ÁRBOLESDE DECISIÓN PARA LA GENERACIÓN DE MÉTRICA CUANTITATIVA EN TÉRMINOS DE CALIDAD EN USABILIDAD PARA APLICATIVOS WEB

La usabilidad como principal factor en la aceptación y credibilidad de los sitios o páginas web, juega un papel y rol importante en el mercado actual, generando mayor interés en el área de desarrollo de software, por lo cual se generó un test basado en árboles de decisiones evaluado bajo características undamentales de los sitios web como la navegabilidad, diseño, búsqueda y contenido, acotados bajo una línea de importancia frente a otras. Dichos factores se componen de elementos que permiten la cuantificación y evaluación de los sitios obteniendo como resultado un valor representativo en materia de usabilidad de la página en la cual se efectuó el test. Mediante la investigación se realizaron múltiples análisis permitiendo concluir que la usabilidad permite mejorar y reducir el espacio o línea de aprendizaje entre el usuario y el sitio web, mediante la retroalimentación del test establecid

Multiplex Real-Time PCR Assay Using TaqMan Probes for the Identification of Trypanosoma cruzi DTUs in Biological and Clinical Samples

Background: Trypanosoma cruzi has been classified into six Discrete Typing Units (DTUs), designated as TcI–TcVI. In order to effectively use this standardized nomenclature, a reproducible genotyping strategy is imperative. Several typing schemes have been developed with variable levels of complexity, selectivity and analytical sensitivity. Most of them can be only applied to cultured stocks. In this context, we aimed to develop a multiplex Real-Time PCR method to identify the six T. cruzi DTUs using TaqMan probes (MTq-PCR).Methods/Principal Findings: The MTq-PCR has been evaluated in 39 cultured stocks and 307 biological samples from vectors, reservoirs and patients from different geographical regions and transmission cycles in comparison with a multi-locus conventional PCR algorithm. The MTq-PCR was inclusive for laboratory stocks and natural isolates and sensitive for direct typing of different biological samples from vectors, reservoirs and patients with acute, congenital infection or Chagas reactivation. The first round SL-IR MTq-PCR detected 1 fg DNA/reaction tube of TcI, TcII and TcIII and 1 pg DNA/reaction tube of TcIV, TcV and TcVI reference strains. The MTq-PCR was able to characterize DTUs in 83% of triatomine and 96% of reservoir samples that had been typed by conventional PCR methods. Regarding clinical samples, 100% of those derived from acute infected patients, 62.5% from congenitally infected children and 50% from patients with clinical reactivation could be genotyped. Sensitivity for direct typing of blood samples from chronic Chagas disease patients (32.8% from asymptomatic and 22.2% from symptomatic patients) and mixed infections was lower than that of the conventional PCR algorithm.Conclusions/Significance: Typing is resolved after a single or a second round of Real-Time PCR, depending on the DTU. This format reduces carryover contamination and is amenable to quantification, automation and kit production.This work received financial support from the Ministry of Science and Technology of Argentina [PICT 2011-0207 to AGS] and the National Scientific and Technical Research Council in Argentina (CONICET) [PIP 112 2011-010-0974 to AGS]. Work related to evaluation of biological samples was partially sponsored by the Pan-American Health Organization (PAHO) [Small Grants Program PAHO-TDR]; the Drugs and Neglected Diseases Initiative (DNDi, Geneva, Switzerland), Wellcome Trust (London, United Kingdom), SANOFI-AVENTIS (Buenos Aires, Argentina) and the National Council for Science and Technology in Mexico (CONACYT) [FONSEC 161405 to JMR]

Evaluation of Quality, Antioxidant Capacity, and Digestibility of Chickpea (Cicer arietinum L. cv Blanoro) Stored under N2 and CO2 Atmospheres

The aim of this work was to monitor the quality, antioxidant capacity and digestibility of chickpea exposed to different modified atmospheres. Chickpea quality (proximal analysis, color, texture, and water absorption) and the antioxidant capacity of free, conjugated, and bound phenol fractions obtained from raw and cooked chickpea, were determined. Cooked chickpea was exposed to N2 and CO2 atmospheres for 0, 25, and 50 days, and the antioxidant capacity was analyzed by DPPH (2,2′-diphenyl-1-picrylhydrazyl), ABTS (2,2′-azino-bis-[3ethylbenzothiazoline-6-sulfonic acid]), and total phenols. After in vitro digestion, the antioxidant capacity was measured by DPPH, ABTS, FRAP (ferric reducing antioxidant power), and AAPH (2,2′-Azobis [2-methylpropionamidine]). Additionally, quantification of total phenols, and UPLC-MS profile were determined. The results indicated that this grain contain high quality and high protein (18.38%). Bound phenolic compounds showed the highest amount (105.6 mg GAE/100 g) and the highest antioxidant capacity in all techniques. Cooked chickpeas maintained their quality and antioxidant capacity during 50 days of storage at 4 and −20 °C under a nitrogen atmosphere. Free and conjugated phenolic compounds could be hydrolyzed by digestive enzymes, increasing their bioaccessibility and their antioxidant capacity during each step of digestion. The majority compound in all samples was enterodiol, prevailing the flavonoid type in the rest of the identified compounds. Chickpea contains biological interest compounds with antioxidant potential suggesting that this legume can be exploited for various technologies

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts