No abstract

No abstract

Arsenic (As) is the one of the main environmental pollutant and phytoremediation is an effective tool for its removal of the environment. In this study, Pistia stratiotes were exposed to seven As concentrations (0, 3, 7, 10, 13, 16 and 20 µM) and then, the influence of this metalloid on growth, mineral nutrition and photosynthesis were analyzed. It was observed that this species have a high affinity for As and pollutant uptake occurs rapidly. The uptake of Cu, Mn, Fe and P increased until the concentration of 13 µM, decreasing in higher concentrations.The Mg content also decreased from this same concentration. No effects were observed in the uptake of K, Ca and Zn. Growth rate and photosynthetic pigments content were negatively affected by As. Despite this decrease, the growth was maintained up to the concentration of 13 µM of As. The maintenance of growth and the change in nutrients uptake are probably related with the increase in antioxidant capacity of the plant, indicating resistance to the pollutant. In this way, P. stratiotes is probably an efficient phytoremediator of As, even when in concentrations up to one hundred times greater than those permitted in water for human consumption. Key words: Aquatic plant species; environmental pollution; phytoremediation. RESUMEN El Arsénico (As) es uno de los principales contaminantes ambientales y la fitorremediación se presenta como una herramienta efectiva para retirar este elemento del medio ambiente. En el presente estudio se analizó la influencia de este elemento en el crecimiento, nutrición mineral y fotosíntesis dePistia stratiotes bajo siete concentraciones de As (0, 3, 7, 10, 13, 16 y 20 µM

Germplasm classification by species requires specific knowledge on/of the culture of interest. Therefore, efforts aimed at automation of this process are necessary for the efficient management of collections. Automation of germplasm classification through artificial neural networks may be a viable and less laborious strategy. The aims of this study were to verify the classification potential of Capsicum accessions regarding/ the species based on morphological descriptors and artificial neural networks, and to establish the most important descriptors and the best network architecture for this purpose. Five hundred and sixty-four plants from 47 Brazilian Capsicum accessions were evaluated. Neural networks of multilayer perceptron type were used in order to automate the species identification through 17 morphological descriptors. Six network architectures were evaluated, and the number of neurons in the hidden layer ranged from 1 to 6. The relative importance of morphological descriptors in the classification process was established by Garson's method. Corolla color, corolla spot color, calyx annular constriction, fruit shape at pedicel attachment, and fruit color at mature stage were the most important descriptors. The network architecture with 6 neurons in the hidden layer is the most appropriate in this study. The possibility of classifying Capsicum plants regarding/ the species through artificial neural networks with 100 % accuracy was verified.