Network pharmacology analysis has shown that Qu Feng Xuan Bi Formula (QFXBF) is a traditional Chinese herbal medicine used to treat bronchial asthma, with demonstrated remarkable effects; however, its effects on airway inflammation and bronchial asthma alleviation remain unclear. This study aimed to explore the underlying mechanism of QFXBF by assessing toll-like receptor-9 (TLR-9) and the downstream MAPK signaling pathway as well as the immune modulation of the Th1/Th2 balance. The results showed that the aqueous extract of QFXBF increased TLR-9 expression, regulated the downstream genes AP-1, JNK, and ERK1/2, and reduced serum IL-5 levels in the high-dose QFXBF group; meanwhile, following QFXBF treatment, IFN-γ and TNF-α levels were increased, indicating that the Th1/Th2 immune balance was reversed as predicted. These findings provide a mechanism underlying QFXBF’s effects, which significantly improves the treatment and management of patients with bronchial asthma and profoundly impacts medical information processing.
Federated Learning is a distributed machine learning framework that aims to train a global shared model while keeping their data locally, and previous researches have empirically proven the ideal performance of federated learning methods. However, recent researches found the challenge of statistical heterogeneity caused by the non-independent and identically distributed (non-IID), which leads to a significant decline in the performance of federated learning because of the model divergence caused by non-IID data. This statistical heterogeneity is dramatically restricts the application of federated learning and has become one of the critical challenges in federated learning. In this paper, a dynamic weighted model aggregation algorithm based on statistical heterogeneity for federated learning called DWFed is proposed, in which the index of statistical heterogeneity is firstly quantitatively defined through derivation. Then the index is used to calculate the weights of each local model for aggregating federated model, which is to constrain the model divergence caused by non-IID data. Multiple experiments on public benchmark data set reveal the improvements in performance and robustness of the federated models in heterogeneous settings.
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