Efficient Premature Ventricular Contraction Detection Based on Network Dynamics Features

Automatic detection of premature ventricular contractions (PVCs) is essential for early identification of cardiovascular abnormalities and reduction of clinical workload. As the most prevalent arrhythmia, PVCs can cause cardiac failure or sudden death. The difficulty resides in extracting features that effectively reflect the electrocardiogram (ECG) signals. Transition networks (TN), which represent the transition relationships between various phases of a time series, are advantageous for capturing temporal dynamics. Therefore, in order to recognize PVCs, each heartbeat was firstly split into serval segments; then their statistical properties were calculated for the sequence construction; finally, network topology related features were extracted from TN constructed by these sequences of statistical properties, and input into decision trees-based Gentleboost for PVC recognition. The algorithm was trained on MIT-BIH arrhythmia database (MIT-BIH-AR), and tested on St. Petersburg Institute of Cardiological Technics 12-lead arrhythmia database (INCART), wearable ECG database (WECG), and noise stress test database by four evaluation metrics: sensitivity, positive predictivity, F1-score (F1) and area under the curve (AUC). The proposed algorithm achieved an average F1 of 0.9784 and AUC of 0.9975 on MIT-BIH-AR, and proved good generalization ability on INCART and WECG with F1=0.9633 and 0.9467, AUC=0.9887 and 0.9755, respectively. The algorithm also exhibited robustness and noise immunity as evidenced by tests on sensitivity of R-wave peak offset and noise, and real-world daily life conditions. Overall, the proposed PVC detection algorithm based on TN theory offered high classification accuracy, strong robustness, and good generalization ability, with great potential for wearable mobile applications

A Unified Framework for Integrating Semantic Communication and AI-Generated Content in Metaverse

As the Metaverse continues to grow, the need for efficient communication and intelligent content generation becomes increasingly important. Semantic communication focuses on conveying meaning and understanding from user inputs, while AI-Generated Content utilizes artificial intelligence to create digital content and experiences. Integrated Semantic Communication and AI-Generated Content (ISGC) has attracted a lot of attentions recently, which transfers semantic information from user inputs, generates digital content, and renders graphics for Metaverse. In this paper, we introduce a unified framework that captures ISGC two primary benefits, including integration gain for optimized resource allocation and coordination gain for goal-oriented high-quality content generation to improve immersion from both communication and content perspectives. We also classify existing ISGC solutions, analyze the major components of ISGC, and present several use cases. We then construct a case study based on the diffusion model to identify an optimal resource allocation strategy for performing semantic extraction, content generation, and graphic rendering in the Metaverse. Finally, we discuss several open research issues, encouraging further exploring the potential of ISGC and its related applications in the Metaverse.Comment: 8 pages, 6 figure

A Unified Blockchain-Semantic Framework for Wireless Edge Intelligence Enabled Web 3.0

Web 3.0 enables user-generated contents and user-selected authorities. With decentralized wireless edge computing architectures, Web 3.0 allows users to read, write, and own contents. A core technology that enables Web 3.0 goals is blockchain, which provides security services by recording content in a decentralized and transparent manner. However, the explosion of on-chain recorded contents and the fast-growing number of users cause increasingly unaffordable computing and storage resource consumption. A promising paradigm is to analyze the semantic information of contents that can convey precisely the desired meanings without consuming many resources. In this article, we propose a unified blockchain-semantic ecosystems framework for wireless edge intelligence-enabled Web 3.0. Our framework consists of six key components to exchange semantic demands. We then introduce an Oracle-based proof of semantic mechanism to implement on-chain and off-chain interactions of Web 3.0 ecosystems on semantic verification algorithms while maintaining service security. An adaptive Deep Reinforcement Learning-based sharding mechanism on Oracle is designed to improve interaction efficiency, which can facilitate Web 3.0 ecosystems to deal with varied semantic demands. Finally, a case study is presented to show that the proposed framework can dynamically adjust Oracle settings according to varied semantic demands.Comment: 8 pages, 5 figures, 1 tabl

