItemINTRACELLULAR CALCIUM-CONCENTRATION INCREASES IN MONKEY OVIDUCTAL CILIATED CELLS AFTER STIMULATION WITH EXTRACELLULAR ATP(SOC STUDY REPRODUCTION, 1995) VILLALON, MJ; DANOVARO, MC; HINDS, TR
- ItemPIVOT: Prompting for Video Continual Learning(2023) Villa Ojeda, Andres Felipe; Alcazar, Juan Leon; Alfarra, Motasem; Alhamoud, Kumail; Hurtado, Julio; Heilbron, Fabian Caba; Soto, Alvaro; Ghanem, BernardModern machine learning pipelines are limited due to data availability, storage quotas, privacy regulations, and expensive annotation processes. These constraints make it difficult or impossible to train and update large-scale models on such dynamic annotated sets. Continual learning directly approaches this problem, with the ultimate goal of devising methods where a deep neural network effectively learns relevant patterns for new (unseen) classes, without significantly altering its performance on previously learned ones. In this paper, we address the problem of continual learning for video data. We introduce PIVOT, a novel method that leverages extensive knowledge in pre-trained models from the image domain, thereby reducing the number of trainable parameters and the associated forgetting. Unlike previous methods, ours is the first approach that effectively uses prompting mechanisms for continual learning without any in-domain pre-training. Our experiments show that PIVOT improves state-of-the-art methods by a significant 27% on the 20-task ActivityNet setup.
- ItemPIVOT: Prompting for Video Continual Learning(IEEE Computer Soc., 2023) Villa Ojeda, Andres Felipe; Alcazar, Juan Leon; Alfarra, Motasem; Alhamoud, Kumail; Hurtado, Julio; Heilbron, Fabian Caba; Soto, Alvaro; Ghanem, BernardModern machine learning pipelines are limited due to data availability, storage quotas, privacy regulations, and expensive annotation processes. These constraints make it difficult or impossible to train and update large-scale models on such dynamic annotated sets. Continual learning directly approaches this problem, with the ultimate goal of devising methods where a deep neural network effectively learns relevant patterns for new (unseen) classes, without significantly altering its performance on previously learned ones. In this paper, we address the problem of continual learning for video data. We introduce PIVOT, a novel method that leverages extensive knowledge in pre-trained models from the image domain, thereby reducing the number of trainable parameters and the associated forgetting. Unlike previous methods, ours is the first approach that effectively uses prompting mechanisms for continual learning without any in-domain pre-training. Our experiments show that PIVOT improves state-of-the-art methods by a significant 27% on the 20-task ActivityNet setup.
- ItemPreimplantational hamster embryos produce prostaglandin E(2) and platelet activating factor in vitro(SOC STUDY REPRODUCTION, 1996) Hermoso, M; Magness, RR; Bavister, B; Villalon, M
- ItemRENAL FUNCTION IN THE PERIOPERATIVE PERIOD OF CARDIAC SURGERY WITH CARDIOPULMONARY BYPASS, IN NEWBORNS WITH COMPLEX CONGENITAL HEART DISEASES: USE OF THE BIOMARKER KIM-1.(LIPPINCOTT WILLIAMS & WILKINS, 2016) Borchert, E.; Lema, G.; Jalil, R.; Guzman, A. M.; De la Fuente, R.; Gomez, M.; Fuentes, D.
- ItemStudy Designs in Genomic Epidemiology(ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD, 2020) Santos Martin, José Luis; Rivadeneira, FernandoThe choice of an appropriate study design is a critical first step in any genomic epidemiology research, especially considering the current unprecedented capacity of generating massive information derived from genomics and other "-omics” disciplines. In this chapter, we will review the main types of study designs used to discover novel genes or gene variants related to human diseases, as well as to assess their quantitative impact on disease risk. Several study designs are commonly used in the field of epidemiology (for example, cohort, classical case-control, and case-cohort studies). In contrast, other study designs based on the observation of resemblance among relatives come primarily from the field of human genetics, such as family aggregation, adoption, and monozygotic-dizygotic twin studies. Finally, the massive incorporation of genetic markers and next-generation sequencing data has been used in linkage studies of nuclear or multigenerational families, genomic studies of population isolates, and genome-wide association studies.
- ItemSU‐E‐T‐466: TCP and NTCP: Is That All?(2012) Sánchez Nieto, Beatriz; Expósito, M. R.; Terrón, J. A.; Paiusco, M.; Cagni, E.; Ghetti, C.; Filice, S.; Grishchuk, D.; Mateos, Juan Carlos; Roselló, J.; Planes, Domingo; Núñez, L.; Sánchez Doblado, Francisco
- ItemTeledermatology and Artificial Intelligence(2022) Navarrete Dechent, Cristián PatricioBackground: The use of artificial intelligence (AI) algorithms for the diagnosis of skin diseases has shown promise in experimental settings but has not yet been tested in real-life conditions. The COVID-19 pandemic led to a worldwide disruption of health systems, increasing the use of telemedicine. There is an opportunity to include AI algorithms in the teledermatology workflow. Objective: The aim of this study is to test the performance of and physicians’ preferences regarding an AI algorithm during the evaluation of patients via teledermatology. Methods: We performed a prospective study in 340 cases from 281 patients using patient-taken photos during teledermatology encounters. The photos were evaluated by an AI algorithm and the diagnosis was compared with the clinician’s diagnosis. Physicians also reported whether the AI algorithm was useful or not. Results: The balanced (in-distribution) top-1 accuracy of the algorithm (47.6%) was comparable to the dermatologists (49.7%) and residents (47.7%) but superior to the general practitioners (39.7%; P=.049). Exposure to the AI algorithm results was considered useful in 11.8% of visits (n=40) and the teledermatologist correctly modified the real-time diagnosis in 0.6% (n=2) of cases. Algorithm performance was associated with patient skin type and image quality. Conclusions: AI algorithms appear to be a promising tool in the triage and evaluation of lesions in patient-taken photographs via telemedicine.