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In order to deeply understand the cutting mechanism of SiCp / Al in ultrasonic vibration-assisted turning, a prediction model of a cutting temperature field of SiCp / Al composites in UVAC (ultrasonic vibration-assisted cutting) was established. A theoretical model of instantaneous cutting depth and transient shear angle was established considering the real-time changing cutting depth, tool front angle and shear angle characteristics of UVAC. The relationship between cutting speed, shear speed and chip flow speed in UVAC processes is revealed, as well as the shear force and the front cutter friction force. Finally, the influence of heat generated by the heat source zone and shear heat source zone on the temperature rise was calculated, and the temperature field model was established. The experiment of processing SiCp / Al composites by UVAC was carried out. SiCp / Al composites with 25 % volume fraction were turned, and the cutting temperature data were measured and recorded by an infrared thermal imaging device. The cutting speed, cutting depth and feed rate were tested by a single factor, and the changes in cutting temperature under different parameters were compared. Finally, the experimental data were compared with the theoretical values to verify the validity of the theoretical model. [ABSTRACT FROM AUTHOR]

Background: The cytotoxicity of NK cells is largely dependent on IgG Fc receptor CD16a, which mediates antibody-dependent cell-mediated cytotoxicity (ADCC). The high-affinity and non-cleavable CD16 (hnCD16) is developed and demonstrated a multi-tumor killing potential. However, the hnCD16 receptor activates a single CD16 signal and provides limited tumor suppression. How to exploit the properties of hnCD16 and incorporate NK cell-specific activation domains is a promising development direction to further improve the anti-tumor activity of NK cells. Methods: To expand the applications of hnCD16-mediated ADCC for NK cell-based immunotherapy in cancer, we designed the hnCD16 Fusion Receptor (FR) constructs with the ectodomain of hnCD16 fused with NK cell-specific activating domains in the cytoplasm. FR constructs were transduced into CD16-negative NK cell line and human iPSC-derived NK (iNK) cells and effective FR constructs were screened. The up-regulation of immune activation- and cytokine-releasing-related pathways in FR-transduced NK cells was screened and validated by RNA sequencing and multiplex cytokines release assay, respectively. The tumor-killing efficiency was tested in vitro and in vivo via co-culture with tumor cell lines and xenograft mice-bearing human B-cell lymphoma, respectively. Results: We screened the most effective combination to kill B cell lymphoma, which was fused with the ectodomain of hnCD16a, NK-specific co-stimulators (2B4 and DAP10) and CD3ζ in cytoplasmic domains. The screened construct showed excellent cytotoxicity effects and sharp multiple cytokines releasing both in the NK cell line and iNK cells. The transcriptomic analysis and validation assays of hnCD16- and hnCD16FR-transduced NK cells showed that hnCD16FR transduction remodeled immune-related transcriptome in NK cells, where significant upregulation of genes related to cytotoxicity, high cytokines releasing, induced tumor cell apoptosis, and ADCC in comparison with hnCD16 transduction were highlighted. In vivo xenograft studies demonstrated that a single low-dose regimen of engineered hnCD16FR iPSC-derived NK cells co-administered with anti-CD20 mAb treatment mediated potent activity and significantly improved survival. Conclusion: We developed a novel hnCD16FR construct that exhibits more potent cytotoxicity than reported hnCD16, which is a promising approach to treat malignancies with improved ADCC properties. We also offer a rationale for NK activation domains that remodel immune response to enhance CD16 signaling in NK cells. [ABSTRACT FROM AUTHOR]

To address the problem of wet ball milling in a strong noise environment, it is difficult to accurately detect the internal load parameters of the cylinder during grinding. In this paper, a mill load parameter prediction method is proposed based on complementary ensemble empirical mode decomposition (CEEMDAN)-refined composite multiscale dispersion entropy (RCMDE) and-long and short-term memory (LSTM) neural networks. Using this method, the vibration signals of the mill barrel under strong noise were decomposed using the CEEMDAN algorithm, sensitive modal components with strong correlation with the original signal were selected for reconstruction using the correlation coefficient method, and features of the reconstructed signals under different load parameters were extracted through RCMDE. The load characteristic vector of an RCMDE mill was used as the input of LSTM neural networks, and the filling rate, material and ball ratio, and grinding concentration were used as the output to establish the internal load prediction model of wet mill. Experiment results show that the prediction method has a high accuracy, with average absolute percentage errors of the filling rate, feed-to-ball ratio, and grinding concentration of 6.08%, 3.50%, and 3.47%, and average absolute errors were of 0.0167, 0.0146, and 0.0146, respectively. [ABSTRACT FROM AUTHOR]