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Yangyang Hui,1,* Han Wang,2,* Gaoyue Guo,1,* Wanting Yang,1 Xuqian Zhang,3 Jie Yang,1 Fang Yang,4 Xiaoyu Wang,1 Xiaofei Fan,1 Binxin Cui,5 Xin Chen,1 Huanli Jiao,2 Chao Sun1,5 1Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin, People’s Republic of China; 2Department of Health Management, Tianjin Hospital, Tianjin, People’s Republic of China; 3Department of Gastroenterology and Hepatology, China Aerospace Science & Industry Corporation 731 Hospital, Beijing, People’s Republic of China; 4Department of Digestive System, Baodi Clinical College of Tianjin Medical University, Tianjin, People’s Republic of China; 5Department of Gastroenterology, Tianjin Medical University General Hospital Airport Hospital, Tianjin Airport Economic Area, Tianjin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Huanli Jiao; Chao Sun, Tel +8613642060733 ; +8613820846027, Fax +86-022-27813550, Email jiaohuanli@163.com; chaosun@tmu.edu.cnPurpose: The utility of the EuroQol Group 5 Dimension (EQ-5D) measuring health-related quality of life (HRQoL) has been verified; however, knowledge gaps remain concerning predictive performance in cirrhosis. We aimed to identify the optimal threshold for risk stratification and the pronounced domain in the EQ-5D linked to inferior outcomes.Patients and Methods: The X-tile project was used to obtain a threshold, considering the composite outcome of 1-year all-cause mortality or readmission. A restricted cubic spline (RCS) was performed to test the non-linear relationship between the EQ-5D utility value and the primary outcome. Six multivariate Cox regression models incorporating EQ-5D utility value and each of the five domains were constructed.Setting/Participants: Totally, 420 patients with cirrhosis were recruited.Results: The median utility value of the study population was 0.77 and 59.8% reported impairment in minimal one

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient’s state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed.

In the era of 6G, featuring compelling visions of intelligent transportation system, digital twins, remote surveillance is poised to become a ubiquitous practice. The substantial data volume and frequent updates present challenges in wireless networks. To address this, we propose a novel agent-driven generative semantic communication (A-GSC) framework based on reinforcement learning. In contrast to the existing research on semantic communication (SemCom), which mainly focuses on semantic compression or semantic sampling, we seamlessly cascade both together by jointly considering the intrinsic attributes of source information and the contextual information regarding the task. Notably, the introduction of the generative artificial intelligence (GAI) enables the independent design of semantic encoders and decoders. In this work, we develop an agent-assisted semantic encoder leveraging the knowledge based soft actor-critic algorithm, which can track the semantic changes, channel condition, and sampling intervals, so as to perform adaptive semantic sampling. Accordingly, we design a semantic decoder with both predictive and generative capabilities, which consists of two tailored modules. Moreover, the effectiveness of the designed models has been verified based on the dataset generated from CDNet2014, and the performance gain of the overall A-GSC framework in both energy saving and reconstruction accuracy have been demonstrated., Comment: Under review with IEEE Transactions on Wireless Communications

In the era of 6G, featuring compelling visions of digital twins and metaverses, Extended Reality (XR) has emerged as a vital conduit connecting the digital and physical realms, garnering widespread interest. Ensuring a fully immersive wireless XR experience stands as a paramount technical necessity, demanding the liberation of XR from the confines of wired connections. In this paper, we first introduce the technologies applied in the wireless XR domain, delve into their benefits and limitations, and highlight the ongoing challenges. We then propose a novel deployment framework for a broad XR pipeline, termed "GeSa-XRF", inspired by the core philosophy of Semantic Communication (SemCom) which shifts the concern from "how" to transmit to "what" to transmit. Particularly, the framework comprises three stages: data collection, data analysis, and data delivery. In each stage, we integrate semantic awareness to achieve streamlined transmission and employ Generative Artificial Intelligence (GAI) to achieve collaborative refinements. For the data collection of multi-modal data with differentiated data volumes and heterogeneous latency requirements, we propose a novel SemCom paradigm based on multi-modal fusion and separation and a GAI-based robust superposition scheme. To perform a comprehensive data analysis, we employ multi-task learning to perform the prediction of field of view and personalized attention and discuss the possible preprocessing approaches assisted by GAI. Lastly, for the data delivery stage, we present a semantic-aware multicast-based delivery strategy aimed at reducing pixel level redundant transmissions and introduce the GAI collaborative refinement approach. The performance gain of the proposed GeSa-XRF is preliminarily demonstrated through a case study., Comment: Under review with IEEE Network

