Optimization of Extraction Process of Elaeagnus angustifolia Flower Polysaccharide and Its Proliferation on Probiotic

The study aimed to explore the optimal conditions for the extraction of polysaccharide from Elaeagnus angustifolia flower and its effect on probiotic proliferation in vitro. Polysaccharide was extracted from Elaeagnus angustifolia flower using hot water and ultrasonication. The effects of the material-liquid ratio, duration of ultrasonication, extraction time and extraction temperature on the polysaccharide yield were analyzed. The extraction conditions were optimized by response surface methodology, and the effects of different polysaccharide concentrations (0, 0.5%, 1.0%, 1.5%, 2.0%, and 3.0%) on the proliferation and acid production of three probiotics were compared. The results showed that the optimal extraction conditions of Elaeagnus angustifolia flower polysaccharide were as follows: Material-liquid ratio, 1:25 g/mL, duration of ultrasonication, 21 min, extraction temperature, 72℃, extraction time, 62 min. The polysaccharide yield was 12.45%±0.15%, which was close to the theoretical predicted yield (12.587%). The highest OD values of Lactobacillus acidophilus, Bifidobacterium bifidum, and Bifidobacterium adolescentis were obtained at a polysaccharide mass concentration of 2%, being 1.23±0.01, 1.06±0.02, and 1.22±0.02, respectively, and the lowest pH values (5.17±0.04, 5.95±0.04, and 5.52±0.02, respectively). The growth of the three probiotics stabilized after the incubation time reached to 40 h. It indicated that Elaeagnus angustifolia flower polysaccharide promoted the proliferation and acid production of three probiotics. These findings indicate the potential of the polysaccharide from Elaeagnus angustifolia flower as a prebiotic and provide a theoretical basis for further research and the utilization of Elaeagnus angustifolia flower resources

Computation Offloading and Trajectory Control for UAV-Assisted Edge Computing Using Deep Reinforcement Learning

Task offloading has attracted widespread attention in accelerating applications and reducing energy consumption. However, in areas with surging traffic (nucleic acid testing, concerts, etc.), the limited resources of fixed-base stations cannot meet user requirements. Unmanned aerial vehicles (UAVs) can effectively serve as temporary-base stations or aerial access points for mobile devices (MDs). In the UAV-assisted MEC system, we intend to jointly optimize the trajectory and user association to maximize computational efficiency. This problem is a non-convex fractional problem; therefore, it is not feasible to use only a traditional method, such as Dinkelbach’s method, for solving a fractional problem. Therefore, to facilitate online decision making for this joint optimization problem, we introduce deep reinforcement learning (DRL) and propose a double-layer cycle algorithm for maximizing computation efficiency (DCMCE). Specifically, in the outer loop, we model the trajectory planning problem as a Markov decision process, and use deep reinforcement learning to output the best trajectory. In the inner loop, we use Dinkelbach’s method to simplify the fraction problem, and propose a priority function to optimize user association to maximize computational efficiency. Simulation results show that DCMCE achieves higher computational efficiency than the baseline scheme

Computation Offloading and Trajectory Control for UAV-Assisted Edge Computing Using Deep Reinforcement Learning

Task offloading has attracted widespread attention in accelerating applications and reducing energy consumption. However, in areas with surging traffic (nucleic acid testing, concerts, etc.), the limited resources of fixed-base stations cannot meet user requirements. Unmanned aerial vehicles (UAVs) can effectively serve as temporary-base stations or aerial access points for mobile devices (MDs). In the UAV-assisted MEC system, we intend to jointly optimize the trajectory and user association to maximize computational efficiency. This problem is a non-convex fractional problem; therefore, it is not feasible to use only a traditional method, such as Dinkelbach’s method, for solving a fractional problem. Therefore, to facilitate online decision making for this joint optimization problem, we introduce deep reinforcement learning (DRL) and propose a double-layer cycle algorithm for maximizing computation efficiency (DCMCE). Specifically, in the outer loop, we model the trajectory planning problem as a Markov decision process, and use deep reinforcement learning to output the best trajectory. In the inner loop, we use Dinkelbach’s method to simplify the fraction problem, and propose a priority function to optimize user association to maximize computational efficiency. Simulation results show that DCMCE achieves higher computational efficiency than the baseline scheme

