METHODS: In this study, the drawbacks of DTF and PDC are addressed by proposing a novel technique, termed as Efficient Effective Connectivity (EEC), for the estimation of EC between multivariate sources using AR spectral estimation and Granger causality principle. In EEC, a linear predictive filter with AR coefficients obtained via multivariate EEG is used for signal prediction. This leads to the estimation of full-length signals which are then transformed into frequency domain by using Burg spectral estimation method. Furthermore, the newly proposed normalization method addressed the effect on each source in EEC using the sum of maximum connectivity values over the entire frequency range. Lastly, the proposed dynamic thresholding works by subtracting the first moment of causal effects of all the sources on one source from individual connections present for that source.
RESULTS: The proposed method is evaluated using synthetic and real resting-state EEG of 46 healthy controls. A 3D-Convolutional Neural Network is trained and tested using the PDC and EEC samples. The result indicates that compared to PDC, EEC improves the EEG eye-state classification accuracy, sensitivity and specificity by 5.57%, 3.15% and 8.74%, respectively.
CONCLUSION: Correct identification of all connections in synthetic data and improved resting-state classification performance using EEC proved that EEC gives better estimation of directed causality and indicates that it can be used for reliable understanding of brain mechanisms. Conclusively, the proposed technique may open up new research dimensions for clinical diagnosis of mental disorders.
OBJECTIVE: In this work, various polysaccharide/gelatin amorphous hydrogels with the impregnation of oil palm leaf derived total flavonoid enriched extract (OPL-TFEE) were fabricated via one-pot synthesis method to provide multiple crosslinking networks.
METHOD: The bioflavonoids (OPL-TFEE) were derived from Elaeis guineensis leaf using an integrated green extraction and enrichment process. Amorphous hydrogels with good wound healing properties were developed by incorporating 0.3% antioxidant agent into the hybrid polymeric gelling system.
RESULT: The formulations appeared as a semi-solid dark yellow translucent hydrogel with good spreading and consistency characteristics and satisfying aesthetic properties. The FTIR analysis indicated that the bioflavonoid was compatible with the matrix, and the hydrogels showed porous morphological structures when observed under SEM. Furthermore, the hydrogels possessed shear thinning, pseudoplastic, and elastic properties. Bioflavonoids-impregnated polysaccharide/gelatin hydrogel release 95-98% bioflavonoids within 24 h, while the drug release profile followed the Korsmeyer-Peppas kinetic model. The hydrogels showed antioxidant and wound healing properties with no sign of cytotoxicity.
CONCLUSION: Overall, the results revealed bioflavonoid-loaded hydrogels exhibited good physicochemical and biological properties, thus could serve as new innovative formulation in the sustainable advancement of wound care product for promoting wound healing.
REVIEW: This article presents a review on the role of date palm as adsorbents in the removal of unwanted materials such as acid and basic dyes, heavy metals, and phenolic compounds. Many studies on adsorption properties of various low cost adsorbent, such as agricultural waste and activated carbons based on agricultural waste have been reported in recent years.
CONCLUSION: Studies have shown that date palm-based adsorbents are the most promising adsorbents for removing unwanted materials. No previous review is available where researchers can get an overview of the adsorption capacities of date palm-based adsorbent used for the adsorption of different pollutants. This review provides the recent literature demonstrating the usefulness of date palm biomass-based adsorbents in the adsorption of various pollutants.