Microbial enzymes that can hydrolyze organophosphorus compounds have been isolated, identified and characterized from different microbial species in order to use them in biodegradation of organophosphorus compounds. We isolated a bacterial strain Cons002 from an agricultural soil bacterial consortium, which can hydrolyze methyl-parathion (MP) and other organophosphate pesticides. HPLC analysis showed that strain Cons002 is capable of degrading pesticides MP, parathion and phorate. Pulsed-field gel electrophoresis and 16S rRNA amplification were performed for strain characterization and identification, respectively, showing that the strain Cons002 is related to the genus Enterobacter sp. which has a single chromosome of 4.6 Mb and has no plasmids. Genomic library was constructed from DNA of Enterobacter sp. Cons002. A gene called opdE (Organophosphate Degradation from Enterobacter) consists of 753 bp and encodes a protein of 25 kDa, which was isolated using activity methods. This gene opdE had no similarity to any genes reported to degrade organophosphates. When kanamycin-resistance cassette was placed in the gene opdE, hydrolase activity was suppressed and Enterobacter sp. Cons002 had no growth with MP as a nutrients source.

This work presents a methodology that integrates a nonsupervised learning approach (self-organizing map (SOM)) and a supervised one (a Bayesian classifier) for segmenting diseased plants that grow in uncontrolled environments such as greenhouses, wherein the lack of control of illumination and presence of background bring about serious drawbacks. During the training phase two SOMs are used: one that creates color groups of images, which are classified into two groups using K-means and labeled as vegetation and nonvegetation by using rules, and a second SOM that corrects classification errors made by the first SOM. Two color histograms are generated from the two color classes and used to estimate the conditional probabilities of the Bayesian classifier. During the testing phase an input image is segmented by the Bayesian classifier and then it is converted into a binary image, wherein contours are extracted and analyzed to recover diseased areas that were incorrectly classified as nonvegetation. The experimental results using the proposed methodology showed better performance than two of the most used color index methods.

Multiple interactions between population increase-as driving force- and pressure factors can cause damage to human-nature interactions. In this paper, we aim to identify, understand, and assess those interactions that exert effects on environment quality. The assessments of multiple interactions will allow selecting management actions to reduce negative effects, such as the loss of vegetation cover, on the environment. However, multiple interactions hinder the understanding of such complex systems. The relevance of this study is related to the support of the systems thinking approach to achieve two objectives: (1) to build a conceptual framework that facilitates the construction of a network aimed at representing the multiple interactions; (2) to build a closed system for the sake of developing a sustainable environmental management system. Thus, the performance of the implemented management actions is assessed through the feedback loop of the closed system. The proposed conceptual framework and the closed system were applied to the state of Morelos, Mexico. We highlight the following results: the systems thinking approach facilitated the construction of a conceptual framework to build understandable causal network; a set of environmental pathways were derived from the causal network and then combined to define and assess a global environmental state. Environmental pathways are composed of relationships between population increase and pressure variables that exert effects on the environment quality; the feedback loop facilitated the performance analysis of implemented management actions related to natural protected areas. The current results suggest further research to apply this study to diverse systems where multiple interactions between drivers and pressure factors damage human-nature interactions, thus exerting effects on the environmental state.

An important use of environmental indicators is oriented to know their individual impact on the whole environment quality. Nevertheless, most of the important causes of environment affectations are derived from multiple interactions between indicators which correspond more specifically to the environmental reality. The affectations derived from interactions should be analyzed and interpreted through numerical expressions representing a relevant challenge for developers of environmental indicators. To cope with the analysis and interpretation problem, we propose in this work a methodology in two senses: in a bottom-up sense a directed graph is built representing interactions between environmental indicators as behavioral relations, which exert an effect on the state of an environmental issue of a site over time (10 years); in a top-down sense to assist users in the analysis and interpretation of interactions through a computer interface that provides users with the capacity of knowing how and what relational behaviors between indicators are affecting, the most or the least, the performance of the environmental issue being studied. This methodology was applied to the analysis an interpretation of interactions between environmental variables that affect the state of an environmental quality issue related with the State of Morelos in Mexico. The results showed the adequate expressivity of a directed graph to represent interactions allowed to verify the coherence of the numerical values associated with their behaviors during a period of time and with their effects on the environmental issue under study.

En este trabajo varias fórmulas están introducidas que permiten calcular la medir la diferencia entre colores de forma perceptible, utilizando el espacio de colores YIQ. Las formulas clásicas y sus derivados que utilizan los espacios CIELAB y CIELUV requieren muchas transformaciones aritméticas de valores entrantes definidos comúnmente con los componentes de rojo, verde y azul, y por lo tanto son muy pesadas para su implementación en dispositivos móviles. Las fórmulas alternativas propuestas en este trabajo basadas en espacio de colores YIQ son sencillas y se calculan rápidamente, incluso en tiempo real. La comparación está incluida en este trabajo entre las formulas clásicas y las propuestas utilizando dos diferentes grupos de experimentos. El primer grupo de experimentos se enfoca en evaluar la diferencia perceptible utilizando diferentes fórmulas, mientras el segundo grupo de experimentos permite determinar el desempeño de cada una de las fórmulas para determinar su velocidad cuando se procesan imágenes. Los resultados experimentales indican que las formulas propuestas en este trabajo son muy cercanas en términos perceptibles a las de CIELAB y CIELUV, pero son significativamente más rápidas, lo que los hace buenos candidatos para la medición de las diferencias de colores en dispositivos móviles y aplicaciones en tiempo real.