A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm.

Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. Brain-Computer Interface (BCI) is a communication system that allows humans to communicate with their environment by detecting and quantifying control signals produced from different modalities and translating them into voluntary commands for actuating an external device. For that purpose, classification the brain signals with a very high accuracy and minimization of the errors is of profound importance to the researchers. So in this study, a novel framework has been proposed to classify the binary-class electroencephalogram (EEG) data. The proposed framework is tested on BCI Competition IV dataset 1 and BCI Competition III dataset 4a. Artifact removal from EEG data is done through preprocessing, followed by feature extraction for recognizing discriminative information in the recorded brain signals. Signal preprocessing involves the application of independent component analysis (ICA) on raw EEG data, accompanied by the employment of common spatial pattern (CSP) and log-variance for extracting useful features. Six different classification algorithms, namely support vector machine, linear discriminant analysis, k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, have been compared to classify the EEG data accurately. The proposed framework achieved the best classification accuracies with logistic regression classifier for both datasets. Average classification accuracy of 90.42% has been attained on BCI Competition IV dataset 1 for seven different subjects, while for BCI Competition III dataset 4a, an average accuracy of 95.42% has been attained on five subjects. This indicates that the model can be used in real time BCI systems and provide extra-ordinary results for 2-class Motor Imagery (MI) signals classification applications and with some modifications this framework can also be made compatible for multi-class classification in the future.

CFD assessment of the effect of convective mass transport on the intracellular clearance of intracellular triglycerides in macrosteatotic hepatocytes.

Donor livers available to transplant for patients with end-stage liver disease are in severe shortage. One possible avenue to expand the donor pool is to recondition livers that would be otherwise discarded due to excessive fat content. Severely steatotic livers (also known as fatty livers) are highly susceptible to ischemia-reperfusion injury and as a result, primary liver non-function post-transplantation. Prior studies in isolated perfused rat livers suggest that "defatting" may be possible in a timeframe of a few hours; thus, it is conceivable that fatty liver grafts could be recovered by machine perfusion to clear stored fat from the organ prior to transplantation. However, studies using hepatoma cells and adult hepatocytes made fatty in culture report that defatting may take several days. Because cell culture studies were done in static conditions, we hypothesized that the defatting kinetics are highly sensitive to flow-mediated transport of metabolites. To investigate this question, we experimentally evaluated the effect of increasing flow rate on the defatting kinetics of cultured HepG2 cells and developed an in silico combined reaction-transport model to identify possible rate-limiting steps in the defatting process. We found that in cultured fatty HepG2 cells, the time required to clear stored fat down to lean control cells can be reduced from 48 to 4-6 h by switching from static to flow conditions. The flow required resulted in a fluid shear of .008 Pa, which did not adversely affect hepatic function. The reaction-transport model suggests that the transport of L-carnitine, which is the carrier responsible for taking free fatty acids into the mitochondria, is the key rate-limiting process in defatting that was modulated by flow. Therefore, we can ensure higher levels of L-carnitine uptake by the cells by choosing flow rates that minimize the limiting mass transport while minimizing shear stress.

Metabolic Flux Distribution during Defatting of Steatotic Human Hepatoma (HepG2) Cells.

Methods that rapidly decrease fat in steatotic hepatocytes may be helpful to recover severely fatty livers for transplantation. Defatting kinetics are highly dependent upon the extracellular medium composition; however, the pathways involved are poorly understood. Steatosis was induced in human hepatoma cells (HepG2) by exposure to high levels of free fatty acids, followed by defatting using plain medium containing no fatty acids, or medium supplemented with a cocktail of defatting agents previously described before. We measured the levels of 28 extracellular metabolites and intracellular triglyceride, and fed the data into a steady-state mass balance model to estimate strictly intracellular fluxes. We found that during defatting, triglyceride content decreased, while beta-oxidation, the tricarboxylic acid cycle, and the urea cycle increased. These fluxes were augmented by defatting agents, and even more so by hyperoxic conditions. In all defatting conditions, the rate of extracellular glucose uptake/release was very small compared to the internal supply from glycogenolysis, and glycolysis remained highly active. Thus, in steatotic HepG2 cells, glycolysis and fatty acid oxidation may co-exist. Together, these pathways generate reducing equivalents that are supplied to mitochondrial oxidative phosphorylation.