Communication Efficiency Optimization of Federated Learning for Computing and Network Convergence of 6G Networks

Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global models. However, factors such as network topology and device computing power can affect its training or communication process in complex network environments. A new network architecture and paradigm with computing-measurable, perceptible, distributable, dispatchable, and manageable capabilities, computing and network convergence (CNC) of 6G networks can effectively support federated learning training and improve its communication efficiency. By guiding the participating devices' training in federated learning based on business requirements, resource load, network conditions, and arithmetic power of devices, CNC can reach this goal. In this paper, to improve the communication efficiency of federated learning in complex networks, we study the communication efficiency optimization of federated learning for computing and network convergence of 6G networks, methods that gives decisions on its training process for different network conditions and arithmetic power of participating devices in federated learning. The experiments address two architectures that exist for devices in federated learning and arrange devices to participate in training based on arithmetic power while achieving optimization of communication efficiency in the process of transferring model parameters. The results show that the method we proposed can (1) cope well with complex network situations (2) effectively balance the delay distribution of participating devices for local training (3) improve the communication efficiency during the transfer of model parameters (4) improve the resource utilization in the network.Comment: 13 pages, 11 figures, accepted by Frontiers of Information Technology & Electronic Engineerin

Efficacy and acceptability of anti-inflammatory agents in major depressive disorder: a systematic review and meta-analysis

BackgroundAnti-inflammatory agents have emerged as a potential new therapy for major depressive disorder (MDD). In this meta-analysis, our aim was to evaluate the antidepressant effect of anti-inflammatory agents and compare their efficacy.MethodsWe conducted a comprehensive search across multiple databases, including PubMed, Embase, Web of Science, Cochrane Review, Cochrane Trial, and ClinicalTrials.gov, to identify eligible randomized clinical trials. The primary outcome measures of our meta-analysis were efficacy and acceptability, while the secondary outcome measures focused on remission rate and dropout rate due to adverse events. We used odds ratio (OR) and 95% confidence interval (95% CI) to present our results.ResultsA total of 48 studies were included in our analysis. In terms of efficacy, anti-inflammatory agents demonstrated a significant antidepressant effect compared to placebo (OR = 2.04, 95% CI: 1.41–2.97, p = 0.0002). Subgroup analyses revealed that anti-inflammatory agents also exhibited significant antidepressant effects in the adjunctive therapy subgroup (OR = 2.17, 95% CI: 1.39–3.37, p = 0.0006) and in MDD patients without treatment-resistant depression subgroup (OR = 2.33, 95% CI: 1.53–3.54, p < 0.0001). Based on the surface under the cumulative ranking curve (SUCRA) value of network meta-analysis, nonsteroidal anti-inflammatory drugs (NSAIDs) (SUCRA value = 81.6) demonstrated the highest acceptability among the included anti-inflammatory agents.ConclusionIn summary, our meta-analysis demonstrates that anti-inflammatory agents have significant antidepressant effects and are well-accepted. Furthermore, adjunctive therapy with anti-inflammatory agents proved effective in treating MDD. Among the evaluated anti-inflammatory agents, NSAIDs exhibited the highest acceptability, although its efficacy is comparable to placebo.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=422004), identifier CRD42023422004

Estimation of Leaf Water Use Efficiency Threshold Values for Water Stress in Winter Wheat (Triticum aestivum L.)

Drought significantly threatens crop productivity and food security worldwide. However, the severity of drought is predicted to increasingly intensify in the future. To provide an antidrought strategy for farmers and breeders, the response of stomatal behavior of crops to water stress should be well studied. In this study, a lysimeter experiment was conducted to study the relationship between gas exchange parameters and grain yields of winter wheat. Light, moderate, and severe drought levels were imposed at seedling, jointing, heading, and filling stages. The results showed that crop evapotranspiration (ETc, mm) of winter wheat during the entire growing season was limited by drought imposed at any growth stage, and ETc under severe drought treatment was always the lowest. The stomatal limitation value had a significant linear correlation with the stomatal conductance (Gs, μmol mol H2O m–2 s–1) and transpiration rate (Tr, mmol H2O m–2 s–1). Light and moderate drought levels at the seedling stage did not generate irreversible physiological stress on wheat plants, while severe drought at any growth stage caused significant reduction in gas exchange parameters and grain yields. Theoretical threshold values of leaf water use efficiency (WUEl) for light, moderate, and severe drought levels were 2.62, 3.36, and 4.11 μmol mmol–1, respectively. The threshold values are useful to provide theoretical reference for achieving smart irrigation in the North China Plain

Radix Pueraria lobata polysaccharide relieved DSS-induced ulcerative colitis through modulating PI3K signaling

Polysaccharides are the essential active constituents of Radix Pueraria lobata. Numerous studies have shown that polysaccharides can play important roles in ulcerative colitis (UC). This study aimed to determine the protective effects of Radix Pueraria lobata polysaccharide (PPL) against UC. The pharmacological effects of PPL on the dextran sulfate sodium (DSS)-induced UC mice, changes in gut microbiota and metabolism, and DSS-induced Caco-2 cell injury were assessed in this study. Both the PPL inhibited the DSS-induced inflammation both in vivo and in vitro. Mechanistically, PPL inhibited inflammation by decreasing the activation of p-PI3K. Knocking down the PI3K gene resulted in abolishing the inhibitory effects of PPL on mouses and Caco-2 cells. The study concluded that the effects of PPL were completely dependent on PI3K

Effect of sulfamethazine on anaerobic digestion of manure mediated by biochar

Antibiotic contamination from animal production and wastewater treatment process will release antibiotic resistant genes to the environment and potentially threaten human health. Meanwhile, the residual antibiotic in manure could have inactive impacts on anaerobic digestion (AD). This study explores the effect of sulfamethazine on manure AD mediated by biochar. The results show that biochar weakens the adverse effects of sulfamethazine on AD by adsorption sulfamethazine during the initial stage (0-3 days) of AD and promoting the growth of hydrolytic bacteria (especially Firmicutes and Bacteroidetes) and methanogens (especially Methanothrix and Methanosarcina). Besides, the presence of biochar improves the biogas production capacity of AD and promotes microbial diversity and community richness. Thus, the addition of biochar greatly reduces sulfamethazine and is testified to be a desirable strategy to mitigate the inhibition of sulfamethazine on AD