Cycloastragenol induces apoptosis and protective autophagy through AMPK/ULK1/mTOR axis in human non-small cell lung cancer cell lines.

OBJECTIVE: Studies have demonstrated that cycloastragenol induces antitumor effects in prostate, colorectal and gastric cancers; however, its efficacy for inhibiting the proliferation of lung cancer cells is largely unexplored. This study explores the efficacy of cycloastragenol for inhibiting non-small cell lung cancer (NSCLC) and elucidates the underlying molecular mechanisms. METHODS: The effects of cycloastragenol on lung cancer cell proliferation were assessed using an adenosine triphosphate monitoring system based on firefly luciferase and clonogenic formation assays. Cycloastragenol-induced apoptosis in lung cancer cells was evaluated using dual staining flow cytometry with an annexin V-fluorescein isothiocyanate/propidium iodide kit. To elucidate the role of cycloastragenol in the induction of apoptosis, apoptosis-related proteins were examined using Western blots. Immunofluorescence and Western blotting were used to determine whether cycloastragenol could induce autophagy in lung cancer cells. Genetic techniques, including small interfering RNA technology, were used to investigate the underlying mechanisms. The effects against lung cancer and biosafety of cycloastragenol were evaluated using a mouse subcutaneous tumor model. RESULTS: Cycloastragenol triggered both autophagy and apoptosis. Specifically, cycloastragenol promoted apoptosis by facilitating the accumulation of phorbol-12-myristate-13-acetate-induced protein 1 (NOXA), a critical apoptosis-related protein. Moreover, cycloastragenol induced a protective autophagy response through modulation of the adenosine 5'-monophosphate-activated protein kinase (AMPK)/unc-51-like autophagy-activating kinase (ULK1)/mammalian target of rapamycin (mTOR) pathway. CONCLUSION: Our study sheds new light on the antitumor efficacy and mechanism of action of cycloastragenol in NSCLC. This insight provides a scientific basis for exploring combination therapies that use cycloastragenol and inhibiting the AMPK/ULK1/mTOR pathway as a promising approach to combating lung cancer. Please cite this article as follows: Zhu LH, Liang YP, Yang L, Zhu F, Jia LJ, Li HG. Cycloastragenolinduces apoptosis and protective autophagy through AMPK/ULK1/mTOR axis in human non-small celllung cancer cell lines. J Integr Med. 2024: Epub ahead of print.

Implementation of Lightweight Convolutional Neural Networks with an Early Exit Mechanism Utilizing 40 nm CMOS Process for Fire Detection in Unmanned Aerial Vehicles.

The advancement of unmanned aerial vehicles (UAVs) enables early detection of numerous disasters. Efforts have been made to automate the monitoring of data from UAVs, with machine learning methods recently attracting significant interest. These solutions often face challenges with high computational costs and energy usage. Conventionally, data from UAVs are processed using cloud computing, where they are sent to the cloud for analysis. However, this method might not meet the real-time needs of disaster relief scenarios. In contrast, edge computing provides real-time processing at the site but still struggles with computational and energy efficiency issues. To overcome these obstacles and enhance resource utilization, this paper presents a convolutional neural network (CNN) model with an early exit mechanism designed for fire detection in UAVs. This model is implemented using TSMC 40 nm CMOS technology, which aids in hardware acceleration. Notably, the neural network has a modest parameter count of 11.2 k. In the hardware computation part, the CNN circuit completes fire detection in approximately 230,000 cycles. Power-gating techniques are also used to turn off inactive memory, contributing to reduced power consumption. The experimental results show that this neural network reaches a maximum accuracy of 81.49% in the hardware implementation stage. After automatic layout and routing, the CNN hardware accelerator can operate at 300 MHz, consuming 117 mW of power.

Coherently parallel fiber-optic distributed acoustic sensing using dual Kerr soliton microcombs.

Fiber-optic distributed acoustic sensing (DAS) has proven to be a revolutionary technology for the detection of seismic and acoustic waves with ultralarge scale and ultrahigh sensitivity, and is widely used in oil/gas industry and intrusion monitoring. Nowadays, the single-frequency laser source in DAS becomes one of the bottlenecks limiting its advance. Here, we report a dual-comb-based coherently parallel DAS concept, enabling linear superposition of sensing signals scaling with the comb-line number to result in unprecedented sensitivity enhancement, straightforward fading suppression, and high-power Brillouin-free transmission that can extend the detection distance considerably. Leveraging 10-line comb pairs, a world-class detection limit of 560 fε/√Hz@1 kHz with 5 m spatial resolution is achieved. Such a combination of dual-comb metrology and DAS technology may open an era of extremely sensitive DAS at the fε/√Hz level, leading to the creation of next-generation distributed geophones and sonars.

A Multiplier-Free Convolution Neural Network Hardware Accelerator for Real-Time Bearing Condition Detection of CNC Machinery.

In various industrial domains, machinery plays a pivotal role, with bearing failure standing out as the most prevalent cause of malfunction, contributing to approximately 41% to 44% of all operational breakdowns. To address this issue, this research employs a lightweight neural network, boasting a mere 8.69 K parameters, tailored for implementation on an FPGA (field-programmable gate array). By integrating an incremental network quantization approach and fixed-point operation techniques, substantial memory savings amounting to 63.49% are realized compared to conventional 32-bit floating-point operations. Moreover, when executed on an FPGA, this work facilitates real-time bearing condition detection at an impressive rate of 48,000 samples per second while operating on a minimal power budget of just 342 mW. Remarkably, this system achieves an accuracy level of 95.12%, showcasing its effectiveness in predictive maintenance and the prevention of costly machinery failures.

CNN Hardware Accelerator for Real-Time Bearing Fault Diagnosis.

This paper introduces a one-dimensional convolutional neural network (CNN) hardware accelerator. It is crafted to conduct real-time assessments of bearing conditions using economical hardware components, implemented on a field-programmable gate array evaluation platform, negating the necessity to transfer data to a cloud-based server. The adoption of the down-sampling technique augments the visible time span of the signal in an image, thereby enhancing the accuracy of the bearing condition diagnosis. Furthermore, the proposed method of quaternary quantization enhances precision and shrinks the memory demand for the neural network model by an impressive 89%. Provided that the current signal data sampling rate stands at 64 K samples/s, the proposed design can accomplish real-time fault diagnosis at a clock frequency of 100 MHz. Impressively, the response duration of the proposed CNN hardware system is a mere 0.28 s, with the fault diagnosis precision reaching a remarkable 96.37%.