Maloney Korsholm (shakepoint32)
The neurophysiological characteristics of sustained attention states are unclear in discrete multi-finger force control tasks. In this study, we developed an immersive visuo-haptic task for conducting stimulus-response measurements. Visual cues were randomly provided to signify the required amplitude and tolerance of fingertip force. Participants were required to respond to the visual cues by pressing force transducers using their fingertips. Response time variation was taken as a behavioral measure of sustained attention states during the task. 50% low-variability trials were classified as the optimal state and the other high-variability trials were classified as the suboptimal state using z-scoring over time. A 64-channel electroencephalogram (EEG) acquisition system was used to collect brain activities during the tasks. The haptics-elicited potential amplitude at 20 ~ 40 ms in latency and over the frontal-central region significantly decreased in the optimal state. Furthermore, the alpha-band power in the spectra of 8 ~ 13 Hz was significantly suppressed in the frontal-central, right temporal, and parietal regions in the optimal state. Taken together, we have identified neuroelectrophysiological features that were associated with sustained attention during multi-finger force control tasks, which would be potentially used in the development of closed-loop attention detection and training systems exploiting haptic interaction.Epilepsy is one of the most chronic brain disorder recorded from since 2000 BC. Almost one-third of epileptic patients experience seizures attack even with medicated treatment. The menace of SUDEP (Sudden unexpected death in epilepsy) in an adult epileptic patient is approximately 8-17% more and 34% in a children epileptic patient. The expert neurologist manually analyses the Electroencephalogram (EEG) signals for epilepsy diagnosis. The non-stationary and complex nature of EEG signals this task more error-prone, time-consuming and even expensive. Hence, it is essential to develop automatic epilepsy detection techniques to ensure an appropriate identification and treatment of this disease. Nowadays, graph-theory has been considered as a prominent approach in the neuroscience field. The network-based approach characterizes a hidden sight of brain activity and brain-behavior mapping. The graph-theory not even helps to understand the underlying dynamics of EEG signals at microscopic, mesoscopic, and macroscopic level but also provide the correlation among them. This paper provides a review report about graph-theory based automated epilepsy detection methods. Furthermore, it will assist the expert's neurologist and researchers with the information of complex network-based epilepsy detection and aid the technician for developing an intelligent system that improving the diagnosis of epilepsy disorder.Phytopathogens are responsible for huge losses in the agriculture sector. Amongst them, fungal phytopathogen is quite difficult to control. Many chemicals are available in the market, claiming the high activity against them. However, the development of resistance by the fungal pathogen is the main concern to overcome their menace. Nanotechnology-based products can be a potential alternative to conventional fungicides. Amongst various nanoparticles, Copper nanoparticles (CuNPs) are appearing to be a promising antifungal candidate. It can be synthesized by various methods, but the myco-fabrication appears to be an environmental-friendly approach. Selleckchem TC-S 7009 Hence, the present study is an attempt to synthesize CuNPs using Aspergillus flavus. The myco-fabricated CuNPs were characterized by UV spectrophotometer, Fourier transform infrared spectroscopy (FTIR), Nanoparticles tracking and analysis system (NTA), Transmission Electron Microscopy (TEM), X-ray diffraction (XRD) and Zeta potential measurement. Myco-fabricated CuNPs showed maximum absorbance at 602 nm and particle size ranging 5-12 nm with the le
