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Abstract Objective Gastric cancer (GC) is one of the most prevalent malignant tumors of the digestive system. The advanced metastasis of gastric cancer severely limits the conventional approaches for its treatment, while certain traditional Chinese medicinal compounds have been reported to possess promising abilities in inhibiting tumor metastasis. Such as Poria (PA), known as Fu Ling in Chinese, is a commonly used traditional Chinese medicinal herb derived from Poria cocos, a fungus belonging to the polyporaceae family. Methods The proliferation capacity of cells was measured using the MTT assay, while the invasion and migration abilities of cells after treatment with different concentrations of PA were evaluated through wound healing assay and Transwell assay. The differential expression of mRNA was analyzed using qPCR. The in vivo growth of tumors was assessed by subcutaneous tumor formation in mice. Results Both in vivo and in vitro experiments have demonstrated that PA significantly inhibits the proliferation of GC. Moreover, in vitro experiments have revealed that PA not only suppresses the invasion and migration of GC cells but also reverses TNF-β-induced EMT. Further experiments have revealed that PA inhibits cell invasion, migration and EMT by inducing ferroptosis in GC cells. Conclusion In brief, the present study shows that PA inhibits tumor metastasis by inducing ferroptosis in GC cells. Our findings suggest that PA may have therapeutic potential in GC.

The escalating proliferation of space debris poses an increasing risk to spinning satellites, elevating the probability of hazardous collisions that can result in severe damage or total loss of functionality. To address this concern, a pioneering inflatable protective structure is employed to ensure the optimal functionality of spinning satellites. Additionally, a multi-body dynamic modeling method based on spring hinge unfolding/spring expansion is proposed to tackle the complex dynamics of spinning satellites with inflatable protective structures during flight. This method enables analysis of the motion parameters of spinning satellites. First, the structural composition of a spinning satellite with inflatable protective structures is introduced and its flight process is analyzed. Then, an articulated spring hinge unfolding model or a spring expansion model using the Newton–Euler method is established to describe the unfolding or expansion of the spinning satellite with inflatable protective structures during flight. Finally, the effects on the motion parameters of a spinning satellite are analyzed through simulation under various working conditions.

A superhydrophobic fluorinated silica functionalized chitosan (F-CS) aerogel is constructed and fabricated by a simple and sustainable method in this study in order to achieve highly efficient gravity-driven oil/water separation performance. The fluorinated silica functionalization invests the pristine hydrophilic chitosan (CS) aerogel with promising superhydrophobicity with a water contact angle of 151.9°. This novel F-CS aerogel possesses three-dimensional structure with high porosity as well as good chemical stability and mechanical compression property. Moreover, it also shows striking self-cleaning performance and great oil adsorption capacity. Most importantly, the as-prepared aerogels exhibits fast and efficient separation of oil/water mixture by the gravity driven process with high separation efficiency. These great performances render the prepared F-CS aerogel a good candidate for oil/water separation in practical industrial application.

Despite their tremendous success in various machine learning tasks, deep neural networks (DNNs) are inherently vulnerable to adversarial examples, which are maliciously crafted inputs to cause DNNs to misbehave. Intensive research has been conducted on this phenomenon in simple tasks (e.g., image classification). However, little is known about this adversarial vulnerability for object detection, a much more complicated task, which often requires specialized DNNs and multiple additional components. In this paper, we present DETECTSEC, a uniform platform for robustness analysis of object detection models. Currently, DETECTSEC implements 13 representative adversarial attacks with 7 utility metrics and 13 defenses on 18 standard object detection models. Leveraging DETECTSEC, we conduct the first rigorous evaluation of adversarial attacks on the state-of-the-art object detection models. We analyze the impact of the factors including DNN architecture and capacity on the model robustness. We show that many conclusions about adversarial attacks and defenses in image classification tasks do not transfer to object detection tasks, for example, the targeted attack is stronger than the untargeted attack for two-stage detectors. Our findings will aid future efforts in understanding and defending against adversarial attacks in complicated tasks. In addition, we compare the robustness of different detection models and discuss their relative strengths and weaknesses. The platform DETECTSEC will be open source as a unique facility for further research on adversarial attacks and defenses in object detection tasks. [ABSTRACT FROM AUTHOR]