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Hydroxychloroquine had no effect on the release of sFlt-1, sEng, TNF-α, activin A, or 8-isoprostane from placental explants exposed to hypoxic injury or oxidative stress. However, hydroxychloroquine mitigated TNF-α-induced HUVEC production of 8-isoprostane and Nicotinanamide adenine dinucleotide phosphate (NADPH) oxidase expression. Hydroxychloroquine also mitigated TNF-α and preeclamptic serum-induced HUVEC monolayer permeability and rescued the loss of zona occludens protein zona occludens 1 (ZO-1). Although hydroxychloroquine had no apparent effects on trophoblast function, it may be a useful endothelial protectant in women presenting with preeclampsia.Present-day lifestyles associated with high calorie-fat intake and accumulation, as well as energy imbalance, have led to the development of obesity and its comorbidities, which have emerged as some of the major health issues globally. To combat the disease, many studies have reported the anti-obesity effects of natural compounds in foods, with some advantages over chemical treatments. Carotenoids, such as xanthophyll derived from seaweeds, have attracted the attention of researchers due to their notable biological activities, which are associated mainly with their antioxidant properties. Their involvement in oxidative stress modulation, the regulation of major transcription factors and enzymes, and their antagonistic effects on various obesity parameters have been examined in both in vitro and in vivo studies. The present review is a collation of published research over the last decade on the antioxidant properties of seaweed xanthophyll carotenoids, with a focus on fucoxanthin and astaxanthin and their mechanisms of action in obesity prevention and treatment.Neurodegenerative diseases, particularly Parkinson's and Alzheimer's, have common features protein accumulation, cell death with mitochondrial involvement and oxidative stress. Patients are treated to cure the symptoms, but the treatments do not target the causes; so, the disease is not stopped. It is interesting to look at the side of nutrition which could help prevent the first signs of the disease or slow its progression in addition to existing therapeutic strategies. Lipids, whether in the form of vegetable or animal oils or in the form of fatty acids, could be incorporated into diets with the aim of preventing neurodegenerative diseases. These different lipids can inhibit the cytotoxicity induced during the pathology, whether at the level of mitochondria, oxidative stress or apoptosis and inflammation. The conclusions of the various studies cited are oriented towards the preventive use of oils or fatty acids. The future of these lipids that can be used in therapy/prevention will undoubtedly involve a better delivery to the body and to the brain by utilizing lipid encapsulation.This paper relates to the separation of single channel source signals from a single mixed signal by means of independent component analysis (ICA). The proposed idea lies in a time-frequency representation of the mixed signal and the use of ICA on spectral rows corresponding to different time intervals. In our approach, in order to reconstruct true sources, we proposed a novelty idea of grouping statistically independent time-frequency domain (TFD) components of the mixed signal obtained by ICA. The TFD components are grouped by hierarchical clustering and k-mean partitional clustering. The distance between TFD components is measured with the classical Euclidean distance and the β distance of Gaussian distribution introduced by as. In addition, the TFD components are grouped by minimizing the negentropy of reconstructed constituent signals. The proposed method was used to separate source signals from single audio mixes of two- and three-component signals. The separation was performed using algorithms written by the authors in Matlab. The quality of obtained separation results was evaluated by perceptual tests. The tests showed that the automated separation requires qualitative information about time-frequency characteristics of constituent signals. The best separation results were obtained with the use of the β distance of Gaussian distribution, a distance measure based on the knowledge of the statistical nature of spectra of original constituent signals of the mixed signal.A computationally efficient target parameter estimation algorithm for frequency agile radar (FAR) under jamming environment is developed. First, the barrage noise jamming and the deceptive jamming are suppressed by using adaptive beamforming and frequency agility. Second, the analytical solution of the parameter estimation is obtained by a low-order approximation to the multi-dimensional maximum likelihood (ML) function. Due to that, fine grid-search (FGS) is avoided and the computational complexity is greatly reduced.Crack identification plays an essential role in the health diagnosis of various concrete structures. Among different intelligent algorithms, the convolutional neural networks (CNNs) has been demonstrated as a promising tool capable of efficiently identifying the existence and evolution of concrete cracks by adaptively recognizing crack features from a large amount of concrete surface images. click here However, the accuracy as well as the versatility of conventional CNNs in crack identification is largely limited, due to the influence of noise contained in the background of the concrete surface images. The noise originates from highly diverse sources, such as light spots, blurs, surface roughness/wear/stains. With the aim of enhancing the accuracy, noise immunity, and versatility of CNN-based crack identification methods, a framework of enhanced intelligent identification of concrete cracks is established in this study, based on a hybrid utilization of conventional CNNs with a multi-layered image preprocessing strategy (MLP), of which the key components are homomorphic filtering and the Otsu thresholding method. Relying on the comparison and fine-tuning of classic CNN structures, networks for detection of crack position and identification of crack type are built, trained, and tested, based on a dataset composed of a large number of concrete crack images. The effectiveness and efficiency of the proposed framework involving the MLP and the CNN in crack identification are examined by comparative studies, with and without the implementation of the MLP strategy. Crack identification accuracy subject to different sources and levels of noise influence is investigated.