Many-Body Function Corrected Neural Network with Atomic Attention (MBNN-att) for Molecular Property Prediction.

Recent years have seen a surge of machine learning (ML) in chemistry for predicting chemical properties, but a low-cost, general-purpose, and high-performance model, desirable to be accessible on central processing unit (CPU) devices, remains not available. For this purpose, here we introduce an atomic attention mechanism into many-body function corrected neural network (MBNN), namely, MBNN-att ML model, to predict both the extensive and intensive properties of molecules and materials. The MBNN-att uses explicit function descriptors as the inputs for the atom-based feed-forward neural network (NN).

Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface.

The high dimensional machine learning potential (MLP) that has developed rapidly in the past decade represents a giant step forward in large-scale atomic simulation for complex systems. The long-range interaction and the poor description of chemical reactions are typical problems of high dimensional MLP, which are mainly caused by the poor structure discrimination of the atom-centered ML model. Herein, we propose a low-cost neural-network-based MLP architecture for fitting global potential energy surface data, namely, G-MBNN, that can offer improved energy and force resolution on a complex potential energy surface.

Fault severity assessment of rolling bearing based on optimized multi-dictionaries matching pursuit and Lempel-Ziv complexity.

For the safe working of rolling bearing, this paper presents a fault severity assessment method through optimized multi-dictionaries matching pursuit (OMMP) and Lempel-Ziv (LZ) complexity. To solve the redundancy problem of over-complete dictionary, the OMMP is proposed by introducing the quantum particle swarm optimization into matching pursuit for best representing the original vibration signal. And then, LZ complexity is calculated as an index of fault severity assessment by reconstructed signal.

[Effects of SiRNA-EGFR on the expression of hyaluronidase gene EGFR in human breast cancer cells].

Objective: To study the effect of SiRNA-EGFR on the expression of hyaluronidase gene in human breast cancer cells.

Methods: Reverse transcription-polymerse chain reaction was used to detect the changes in the expression of EGFR mRNA in human breast cancer cell lines MDA-MB-231, MDA-MB-435S, ZR-75 and ZR-75-30 after transfection by SiRNA-EGFR.

Results: After transfection with SiRNA-EGFR, the expression levels of EGFR were significantly inhibited in MDA-MB-231, MDA-MB-435S, ZR-75 and ZR-75-30 cells (P<0.

[Effects of RNA interference on epidermal growth factor receptor expression in breast cancer cells: a study in tumor-bearing nude mice].

Objective: To investigate the effect of cationic liposome-mediated RNA interference (RNAi) in silencing epidermal growth factor (EGF) receptor (EGFR) gene in breast cancer cells in vivo.

Methods: A small interfering RNA (siRNA) targeting EGFR gene was constructed and transfected into human breast cancer cell in vitro via cationic liposome. The transfected cells were inoculated into nude mice, and the tumor growth inhibition rate was calculated.