Effects of Different Farming Modes on Salmo trutta fario Growth and Intestinal Microbial Community.

The gut microbiota plays a pivotal role in upholding intestinal health, fostering intestinal development, fortifying organisms against pathogen intrusion, regulating nutrient absorption, and managing the body's lipid metabolism. However, the influence of different cultivation modes on the growth indices and intestinal microbes of Salmo trutta fario remains underexplored. In this study, we employed high-throughput sequencing and bioinformatics techniques to scrutinize the intestinal microbiota in three farming modes: traditional pond aquaculture (TPA), recirculating aquaculture (RA), and flow-through aquaculture (FTA). We aimed to assess the impact of different farming methods on the water environment and Salmo trutta fario's growth performance. Our findings revealed that the final weight and weight gain rate in the FTA model surpassed those in the other two. Substantial disparities were observed in the composition, relative abundance, and diversity of Salmo trutta fario gut microbiota under different aquaculture modes. Notably, the dominant genera of Salmo trutta fario gut microbiota varied across farming modes: for instance, in the FTA model, the most prevalent genera were SC-I-84 (7.34%), Subgroup_6 (9.93%), and UTCFX1 (6.71%), while, under RA farming, they were Bacteroidetes_vadinHA17 (10.61%), MBNT15 (7.09%), and Anaeromyxoactor (6.62%). In the TPA model, dominant genera in the gut microbiota included Anaeromyxobacter (8.72%), Bacteroidetes_vadinHA17 (8.30%), and Geobacter (12.54%). From a comparative standpoint, the genus-level composition of the gut microbiota in the RA and TPA models exhibited relative similarity. The gut microbiota in the FTA model showcased the most intricate functional diversity, while TPA farming displayed a more intricate interaction pattern with the gut microbiota. Transparency, pH, dissolved oxygen, conductivity, total dissolved solids, and temperature emerged as pivotal factors influencing Salmo trutta fario gut microbiota under diverse farming conditions. These research findings offer valuable scientific insights for fostering healthy aquaculture practices and disease prevention and control measures for Salmo trutta fario, holding substantial significance for the sustainable development of the cold-water fish industry in the Qinghai-Tibet Plateau.

Deefgea salmonis sp. nov., isolated from gills of rainbow trout (Oncorhynchus mykiss).

A Gram-stain-negative, milky white, aerobic, rod-shaped bacterium named strain H3-26T was isolated from gills of Oncorhynchus mykiss in Lhasa, Tibet Autonomous Region, PR China. Strain H3-26T grew at 4-30 °C and pH 5.0-11.0 (optimum, 25 °C and pH 7.0) with 0-1% (w/v) NaCl (optimum, 0%). The 16S rRNA gene sequence of strain H3-26T showed the highest similarity to Deefgea rivuli WB 3.4-79T (98.42%), followed by Deefgea chitinilytica Nsw-4T (96.91%). Phylogenetic analysis based on 16S rRNA genes indicated that strain H3-26T was a new member of the genus Deefgea. The digital DNA-DNA hybridization and average nucleotide identity values between the genome sequence of strain H3-26T and Deefgea spp. were 21.2-21.9% and 76.3-77.4%, respectively. The genomic DNA G+C content of strain H3-26T was 48.74%. The predominant fatty acids were C12:0, C14:0, C16:0 and C16:1 ω7c. Based on phenotypic, phylogenetic, and genotypic data, strain H3-26T is considered to represent a novel species of the genus Deefgea, for which the name Deefgea salmonis sp. nov. is proposed. The type strain is H3-26T (= JCM 35050T = CICC 25103T).

Slot Self-Allocation Based MAC Protocol for Energy Harvesting Nano-Networks.

