Search

With the development of new Internet services such as computation-intensive and delay-sensitive tasks, the traditional "Best Effort" network transmission mode has been greatly challenged. The network system is urgently required to provide end-to-end transmission determinacy and computing determinacy for new applications to ensure the safe and efficient operation of services. Based on the research of the convergence of computing and networking, a new network paradigm named deterministic computing power networking (Det-CPN) is proposed. In this article, we firstly introduce the research advance of computing power networking. And then the motivations and scenarios of Det-CPN are analyzed. Following that, we present the system architecture, technological capabilities, workflow as well as key technologies for Det-CPN. Finally, the challenges and future trends of Det-CPN are analyzed and discussed.

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID-19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial.MethodsWe conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochrans Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results.ResultsWe found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92-0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy.ConclusionsOur study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential, Funding Agencies|This study was supported by Natural Science Foundation of Sichuan Province, China (Grant No. 2022NSFSC0764) and Post-Doctor Research Project of Sichuan University (Grant Nos.2022SCU12025 and 2022SCU12020). [2022SCU12025]; Natural Science Foundation of Sichuan Province, China [2022SCU12020]; Post-Doctor Research Project of Sichuan University; [2022NSFSC0764]

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID-19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial.MethodsWe conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochrans Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results.ResultsWe found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92-0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy.ConclusionsOur study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential, Funding Agencies|This study was supported by Natural Science Foundation of Sichuan Province, China (Grant No. 2022NSFSC0764) and Post-Doctor Research Project of Sichuan University (Grant Nos.2022SCU12025 and 2022SCU12020). [2022SCU12025]; Natural Science Foundation of Sichuan Province, China [2022SCU12020]; Post-Doctor Research Project of Sichuan University; [2022NSFSC0764]

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID-19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial.MethodsWe conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochrans Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results.ResultsWe found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92-0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy.ConclusionsOur study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential, Funding Agencies|This study was supported by Natural Science Foundation of Sichuan Province, China (Grant No. 2022NSFSC0764) and Post-Doctor Research Project of Sichuan University (Grant Nos.2022SCU12025 and 2022SCU12020). [2022SCU12025]; Natural Science Foundation of Sichuan Province, China [2022SCU12020]; Post-Doctor Research Project of Sichuan University; [2022NSFSC0764]

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID-19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial.MethodsWe conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochrans Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results.ResultsWe found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92-0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy.ConclusionsOur study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential, Funding Agencies|This study was supported by Natural Science Foundation of Sichuan Province, China (Grant No. 2022NSFSC0764) and Post-Doctor Research Project of Sichuan University (Grant Nos.2022SCU12025 and 2022SCU12020). [2022SCU12025]; Natural Science Foundation of Sichuan Province, China [2022SCU12020]; Post-Doctor Research Project of Sichuan University; [2022NSFSC0764]

With the rapid development of the Industrial Internet of Things (IIoT), massive IIoT devices connect to industrial networks via wired and wireless. Furthermore, industrial networks pose new requirements on communications, such as strict latency boundaries, ultra-reliable transmission, and so on. To this end, time-sensitive networking (TSN) embedded fifth-generation (5G) wireless communication technology (i.e., TSN-5G networks), is considered the most promising solution to address these challenges. TSN can provide deterministic end-to-end latency and reliability for real-time applications in wired networks. 5G supports ultra-reliable and low-latency communications (uRLLC), providing increased flexibility and inherent mobility support in the wireless network. Thus, the integration of TSN and 5G provides numerous benefits, including increased flexibility, lower commissioning costs, and seamless interoperability of various devices, regardless of whether they use a wired or wireless interface. Nonetheless, the potential barriers between the TSN and 5G systems, such as clock synchronization and end-to-end traffic scheduling, are inevitable. Time synchronization has been studied in many works, so this paper focuses on the end-to-end traffic scheduling problem in TSN-5G networks. We propose a novel integrated TSN and 5G industrial network architecture, where the 5G system acts as a logical TSN-capable bridge. Based on this network architecture, we design a Double Q-learning based hierarchical particle swarm optimization algorithm (DQHPSO) to search for the optimal scheduling solution. The DQHPSO algorithm adopts a level-based population structure and introduces Double Q-learning to adjust the number of levels in the population, which evades the local optimum to further improve the search efficiency. Extensive simulations demonstrate that the DQHPSO algorithm can increase the scheduling success ratio of time-triggered flows compared to other algorithms.

