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Resumen: Introducción: La Enfermedad Pulmonar Obstructiva Crónica (EPOC) es una afección del pulmón donde, inicialmente, el usuario no presenta molestias, lo que dificulta un diagnóstico oportuno. Para corregir algunos de los cambios que genera el EPOC a nivel pulmonar se ha estudiado el efecto de la válvula de presión espiratoria positiva (PEP), esta es utilizada para proporcionar una terapia respiratoria mediante la aplicación de presión espiratoria positiva. Materiales y Métodos: Para este trabajo se incluyeron en la búsqueda artículos científicos, revistas y revisiones sistemáticas publicados en los últimos cinco años con el fin de tener evidencia actualizada respecto a la válvula espiratoria positiva (PEP) y su implicancia en el tratamiento de la EPOC. Conclusiones: Se plantea que la aplicación de PEP mediante la válvula como instrumento pareciera mejorar la tolerancia al ejercicio, la higiene bronquial y disminuye la disnea, como también se propone que la PEP puede ayudar a mejorar el control de la hipoxemia en usuarios con diagnóstico de EPOC, sin embargo, no existe suficiente evidencia científica ni resultados evidentes. Abstract: Introduction: The chronic obstructive pulmonary disease (COPD) is a condition of the lung where the patient does not initially present discomfort, which makes early diagnosis difficult.To correct some of the changes caused by (COPD) exist the effect of the Expiratory Positive Pressure Valve (PEP) that has been studied to use it to provide respiratory therapy through the application of positive respiratory therapy. Materials and Methods: This article includes scientific information, journals and systematic reviews published in the last five years in order to have evidence about the positive spiral (PEP) and its participation in treatment (COPD). Conclusions: The application of PEP through the valve as an instrument that improves tolerance with exercise, bronchial hygiene and finally decreases dyspnea.Also it helps to improve control of hypoxia in users with the diagnosis of (COPD). But there is not enough scientific evidence or clear results about it. Palabras clave: Enfermedad Pulmonar Obstructiva Crónica (EPOC), Presión Espiratoria Positiva (PEP), Rehabilitación pulmonar., Key words: Chronic Obstructive Pulmonary Disease (COPD), Expiratory Positive Pressure (PEP), and pulmonary rehabilitation.

El propósito de este estudio fue establecer las normas para el Cuestionario de Metacomprensión Lectora (MCL) de Peronard, Crespo, Velásquez y Viramonte (2002) para la población escolar de octavo año básico, segundo y cuarto año medio, científico-humanista de la V Región de Chile, con la finalidad de posibilitar la correcta aplicación, análisis e interpretación de sus resultados con relación a los puntajes de corte establecidos.El cuestionario MCL (instrumento de 48 preguntas) se aplicó a una muestra de 3297 sujetos que cursaban octavo año básico, segundo y cuarto año medio en el año 2002, seleccionados al azar a través de un muestreo estratificado en dos etapas. En la etapa de tipificación, se determinaron los puntajes de corte con las puntuaciones brutas y puntuaciones estandarizadas T.Respecto a sus características psicométricas, la validez de contenido fue evaluada por un grupo de jueces expertos en la temática. La validez de constructo se estableció mediante un análisis factorial preliminar, que permitió examinar la estructura de los factores que componen el cuestionario, el que arrojó una variabilidad acumulada de 24,5% para seis factores. La validez de criterio, referida a la validez concurrente del instrumento se constató en las aplicaciones preliminares de esta versión y fue corroborada a través de criterio de jueces. Los resultados mostraron que el cuestionario es altamente confiable, coeficiente alfa de Cronbach 0,8124; y que discrimina y diferencia adecuadamente entre los grados o niveles del constructo que mide, con un índice de 61%.Además, se elaboraron las categorías interpretativas y el manual de uso correspondiente como así mismo un software de corrección (ISSN Nº 132219).

The OMICAS alliance is part of the Colombian government’s Scientific Ecosystem, established between 2017-2018 to promote world-class research, technological advancement and improved competency of higher education across the nation. Since the program’s kick-off, OMICAS has focused on consolidating and validating a multi-scale, multi-institutional, multi-disciplinary strategy and infrastructure to advance discoveries in plant science and the development of new technological solutions for improving agricultural productivity and sustainability. The strategy and methods described in this article, involve the characterization of different crop models, using high-throughput, real-time phenotyping technologies as well as experimental tissue characterization at different levels of the omics hierarchy and under contrasting conditions, to elucidate epigenome-, genome-, proteome- and metabolome-phenome relationships. The massive data sets are used to derive in-silico models, methods and tools to discover complex underlying structure-function associations, which are then carried over to the production of new germplasm with improved agricultural traits. Here, we describe OMICAS’ R&D trans-disciplinary multi-project architecture, explain the overall strategy and methods for crop-breeding, recent progress and results, and the overarching challenges that lay ahead in the field.

