miRNome targeting NF-κB signaling orchestrates macrophage-triggered cancer metastasis and recurrence.

Whether and how tumor intrinsic signature determines macrophage-elicited metastasis remain elusive. Here, we show, in detailed studies of data regarding 7,477 patients of 20 types of human cancers, that only 13.8% ± 2.6%/27.9% ± 3.03% of patients with high macrophage infiltration index exhibit early recurrence/vascular invasion. In parallel, although macrophages enhance the motility of various hepatoma cells, their enhancement intensity is significantly heterogeneous. We identify that the expression of malignant Dicer, a ribonuclease that cleaves miRNA precursors into mature miRNAs, determines macrophage-elicited metastasis. Mechanistically, the downregulation of Dicer in cancer cells leads to defects in miRNome targeting NF-κB signaling, which in turn enhances the ability of cancer cells to respond to macrophage-related inflammatory signals and ultimately promotes metastasis. Importantly, transporting miR-26b-5p, the most potential miRNA targeting NF-κB signaling in hepatocellular carcinoma, can effectively reverse macrophage-elicited metastasis of hepatoma in vivo. Our results provide insights into the crosstalk between Dicer-elicited miRNome and cancer immune microenvironments and suggest that strategies to remodel malignant cell miRNome may overcome pro-tumorigenic activities of inflammatory cells.

Targeting Ferroptosis-Elicited Inflammation Suppresses Hepatocellular Carcinoma Metastasis and Enhances Sorafenib Efficacy.

Triggering ferroptosis, an iron-dependent form of cell death, has recently emerged as an approach for treating cancer. A better understanding of the role and regulation of ferroptosis is needed to realize the potential of this therapeutic strategy. Here, we observed extensive activation of ferroptosis in hepatoma cells and human hepatocellular carcinoma (HCC) cases. Patients with low to moderate activation of ferroptosis in tumors had the highest risk of recurrence compared to patients with no or high ferroptosis. Upon encountering ferroptotic liver cancer cells, aggregated macrophages efficiently secreted proinflammatory IL1ß to trigger neutrophil-mediated sinusoidal vascular remodeling, thereby creating favorable conditions for aggressive tumor growth and lung metastasis. Mechanistically, hyaluronan fragments released by cancer cells acted via an NF-κB-dependent pathway to upregulate IL1ß precursors and the NLRP3 inflammasome in macrophages, and oxidized phospholipids secreted by ferroptotic cells activated the NLRP3 inflammasome to release functional IL1ß. Depleting either macrophages or neutrophils or neutralizing IL1ß in vivo effectively abrogated ferroptosis-mediated liver cancer growth and lung metastasis. More importantly, the ferroptosis-elicited inflammatory cellular network served as a negative feedback mechanism that led to therapeutic resistance to sorafenib in HCC. Targeting the ferroptosis-induced inflammatory axis significantly improved the therapeutic efficacy of sorafenib in vivo. Together, this study identified a role for ferroptosis in promoting HCC by triggering a macrophage/IL1ß/neutrophil/vasculature axis. SIGNIFICANCE: Ferroptosis induces a favorable tumor microenvironment and supports liver cancer progression by stimulating an inflammatory cellular network that can be targeted to suppress metastasis and improve the efficacy of sorafenib.

Cost-Sensitive Siamese Network for PCB Defect Classification.

After the production of printed circuit boards (PCB), PCB manufacturers need to remove defected boards by conducting rigorous testing, while manual inspection is time-consuming and laborious. Many PCB factories employ automatic optical inspection (AOI), but this pixel-based comparison method has a high false alarm rate, thus requiring intensive human inspection to determine whether alarms raised from it resemble true or pseudo defects. In this paper, we propose a new cost-sensitive deep learning model: cost-sensitive siamese network (CSS-Net) based on siamese network, transfer learning and threshold moving methods to distinguish between true and pseudo PCB defects as a cost-sensitive classification problem. We use optimization algorithms such as NSGA-II to determine the optimal cost-sensitive threshold. Results show that our model improves true defects prediction accuracy to 97.60%, and it maintains relatively high pseudo defect prediction accuracy, 61.24% in real-production scenario. Furthermore, our model also outperforms its state-of-the-art competitor models in other comprehensive cost-sensitive metrics, with an average of 33.32% shorter training time.

A Seismic Data Acquisition System Based on Wireless Network Transmission.

A seismic data acquisition system based on wireless network transmission is designed to improve the low-frequency response and low sensitivity of the existing acquisition system. The system comprises of a piezoelectric transducer, a high-resolution data acquisition system, and a wireless communication module. A seismic piezoelectric transducer based on a piezoelectric simply supported beam using PMN-PT is proposed. High sensitivity is obtained by using a new piezoelectric material PMN-PT, and a simply supported beam matching with the PMN-PT wafer is designed, which can provide a good low-frequency response. The data acquisition system includes an electronic circuit for charge conversion, filtering, and amplification, an FPGA, and a 24-bit analog-to-digital converter (ADC). The wireless communication was based on the ZigBee modules and the WiFi modules. The experimental results show that the application of the piezoelectric simply supported beam based on PMN-PT can effectively improve the sensitivity of the piezoelectric accelerometer by more than 190%, compared with the traditional PZT material. At low frequencies, the fidelity of the PMN-PT piezoelectric simply supported beam is better than that of a traditional central compressed model, which is an effective expansion of the bandwidth to the low-frequency region. The charge conversion, filtering, amplification, and digitization of the output signal of the piezoelectric transducer are processed and, finally, are wirelessly transmitted to the monitoring centre, achieving the design of a seismic data acquisition system based on wireless transmission.