Determination of the acid dissociation constant of the biosurfactant monorhamnolipid in aqueous solution by potentiometric and spectroscopic methods

The acid dissociation constant in water for a monorhamnolipid mixture extracted from Pseudomonas aeruginosa ATCC 9027 has been determined using potentiometry and two spectroscopic approaches at concentrations below and above the critical micelle concentration (cmc). Potentiometric titrations resulted in pKa values ranging from 4.28 +/- 0.16 to 5.50 +/- 0.06 depending on concentration. 1H NMR spectrochemical titrations at concentrations below the cmc revealed a pKa value of 4.39 +/- 0.06. ATR-FT-IR spectrochemical titrations on solutions well above the cmc gave a pKa value of 4.84 +/- 0.05. The value of 4.28 for the free rhamnolipid molecule for concentrations below the cmc differs markedly from that reported previously. However, the pKa of 5.50 for surface-adsorbed and solution aggregates correlates closely to that previously reported. Differences in these pKa values are rationalized in terms of the pH- and concentration-dependent aggregation behavior of rhamnolipids in aqueous solution.     

Comparative electrochemical inactivation of bacteria and bacteriophage

Electric fields and currents have been shown to be capable of disinfecting drinking water and reducing the numbers of bacteria and yeast in food. However, little research has been conducted regarding the effectiveness of electric fields and currents in the inactivation of viruses. The objective of this study was to compare the ability of bacteria and bacteriophage to survive exposure to direct electric current in an electrochemical cell, where they would be subject to irreversible membrane permeabilization processes, direct oxidation of cellular/viral constituents by electric current, and disinfection by electrochemically generated oxidants. Suspensions of the bacteria Escherichia coli and Pseudomonas aeruginosa and bacteriophage MS2 and PRD1 at both high (approximately 1 x 10(6)CFU or PFU/mL) and low (approximately 1 x 10(3)CFU or PFU/mL) population densities were exposed to currents ranging from 25 to 350 mA in 5s pulses. Post-exposure plaque counts of the bacteriophage were proportionally higher than bacterial culturable counts at corresponding current exposures. E. coli and MS2 were then exposed to 5 mA for 20 min at both high and low population densities. The inactivation rate of E. coli was 2.1-4.3 times greater than that of MS2. Both bacteria and bacteriophage were more resistant to exposure to direct current at higher population densities. Also, amelioration of inactivation within the electrochemical cell by the reducing agent glutathione indicates the major mechanism of inactivation in the electrochemical cell is disinfection by electrochemically generated oxidants. The implications of these results are that technologies relying upon direct current to reduce the numbers of microbes in food and water may not be sufficient to reduce the numbers of potentially pathogenic viruses and ensure the safety of the treated food or water.     

An automatic channel selection method based on the standard deviation of wavelet coefficients for motor imagery based brain–computer interfacing

The redundant data in multichannel electroencephalogram (EEG) signals significantly reduces the performance of brain–computer interface (BCI) systems. By removing redundant channels, a channel selection strategy increases the classification accuracy of BCI systems. In this work, a novel channel selection method (stdWC) based on the standard deviation of wavelet coefficients across channels is proposed to identify Motor Imagery (MI) based EEG signals. The wavelet coefficients are calculated by employing a Continuous Wavelet Transform (CWT) filter bank to decompose each trial from the EEG channel. The wavelet coefficient's standard deviation values are obtained across the channels, and these values are then sorted to determine the EEG channels with the highest standard deviation values. The channels with the largest wavelet coefficient divergence are chosen. MI trials are then spatially filtered with the Common Spatial Pattern (CSP), and CWT filter bank-based 2D images are generated from the spatially filtered trials. These images are then classified using a unique nine-layered convolutional neural network (CNN) model that combines two feature maps acquired with differing filter sizes. The proposed framework (stdWC-CSP-CNN) is evaluated using kappa score and classification accuracy on two publically accessible datasets (BCI Competition III dataset IVa and BCI Competition IV dataset 2a). The suggested framework achieved a mean test classification accuracy of 88.8% for dataset IVa from BCI Competition III and 75.03% for dataset 2a from BCI Competition IV, according to the results. The proposed channel selection method outperforms the other channel selection methods examined, according to the results. By rejecting redundant channels, the whole framework can improve the performance of MI-based BCIs.