Statistical and Biological Evaluation of Different Gene Set Analysis Methods

AbstractGene-set analysis (GSA) methods have been widely used in microarray data analysis. Owing to the unusual characteristics of microarray data, such as multi-dimension, small sample size and complicated relationship between genes, no generally accepted methods have been used to detect differentially expressed gene sets (DEGs) up to now. Our group assessed the statistical performance of some commonly used methods through Monte Carlo simulation combined with the analysis of real-world microarray data sets. Not only did we discover a few novel features of GSA methods during experiences, but also we find that some GSA methods are effective only if genes were assumed to be independent. And we also detected that model-based methods (GlobalTest and PCOT2) performed well when analyzing our simulated data sets in which the inter-gene correlation structure was incorporated into each gene set separately for more reasonable. Through analysis of real-world microarray data, we found GlobalTest is more effective. Then we concluded that GlobalTest is a more effective gene set analysis method, and recommended using it with microarray data analysis

Dual Defense: Adversarial, Traceable, and Invisible Robust Watermarking against Face Swapping

The malicious applications of deep forgery, represented by face swapping, have introduced security threats such as misinformation dissemination and identity fraud. While some research has proposed the use of robust watermarking methods to trace the copyright of facial images for post-event traceability, these methods cannot effectively prevent the generation of forgeries at the source and curb their dissemination. To address this problem, we propose a novel comprehensive active defense mechanism that combines traceability and adversariality, called Dual Defense. Dual Defense invisibly embeds a single robust watermark within the target face to actively respond to sudden cases of malicious face swapping. It disrupts the output of the face swapping model while maintaining the integrity of watermark information throughout the entire dissemination process. This allows for watermark extraction at any stage of image tracking for traceability. Specifically, we introduce a watermark embedding network based on original-domain feature impersonation attack. This network learns robust adversarial features of target facial images and embeds watermarks, seeking a well-balanced trade-off between watermark invisibility, adversariality, and traceability through perceptual adversarial encoding strategies. Extensive experiments demonstrate that Dual Defense achieves optimal overall defense success rates and exhibits promising universality in anti-face swapping tasks and dataset generalization ability. It maintains impressive adversariality and traceability in both original and robust settings, surpassing current forgery defense methods that possess only one of these capabilities, including CMUA-Watermark, Anti-Forgery, FakeTagger, or PGD methods

Communication-efficient federated learning for digital twin systems of industrial internet of things

With the rapid development and deployment of Industrial Internet of Things technology, it promotes interconnection and edge applications in smart manufacturing. However, challenges remain, such as yet-to-improve communication efficiency and trade-offs between computing power and energy consumption, which limits the application and further development of IIoT technology. This paper proposes the digital twin systems into the IIoT to build model between physical objects and digital virtual systems to optimize the structure of IIoT. And we further introduce federal learning to train the digital twins model and to improve the communication efficiency of IIoT. In this paper, we first establish the digital twins model of IIoT based on industrial scenario. Moreover, to optimize the communication overhead allocation problem, this paper proposes an improved communication-efficient distribution algorithm, which speeds up the training performance of federated model and ensures the performance of industrial system model by changing the update training mode of client and server and allowing some industrial equipment to participate in federated training. This paper simulates the real-word intelligent camera detection to validate the proposed method. Comparing our proposed method with the existing traditional methods, the results show the advantages of the proposed method can improve the communication performance of the training model

Heparin Alters Viral Serpin, Serp-1, Anti-Thrombolytic Activity to Anti-Thrombotic Activity

Serine protease inhibitors (serpins) regulate coagulation and inflammation. Heparin, a glycosaminoglycan, is an important cofactor for modulation of the inhibitory function of mammalian serpins. The secreted myxoma viral serpin, Serp-1 exerts profound anti-inflammatory activity in a wide range of animal models. Serp-1 anti-inflammatory and anti-atherogenic activity is dependent upon inhibition of the uPA / uPA receptor thrombolytic complex. We demonstrate here that heparin binds to Serp-1 and enhances Serp-1 inhibition of thrombin, a human pro-thrombotic serine protease, in vitro, altering inhibitory activity to a more predominant anti-thrombotic activity. Heparin also facilitates the simultaneous thrombin-mediated cleavage of Serp-1 and prevents formation of a serpin-typical SDS-resistant complex, implying mutual neutralization of Serp-1 and thrombin. In a cell-based assay, heparin facilitates Serp-1 reversal of cellular activation by stabilizing cellular membrane fluidity in thrombin-activated monocytes. In conclusion, heparin and other GAGs serve as cofactors enhancing Serp-1 regulation of local thrombotic and inflammatory pathway