Background Lifestyle factors such as physical fitness, dietary habits, mental stress, and sleep quality, are strong predictors of the occurrence, clinical course, and overall treatment outcomes of common cardiovascular diseases. However, these lifestyle factors are rarely monitored, nor used in daily clinical practice and personalized cardiac care. Moreover, non-adherence to long-term self-reporting of these lifestyle factors is common. In the present study, we evaluate adherence to a continuous unobtrusive and patient-friendly lifestyle monitoring system using evidence-based assessment tools. Methods In a prospective observational trial (N = 100), the project investigates usability of and adherence to a monitoring system for multiple lifestyle factors relevant to cardiovascular disease, i.e., daily physical activity levels, dietary habits, mental stress, smoking, and sleep quality. Patients with coronary artery disease, valvular disease and arrhythmias undergoing an elective intervention are asked to participate. The monitoring system consists of a secured online platform with a custom-built conversational interface—a chatbot—and a wrist-worn wearable medical device. The wrist-worn device collects continuous objective data on physical activity and the chatbot is used to collect self-report data. Participants collect self-reported lifestyle data via the chatbot for a maximum of 4 days every other week; in the same week physiological data are collected for 7 days for 24 h. Data collection starts one week before the intervention and continues until 1-year after discharge. Via a dashboard, patients can observe their lifestyle measures and adherence to self-reporting, set and track personal goals, and share their lifestyle data with practitioners and relatives. The primary outcome of the trial is adherence to using the integrated platform for self-tracking data. The secondary outcomes include system usability, determinants of adherence and the relation

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID-19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial.MethodsWe conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochrans Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results.ResultsWe found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92-0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy.ConclusionsOur study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential, Funding Agencies|This study was supported by Natural Science Foundation of Sichuan Province, China (Grant No. 2022NSFSC0764) and Post-Doctor Research Project of Sichuan University (Grant Nos.2022SCU12025 and 2022SCU12020). [2022SCU12025]; Natural Science Foundation of Sichuan Province, China [2022SCU12020]; Post-Doctor Research Project of Sichuan University; [2022NSFSC0764]

The Internet of intelligence is conceived as an emerging networking paradigm, which will make intelligence as easy to obtain as information. This paper provides an overview of the Internet of intelligence, focusing on motivations, architecture, enabling technologies, applications, and existing challenges. This can provide a good foundation for those who are interested to gain insights into the concept of the Internet of intelligence and the key enablers of this emerging networking paradigm. Specifically, this paper starts by investigating the evolution of networking paradigms and artificial intelligence (AI), based on which we present the motivations of the Internet of intelligence by demonstrating that networking needs intelligence and intelligence needs networking. We then present the layered architecture to characterize the Internet of intelligence systems and discuss the enabling technologies of each layer. Moreover, we discuss the critical applications and their integration with the Internet of intelligence paradigm. Finally, some technical challenges and open issues are summarized to fully exploit the benefits of the Internet of intelligence., Comment: This article has been accepted for publication in a future issue of IEEE Communications Surveys & Tutorials

