A novel polysaccharide from Ulva lactuca (ULP) was purified using a Sepharose CL-4B column. Fourier transform infrared spectroscopy, high-performance liquid chromatography, and nuclear magnetic resonance spectroscopy were employed to analyze the structure of ULP. It consisted of rhamnose (Rha), glucuronic acid (GluA), galactose (Gal), and xylose (Xyl) at a molar ratio of 32.75:22.83:1.07:6.46 with the molecular weight of 2.24 × 10 5 Da. The four major glycosidic residues found in ULP were →The antihyperuricemic activity of ULP was exhibited by detecting related biochemical indexes, urate transporter gene expressions, renal histopathology, and intestinal microbiota shifts. ULP obviously decreased the levels of serum uric acid (UA), blood urea nitrogen, and creatinine, while inhibited serum and hepatic xanthine oxidase activities as well as improved renal injury in hyperuricemic mice. Furthermore, the upregulation of UA excretion genes ABCG2/OAT1 and downregulation of UA resorption genes URAT1 and GLUT9 were detected. In addition, ULP exerted its antihyperuricemic effect through regulating the intestinal microbiome, characterized by elevating the helpful microbial abundance, meanwhile declining the harmful bacterial abundance and restoring the gut microbiome homeostasis. This study demonstrates the antihyperuricemic activity of ULP and its potential effect for the treatment of hyperuricemia-related diseases.
Phyllanthus emblica L. fruits were extracted by a hot water assistant with ultrasonication to obtain aqueous Phyllanthus emblica L. extract (APE). The ameliorating functional dyspepsia (FD) effect of a low dose (150 mg/kg) and a high dose (300 mg/kg) of APE was exhibited by determining the gastrointestinal motility, gastrointestinal hormones, and gut microbiome shifts in reserpine induced FD male balb/c mice. APE increased the gastrointestinal motility including the gastric emptying (GE) rate and small intestinal transit (SIT) rate. The level of serum gastrointestinal hormones such as motilin (MTL) and gastrin (GAS) increased, and the vasoactive intestinal peptide (VIP) level decreased after the administration of APE. Furthermore, the gut microbiome analysis demonstrated that APE could regulate the microbiome structure and restore homeostasis by elevating useful bacterial abundance, while simultaneously decreasing harmful bacterial abundance. This study demonstrated the ameliorating FD effect of APE and its potential efficacy in curing functional gastrointestinal disorders and maintaining a healthy digestive tract.
In this paper, we proposed a novel DE variant named DE-NPC for real parameter single objective optimization. In DE-NPC algorithm, a novel adaptation scheme for the scale factor F is first proposed, which is based on the location information of the population rather than the fitness difference. The adaptation scheme of crossover rate CR in our DE-NPC is based on its success probability. Furthermore, a novel population size reduction scheme is also employed in DE-NPC, which can get a better perception of the landscape of objectives and consequently obtain an overall better performance. The algorithm validation is conducted under our test suite containing 88 benchmarks from CEC2013, CEC2014 and CEC2017 in comparison with several state-of-the-art DE variants. The experiment results show that our novel DE-NPC algorithm is competitive with these state-of-the-art DE variants.
Differential Evolution (DE) is a simple and effective stochastic algorithm for optimization problems, and it became much more popular in recent year because of its easy-implementation and excellent performance. Nevertheless, the performance of DE algorithm is greatly affected by the trial vector generation strategy including mutation strategy and parameter control, both of which still exist some weaknesses, e.g. the premature convergence to some local optima of a mutation strategy and the misleading interaction among control parameters. Therefore in this paper, a novel Di-DE algorithm is proposed to tackle these weaknesses. A depth information based external archive was advanced in our novel mutation strategy, which can get a better perception of the landscape of objective in an optimization. Moreover, a novel grouping strategy was also employed in Di-DE and parameters were updated separately so as to avoid the misleading among parameters. Moreover, a cooperative strategy for information interchange was also advanced aiming at improving the efficiency of the exploration behavior. By absorbing these advancements, the novel Di-DE algorithm can secure better performance in comparison with other famous optimization algorithms. The algorithm validation was conducted on CEC2013 and CEC2017 test suites, and the results revealed the competitiveness of our Di-DE algorithm in comparison with those famous optimization algorithms including Particle Swarm Optimization (PSO) variants, QUasi-Affine TRansformation Evolution (QUATRE) variants and Differential Evolution (DE) variants.
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