Nano-networks are composed of interconnected nano-nodes and can enable unprecedented applications in various fields. Due to the peculiarities of nano-networks, such as high density, extremely limited energy and computational resources, traditional carrier-sensing based Media Access Control (MAC) protocols are not suitable for nano-networks. In this paper, a Slot Self-Allocation based MAC protocol (SSA-MAC) is proposed for energy harvesting nano-networks. Two transmission schemes for centralized and distributed nano-networks are designed, respectively. In centralized nano-networks, nano-nodes can only send packets to the nano-controller in their Self-Allocation Slots (SASs), while, in distributed nano-networks, nano-nodes can only receive packets from surrounding nano-nodes in their SASs. Extensive simulations were conducted to compare the proposed SSA-MAC with PHysical LAyer aware MAC (PHLAME), Receiver-Initiated Harvesting-aware MAC (RIH-MAC) and Energy Efficient Wireless NanoSensor Network MAC (EEWNSN). From the results, it can be concluded that the proposed SSA-MAC achieves better performance and can reduce the collision probability, while improving the energy efficiency of nano-networks.

An Opposition-Based Evolutionary Algorithm for Many-Objective Optimization with Adaptive Clustering Mechanism.

Balancing convergence and diversity has become a key point especially in many-objective optimization where the large numbers of objectives pose many challenges to the evolutionary algorithms. In this paper, an opposition-based evolutionary algorithm with the adaptive clustering mechanism is proposed for solving the complex optimization problem. In particular, opposition-based learning is integrated in the proposed algorithm to initialize the solution, and the nondominated sorting scheme with a new adaptive clustering mechanism is adopted in the environmental selection phase to ensure both convergence and diversity. The proposed method is compared with other nine evolutionary algorithms on a number of test problems with up to fifteen objectives, which verify the best performance of the proposed algorithm. Also, the algorithm is applied to a variety of multiobjective engineering optimization problems. The experimental results have shown the competitiveness and effectiveness of our proposed algorithm in solving challenging real-world problems.

Game theory for Wireless Sensor Networks: a survey.

Game theory (GT) is a mathematical method that describes the phenomenon of conflict and cooperation between intelligent rational decision-makers. In particular, the theory has been proven very useful in the design of wireless sensor networks (WSNs). This article surveys the recent developments and findings of GT, its applications in WSNs, and provides the community a general view of this vibrant research area. We first introduce the typical formulation of GT in the WSN application domain. The roles of GT are described that include routing protocol design, topology control, power control and energy saving, packet forwarding, data collection, spectrum allocation, bandwidth allocation, quality of service control, coverage optimization, WSN security, and other sensor management tasks. Then, three variations of game theory are described, namely, the cooperative, non-cooperative, and repeated schemes. Finally, existing problems and future trends are identified for researchers and engineers in the field.

[Plasma tissue factor and tissue factor pathway inhibitor levels in acute myocardial infarction patients with no-reflow during percutaneous coronary intervention].

OBJECTIVE: To compare plasma tissue factor (TF) and tissue factor pathway inhibitor (TFPI) levels before, immediately and 24 hours after percutaneous coronary intervention (PCI) in acute myocardial infarction (AMI) patients with or without no-reflow. METHODS: Plasma TF and TFPI in AMI patients underwent PCI were measured by enzyme linked immunoadsorbent (ELISA) before, immediately and 24 hours after PCI. RESULTS: The levels of TF and TFPI of no-reflow patients (n = 17) were significantly higher than those of reflow patients (n = 36) at baseline, immediately and 24 hours after PCI [TF: (275.3 +/- 46.2) ng/L vs. (236.8 +/- 44.3) ng/L, (332.7 +/- 41.3) ng/L vs. (282.3 +/- 38.7) ng/L, (315.5 +/- 47.8) ng/L vs. (248.1 +/- 46.9) ng/L; TFPI: (165.2 +/- 38.4) microg/L vs. (128.5 +/- 18.7) microg/L, (176.3 +/- 36.8) microg/L vs. (135.6 +/- 20.3) microg/L, (149.8 +/- 31.7) microg/L vs. (118.7 +/- 19.2) microg/L; all P < 0.01]. Plasma TF was significantly increased in both groups (all P < 0.01) immediately post PCI and the TF was still higher than that before PCI in no-reflow patients (P < 0.05) and returned to baseline level in reflow patients at 24 hours after PCI (P > 0.05), TFPI levels were similar before and after PCI in both groups (all P > 0.05). CONCLUSION: Plasma tissue factor and tissue factor pathway inhibitor might play important roles in the development of no-reflow during PCI in AMI patients.