The ultrasonic process has been examined to exfoliate layered materials and upgrade their properties for a variety of applications in different media. Our previous studies have shown that the ultra-sonication treatment in water without chemicals has a positive influence on the physical and electrochemical performance of layered materials and nanoparticles. In this work, we have probed the impact of ultrasonication on the physical properties and the oxygen evolution reaction (OER) of the NiFe LDH materials under various conditions, including suspension concentration (2.5-12.5 mg mL-1), sonication times (3-20 min) and amplitudes (50-90%) in water, in particular, sonication times and amplitudes. We found that the concentration, amplitude and time play significant roles on the exfoliation of the NiFe LDH material. Firstly, the NiFe LDH nanosheets displayed the best OER performance under ultrasonic conditions with the concentration of 10 mg mL-1 (50% amplitude and 15 min). Secondly, it was revealed that the exfoliation of the NiFe LDH nanosheets in a short time ( less then 10 min) or a higher amplitudes (≥80%) has left a cutdown on the OER activity. Comprehensively, the optimum OER activity was displayed on the exfoliated NiFe LDH materials under ultrasonic condition of 60% (amplitude), 10 mg mL-1 and 15 min. It demanded only 250 mV overpotentials to reach 10 mA cm-2 in 1 M KOH, which was 100 mV less than the starting NiFe LDH material. It was revealed from the mechanism of sonochemistry and the OER reaction that, after exfoliation, the promoted OER performance is ascribed to the enriched Fe3+ at the active sites, easier oxidation of Ni2+ to Ni3+, and the strong electrical coupling of the Ni2+ and Fe3+ during the OER process. This work provides a green strategy to improve the intrinsic activity of layered materials.In this work, extraction of flavonoids from peanut shells has been studied in the presence of ultrasound and the results are compared with Soxhlet and heat reflux extraction for establishing the process intensification benefits. The process optimization for understanding the effects of operating parameters, such as ethanol concentration, particle size, solvent to solid ratio, extraction temperature, ultrasonic power and ultrasonic frequency, on the extraction of flavonoids has been investigated in details. The highest extraction yield (9.263 mg/g) of flavonoids was achieved in 80 min at optimum operating parameters of particle size of 0.285 mm, solvent to solid ratio of 40 ml/g, extraction temperature of 55 °C, ultrasonic power of 120 W and ultrasonic frequency of 45 kHz with 70% ethanol as the solvent. Two kinetic models (i.e. phenomenological model and Peleg's model) have been introduced to describe the extraction kinetic of flavonoids by fitting experimental data and predict kinetic parameters. Good performance with slight loss of goodness of fit of two models was found by comparing their coefficient of determination (R2), root mean square error (RMSE) and/or mean percentage error (MPE) values. This work would provide the reduction of degradation and the economic evaluation for the extraction processes of flavonoids from peanut shells, as well as give a better explanation for the mechanism of ultrasound.Ultrasound imaging is a patient-friendly and robust technique for studying physiological and pathological muscles. An automatic deep learning (DL) system for the analysis of ultrasound images could be useful to support an expert operator, allowing the study of large datasets requiring less human interaction. The purpose of this study is to present a deep learning algorithm for the cross-sectional area (CSA) segmentation in transverse musculoskeletal ultrasound images, providing a quantitative grayscale analysis which is useful for studying muscles, and to validate the results in a large dataset. The dataset included 3917 images of biceps brachii, tibialis anterior and gastrocnemius medialis acquired on 1283 subjects (mean age 50 ± 21 years, 729 male). The algorithm was based on multiple deep-learning architectures, and its performance was compared to a manual expert segmentation. We compared the mean grayscale value inside the automatic and manual CSA using Bland-Altman plots and a correlation analysis. Classification in healthy and abnormal muscles between automatic and manual segmentation were compared using the grayscale value z-scores. In the test set, a Precision of 0.88 ± 0.12 and a Recall of 0.92 ± 0.09 was achieved. The network segmentation performance was slightly less in abnormal muscles, without a loss of discrimination between healthy and abnormal muscle images.  https://www.selleckchem.com/products/gsk8612.html  Bland-Altman plots showed no clear trend in the error distribution and the two readings have a 0.99 Pearson's correlation coefficient (p less then 0.001, test set). The ICC(A, 1) calculated between the z-score readings was 0.99. The algorithm achieves robust CSA segmentation performance and gives mean grayscale level information comparable to a manual operator. This could provide a helpful tool for clinicians in neuromuscular disease diagnosis and follow-up. The entire dataset and code are made available for the research community.Coronavirus disease 2019 (COVID-19) is an ongoing pandemic. The virus that causes the disease, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), predominantly infects the respiratory tract, which may lead to pneumonia and death in severe cases. Many marine compounds have been found to have immense medicinal value and have gained approval from the Food and Drug Administration (FDA), and some are being tested in clinical trials. In the current investigation, we redirected a number of marine compounds toward SARS-CoV-2 by targeting the main protease (Mpro, PDB ID 6Y2F), subjecting them to several advanced computational techniques using co-crystallised ligand as the reference compound. The results of the binding affinity studies showed that two compounds, eribulin mesylate (eri) and soblidotin (sob), displayed higher docking scores than did the reference compound. When these compounds were assessed using molecular dynamics simulation, it was evident that they demonstrated stable binding at the binding pocket of the target protein.