Showing papers by "Jian Wang published in 2023"
TL;DR: In this paper , a credible method for performance evaluation of network timing service was proposed, based on one kind of disciplined time standard with GNSS time transfer, NIMDO, and performance of the NTP timing service were characterized over multiple baselines on both the Internet and the Intranet, and the network delay and the timing offset were analyzed in detail.
Abstract: One credible method for performance evaluation of network timing service was proposed, based on one kind of disciplined time standard with GNSS time transfer, NIMDO. The performance of the NTP timing service were characterized over multiple baselines on both the Internet and the Intranet, and the network delay and the timing offset were analyzed in detail. The results show that on the Internet the averaged timing offsets via the NTP servers are roughly hundreds of μs to several ms level, and on the Intranet the averaged timing offsets via the NTP servers are at about 20 μs level. NTP timing service was mainly affected by white phase noise, and flicker phase noise at the different sites. The maximum change of the timing offset was one-half of the maximum change of RTDelay. The uncertainties have been separately evaluated as less than 30 ms on the Internet and less than 60 μs on the Intranet for NTP timing service.
TL;DR: Wang et al. as discussed by the authors showed that renal tubule-specific deletion of ASIC1a in mice attenuated renal IRI, and reduced the expression of NLRP3, ASC, cleaved-caspase-1, GSDMD-N, and IL-1β.
Abstract: Abstract Ischemia-reperfusion injury (IRI) is the main cause of acute kidney injury (AKI), and there is no effective therapy. Microenvironmental acidification is generally observed in ischemic tissues. Acid-sensing ion channel 1a (ASIC1a) can be activated by a decrease in extracellular pH which mediates neuronal IRI. Our previous study demonstrated that, ASIC1a inhibition alleviates renal IRI. However, the underlying mechanisms have not been fully elucidated. In this study, we determined that renal tubule-specific deletion of ASIC1a in mice (ASIC1a fl/fl /CDH16 cre ) attenuated renal IRI, and reduced the expression of NLRP3, ASC, cleaved-caspase-1, GSDMD-N, and IL-1β. Consistent with these in vivo results, inhibition of ASIC1a by the specific inhibitor PcTx-1 protected HK-2 cells from hypoxia/reoxygenation (H/R) injury, and suppressed H/R-induced NLRP3 inflammasome activation. Mechanistically, the activation of ASIC1a by either IRI or H/R induced the phosphorylation of NF-κB p65, which translocates to the nucleus and promotes the transcription of NLRP3 and pro-IL-1β. Blocking NF-κB by treatment with BAY 11-7082 validated the roles of H/R and acidosis in NLRP3 inflammasome activation. This further confirmed that ASIC1a promotes NLRP3 inflammasome activation, which requires the NF-κB pathway. In conclusion, our study suggests that ASIC1a contributes to renal IRI by affecting the NF-κB/NLRP3 inflammasome pathway. Therefore, ASIC1a may be a potential therapeutic target for AKI. Key messages Knockout of ASIC1a attenuated renal ischemia-reperfusion injury. ASIC1a promoted the NF-κB pathway and NLRP3 inflammasome activation. Inhibition of the NF-κB mitigated the NLRP3 inflammasome activation induced by ASIC1a.
TL;DR: In this article , the authors evaluated the clinical utility of serial circulating tumor DNA (ctDNA) sequencing in early prediction of the efficacy of chemohormonal therapy in patients with metastatic hormone-sensitive prostate cancer (mHSPC).
TL;DR: In this article , a Canny enhanced high-resolution neural network (C-HRNet) is proposed based on Canny edge features and the HRNet that maintains high resolution representations throughout the process.
Abstract: Satellite image based land cover classification, which falls under the category of semantic segmentation, is critical for many global and environmental applications. Deep learning has been proven to be excellent in semantic segmentation. However, mainstream neural networks formed by connecting high-to-low convolutions in series are prone to losing image information, which affects the accuracy of semantic segmentation. Besides, it is difficult to distinguish adjacent land cover classes with similar colors using only RGB information presented by satellite images. Striven to maintain high-resolution representations and improve the inter-class distinguishability, a Canny enhanced high-resolution neural network (C-HRNet) is proposed based on Canny edge features and the high-resolution neural network (HRNet) that maintains high-resolution representations throughout the process. Meanwhile, we construct a novel dataset for model evaluation, which provides an automatic dataset construction method, making the dataset more efficient in construction and richer in samples. Extensive experiments are conducted on datasets at different granularities. Quantitative results demonstrate that for large-scale fine-grained scenarios, C-HRNet outperforms state-of-the-art semantic segmentation networks due to the accurate spatial localization ability of Canny edge features. For small-scale coarse-grained scenarios, Canny extracts a large number of edge features that highlight the positional differences between adjacent instances belonging to the same land cover class, which slightly degrades the performance of C-HRNet, but it can still provide reliable land cover classification results. Based on this conclusion, we apply C-HRNet to large-scale wireless channel simulations that are location-sensitive and require fine-grained semantic segmentation, which are proven to be accurate and effective.
TL;DR: In this paper , the effects of different weight allocation for multi-measurements of GNSS time transfer were analyzed, and a federated Kalman filter was designed and applied to fuse multi-GNSS measurements combined with the standard deviation-allocated weight.
