Fusion of BERH-2 rat hepatocellular carcinoma cells with activated B cells produced hybrid cells that lost their tumorigenicity and became immunogenic. Syngeneic rats injected with BERH-2-B hybrid cells became resistant to challenge with parental BERH-2 cells, and rats with established BERH-2 hepatomas were cured by subsequent injection of BERH-2-B cells. Both CD4+ and CD8+ cells were essential for the induction of protective immunity; however, only CD8+ cells were required for the eradication of BERH-2 tumors. The generation of hybrid tumor cells that elicit antitumor immune responses may be a useful strategy for cancer immunotherapy.

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A novel chitosan-based polymeric surfactant, Oleoyl-Carboxymethyl chitosan (O-CMCS) was produced as a coagulation agent to clean the residual oil from the wastewater of oil extraction (WWOE). O-CMCS was synthesized by reacting carboxymethyl chitosan (CMCS) with oleoyl chloride. The chemical structures of chitosan and its derivatives were characterized by FTIR. The destabilization of oil in water was successfully actualized by the application of O-CMCS (the optimal adsorbent with a dosage of 0.2 g/L; mixing time Ն3 min; the broader pH 0-8.05; the temperature Յ45°C; initial concentration of residual oil Ͻ140 mg/L), which showed a enhancement for the effective adsorption for the residue oil from WWOE.

Molecular properties play a crucial role in material discovery, protein interaction and drug development. The appearance of Graph Neural Network(GNN) significantly improved the performance of molecular property prediction. However, nodes in GNN only update the features of neighbor nodes, resulting in insufficient ability to encode global feature information. The self attention mechanism in transformer encodes the global information of molecules, while its spatial information is insufficient. Since molecules are three-dimensional spatial structures, spatial geometry information is an important attribute of molecules. Therefore, a transformer based geometric enhanced graph neural network (TG-GNN) is proposed to combine global and local molecule information with three-dimensional spatial structures to predict molecular properties. In this network, Graph neural network Geometric Feature Fusion Module (GGFF) and Transformer Geometric Feature Enhancement Module (TGF) are proposed to enhance the spatial geometry learning ability of the network. The GGFF module constructs a parallel graph neural network using bond and bond angle. The introduction of bond angle effectively complements the spatial information of the network. The TGF module introduces the coordinates and centrality degree features into the transformer to enhance the geometric expression ability of the module. GGFF module and TGF module encode local information and global information of molecules at the same time on the QM9 and OMDB dataset.