Genetic Manipulation Toolkits in Apicomplexan Parasites

Apicomplexan parasites are a group of intracellular pathogens of great medical and veterinary importance, including Toxoplasma gondii and Plasmodium , which cause toxoplasmosis and malaria, respectively. Efficient and accurate manipulation of their genomes is essential to dissect their complex biology and to design new interventions. Over the past several decades, scientists have continually optimized the methods for genetic engineering in these organisms, and tremendous progress has been made. Here, we review the genetic manipulation tools currently used in several apicomplexan parasites, and discuss their advantages and limitations. The widely used CRISPR/Cas9 genome editing technique has been adapted in several apicomplexans and shown promising efficiency. In contrast, conditional gene regulation is available in only a limited number of organisms, mainly Plasmodium and Toxoplasma , thus posing a research bottleneck for other parasites. Conditional gene regulation can be achieved with tools that regulate gene expression at the DNA, RNA or protein level. However, a universal tool to address all needs of conditional gene manipulation remains lacking. Understanding the scope of application is key to selecting the proper method for gene manipulation

RNA interference-mediated silencing of BACE and APP attenuates the isoflurane-induced caspase activation

<p>Abstract</p> <p>Background</p> <p>β-Amyloid protein (Aβ) has been shown to potentiate the caspase-3 activation induced by the commonly used inhalation anesthetic isoflurane. However, it is unknown whether reduction in Aβ levels can attenuate the isoflurane-induced caspase-3 activation. We therefore set out to determine the effects of RNA interference-mediated silencing of amyloid precursor protein (APP) and β-site APP-cleaving enzyme (BACE) on the levels of Aβ and the isoflurane-induced caspase-3 activation.</p> <p>Methods</p> <p>H4 human neuroglioma cells stably transfected to express full-length human APP (H4-APP cells) were treated with small interference RNAs (siRNAs) targeted at silencing BACE and APP for 48 hours. The cells were then treated with 2% isoflurane for six hours. The levels of BACE, APP, and caspase-3 were determined using Western blot analysis. Sandwich Enzyme-linked immunosorbent assay (ELISA) was used to determine the extracellular Aβ levels in the conditioned cell culture media.</p> <p>Results</p> <p>Here we show for the first time that treatment with BACE and APP siRNAs can decrease levels of BACE, full-length APP, and APP c-terminal fragments. Moreover, the treatment attenuates the Aβ levels and the isoflurane-induced caspase-3 activation. These results further suggest a potential role of Aβ in the isoflurane-induced caspase-3 activation such that the reduction in Aβ levels attenuates the isoflurane-induced caspase-3 activation.</p> <p>Conclusion</p> <p>These findings will lead to more studies which aim at illustrating the underlying mechanism by which isoflurane and other anesthetics may affect Alzheimer's disease neuropathogenesis.</p

On the special oxidation mechanism of a Mg-Y-Al alloy contained LPSO phase at high temperatures

This work investigated the oxidation of Mg-11Y-1Al alloy in Ar-20%O2 at 500{\deg}through multiscale characterization. The results show that the network-like long-period stacking ordered(LPSO) phase decomposed into a needle-like LPSO phase and a polygonal Mg24Y5 phase. The needle-like LPSO phase resulted in the formation of a high-dense of needle-like oxide at the oxidation front of the area initially occupied by the network-like LPSO phase. The further inward oxygen would diffuse along the needle-like oxide-matrix interfaces and react with Y in the surrounding Mg matrix, resulting in the lateral growth of these needle-like oxides. Finally, the discrete needle-like oxides were interconnected to form a thicker and continuous oxide scale which could be more effective in hindering the elemental diffusion. Meanwhile, Al could partially enter the Y2O3 oxide scale and formed a strengthened (Y,Al)O oxide scale which could show a greater resistance to cracking and debonding