Research on the Prediction of Rigid Frame-Continuous Girder Bridge Deflection Using BP and RBF Neural Networks

To solve the problem of excessive deflection in the post-operation process of a rigid frame-continuous girder bridge and provide a basis for the setting of its initial camber, this paper, based on the results of finite element analysis, uses three methods to predict and verify the deflection of a rigid frame-continuous girder bridge. The results show that the average deflection method can be used to fit the average deflection value for a relatively long period of time and predict the average deflection value for the next longer period of time. Both the back-propagation (BP) neural network model and the radial basis function (RBF) neural network model can predict deflection well, but the RBF neural network model has higher prediction accuracy, with a mean absolute error (MAE) of 2.55 cmm and a relative error not exceeding 1%. The prediction model established by the RBF neural network has higher stability, better generalization ability, and better overall prediction performance. The established model has some reference significance for similar engineering projects and can achieve the optimization of structural parameters

Trypsin Isoinhibitors with Antiproliferative Activity toward Leukemia Cells from Phaseolus vulgaris cv “White Cloud Bean”

A purification protocol that comprised ion exchange chromatography on DEAE-cellulose, affinity chromatography on Affi-gel blue gel, ion exchange chromatography on SP-Sepharose, and gel filtration by FPLC on Superdex 75 was complied to isolate two trypsin inhibitors from Phaseolus vulgaris cv “White Cloud Bean”. Both trypsin inhibitors exhibited a molecular mass of 16 kDa and reduced the activity of trypsin with an IC50 value of about 0.6 μM. Dithiothreitol attenuated the trypsin inhibitory activity, signifying that an intact disulfide bond is indispensable to the activity. [Methyl-3H] thymidine incorporation by leukemia L1210 cells was inhibited with an IC50 value of 28.8 μM and 21.5 μM, respectively. They were lacking in activity toward lymphoma MBL2 cells and inhibitory effect on HIV-1 reverse transcriptase and fungal growth when tested up to 100 μM

Isolation of a laccase with HIV-1 reverse transcriptase inhibitory activity from fresh fruiting bodies of the <i>Lentinus edodes</i> (Shiitake mushroom)<i></i>

88-94A laccase with a molecular mass of 67 kDa and inhibitory activity toward HIV-1 reverse transcriptase (IC50 = 7.5 M) was isolated from fresh fruiting bodies of the Lentinus edodes (Shiitake mushroom). Its characteristics were compared with those of laccases from cultured mushroom mycelia reported earlier. The laccase was unadsorbed on DEAE-cellulose, Affi-gel blue gel and CM-cellulose, but was adsorbed on Con A-Sepharose. About 50-fold purification was achieved with a 19.2% yield of the enzyme. The activity of the enzyme increased steadily from 20°C to 70°C. The activity disappeared after exposure to the boiling temperature for 10 min. Its optimal pH was 4 and very little enzyme activity remained at and above pH 10. The laccase inhibited HIV-1 reverse transcriptase with an IC50 of 7.5 M, but did not demonstrate any antifungal or anti-proliferative activity

Spatiotemporal adaptive attention graph convolution network for city-level air quality prediction

Abstract Air pollution is a leading cause of human diseases. Accurate air quality predictions are critical to human health. However, it is difficult to extract spatiotemporal features among complex spatiotemporal dependencies effectively. Most existing methods focus on constructing multiple spatial dependencies and ignore the systematic analysis of spatial dependencies. We found that besides spatial proximity stations, functional similarity stations, and temporal pattern similarity stations, the shared spatial dependencies also exist in the complete spatial dependencies. In this paper, we propose a novel deep learning model, the spatiotemporal adaptive attention graph convolution model, for city-level air quality prediction, in which the prediction of future short-term series of PM2.5 readings is preferred. Specifically, we encode multiple spatiotemporal dependencies and construct complete spatiotemporal interactions between stations using station-level attention. Among them, we design a Bi-level sharing strategy to extract shared inter-station relationship features between certain stations efficiently. Then we extract multiple spatiotemporal features with multiple decoders, which it is extracted from the complete spatial dependencies between stations. Finally, we fuse multiple spatiotemporal features with a gating mechanism for multi-step predictions. Our model achieves state-of-the-art experimental results in several real-world datasets

A novel alkaline protease with antiproliferative activity from fresh fruiting bodies of the toxic wild mushroom Amanita farinosa

A novel protease with a molecular mass of 15 kDa was purified from fresh fruiting bodies of the wild mushroom Amanita farinosa. The purification protocol entailed anion exchange chromatography on DEAE-cellulose, affinity chromatography on Affi-gel blue gel, cation exchange chromatography on SP-Sepharose, and gel filtration by fast protein liquid chromatography on Superdex 75. The protease was unadsorbed on DEAE-cellulose but adsorbed on Affi-gel blue gel and SP-Sepharose. It demonstrated a single 15-kDa band in sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS/PAGE) and a 15-kDa peak in gel filtration. The optimal pH and optimal temperature of the protease were pH 8.0 and 65 °C, respectively. Proliferation of human hepatoma HepG2 cells was inhibited by the protease with an IC50 of 25 µM. The protease did not have antifungal or ribonuclease activity

Effect of crystalline admixture and superabsorbent polymer on the self-healing and mechanical properties of basalt fibre mortars

This study investigated the effects of a crystalline admixture (CA) and superabsorbent polymer (SAP) on the self-healing and mechanical properties of basalt fibre mortars. Uniaxial compression experiments were conducted on basalt fibre mortar specimens to investigate the effects of the two admixtures and different admixture ratios on the strength repair ability of basalt fibre mortar at different maintenance ages after pre-cracking, and microscopic observations of cracks and their healing products were conducted using optical microscopy, scanning electron microscopy, and energy dispersive spectroscopy to verify the experimental results. The results showed that CA has a noticeable advantage in the self-healing of microcracks by producing a dense material through chemical reactions, whereas SAP can effectively fill wider cracks and reduce their width through physical expansion. Compared with CA, SAP had a greater effect on the compressive strength of the basalt fibre mortars. The simultaneous dosing of CA and SAP in appropriate amounts can effectively combine the advantages of CA and SAP to optimise the self-healing effect of basalt fibre mortars, generating self-healing fillers based on calcium silicate and calcium carbonate in the cracks and enhancing the repair strength of basalt fibre mortars with a self-healing rate of 103%