Classifying multi-level stress responses from brain cortical EEG in Nurses and Non-health professionals using Machine Learning Auto Encoder

ObjectiveMental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress.MethodsTo overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers.ResultsThe results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%

A Bacterial Acetyltransferase Destroys Plant Microtubule Networks and Blocks Secretion

The eukaryotic cytoskeleton is essential for structural support and intracellular transport, and is therefore a common target of animal pathogens. However, no phytopathogenic effector has yet been demonstrated to specifically target the plant cytoskeleton. Here we show that the Pseudomonas syringae type III secreted effector HopZ1a interacts with tubulin and polymerized microtubules. We demonstrate that HopZ1a is an acetyltransferase activated by the eukaryotic co-factor phytic acid. Activated HopZ1a acetylates itself and tubulin. The conserved autoacetylation site of the YopJ / HopZ superfamily, K289, plays a critical role in both the avirulence and virulence function of HopZ1a. Furthermore, HopZ1a requires its acetyltransferase activity to cause a dramatic decrease in Arabidopsis thaliana microtubule networks, disrupt the plant secretory pathway and suppress cell wall-mediated defense. Together, this study supports the hypothesis that HopZ1a promotes virulence through cytoskeletal and secretory disruption

Effect of mechanical resistance on cognitive conflict in physical human-robot collaboration

© 2019 IEEE. Physical Human-Robot Collaboration (pHRC) is about the interaction between one or more human operator(s) and one or more robot(s) in direct contact and voluntarily exchanging forces to accomplish a common task. In any pHRC, the intuitiveness of the interaction has always been a priority, so that the operator can comfortably and safely interact with the robot. So far, the intuitiveness has always been described in a qualitative way. In this paper, we suggest an objective way to evaluate intuitiveness, known as prediction error negativity (PEN) using electroencephalogram (EEG). PEN is defined as a negative deflection in event related potential (ERP) due to cognitive conflict, as a consequence of a mismatch between perception and reality. Experimental results showed that the forces exchanged between robot and human during pHRC modulate the amplitude of PEN, representing different levels of cognitive conflict. We also found that PEN amplitude significantly decreases (mathrm {p} lt 0.05) when a mechanical resistance is being applied smoothly and more time in advance before an invisible obstacle, when compared to a scenario in which the resistance is applied abruptly before the obstacle. These results indicate that an earlier and smoother resistance reduces the conflict level. Consequently, this suggests that smoother changes in resistance make the interaction more intuitive

Catastrophic Drop Breakup in Electric Field

We report novel observations revealing the catastrophic breakup of water drops containing surfactant molecules, which are suspended in oil and subjected to an electric field of strength similar to 10(5) V/m. The observed breakup was distinctly different from the gradual end pinch-off or tip-streaming modes reported earlier in the literature. There was no observable characteristic deformation of the drop prior to breakup. The time scales involved in the breakup and the resultant droplet sizes were much smaller in the phenomenon observed by us. We hypothesize that this mode of drop breakup is obtained by the combined effect of an external electric field that imposes tensile stresses on the surface of the drop, and characteristic stress-strain behavior for tensile deformation exhibited by the liquid drop in the presence of a suitable surfactant, which not only lowers the interfacial tension (and hence the cohesive strength) of the drop but also simultaneously renders the interface nonductile or brittle at high enough concentration. We have identified the relevant thermodynamic parameter, viz., the sum of interfacial tension, sigma, and the Gibbs elasticity, epsilon, which plays a decisive role in determining the mode of drop breakup. The parameter (epsilon + sigma) represents the internal restoration stress of a liquid drop opposing rapid, short-time-scale perturbations or local deformations in the drop shape under the influence of external impulses or stresses. A thermodynamic "state" diagram of (epsilon + sigma) versus interfacial area per surfactant molecule adsorbed at the drop interface shows a "maximum" at a critical transition concentration (ctc). Below this concentration of the surfactant, the drop undergoes tip streaming or pinch off. Above this concentration, the drop may undergo catastrophic disintegration if the external stress is high enough to overcome the ultimate cohesive strength of the drop's interface

Renewable energy integration: Opportunities and challenges

Renewable energy (RE) is staring to be used as the panacea for solving current climate change or global warming threats. Therefore, government, utilities and research communities are working together to integrate large-scale RE into the power grid. However, there are a number of potential challenges in integrating RE with the existing grid. The major potential challenges are as follows: unpredictable power generation, week grid system and impacts on power quality (PQ) and reliability. This chapter investigates the potential challenges in integrating RE as well as distributed energy resources (DERs) with the smart power grid including the possible deployment issues for a sustainable future both nationally and internationally. Initially, the prospects of RE with their possible deployment issues were investigated. Later, a prediction model was proposed that informs the typical variation in energy generation as well as effect on grid integration using regression algorithms. This chapter also investigates the potential challenges in integrating RE into the grid through experimental and simulation analyses. © Springer-Verlag London 2013