Abstract: Cerrado is the second largest biome in Brazil and majorly contributes to the country's grain production. Previous studies on soil metagenomics from the Cerrado revealed an outstanding microbial diversity. In this study, the abundance of pathogenic fungi was analyzed using metagenomic sequences of the Cerrado soils under native vegetation, and under agriculture with no-tillage and conventional tillage. In total, 128,627 sequences of fungi were identified, with 43,439 representing pathogenic fungi and were distributed as follows: native 17,301 (40%), no-tillage 13,780 (32%), and conventional tillage 12,358 (28%). We identified 41 pathogenic fungal species associated with human and animal infections. The data analysis revealed that the native soils had a higher relative abundance of fungal sequences, similar to pathogenic species sequences, in relation to the total eukaryotic sequences, than the conventional tillage and no-tillage treatments, which observed a reduction in fungal abundance because of anthropogenic activities.

A prevenção de fraude em cartão de crédito é uma importante aplicação comercial para aplicação de abordagens de métodos de previsão e inteligência computacional. A inteligência computacional é uma associação de metodologias bio-inspiradas que incluem, como principais membros, as redes neurais, sistemas nebulosos, computação evolutiva, inteligência coletiva e computação probabilística. Recentemente, a aplicação de técnicas da inteligência computacional no suporte de tarefas de serviço, tais como detecção e identificação de anomalia, classificação de padrão, diagnóstico, prognóstico, estimação e controle, tem emergido nos ambientes industrial e comercial. Este artigo apresenta um sistema computacional eficiente baseado em metodologias da inteligência computacional para detecção de fraude em operações reais de dados de cartão de crédito em transações de comércio eletrônico. O novo método proposto é denominado FControl® de detecção de fraude e classificação integra conceitos de sistemas inteligentes híbridos baseado em abordagens de redes neurais, sistemas nebulosos e computação evolutiva. O método proposto gera soluções de qualidade em termos de eficiência e sucesso de previsão. O programa computacional do FControl® está atualmente instalado em um Intel Pentium IV 2.4 MHz (bi-processador e RAM de 4 Gbytes) na companhia Ciashop E-Commerce e está correntemente em uso para detecção de fraude para 250 companhias com serviços de comércio eletrônico.

The prevention of credit card fraud is an important commercial application for prediction methods and computational intelligence of pattern recognition approaches. Computational intelligence is an association of bio-inspired computational methodologies which are founded principally on neural networks, fuzzy systems, evolutionary computation, swarm intelligence, and probabilistic computation. The application of computational intelligence techniques in the support of service tasks such as anomaly detection and identification, pattern classification, diagnostics, prognostics, estimation and control has recently emerged in industrial and commercial environments. This paper presents an efficient system based on computational intelligence methodologies for the detection of fraud in the operation of real credit card data in E-commerce transactions. This new proposed method, called FControl® for fraud detection and classification, integrates concepts of hybrid intelligent systems based on neural networks, fuzzy systems and evolutionary computation approaches. The proposed method generates quality solutions in terms of prediction efficiency and success. The computational program of the FControl® system has been installed on an Intel Pentium IV 2.4 MHz (bi-processor and 4 Gbytes RAM) at the company Ciashop E-Commerce and is currently in use for fraud detection by 250 companies that offer E-commerce services.