COPPER-INDUCED OXIDATIVE STRESS IN MAIZE SHOOTS ( ZEA MAYS L.): H 2 O 2 ACCUMULATION AND PEROXIDASES MODULATION

The effect of copper excess on growth, H 2 O 2 level and peroxidase activities were studied in maize shoots. Ten-day-old seedlings were cultured in nutrient solution that contained Cu 2+ ions at various concentra- tions (50 and 100 μM) for seven days. High concentrations of Cu 2+ ions caused significant decrease both in matter production and elongation of maize shoots. In addition, treatment with CuSO 4 increased levels of H 2 O 2 and induced changes in several peroxidase activities. Moreover, the disturbance of the physio- logical parameters was accompanied by the modulation of the peroxidase activities: GPX (Guaiacol per- oxidase, EC 1.11.1.7), CAPX (Coniferyl alcohol peroxidase, EC 1.11.1.4) and APX (Ascorbate peroxi- dase, EC.1.11.1.11). Furthermore, this modulation becomes highly significant, especially, in the presence of 100 μM of CuSO 4

Graph Embedding Using Constant Shift Embedding

In the literature, although structural representations (e.g. graph) are more powerful than feature vectors in terms of representational abilities, many robust and efficient methods for classification (unsupervised and supervised) have been developed for feature vector representations. In this paper, we propose a graph embedding technique based on the constant shift embedding which transforms a graph to a real vector. This technique gives the abilities to perform the graph classification tasks by procedures based on feature vectors. Through a set of experiments we show that the proposed technique outperforms the classification in the original graph domain and the other graph embedding techniques

A Comparison of Explicit and Implicit Graph Embedding Methods for Pattern Recognition

International audienceIn recent years graph embedding has emerged as a promising solution for enabling the expressive, convenient, powerful but computa tional expensive graph based representations to benefit from mature, less expensive and efficient state of the art machine learning models of statistical pattern recognition. In this paper we present a comparison of two implicit and three explicit state of the art graph embedding methodologies. Our preliminary experimentation on different chemoinformatics datasets illustrates that the two implicit and three explicit graph embedding approaches obtain competitive performance for the problem of graph classification

Comparing Graph Similarity Measures for Graphical Recognition

In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique