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01 januari 2023, Item does not contain fulltext, BACKGROUND & AIMS: Chronic hepatitis C virus (HCV) infection can be cured with direct-acting antivirals (DAAs). However, not all sequelae of chronic hepatitis C appear to be completely reversible after sustained virologic response (SVR). Recently, chronic viral infections have been shown to be associated with biological age acceleration defined by the epigenetic clock. The aim of this study was to investigate whether chronic HCV infection is associated with epigenetic changes and biological age acceleration and whether this is reversible after SVR. METHODS: We included 54 well-characterized individuals with chronic hepatitis C who achieved SVR after DAA therapy at three time points: DAA treatment initiation, end of treatment, and long-term follow-up (median 96 weeks after end of treatment). Genome-wide DNA methylation status was determined in peripheral blood mononuclear cells (PBMCs) and used to calculate epigenetic age acceleration (EAA) using Horvath's clock. RESULTS: Individuals with HCV had an overall significant EAA of 3.12 years at baseline compared with -2.61 years in the age- and sex-matched reference group (p <0.00003). HCV elimination resulted in a significant long-term increase in DNA methylation dominated by hypermethylated CpGs in all patient groups. Accordingly, EAA decreased to 1.37 years at long-term follow-up. The decrease in EAA was significant only between the end of treatment and follow-up (p = 0.01). Interestingly, eight individuals who developed hepatocellular carcinoma after SVR had the highest EAA and showed no evidence of reversal after SVR. CONCLUSIONS: Our data contribute to the understanding of the biological impact of HCV elimination after DAA therapy and demonstrate that HCV elimination can lead to "reverse inflammaging". In addition, our data support the potential use of biological age as a biomarker for HCV sequelae after SVR. IMPACT AND IMPLICATIONS: Chronic hepatitis C virus infection is now curable with direct-acting antivirals, b

Contains fulltext : 297155.pdf (Publisher’s version ) (Open Access)

Item does not contain fulltext

Contains fulltext : 250184.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 250184.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 240766.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 240766.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 240766.pdf (Publisher’s version ) (Open Access)

Device identification is of great importance in system management and network security. Especially, it is the priority in industrial internet of things (IIoT) scenario. Since there are massive devices producing various kinds of information in manufacturing process, the robustness, reliability, security and real-time control of the whole system is based on the identification of the massive IIoT devices. Previous IIoT device identification solutions are mostly based on a centralized architecture, which brings a lot of problems in scalability and security. In addition, most traditional identification systems can only identify inherent types of devices which is not suitable for the adaptive device management in IIoT. In order to solve these problems, this paper proposes a Intelligent Identification Scheme(IIS) of Massive IoT Devices, a completely distributed intelligent identification scheme of massive IIoT devices. The scheme changes the traditional centralized architecture and realizes more efficient clustering identification of massive IIoT devices. Moreover, IIS can identify more and more types of devices intelligently with the continuous learning ability since the identification model is constantly updated according to the ledger which is maintained by all gateways collaboratively. We also conduct experiments to evaluate the performance of IIS based on the data obtained from real IIoT devices, which proves that IIS is efficient in device identification and intelligent for the adaptive device management in IIoT.

Recent years, analysis dictionary learning (ADL) and its applications for classification have been well developed, due to its flexible projective ability and low classification complexity. With the learned analysis dictionary, test samples can be transformed into a sparse subspace for classification efficiently. However, the underling locality of sample data has rarely been explored in analysis dictionary to enhance the discriminative capability of the classifier. In this paper, we propose a novel locality constrained analysis dictionary learning model with a synthesis K-SVD algorithm (SK-LADL). It considers the intrinsic geometric properties by imposing graph regularization to uncover the geometric structure for the image data. Through the learned analysis dictionary, we transform the image to a new and compact space where the manifold assumption can be further guaranteed. thus, the local geometrical structure of images can be preserved in sparse representation coefficients. Moreover, the SK-LADL model is iteratively solved by the synthesis K-SVD and gradient technique. Experimental results on image classification validate the performance superiority of our SK-LADL model., Comment: 12 pages, 2 figures

In this study a method for measuring the regional hydrological connectivity and water supply requirement of wetlands in Western Jilin Province (WJL) is defined and verified. The ecological water requirements of 216 wetlands, under various guarantee frequencies, were calculated and analyzed. In addition, twelve indicators were used to establish an evaluation index system, to characterize their ecological water requirements. The results showed that 12 hydrologically connected regions were delineated in WJL, and the 216 wetlands can be classified to six grades by AHP (Analytic Hierarchy Process) method. The annual minimum, suitable, and maximum ecological water requirements of these wetlands were found to be 7.72×108 m3, 13.76×108 m3, 26.15×108 m3 respectively, with 50% guarantee frequency. The primary water supply to the lakes and marshes was found to be from regional sources that provided a scientific basis for the allocation of water resources from the interconnected river-lake system network (IRLSN) in WJL. © 2016, ALÖKI Kft., Budapest, Hungary.

In this study a method for measuring the regional hydrological connectivity and water supply requirement of wetlands in Western Jilin Province (WJL) is defined and verified. The ecological water requirements of 216 wetlands, under various guarantee frequencies, were calculated and analyzed. In addition, twelve indicators were used to establish an evaluation index system, to characterize their ecological water requirements. The results showed that 12 hydrologically connected regions were delineated in WJL, and the 216 wetlands can be classified to six grades by AHP (Analytic Hierarchy Process) method. The annual minimum, suitable, and maximum ecological water requirements of these wetlands were found to be 7.72×108 m3, 13.76×108 m3, 26.15×108 m3 respectively, with 50% guarantee frequency. The primary water supply to the lakes and marshes was found to be from regional sources that provided a scientific basis for the allocation of water resources from the interconnected river-lake system network (IRLSN) in WJL. © 2016, ALÖKI Kft., Budapest, Hungary.

Zhang, L., Hou, G.L., Zhang, G.X., Liu, Z.L., Sun, G.Z. & Li, M.N. (2016). Calculation of wetlands ecological water requirement in China’s Western Jilin province based on regionalization and gradation techniques. Applied Ecology and Environmental Research, 14(3), 463-478. Available here.