Deterministic IP (DIP) networking is a promising technique that can provide delay-bounded transmission in large-scale networks. Nevertheless, DIP faces several challenges in the mixed traffic scenarios, including (i) the capability of ultra-low latency communications, (ii) the simultaneous satisfaction of diverse QoS requirements, and (iii) the network efficiency. The problems are more formidable in the dynamic surroundings without prior knowledge of traffic demands. To address the above-mentioned issues, this paper designs a flexible DIP (FDIP) network. In the proposed network, we classify the queues at the output port into multiple groups. Each group operates with different cycle lengths. FDIP can assign the time-sensitive flows with different groups, hence delivering diverse QoS requirements, simultaneously. The ultra-low latency communication can be achieved by specific groups with short cycle lengths. Moreover, the flexible scheduling with diverse cycle lengths improves resource utilization, hence increasing the throughput (i.e., the number of acceptable time-sensitive flows). We formulate a throughput maximization problem that jointly considers the admission control, transmission path selection, and cycle length assignment. A branch and bound (BnB)-based heuristic is developed. Simulation results show that the proposed FDIP significantly outperforms the standard DIP in terms of both the throughput and the latency guarantees.

With the explosive growth of network traffic, content delivery in the Internet is an increasing concern. To fundamentally deal with this problem, in this article, we propose a data-driven intelligent future network from the perspectives of design philosophy, architecture, and use cases, respectively. The novel proposal fully absorbs the advantages of future network architectures and technologies, and efficiently improves the utilization of system resources and realizes intelligent content distribution while meeting the demands of network services. Moreover, we also outline some challenges and future research directions in this field. © 1979-2012 IEEE.

Efforts to understand the influence of historical global warming on individual extreme climate events have increased over the past decade. However, despite substantial progress, events that are unprecedented in the local observational record remain a persistent challenge. Leveraging observations and a large climate model ensemble, we quantify uncertainty in the influence of global warming on the severity and probability of the historically hottest month, hottest day, driest year, and wettest 5-d period for different areas of the globe. We find that historical warming has increased the severity and probability of the hottest month and hottest day of the year at >80% of the available observational area. Our framework also suggests that the historical climate forcing has increased the probability of the driest year and wettest 5-d period at 57% and 41% of the observed area, respectively, although we note important caveats. For the most protracted hot and dry events, the strongest and most widespread contributions of anthropogenic climate forcing occur in the tropics, including increases in probability of at least a factor of 4 for the hottest month and at least a factor of 2 for the driest year. We also demonstrate the ability of our framework to systematically evaluate the role of dynamic and thermodynamic factors such as atmospheric circulation patterns and atmospheric water vapor, and find extremely high statistical confidence that anthropogenic forcing increased the probability of record-low Arctic sea ice extent.

Recently, there are significant advances in the areas of networking, caching and computing. Nevertheless, these three important areas have traditionally been addressed separately in the existing research. In this paper, we present a novel framework that integrates networking, caching and computing in a systematic way and enables dynamic orchestration of these three resources to improve the end-to-end system performance and meet the requirements of different applications. Then, we consider the bandwidth, caching and computing resource allocation issue and formulate it as a joint caching/computing strategy and servers selection problem to minimize the combination cost of network usage and energy consumption in the framework. To minimize the combination cost of network usage and energy consumption in the framework, we formulate it as a joint caching/computing strategy and servers selection problem. In addition, we solve the joint caching/computing strategy and servers selection problem using an exhaustive-search algorithm. Simulation results show that our proposed framework significantly outperforms the traditional network without in-network caching/computing in terms of network usage and energy consumption.

Detecting extreme events in large datasets is a major challenge in climate science research. Current algorithms for extreme event detection are build upon human expertise in defining events based on subjective thresholds of relevant physical variables. Often, multiple competing methods produce vastly different results on the same dataset. Accurate characterization of extreme events in climate simulations and observational data archives is critical for understanding the trends and potential impacts of such events in a climate change content. This study presents the first application of Deep Learning techniques as alternative methodology for climate extreme events detection. Deep neural networks are able to learn high-level representations of a broad class of patterns from labeled data. In this work, we developed deep Convolutional Neural Network (CNN) classification system and demonstrated the usefulness of Deep Learning technique for tackling climate pattern detection problems. Coupled with Bayesian based hyper-parameter optimization scheme, our deep CNN system achieves 89\%-99\% of accuracy in detecting extreme events (Tropical Cyclones, Atmospheric Rivers and Weather Fronts

The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa.

The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa.