Proteomics may help to detect subtle pollution-related changes, such as responses to mixture pollution at low concentrations, where clear signs of toxicity are absent. Also proteomics provide potential in the discovery of new sensitive biomarkers for environmental pollution. We utilized SELDI-TOF MS (surface enhanced laser desorption. / ionization time-of-flight mass spectrometry) to analyze the proteomic profile of Heterocypris incongruens exposed to several heavy metals (lead, mercury, copper, cadmium and chromium) and pesticides (emamectin benzoate, endosulfan, cypermethrin, mancozeb and paraquat dichloride). Several highly significant biomarkers were selected to make a model of classification analysis. data sets obtained from H. incongruens exposed to pollutants were investigated for differential protein expression by SELDI-TOF MS and decision tree classification. Decision tree model was developed with training set, and then validated with test set from profiling data of H. incongruens. Machine learning techniques provide a promising approach to process the information from mass spectrometry data. Even thought the identification of protein would be ideal, class discrimination does not need it. In the future, this decision tree model would be validated with various levels of pollutants to apply field samples.

Systemic acquired resistance is an important component of the disease resistance repertoire of plants. A novel syntheticchemical, Benzo (1,2,3) thiadiazole-7-carbothioic acid S-methyl ester (BTH), was shown to induce acquired resistance in wheat.BTH prote