Atherosclerosis remains a major driver for cardiovascular disease (CVD), despite advancements in traditional risk factor management therapies. Recent evidence emphasizes the crucial role of the gut microbiome in the progression of atherosclerosis and plaque rupture, highlighting a promising therapeutic avenue. This review focuses on the intertwined relationship between the gut microbiome, its metabolites, and atherosclerosis and CVD, also highlighting the potential therapeutic role of probiotics and prebiotics. Given the diverse and unique gut microbiota signatures among individuals, a one-size-fits-all therapeutic approach is unlikely to be effective. Personalized treatment strategies are therefore necessary. Here, we discussed how Artificial Intelligence (AI) can be leveraged to analyze individual gut microbiome profiles from microbiome sequencing, predict treatment response, and optimize therapeutic strategies based on individual patients, which would significantly improve outcomes of the treatment for atherosclerosis patients.

Trusted execution environment (TEE) technology has found many applications in mitigating various security risks in an efficient manner, which is attractive for critical infrastructure protection. First, the natural of critical infrastructure requires it to be well protected from various cyber attacks. Second, performance is usually important for critical infrastructure and it cannot afford an expensive protection mechanism. While a large number of TEE-based critical infrastructure protection systems have been proposed to address various security challenges (e.g., secure sensing and reliable control), most existing works ignore one important feature, i.e., devices comprised the critical infrastructure may be equipped with multiple incompatible TEE technologies and belongs to different owners. This feature makes it hard for these devices to establish mutual trust and form a unified TEE environment. To address these challenges and fully unleash the potential of TEE technology for critical infrastructure protection, we propose DHTee, a decentralized coordination mechanism. DHTee uses blockchain technology to support key TEE functions in a heterogeneous TEE environment, especially the attestation service. A Device equipped with one TEE can interact securely with the blockchain to verify whether another potential collaborating device claiming to have a different TEE meets the security requirements. DHTee is also flexible and can support new TEE schemes without affecting devices using existing TEEs that have been supported by the system.

Large language models (LLMs) have showcased remarkable skills, rivaling or even exceeding human intelligence in certain areas. Their proficiency in translation is notable, as they may replicate the nuanced, preparatory steps of human translators for high-quality outcomes. Although there have been some notable work exploring using LLMs for code-to-code translation, there has not been one for smart contracts, especially when the target language is unseen to the LLMs. In this work, we introduce our novel framework SolMover, which consists of two different LLMs working in tandem in a framework to understand coding concepts and then use that to translate code to an unseen language. We explore the human-like learning capability of LLMs with a detailed evaluation of the methodology to translate existing smart contracts written in Solidity to a low-resource one called Move. Specifically, we enable one LLM to understand coding rules for the new language to generate a planning task, for the second LLM to follow, which does not have planning capability but does have coding. Experiments show that SolMover brings a significant improvement over gpt-3.5-turbo-1106 and outperforms both Palm2 and Mixtral-8x7B-Instruct. Our further analysis shows that employing our bug mitigation technique even without the framework still improves code quality for all models

With the recent advances in concepts like decentralized “cloud” and blockchain-enabled decentralized computing environments, the legacy modeling and orchestration tools developed to support centrally managed cloud-based ICT infrastructures are challenged by such a new paradigm built on top of decentralization. On the other hand, decentralized “cloud” and computing infrastructures need to support many Dapp use cases. As the complexity of these targeted application scenarios increases, there is an urgent need for developing automation and modeling tools for deploying and managing decentralized infrastructures. Instead of creating such tools from scratch, a natural approach is extending mature infrastructure modeling tools for Dapps and decentralized computing environments. To this end, in this work, we have developed extensions to the TOSCA domain-specific language to support smart contract specification of decentralized computing infrastructures for supporting Dapps, where smart contracts or chain codes manage a decentralized computing environment. The result is blockchainbased orchestration and automation for decentralized “cloud” and computing environments that use existing infrastructure as code tools to deploy and manage decentralized applications.