ABSTRACT
The most commercialized Bt maize plants in Europe were transformed with genes which express a truncated form of the insecticidal delta-endotoxin (Cry1Ab) from the soil bacterium Bacillus thuringiensis (Bt) specifically against Lepidoptera. Studies on the effect of transgenic maize on non-target arthropods have mainly converged on beneficial insects. However, considering the worldwide extensive cultivation of Bt maize, an increased availability of information on their possible impact on non-target pests is also required. In this study, the impact of Bt-maize on the non-target corn leaf aphid, Rhopalosiphum maidis, was examined by comparing biological traits and demographic parameters of two generations of aphids reared on transgenic maize with those on untransformed near-isogenic plants. Furthermore, free and bound phenolics content on transgenic and near-isogenic plants were measured. Here we show an increased performance of the second generation of R. maidis on Bt-maize that could be attributable to indirect effects, such as the reduction of defense against pests due to unintended changes in plant characteristics caused by the insertion of the transgene. Indeed, the comparison of Bt-maize with its corresponding near-isogenic line strongly suggests that the transformation could have induced adverse effects on the biosynthesis and accumulation of free phenolic compounds. In conclusion, even though there is adequate evidence that aphids performed better on Bt-maize than on non-Bt plants, aphid economic damage has not been reported in commercial Bt corn fields in comparison to non-Bt corn fields. Nevertheless, Bt-maize plants can be more easily exploited by R. maidis, possibly due to a lower level of secondary metabolites present in their leaves. The recognition of this mechanism increases our knowledge concerning how insect-resistant genetically modified plants impact on non-target arthropods communities, including tritrophic web interactions, and can help support a sustainable use of genetically modified crops.
Subject(s)
Aphids , Bacillus thuringiensis , Animals , Aphids/genetics , Bacillus thuringiensis/genetics , Bacterial Proteins/genetics , Crops, Agricultural , Demography , Endotoxins/genetics , Hemolysin Proteins/genetics , Pest Control, Biological , Plants, Genetically Modified/genetics , Zea maysSubject(s)
Algorithms , Eye Movements/physiology , Electrooculography , Humans , Man-Machine SystemsSubject(s)
Computational Biology/methods , Genetic Diseases, Inborn/genetics , Genetic Diseases, Inborn/metabolism , Algorithms , Computer Simulation , Gene Expression Profiling , Gene Expression Regulation , Gene-Environment Interaction , Genetic Predisposition to Disease , Humans , Mutation , SoftwareABSTRACT
The identification of causes of genetic diseases has been carried out by several approaches with increasing complexity. Innovation of genetic methodologies leads to the production of large amounts of data that needs the support of statistical and computational methods to be correctly processed. The aim of the paper is to provide an overview of statistical and computational methods paying attention to methods for the sequence analysis and complex diseases.
Subject(s)
Computational Biology/methods , Genetic Diseases, Inborn/genetics , Statistics as Topic , Algorithms , Bayes Theorem , DNA/chemistry , Frameshift Mutation , Gene Deletion , Humans , Markov Chains , Models, Statistical , Mutation , Polymorphism, Single Nucleotide , Quantitative Trait Loci , RNA Splicing , Research Design , SoftwareABSTRACT
Kernel Methods are algorithms that, by replacing the inner product with an appropriate positive definite function, implicitly perform a nonlinear mapping of the input data into a high-dimensional feature space. In this paper, we present a kernel method for clustering inspired by the classical K-Means algorithm in which each cluster is iteratively refined using a one-class Support Vector Machine. Our method, which can be easily implemented, compares favorably with respect to popular clustering algorithms, like K-Means, Neural Gas, and Self-Organizing Maps, on a synthetic data set and three UCI real data benchmarks (IRIS data, Wisconsin breast cancer database, Spam database).
