Multimodal analysis of electroencephalographic and electrooculographic signals.

Electrooculography (EOG) is a method to concurrently obtain electrophysiological signals accompanying an Electroencephalography (EEG), where both methods have a common cerebral pattern and imply a similar medical significance. The most common electrophysiological signal source is EOG that contaminated the EEG signal and thereby decreases the accuracy of measurement and the predicated signal strength. In this study, we introduce a method to improve the correction efficiency for EOG artifacts (EOAs) on raw EEG recordings: We retrieve cerebral information from three EEG signals with high system performance and accuracy by applying feature engineering and a novel machine-learning (ML) procedure. To this end, we use two adaptive algorithms for signal decomposition to remove EOAs from multichannel EEG signals: empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition (CEEMD), both using the Hilbert-Huang transform. First, the signal components are decomposed into multiple intrinsic mode functions. Next, statistical feature extraction and dimension reduction using principal component analysis are employed to select optimal feature sets for the ML procedure that is based on classification and regression models. The proposed CEEMD algorithm enhances the accuracy compared to the EMD algorithm and considerably improves the multi-sensory classification of EEG signals. Models of three different categories are applied, and the classification is based on a K-nearest neighbor (k-NN) algorithm, a decision tree (DT) algorithm, and a support vector machine (SVM) algorithm with accuracies of 94% for K-NN, 75% for DT, and 69% for SVM. For each classification model, a regression learner is used to assist as an evidence rule for the proposed artificial system and to influence the learning process from classification and regression models. The regression learning algorithms applied include algorithms based on an ensemble of trees (ET), a DT, and a SVM. We find that the ET-based regression model exhibits a determination coefficient R2 = 1.00 outperforming the other two approaches with R2 = 0.80 for DT and R2 = 0.76 for SVM.

Glucose modulates copper induced changes in photosynthesis, ion uptake, antioxidants and proline in Cucumis sativus plants.

Glucose is recognized as signaling molecule that regulates growth and development of plants under various environmental cues, but their effect in regulation of copper induced toxicity in plants is not yet investigated. This study revealed the effect of exogenously sourced glucose on Cucumber plants exposed to increasing concentration of copper. Glucose mediated response on growth performance, photosynthetic efficiency, antioxidant enzymes, oxidative stress markers, ion uptake were analyzed in the presence and absence of copper. Glucose alone and in combination with lower concentration of copper improved the growth, photosynthetic performance, and antioxidant capacity of cucumber plants. However, higher concentrations of copper alone showed oxidative damage through increased electrolyte leakage, H2O2 accumulation, lipid peroxidation and reduced uptake of macronutrients. Application of glucose to copper-stressed plants enhanced activities of Rubisco, antioxidant enzymes, proline accumulation and maintained copper level in aerial parts of plants. These enhanced activities of antioxidant enzymes, proline accumulation, uptake of NPK and maintained equilibrium of copper in plants, leading to detoxification of copper stress in cucumber plants. This study provides an understanding that exogenous application of glucose can be employed as vital biochemical approach in alleviating copper-induced toxicity and could be utilized as phytoremediation technique for removal of excess transition metal from polluted soil.

Exogenous methylglyoxal enhances the reactive aldehyde detoxification capability and frost-hardiness of wheat.

Cold-acclimation is essential for the development of adequate frost-hardiness in cereals and therefore sudden freezes can cause considerable damage to the canopy. However, timely adding of an appropriate signal in the absence of cold acclimation may also harden wheat for the upcoming freeze. The feasibility of the promising signal molecule methylglyoxal was tested here for such applications and the signal mechanism was studied in bread wheat (Triticum aestivum L.) and durum wheat (Triticum turgidum L. ssp. durum). Spraying with 10 mM methylglyoxal did not decrease the fresh weight and photosynthetic parameters in most wheat varieties at growth temperature (21 °C). Photosynthetic parameters even improved and chlorophyll content increased in some cases. Increased transcript level of glutathione-S-transferases and omega-3 fatty acid desaturases was detected by qPCR 6 h after the last methylglyoxal spray. Aldo-keto reductase and glyoxalase enzyme activities, as well as sorbitol content of wheat plants increased 24 h after the last 10 mM methylglyoxal spray in most of the cultivars. These mechanisms may explain the increased freezing survival of methylglyoxal pretreated wheat plants from less than 10% to over 30%. Our results demonstrate that exogenous methylglyoxal treatment can be safely added to wheat plants as preparatory treatment without detrimental effects but inducing some of the stress-protective mechanisms, which contribute to frost-hardiness.