The burgeoning generative artificial intelligence technology offers novel insights into the development of semantic communication (SemCom) frameworks. These frameworks hold the potential to address the challenges associated with the black-box nature inherent in existing end-to-end training manner for the existing SemCom framework, as well as deterioration of the user experience caused by the inevitable error floor in deep learning-based SemCom. In this paper, we focus on the widespread remote monitoring scenario, and propose a semantic change driven generative SemCom framework. Therein, the semantic encoder and semantic decoder can be optimized independently. Specifically, we develop a modular semantic encoder with value of information based semantic sampling function. In addition, we propose a conditional denoising diffusion probabilistic mode-assisted semantic decoder that relies on received semantic information from the source, namely, the semantic map, and the local static scene information to remotely regenerate scenes. Moreover, we demonstrate the effectiveness of the proposed semantic encoder and decoder as well as the considerable potential in reducing energy consumption through simulation based on the realistic $\mathcal{F}$ composite channel fading model. The code is available at https://github.com/wty2011jl/SCDGSC.git.

Considering the infrastructure deployment cost and energy consumption, it is unrealistic to provide seamless coverage of the vehicular network. The presence of uncovered areas tends to hinder the prevalence of the in-vehicle services with large data volume. To this end, we propose a predictive cooperative multi-relay transmission strategy (PreCMTS) for the intermittently connected vehicular networks, fulfilling the 6G vision of semantic and green communications. Specifically, we introduce a task-driven knowledge graph (KG)-assisted semantic communication system, and model the KG into a weighted directed graph from the viewpoint of transmission. Meanwhile, we identify three predictable parameters about the individual vehicles to perform the following anticipatory analysis. Firstly, to facilitate semantic extraction, we derive the closed-form expression of the achievable throughput within the delay requirement. Then, for the extracted semantic representation, we formulate the mutually coupled problems of semantic unit assignment and predictive relay selection as a combinatorial optimization problem, to jointly optimize the energy efficiency and semantic transmission reliability. To find a favorable solution within limited time, we proposed a low-complexity algorithm based on Markov approximation. The promising performance gains of the PreCMTS are demonstrated by the simulations with realistic vehicle traces generated by the SUMO traffic simulator., Comment: Accepted by IEEE Transactions on Communications

With the increasing demand for intelligent services, the sixth-generation (6G) wireless networks will shift from a traditional architecture that focuses solely on high transmission rate to a new architecture that is based on the intelligent connection of everything. Semantic communication (SemCom), a revolutionary architecture that integrates user as well as application requirements and meaning of information into the data processing and transmission, is predicted to become a new core paradigm in 6G. While SemCom is expected to progress beyond the classical Shannon paradigm, several obstacles need to be overcome on the way to a SemCom-enabled smart wireless Internet. In this paper, we first highlight the motivations and compelling reasons of SemCom in 6G. Then, we outline the major 6G visions and key enabler techniques which lay the foundation of SemCom. Meanwhile, we highlight some benefits of SemCom-empowered 6G and present a SemCom-native 6G network architecture. Next, we show the evolution of SemCom from its introduction to classical SemCom related theory and modern AI-enabled SemCom. Following that, focusing on modern SemCom, we classify SemCom into three categories, i.e., semantic-oriented communication, goal-oriented communication, and semantic-aware communication, and introduce three types of semantic metrics. We then discuss the applications, the challenges and technologies related to semantics and communication. Finally, we introduce future research opportunities. In a nutshell, this paper investigates the fundamentals of SemCom, its applications in 6G networks, and the existing challenges and open issues for further direction., Comment: arXiv admin note: text overlap with arXiv:2103.05391 by other authors

