Draft Genome Sequence of Chromate-Resistant and Biofilm-Producing Strain Pseudomonas alcaliphila 34

We report the draft genome sequence of Pseudomonas alcaliphila 34, a Cr(VI)-hyperresistant and biofilm-producing bacterium that might be used for the bioremediation of chromate-polluted soils. The genome sequence might be helpful in exploring the mechanisms involved in chromium resistance and biofilm formation

Exploring strategies for classification of external stimuli using statistical features of the plant electrical response

Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes. These electrical signals, when monitored, show both stochastic and deterministic dynamics. In this paper, we compute 11 statistical features from the raw non-stationary plant electrical signal time series to classify the stimulus applied (causing the electrical signal). By using different discriminant analysis-based classification techniques, we successfully establish that there is enough information in the raw electrical signal to classify the stimuli. In the process, we also propose two standard features which consistently give good classification results for three types of stimuli—sodium chloride (NaCl), sulfuric acid (H2SO4) and ozone (O3). This may facilitate reduction in the complexity involved in computing all the features for online classification of similar external stimuli in futur

Forward and inverse modelling approaches for prediction of light stimulus from electrophysiological response in plants

In this paper, system identification approach has been adopted to develop a novel dynamical model for describing the relationship between light as an environmental stimulus and the electrical response as the measured output for a bay leaf (Laurus nobilis) plant. More specifically, the target is to predict the characteristics of the input light stimulus (in terms of on–off timing, duration and intensity) from the measured electrical response – leading to an inverse problem. We explored two major classes of system estimators to develop dynamical models – linear and nonlinear – and their several variants for establishing a forward and also an inverse relationship between the light stimulus and plant electrical response. The best class of models are given by the Nonlinear Hammerstein–Wiener (NLHW) estimator showing good data fitting results over other linear and nonlinear estimators in a statistical sense. Consequently, a few set of models using different functional variants of NLHW has been developed and their accuracy in detecting the on–off timing and intensity of the input light stimulus are compared for 19 independent plant datasets (including 2 additional species viz. Zamioculcas zamiifolia and Cucumis sativus) under similar experimental scenario