Biological activities and phenolic contents of Argania spinosa L (Sapotaceae) leaf extract

Purpose: To investigate the phenolic profile and biological activities of Argania spinosa L. leaves.Methods: The crude methanol extract of leaves of A. spinosa L. Skeels was obtained by ultrasonic extraction, and the total polyphenolic and flavonoid contents were determined by ultra-high performance liquid chromatography-electrospray ionization-quadrupole time-of-flight-mass spectrometry (UPLC-ESIQTOF- MS). In vitro antioxidant activity was determined by 2,2-di-phenyl-1-picryl-hydrazil (DPPH) radical assay and antimicrobial activity evaluated using agar disk diffusion method against reference pathogenic strains (Bacillus subtilis ATCC 6633, Bacillus cereus ATCC 14579, Yersinia enterocolitica ATCC 23715, Staphylococcus aureus ATCC 25923, Pseudomonas aeruginosa ATCC 27853, Escherichia coli ATCC 25922). Cytotoxic activity was evaluated by methyl-thiazolyldiphenyl-tetrazolium bromide (MTT) assay.Results: The results revealed abundant polyphenols and flavonoids (221.39 ± 5.70 μg GAEq/1 g and 66.86 ± 3.36 μg CAEq/1 g, respectively) in the leaf extract. UPLC-DAD-ESI-QTOF-MS profiling showed the presence of myrecitin-3-galactoside, myrecitin-3-gluctoside, myrecitin-3-xyloside, quercetin-3- galactoside, quercetin-3-glucoside, quercetin-3-arabinofuranoside, and quercetin-3-rhamnoside. DPPH assay of the leaf extract yielded a half-maximal effective concentration (EC50) value of 125.60 ± 1.87 μg. A. spinosa L. leaves exhibited antibacterial activity against Gram (+) and Gram (-) bacteria, with particularly marked activity against B. subtilis (inhibition zone, 16 mm). Cytotoxicity data showed that the extract inhibited the proliferation of PC3 cells (IC50 ~ 600 μg).Conclusion: A. spinosa L. leaf extract is rich in valuable biologically active compounds and could represent a new resource for natural and preventive therapies.Keywords: Argania spinosa, Polyphenols, Flavonoids, Antioxidant, Antibacterial, Cytotoxi

Active Re-identification Attacks on Periodically Released Dynamic Social Graphs

Active re-identification attacks pose a serious threat to privacy-preserving social graph publication. Active attackers create fake accounts to build structural patterns in social graphs which can be used to re-identify legitimate users on published anonymised graphs, even without additional background knowledge. So far, this type of attacks has only been studied in the scenario where the inherently dynamic social graph is published once. In this paper, we present the first active re-identification attack in the more realistic scenario where a dynamic social graph is periodically published. The new attack leverages tempo-structural patterns for strengthening the adversary. Through a comprehensive set of experiments on real-life and synthetic dynamic social graphs, we show that our new attack substantially outperforms the most effective static active attack in the literature by increasing the success probability of re-identification by more than two times and efficiency by almost 10 times. Moreover, unlike the static attack, our new attack is able to remain at the same level of effectiveness and efficiency as the publication process advances. We conduct a study on the factors that may thwart our new attack, which can help design graph anonymising methods with a better balance between privacy and utility

Dynamic Community Detection into Analyzing of Wildfires Events

The study and comprehension of complex systems are crucial intellectual and scientific challenges of the 21st century. In this scenario, network science has emerged as a mathematical tool to support the study of such systems. Examples include environmental processes such as wildfires, which are known for their considerable impact on human life. However, there is a considerable lack of studies of wildfire from a network science perspective. Here, employing the chronological network concept -- a temporal network where nodes are linked if two consecutive events occur between them -- we investigate the information that dynamic community structures reveal about the wildfires' dynamics. Particularly, we explore a two-phase dynamic community detection approach, i.e., we applied the Louvain algorithm on a series of snapshots. Then we used the Jaccard similarity coefficient to match communities across adjacent snapshots. Experiments with the MODIS dataset of fire events in the Amazon basing were conducted. Our results show that the dynamic communities can reveal wildfire patterns observed throughout the year.Comment: 16 pages, 8 figure