The development of emerging applications, such as autonomous transportation systems, is expected to result in an explosive growth in mobile data traffic. As the available spectrum resource becomes more and more scarce, there is a growing need for a paradigm shift from Shannon's Classical Information Theory (CIT) to semantic communication (SemCom). Specifically, the former adopts a "transmit-before-understanding " approach while the latter leverages artificial intelligence (AI) techniques to "understand-before-transmit, " thereby alleviating bandwidth pressure by reducing the amount of data to be exchanged without negating the semantic effectiveness of the transmitted symbols. However, the semantic extraction (SE) procedure incurs costly computation and storage overheads. In this article, we introduce an edge-driven training, maintenance, and execution of SE. We further investigate how edge intelligence can be enhanced with SemCom through improving the generalization capabilities of intelligent agents at lower computation overheads and reducing the communication overhead of information exchange. Finally, we present a case study involving semantic-aware resource optimization for the wireless powered Internet of Things (IoT).

The development of emerging applications, such as autonomous transportation systems, are expected to result in an explosive growth in mobile data traffic. As the available spectrum resource becomes more and more scarce, there is a growing need for a paradigm shift from Shannon's Classical Information Theory (CIT) to semantic communication (SemCom). Specifically, the former adopts a "transmit-before-understanding" approach while the latter leverages artificial intelligence (AI) techniques to "understand-before-transmit", thereby alleviating bandwidth pressure by reducing the amount of data to be exchanged without negating the semantic effectiveness of the transmitted symbols. However, the semantic extraction (SE) procedure incurs costly computation and storage overheads. In this article, we introduce an edge-driven training, maintenance, and execution of SE. We further investigate how edge intelligence can be enhanced with SemCom through improving the generalization capabilities of intelligent agents at lower computation overheads and reducing the communication overhead of information exchange. Finally, we present a case study involving semantic-aware resource optimization for the wireless powered Internet of Things (IoT).

BackgroundThe connection between gut microbiota and metabolism and its role in the pathogenesis of diabetes are increasingly recognized. The objective of this study was to quantitatively measure Bifidobacterium and Lactobacillus species, members of commensal bacteria found in human gut, in type 2 diabetic patients (T2D) patients from Southern China.MethodsFifty patients with T2D and thirty control individuals of similar body mass index (BMI) were recruited from Southern China. T2D and control subjects were confirmed with both oral glucose tolerance test (OGTT) and HbA(1c) measurements. Bifidobacterium and Lactobacillus species in feces were measured by real-time quantitative PCR. Data were analyzed with STATA 11.0 statistical software.ResultsIn comparison to control subjects T2D patients had significantly more total Lactobacillus (+18%), L. bugaricus (+13%), L. rhamnosum (+37%) and L. acidophilus (+48%) (P < 0.05). In contrast, T2D patients had less amounts of total Bifidobacteria (-7%) and B. adolescentis (-12%) (P < 0.05). Cluster analysis showed that gut microbiota pattern of T2D patients is characterized by greater numbers of L. rhamnosus and L. acidophillus, together with lesser numbers of B. adolescentis (P < 0.05).ConclusionThe gut microflora in T2D patients is characterized by greater numbers of Lactobacillus and lesser numbers of Bifidobacterium species.

BackgroundThe connection between gut microbiota and metabolism and its role in the pathogenesis of diabetes are increasingly recognized. The objective of this study was to quantitatively measure Bifidobacterium and Lactobacillus species, members of commensal bacteria found in human gut, in type 2 diabetic patients (T2D) patients from Southern China.MethodsFifty patients with T2D and thirty control individuals of similar body mass index (BMI) were recruited from Southern China. T2D and control subjects were confirmed with both oral glucose tolerance test (OGTT) and HbA(1c) measurements. Bifidobacterium and Lactobacillus species in feces were measured by real-time quantitative PCR. Data were analyzed with STATA 11.0 statistical software.ResultsIn comparison to control subjects T2D patients had significantly more total Lactobacillus (+18%), L. bugaricus (+13%), L. rhamnosum (+37%) and L. acidophilus (+48%) (P < 0.05). In contrast, T2D patients had less amounts of total Bifidobacteria (-7%) and B. adolescentis (-12%) (P < 0.05). Cluster analysis showed that gut microbiota pattern of T2D patients is characterized by greater numbers of L. rhamnosus and L. acidophillus, together with lesser numbers of B. adolescentis (P < 0.05).ConclusionThe gut microflora in T2D patients is characterized by greater numbers of Lactobacillus and lesser numbers of Bifidobacterium species.