Bacterial panicle blight (BPB) caused by Burkholderia glumae is one of the main concerns for rice production in the Americas since bacterial infection can interfere with the grain-filling process and under severe conditions can result in high sterility. B. glumae has been detected in several rice-growing areas of Colombia and other countries of Central and Andean regions in Latin America, although evidence of its involvement in decreasing yield under these conditions is lacking. Analysis of different parameters in trials established in three rice-growing areas showed that, despite BPB presence, severity did not explain the sterility observed in fields. PCR tests for B. glumae confirmed low infection in all sites and genotypes, only 21.4% of the analyzed samples were positive for B. glumae. Climate parameters showed that Montería and Saldaña registered maximum temperature above 34°C, minimum temperature above 23°C, and Relative Humidity above 80%, conditions that favor the invasion model described for this pathogen in Asia. Our study found that in Colombia, minimum temperature above 23°C during 10 days after flowering is the condition that correlates with disease incidence. Therefore, this correlation, and the fact that Montería and Saldaña had a higher level of infected samples according to PCR tests, high minimum temperature, but not maximum temperature, seems to be determinant for B. glumae colonization under studied field conditions. This knowledge is a solid base line to design strategies for disease control, and is also a key element for breeders to develop strategies aimed to decrease the effect of B. glumae and high night-temperature on rice yield under tropical conditions.

With the aim to find new sources of resistance to rice hoja blanca (white leaf) disease, transmitted by the insect Tagosodes orizicolus, 660 genotypes were evaluated under greenhouse and field conditions. Seven resistant genotypes were identified, and genomic studies were performed to demonstrate that the resistance in these sources is genetically different from that of Fedearroz 2000, which is currently the variety with the most resistance to hoja blanca. These new resistance sources constitute a resource that can be used to sustainably extend hoja blanca disease management throughout all of the rice-growing regions of tropical America. This is the first report of hoja blanca resistance in indica rice and different from that of Fedearroz 2000.

Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R2 = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein.

Number of spikelets per panicle (NSP) is a key trait to increase yield potential in rice (O. sativa). The architecture of the rice inflorescence which is mainly determined by the length and number of primary (PBL and PBN) and secondary (SBL and SBN) branches can influence NSP. Although several genes controlling panicle architecture and NSP in rice have been identified, there is little evidence of (i) the genetic control of panicle architecture and NSP in different environments and (ii) the presence of stable genetic associations with panicle architecture across environments. This study combines image phenotyping of 225 accessions belonging to a genetic diversity array of indica rice grown under irrigated field condition in two different environments and Genome Wide Association Studies (GWAS) based on the genotyping of the diversity panel, providing 83,374 SNPs.Accessions sown under direct seeding in one environement had reduced Panicle Length (PL), NSP, PBN, PBL, SBN and SBL compared to those established under transplanting in the second environment. Across environments, NSP was significantly and positively correlated with PBN, SBN and PBL. However, the length of branches (PBL and SBL) was not significantly correlated with variables related to number of branches (PBN and SBN ), suggesting independent genetic control.Twenty- three GWAS sites were detected with P-values ≤ 1.0E-04 and 27 GWAS sites with p ≤ 5.9E-04. We found 17 GWAS sites related to NSP, 10 for PBN and 11 for SBN, 7 for PBL and 11 for SBL. This study revealed new regions related to NSP, but only three associations were related to both branching number (PBN and SBN) and NSP. Two GWAS sites associated with SBL and SBN were stable across contrasting environments and were not related to genes previously reported.The new regions reported in this study can help improving NSP in rice for both direct seeded and transplanted conditions. The integrated approach of high-throughput phenotyping, multi-environment field trials and GWAS has the potential to dissect complex traits, such as NSP, into less complex traits and to match single nucleotide polymorphisms with relevant function under different environments, offering a potential use for molecular breeding.

Seasonal and inter-annual climate variability have become important issues for farmers, and climate change has been shown to increase them. Simultaneously farmers and agricultural organizations are increasingly collecting observational data about in situ crop performance. Agriculture thus needs new tools to cope with changing environmental conditions and to take advantage of these data. Data mining techniques make it possible to extract embedded knowledge associated with farmer experiences from these large observational datasets in order to identify best practices for adapting to climate variability. We introduce new approaches through a case study on irrigated and rainfed rice in Colombia. Preexisting observational datasets of commercial harvest records were combined with in situ daily weather series. Using Conditional Inference Forest and clustering techniques, we assessed the relationships between climatic factors and crop yield variability at the local scale for specific cultivars and growth stages. The analysis showed clear relationships in the various location-cultivar combinations, with climatic factors explaining 6 to 46% of spatiotemporal variability in yield, and with crop responses to weather being non-linear and cultivar-specific. Climatic factors affected cultivars differently during each stage of development. For instance, one cultivar was affected by high nighttime temperatures in the reproductive stage but responded positively to accumulated solar radiation during the ripening stage. Another was affected by high nighttime temperatures during both the vegetative and reproductive stages. Clustering of the weather patterns corresponding to individual cropping events revealed different groups of weather patterns for irrigated and rainfed systems with contrasting yield levels. Best-suited cultivars were identified for some weather patterns, making weather-site-specific recommendations possible. This study illustrates the potential of data mining for adding value to existing observational data in agriculture by allowing embedded knowledge to be quickly leveraged. It generates site-specific information on cultivar response to climatic factors and supports on-farm management decisions for adaptation to climate variability.