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Resumen Introducción. La pitahaya, que pertenece a la familia de las cactáceas, es una fruta exótica de múltiples usos gastronómicos y nutracéuticos, y se consume desde tiempos precolombinos. En Costa Rica su consumo no es tradicional y el cultivo comercial no es muy conocido por los agricultores, por lo que es necesario estimular el interés como cultivo alternativo y económicamente rentable principalmente para la región Chorotega y Región Pacífico Central. Objetivo. Evaluar el efecto de diferentes tamaños de cladodios y soluciones naturales (bioestimulantes), sobre el crecimiento, desarrollo de raíces y brotes. Materiales y métodos. El experimento se desarrolló en la Finca Experimental de Santa Cruz de la Universidad de Costa Rica, durante 2016 - 2017. Se desarrollaron dos experimentos; en el primero se utilizaron cladodios de 40 cm bajo tres soluciones naturales (agua de pipa, coco y lentejas) a concentraciones de 150, 300 y 600 ml.L-1. En el segundo, se evaluó el tamaño de los cladodios entre los 20 y 110 cm. Todos los cladodios se sembraron durante 60 días en bolsas de vivero (2 L) y luego se midió el largo y ancho del brote, longitud y número de raíces, peso seco y fresco de raíces, y la medida alométrica''LxA''. Resultados. La solución con agua de coco a 300 y 600 ml.L-1mostró 8 cm más de longitud del brote que el testigo, mientras que el agua de pipa a 300 ml.L-1evidenció el mayor peso seco y fresco de las raíces. Asimismo, se demostró que a un mayor tamaño del cladodio se presenta una mayor brotación. Conclusión. Tamaños de cladodios de 400 cm2en LxA (> 60 cm) garantiza el mejor desarrollo de la semilla asexual y los cladodios que presenten un área de 200 y 400 cm2(≈ 30 - 60 cm) son de calidad media. Abstract Introduction. Dragon fruit, which belongs to the cactacea family, is an exotic fruit with multiple gastronomic and nutraceutical uses, and has been consumed since pre-Columbian times. In Costa Rica its consumption is not traditional and commercial cultivation is not well known by farmers, thus it is necessary to stimulate interest as an alternative and economically profitable crop, mainly for the Chorotega and Central Pacific region. Objective. To evaluate the effect of different cladode sizes and natural solutions (biostimulants) on growth, root development and shoots. Material and methods. The experiment was conducted at the Santa Cruz Experimental Farm of the University of Costa Rica, during 2016 and 2017. Two experiments were developed; in the first, 40 cm cladodes were used under three natural solutions (green coconut water, mature coconut water, and lentil extract) at concentrations of 150, 300 and 600 ml.L-1. In the second, cladode size between 20 and 110 cm was evaluated. All cladodes were sown for 60 days in nursery bags (2 L) and then length and width of the shoots, length and number of roots, dry and fresh weight of roots, and the allometric measurement''LxA'' were measured. Results. The solution with mature coconut water at 300 and 600 ml.L-1showed 8 cm longer shoots than the control, while green coconut water at 300 ml.L-1showed the highest dry and fresh root weight. Likewise, it was shown that a larger cladode size presents greater shoots. Conclusion. Cladode size of 400 cm2in LxA (> 60 cm) guarantee the best development of the asexual seed and cladodes with an area of 200 - 400 cm2(≈ 30 - 60 cm) are of medium quality.

Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition. The approach presented here is based on the analysis of brightness distribution patterns present in rectangular segments (here called "characteristic vectors") from the ultrasound digital images. In a first step we identified and eliminated the skin and subcutaneous tissue (fat and muscle) in lung ultrasound frames, and the "characteristic vectors"were analyzed using standard neural networks using artificial intelligence methods. We analyzed 60 lung ultrasound frames corresponding to 21 children under age 5 years (15 children with confirmed pneumonia by clinical examination and X-rays, and 6 children with no pulmonary disease) from a hospital based population in Lima, Peru. Lung ultrasound images were obtained using an Ultrasonix ultrasound device. A total of 1450 positive (pneumonia) and 1605 negative (normal lung) vectors were analyzed with standard neural networks, and used to create an algorithm to differentiate lung infiltrates from healthy lung. A neural network was trained using the algorithm and it was able to correctly identify pneumonia infiltrates, with 90.9% sensitivity and 100% specificity. This approach may be used to develop operator-independent computer algorithms for pneumonia diagnosis using ultrasound in young children.

Pneumonia is one of the major causes of child mortality, but it is curable if one can achieves early diagnostics. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. The use of ultrasound to detect pneumonia is limited by the image analysis for interpretation, which is carried by human experts. Pattern recognition and image analysis is a potential tool to facilitate recognition of pneumonia consolidates in absence of medical experts for automatic diagnostics. To perform an automatic analysis of lung ultrasound images for pneumonia detection, the noise introduced by the image portion of the skin, notably complicates the processing and interpretation. This paper presents a methodology to recognize and eliminate the portion of the skin in lung ultrasound images.

International audience; Pneumonia is one of the major causes of child mortality. Unfortunately, in developing countries there is a lack of infrastructure and medical experts in rural areas to provide the required diagnostics opportunely. Lung ultrasound echography has proved to be an important tool to detect lung consolidates as evidence of pneumonia. This paper presents a method for automatic diagnostics of pneumonia using ultrasound imaging of the lungs. The approach presented here is based on the analysis of patterns present in rectangular segments from the ultrasound digital images. Specific features from the characteristic vectors were obtained and classified with standard neural networks. A training and testing set of positive and negative vectors were compiled. Vectors obtained from a single patient were included only in the testing or in the training set, but never in both. Our approach was able to correctly classify vectors with evidence of pneumonia, with 91.5% sensitivity and 100% specificity.