Combining ICA Clustering and Power Spectral Density for Feature Extraction of Mental Fatigue of Spinal Cord Injury Patients

© 2019 IEEE. This paper presents the combination of clustering-based independent component analysis (ICASSO) and power spectral density (PSD) as a features extractor of mental fatigue from spinal cord injury (SCI) patients. Initially, the results show that SCI and abled-bodied groups have no differences in EEG for alert and mental fatigue states. Further, the coefficient determination (R2) is calculated for testing the variation of data alert vs. fatigue on the SCI group, resulting in a lower R2 for proposed combination of ICASSO and PSD method compared to the PSD method only. With the lower R2 values, this shows that the proposed method ICASSO and PSD is able to provide superior distinction for separating fatigue vs. alert data variation. The statistical significance is found across four EEG bands and EEG channels

EEG-based driver fatigue detection using hybrid deep generic model

© 2016 IEEE. Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG

Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification

© 2017 IEEE. This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system

Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module

© 2015 IEEE. An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p < 0.05)

Improving EEG-based driver fatigue classification using sparse-deep belief networks

© 2017 Chai, Ling, San, Naik, Nguyen, Tran, Craig and Nguyen. This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively

Post-translational regulation of metabolism in fumarate hydratase deficient cancer cells.

Deregulated signal transduction and energy metabolism are hallmarks of cancer and both play a fundamental role in tumorigenesis. While it is increasingly recognised that signalling and metabolism are highly interconnected, the underpinning mechanisms of their co-regulation are still largely unknown. Here we designed and acquired proteomics, phosphoproteomics, and metabolomics experiments in fumarate hydratase (FH) deficient cells and developed a computational modelling approach to identify putative regulatory phosphorylation-sites of metabolic enzymes. We identified previously reported functionally relevant phosphosites and potentially novel regulatory residues in enzymes of the central carbon metabolism. In particular, we showed that pyruvate dehydrogenase (PDHA1) enzymatic activity is inhibited by increased phosphorylation in FH-deficient cells, restricting carbon entry from glucose to the tricarboxylic acid cycle. Moreover, we confirmed PDHA1 phosphorylation in human FH-deficient tumours. Our work provides a novel approach to investigate how post-translational modifications of enzymes regulate metabolism and could have important implications for understanding the metabolic transformation of FH-deficient cancers with potential clinical applications

Glycosylation characterization of therapeutic mAbs by top- and middle-down mass spectrometry

A reference monoclonal antibody IgG1 and a fusion IgG protein were analyzed by top- and middle-down mass spectrometry with multiple fragmentation techniques including electron transfer dissociation (ETD) and matrix-assisted laser desorption ionization in-source decay (MALDI-ISD) to investigate heterogeneity of glycosylated protein species. Specifically, glycan structure, sites, relative abundance levels, and termini structural conformation were investigated by use of Fourier transform ion cyclotron resonance (FT-ICR) or high performance liquid chromatography electrospray ionization (HPLC-ESI) linked to an Orbitrap. Incorporating a limited enzymatic digestion by immunoglobulin G-degrading enzyme Streptococcus pyogenes (IdeS) with MALDI-ISD analysis extended sequence coverage of the internal region of the proteins without pre-fractionation. The data in this article is associated with the research article published in Journal of Proteomics (Tran et al., 2015)

OXSR1 inhibits inflammasome activation by limiting potassium efflux during mycobacterial infection.

Pathogenic mycobacteria inhibit inflammasome activation to establish infection. Although it is known that potassium efflux is a trigger for inflammasome activation, the interaction between mycobacterial infection, potassium efflux, and inflammasome activation has not been investigated. Here, we use Mycobacterium marinum infection of zebrafish embryos and Mycobacterium tuberculosis infection of THP-1 cells to demonstrate that pathogenic mycobacteria up-regulate the host WNK signalling pathway kinases SPAK and OXSR1 which control intracellular potassium balance. We show that genetic depletion or inhibition of OXSR1 decreases bacterial burden and intracellular potassium levels. The protective effects of OXSR1 depletion are at least partially mediated by NLRP3 inflammasome activation, caspase-mediated release of IL-1β, and downstream activation of protective TNF-α. The elucidation of this druggable pathway to potentiate inflammasome activation provides a new avenue for the development of host-directed therapies against intracellular infections

Adsorption mechanism of hexavalent chromium onto layered double hydroxides-based adsorbents: A systematic in-depth review

© 2019 Elsevier B.V. An attempt has been made in this review to provide some insights into the possible adsorption mechanisms of hexavalent chromium onto layered double hydroxides-based adsorbents by critically examining the past and present literature. Layered double hydroxides (LDH) nanomaterials are typical dual-electronic adsorbents because they exhibit positively charged external surfaces and abundant interlayer anions. A high positive zeta potential value indicates that LDH has a high affinity to Cr(VI) anions in solution through electrostatic attraction. The host interlayer anions (i.e., Cl−, NO3−, SO42−, and CO32−) provide a high anion exchange capacity (53–520 meq/100 g) which is expected to have an excellent exchangeable capacity to Cr(VI) oxyanions in water. Regarding the adsorption-coupled reduction mechanism, when Cr(VI) anions make contact with the electron-donor groups in the LDH, they are partly reduced to Cr(III) cations. The reduced Cr(III) cations are then adsorbed by LDH via numerous interactions, such as isomorphic substitution and complexation. Nonetheless, the adsorption-coupled reduction mechanism is greatly dependent on: (1) the nature of divalent and trivalent salts utilized in LDH preparation, and the types of interlayer anions (i.e., guest intercalated organic anions), and (3) the adsorption experiment conditions. The low Brunauer–Emmett–Teller specific surface area of LDH (1.80–179 m2/g) suggests that pore filling played an insignificant role in Cr(VI) adsorption. The Langmuir maximum adsorption capacity of LDH (Qomax) toward Cr(VI) was significantly affected by the natures of used inorganic salts and synthetic methods of LDH. The Qomax values range from 16.3 mg/g to 726 mg/g. Almost all adsorption processes of Cr(VI) by LDH-based adsorbent occur spontaneously (ΔG° 0) and increase the randomness (ΔS° >0) in the system. Thus, LDH has much potential as a promising material that can effectively remove anion pollutants, especially Cr(VI) anions in industrial wastewater

Removal of various contaminants from water by renewable lignocellulose-derived biosorbents: a comprehensive and critical review

© 2019, © 2019 Taylor & Francis Group, LLC. Contaminants in water bodies cause potential health risks for humans and great environmental threats. Therefore, the development and exploration of low-cost, promising adsorbents to remove contaminants from water resources as a sustainable option is one focus of the scientific community. Here, we conducted a critical review regarding the application of pristine and modified/treated biosorbents derived from leaves for the removal of various contaminants. These include potentially toxic cationic and oxyanionic metal ions, radioactive metal ions, rare earth elements, organic cationic and anionic dyes, phosphate, ammonium, and fluoride from water media. Similar to lignocellulose-based biosorbents, leaf-based biosorbents exhibit a low specific surface area and total pore volume but have abundant surface functional groups, high concentrations of light metals, and a high net surface charge density. The maximum adsorption capacity of biosorbents strongly depends on the operation conditions, experiment types, and adsorbate nature. The absorption mechanism of contaminants onto biosorbents is complex; therefore, typical experiments used to identify the primary mechanism of the adsorption of contaminants onto biosorbents were thoroughly discussed. It was concluded that byproduct leaves are renewable, biodegradable, and promising biosorbents which have the potential to be used as a low-cost green alternative to commercial activated carbon for effective removal of various contaminants from the water environment in the real-scale plants