A Robust and Energy-Efficient Weighted Clustering Algorithm on Mobile Ad Hoc Sensor Networks †

In an Ad hoc sensor network, nodes have characteristics of limited battery energy, self-organization and low mobility. Due to the mobility and heterogeneity of the energy consumption in the hierarchical network, the cluster head and topology are changed dynamically. Therefore, topology control and energy consumption are growing to be critical in enhancing the stability and prolonging the lifetime of the network. In order to improve the survivability of Ad hoc network effectively, this paper proposes a new algorithm named the robust, energy-efficient weighted clustering algorithm (RE2WCA). For the homogeneous of the energy consumption; the proposed clustering algorithm takes the residual energy and group mobility into consideration by restricting minimum iteration times. In addition, a distributed fault detection algorithm and cluster head backup mechanism are presented to achieve the periodic and real-time topology maintenance to enhance the robustness of the network. The network is analyzed and the simulations are performed to compare the performance of this new clustering algorithm with the similar algorithms in terms of cluster characteristics, lifetime, throughput and energy consumption of the network. The result shows that the proposed algorithm provides better performance than others

Characterization and expression analysis of g- and c-type lysozymes in Dabry's sturgeon (Acipenser dabryanus)

Dabry's sturgeon (Acipenser dabryanus) is mainly distributed in the upper Yangtze River. Although extensively farmed, little information is available on its innate immune system. In this study, we conducted de novo transcriptome assembly of the head kidney to create a comprehensive dataset for A. dabryanus. A total of 51,324,686 high quality reads were obtained from head kidney cDNA library by the Illumina sequencing platform and 131,261 unigenes were determined to contain complete ORFs. The complete coding sequences of g- and c-type lysozymes were identified from unigenes, and designated as ADLysG and ADLysC. Aeromonas hydrophila infection of Dabry's sturgeon caused a significant increase (P\u202f<\u202f0.05) in blood for both lysozyme types, confirming their active defensive role against bacterial infections. This research provides the first characterization of these enzymes in an ancestral chondrostean. These data suggest that ADLysG and ADLysC have the potential for immune defense system against bacterial infection

Physicochemical properties and micro-interaction between micro-nanoparticles and anterior corneal multilayer biological interface film for improving drug delivery efficacy: the transformation of tear film turnover mode

AbstractRecently, various novel drug delivery systems have been developed to overcome ocular barriers in order to improve drug efficacy. We have previously reported that montmorillonite (MT) microspheres (MPs) and solid lipid nanoparticles (SLNs) loaded with the anti-glaucoma drug betaxolol hydrochloride (BHC) exhibited sustained drug release and thus intraocular pressure (IOP) lowering effects. Here, we investigated the effect of physicochemical particle parameters on the micro-interactions with tear film mucins and corneal epithelial cells. Results showed that the MT-BHC SLNs and MT-BHC MPs eye drops significantly prolonged the precorneal retention time due to their higher viscosity and lower surface tension and contact angle compared with the BHC solution, with MT-BHC MPs exhibiting the longest retention due to their stronger hydrophobic surface. The cumulative release of MT-BHC SLNs and MT-BHC MPs was up to 87.78% and 80.43% after 12 h, respectively. Tear elimination pharmacokinetics study further confirmed that the prolonged precorneal retention time of the formulations was due to the micro-interaction between the positively charged formulations and the negatively charged tear film mucins. Moreover, the area under the IOP reduction curve (AUC) of MT-BHC SLNs and MT-BHC MPs was 1.4 and 2.5 times that of the BHC solution. Accordingly, the MT-BHC MPs also exhibit the most consistent and long-lasting IOP-lowering effect. Ocular irritation experiments showed no significant toxicity of either. Taken together, MT MPs may have the potential for more effective glaucoma treatment