Search Results (4)

The NLRP3 inflammasome, plays a critical role in the pathogenesis of rheumatoid arthritis (RA) by activating inflammatory cytokines such as IL1β and IL18. Targeting NLRP3 has emerged as a promising therapeutic strategy for RA. In this study, a multidisciplinary approach combining machine learning, quantitative structure–activity relationship (QSAR) modeling, structure–activity landscape index (SALI), docking, molecular dynamics (MD), and molecular mechanics Poisson–Boltzmann surface area MM/PBSA assays was employed to identify novel NLRP3 inhibitors. The ChEMBL database was used to retrieve compounds with known IC₅₀ values to train machine learning (ML) models using the Lazy Predict package. After data pre-processing, 401 non-redundant structures were selected for exploratory data analysis (EDA). PubChem and MACCS fingerprints were used to predict the inhibitory activities of the compounds. SALI was used to identify structurally similar compounds with significantly different biological activities. The compounds were docked using MOE to assess their binding affinities and interactions with key residues in NLRP3. The models were evaluated, and a comparative analysis revealed that the ensemble Random Forest (RF) model (PubChem fingerprints) with RMSE (0.731), R² (0.622), and MAPE (8.988) and bootstrap aggregating model (MACCS fingerprints) with RMSE (0.687), R² (0.666), and MAPE (9.216) on the testing set performed well, in accordance with the Organization for Economic Cooperation and Development (OECD) guidelines. Out of all docked compounds, the two most promising compounds (ChEMBL5289544 and ChEMBL5219789) with binding scores of −7.5 and −8.2 kcal/mol were further investigated by MD to evaluate their stability and dynamic behavior within the binding site. MD simulations (200 ns) revealed strong structural stability, flexibility, and interactions in the selected complexes. MM/PBSA binding free energy calculations revealed that van der Waals and electrostatic forces were the key drivers of the binding of the protein with ligands. The outcomes obtained can be used to design more potent and selective NLRP3 inhibitors as therapeutic agents for the treatment of inflammatory diseases such as RA. However, concerns related to the lack of large datasets, experimental validation, and high computational costs remain. Full article

Federated learning (FL) is a distributed neural network training paradigm with privacy protection. With the premise of ensuring that local data isn’t leaked, multi-device cooperation trains the model and improves its normalization. Unlike centralized training, FL is susceptible to heterogeneous data, biased gradient estimations hinder convergence of the global model, and traditional sampling techniques cannot apply FL due to privacy constraints. Therefore, this paper proposes a novel FL framework, federated lazy aggregation (FedLA), which reduces aggregation frequency to obtain high-quality gradients and improve robustness in non-IID. To judge the aggregating timings, the change rate of the models’ weight divergence (WDR) is introduced to FL. Furthermore, the collected gradients also facilitate FL walking out of the saddle point without extra communications. The cross-device momentum (CDM) mechanism could significantly improve the upper limit performance of the global model in non-IID. We evaluate the performance of several popular algorithms, including FedLA and FedLA with momentum (FedLAM). The results show that FedLAM achieves the best performance in most scenarios and the performance of the global model can also be improved in IID scenarios. Full article

Brittleness plays an important role in assessing the stability of the surrounding rock mass in deep underground projects. To this end, the present study deals with developing a robust evolutionary programming paradigm known as linear genetic programming (LGP) for estimating the brittleness index (BI). In addition, the bootstrap aggregate (Bagged) regression tree (BRT) and two efficient lazy machine learning approaches, namely local weighted linear regression (LWLR) and KStar approach, were examined to validate the LGP model. To the best of our knowledge, this is the first attempt to estimate the BI through the LGP model. A tunneling project in Pahang state, Malaysia, was investigated, and the requirement datasets were measured to construct the proposed models. According to the results from the testing phase, the LGP model yielded the best statistical indicators (R = 0.9529, RMSE = 0.4838, and I_A = 0.9744) for modeling BI, followed by LWLR (R = 0.9490, RMSE = 0.6607, and I_A = 0.9400), BRT (R = 0.9433, RMSE = 0.6875, and I_A = 0.9324), and KStar (R = 0.9310, RMSE = 0.7933, and I_A = 0.9095), respectively. In addition, the sensitivity analysis demonstrated that the dry density factor demonstrated the most effective prediction of BI. Full article

Sensor networks are estimated to drive the formation of the future Internet, with stream reasoning responsible for analysing sensor data. Stream reasoning is defined as real time logical reasoning on large, noisy, heterogeneous data streams, aiming to support the decision process of large numbers of concurrent querying agents. In this research we exploited non-monotonic rule-based systems for handling inconsistent or incomplete information and also ontologies to deal with heterogeneity. Data is aggregated from distributed streams in real time and plausible rules fire when new data is available. The advantages of lazy evaluation on data streams were investigated in this study, with the help of a prototype developed in Haskell. Full article