ABSTRACT
El efecto auxin-like consiste en la promoción del crecimiento vegetal por ácidos húmicos (AH). En este trabajo, se evaluó el efecto auxin-like de ácidos húmicos, mediante la determinación del área foliar y la superficie radicular de plántulas de maíz. Los AH fueron obtenidos por solubilización alcalina de carbón, denominados AH-NaOH y mediante solubilización bacteriana de carbón, designados AH-BSC3 y AH-BSC25. Se probó la aplicación de AH, mediante inmersión de semillas en soluciones de 20, 80 y 150mgAH/L y por aspersión foliar sobre plántulas de dos semanas. El ensayo, se desarrolló bajo condiciones de casa de malla y constó de cinco repeticiones por tratamiento; 30 días después de la siembra en suelo, se determinó el área foliar, utilizando el software ImageJ y la superficie total de raíces, mediante un algoritmo desarrollado en Matlab. Se registraron incrementos significativos en el área foliar de plantas tratadas mediante la inmersión de semillas, en soluciones de AH-BSC-3-80mg/L y 150mg/L, AH-BSC-25-20mg/L y 150mg/L, y AH-NaOH-80mg/L y plantas tratadas mediante aspersión foliar de AH-BSC-3-150mg/L, AH-BSC-25-150mg/L y AH-NaOH-150mg/L. También, se evidenciaron incrementos significativos en la superficie total de raíces de plántulas tratadas con AH-BSC25-150mg/L, AH-NaOH-80mg/L y AH-BSC3-80mg/L y todos los tratamientos de AH por aspersión. Se presentó el efecto auxin-like de AH sobre el desarrollo radicular y foliar de plántulas de maíz, a través del análisis digital de imágenes; esta herramienta puede producir información precisa para medir algunos parámetros del proceso vegetal.
The auxin-like effect consists in plant growth promotion by humic acids (HA). In this research the auxin-like effect was determined by digital image processing, both foliar and root surface of corn plants were assessed. The HA were obtained through alcaline solubilization coal, HA-NaOH and through coal bacterial solubilization, HA-CSB3 and HA-CSB25. The application of the HA solutions of 20, 80 and 150mgHA/L was evaluated through seed immersion and foliar spray on two week-old plants. The experiment was carried out under shade cloth with five replicates by treatments; 30 days after planting in soil, leaf area was measured using ImageJ software and total root surface through an algorithm of Matlab. Significant increase in leaf area of plants treated by immersion of seeds in HA-BSC3-80mg/L and 150mg/L, AH-CSB25-20mg/L and 150mg/L, and HA-NaOH-80mg/L was found, as well as in plants treated by foliar aspersion of HA-CSB3-150mg/L, AH-CSB 25-150mg/L and HA-NaOH-150mg/L. Moreover significant increase in total root surface of plants treated with HA-CSB25-150mg/L, HA-NaOH-80mg/L and HA-CSB3-80mg/L and all treatments of HA by aspersion was found. The auxin-like effect of humic acids on maize plants was evidenced through digital image analysis; this tool can produce accurate information to measure some plant development parameters.
ABSTRACT
OBJECTIVE: To evaluate the reproducibility of the calculation of breast density with DM-Scan software, which is based on the semiautomatic segmentation of fibroglandular tissue, and to compare it with the reproducibility of estimation by visual inspection. MATERIAL AND METHODS: The study included 655 direct digital mammograms acquired using craniocaudal projections. Three experienced radiologists analyzed the density of the mammograms using DM-Scan, and the inter- and intra-observer agreement between pairs of radiologists for the Boyd and BI-RADS® scales were calculated using the intraclass correlation coefficient. The Kappa index was used to compare the inter- and intra-observer agreements with those obtained previously for visual inspection in the same set of images. RESULTS: For visual inspection, the mean interobserver agreement was 0,876 (95% CI: 0,873-0,879) on the Boyd scale and 0,823 (95% CI: 0,818-0,829) on the BI-RADS® scale. The mean intraobserver agreement was 0,813 (95% CI: 0,796-0,829) on the Boyd scale and 0,770 (95% CI: 0,742-0,797) on the BI-RADS® scale. For DM-Scan, the mean inter- and intra-observer agreement was 0,92, considerably higher than the agreement for visual inspection. CONCLUSION: The semiautomatic calculation of breast density using DM-Scan software is more reliable and reproducible than visual estimation and reduces the subjectivity and variability in determining breast density.
Subject(s)
Breast Density , Mammography/methods , Software , Aged , Female , Humans , Middle AgedABSTRACT
Using the k-NN classifier in combination with the first Minkowski metric, in addition to techniques of digital image processing, we developed a computational system platform-independent, which is able to identify, to classify and to count five normal types of leukocytes: neutrophils, eosinophils, basophils, monocytes and lymphocytes. It is important to emphasize that this work does not attempt to diferentiate between smears of leukocytes coming from healthy and sick people; this is because most diseases produce a change in the differential count of leukocytes rather than in theirs forms. In the other side, the system could be used in emerging areas such as the topographic hematology and the chronobiology.
Mediante un clasificador k-NN en combinación con la primera métrica de Minkowski y técnicas de procesamiento digital de imágenes, se desarrolló un sistema computacional independiente de la plataforma, capaz de identificar, clasificar y contar cinco formas normales de leucocitos: neutrófilos, eosinófilos, basófilos, monocitos y linfocitos. Es importante enfatizar que este trabajo no intenta diferenciar entre muestras de leucocitos provenientes de gente sana y enferma, debido a que la mayoría de las enfermedades se detectan principalmente por un cambio en el conteo diferencial de leucocitos más que por cambios en su forma. Finalmente, el contador de leucocitos puede ser usado en áreas emergentes como la hematología topográfica y la cronobiología.
ABSTRACT
The design and test results of a real time QRS detector used in an ECG monitoring system via RF telemetry are presented. The detector is based on the Pan-Tompkins backward difference algorithm ...