Abstract: Relative to single Global Navigation Satellite System (GNSS) measurements, i.e., the measurements from a single GNSS system, a single GNSS code, and a single GNSS receiver, multi-GNSS measurements for time transfer could improve reliability and provide better short-term stability. Previous studies applied equal weighting to different GNSS systems or different GNSS time transfer receivers, which, to some extent, revealed the improvement in the additional short-term stability from the combination of two or more kinds of GNSS measurements. In this study, the effects of the different weight allocation for multi-measurements of GNSS time transfer were analyzed, and a federated Kalman filter was designed and applied to fuse multi-GNSS measurements combined with the standard-deviation-allocated weight. Tests with real data showed that the proposed approach can reduce the noise level to well below about 250 ps for short averaging times.
TL;DR: In this paper , a comparative analysis based on MEMS (microelectro-mechanical system) accelerometer data was conducted between the non-impacts and the impacts of the above factors.
Abstract: The stability of the Great Wall is mainly affected by traffic vibrations and natural hazards, such as strong winds, heavy rainfall, and thunderstorms, which are extremely harmful to the safety of the Great Wall. To determine the impact of the above factors on the Great Wall, a comparative analysis based on MEMS (micro-electro-mechanical system) accelerometer data was conducted between the non-impacts and the impacts of the above factors. An analysis of the relationship between vibration acceleration and each potential hazard based on a visual time series chart was presented using the data of accelerometers, traffic video, meteorology, rainfall, and wind. According to the results, traffic vibration is one of the primary dangerous factors affecting the stability of the Great Wall, Moreover, the intensity of the vibrations increases with the traffic flow. Thunderstorms also influence the stability of the Great Wall, with enhanced thunderstorm excitation resulting in increased vibration displacement. Furthermore, wind load is an influencing factor, with average wind speeds greater than 9 m/s significantly affecting the stability of the Great Wall. Rainfall has no impact on the stability of the Great Wall in the short term. This research can provide important guidance for risk assessment and protection of the Great Wall.
TL;DR: In this paper , RNA-seq analyses revealed that HCMV tegument protein UL23 could regulate the expression of many ISGs under IFN-γ treatment or infection, and UL23 appeared to resist the antiviral effect of IFNγ by downregulating expression of APOL1, CMPK2, and LGALS9.
Abstract: Interferon-γ (IFN-γ) is a critical component of innate immune responses in humans to combat infection by many viruses, including human cytomegalovirus (HCMV). IFN-γ exerts its biological effects by inducing hundreds of IFN-stimulated genes (ISGs). In this study, RNA-seq analyses revealed that HCMV tegument protein UL23 could regulate the expression of many ISGs under IFN-γ treatment or HCMV infection. We further confirmed that among these IFN-γ stimulated genes, individual APOL1 (Apolipoprotein-L1), CMPK2 (Cytidine/uridine monophosphate kinase 2), and LGALS9 (Galectin-9) could inhibit HCMV replication. Moreover, these three proteins exhibited a synergistic effect on HCMV replication. UL23-deficient HCMV mutants induced higher expression of APOL1, CMPK2, and LGALS9, and exhibited lower viral titers in IFN-γ treated cells compared with parental viruses expressing full functional UL23. Thus, UL23 appears to resist the antiviral effect of IFN-γ by downregulating the expression of APOL1, CMPK2, and LGALS9. This study highlights the roles of HCMV UL23 in facilitating viral immune escape from IFN-γ responses by specifically downregulating these ISGs.
10 Feb 2023
TL;DR: Wang et al. as discussed by the authors analyzed the correlation between traffic flow and the vibration acceleration of enemy stations on the Great Wall and established a vibration data denoising method based on variational mode decomposition (VMD) combined with FLANDRIN criterion, and removed the high-frequency noise of vibration acceleration; to denoise the acceleration data, an integrated VMD and Hilbert-Huang transform (HHT) time-frequency feature extraction model was introduced to extract the instantaneous vibration frequency and intensity.
Abstract: With the rapid development of the modern transportation network, the phenomenon of roads crossing the Great Wall is increasing day by day, making ground traffic vibration an important factor affecting the safety of the Great Wall. This paper firstly analyzes the correlation between traffic flow and the vibration acceleration of enemy stations on the Great Wall; then establishes a vibration data denoising method based on variational mode decomposition (VMD) combined with FLANDRIN criterion, and removes the high-frequency noise of vibration acceleration; To denoise the acceleration data, an integrated VMD and Hilbert-Huang transform (HHT) time-frequency feature extraction model was introduced to extract the instantaneous vibration frequency and intensity. The results show that the ground traffic vibration has a great influence on the short-cut Great Wall, which leads to the vibration of the enemy stations on the Great Wall, and the vibration frequency is 0.27 Hz; the VMD-HHT model can accurately obtain the instantaneous vibration frequency and intensity characteristics of the enemy stations on the Great Wall. This research can provide an important reference for the real-time safety monitoring and protection of the Great Wall.
TL;DR: In this paper , the authors showed that blocking extracellular Y-box protein (YB)-1 reduced the effects induced by hypoxia and NET formation in the kidney and significantly limited acute kidney injury.
TL;DR: In this article , an integrated vibration monitoring system that includes a GNSS receiver and 3-axis MEMS accelerometers was developed to obtain the dynamic responses under the thunder loading, and a new denoising algorithm for thunderstorm-induced vibration data was proposed based on variational mode decomposition (VMD) and the characteristics of white noise, and the low-frequency disturbance was separated from the GNSS displacement time series.
Abstract: Dynamic response monitoring is of great significance for large engineering structural anomaly diagnosis and early warning. Although the global navigation satellite system (GNSS) has been widely used to measure the dynamic structural response, it has the limitation of a relatively low sampling rate. The micro-electro-mechanical system (MEMS) accelerometer has a high sampling frequency, but it belongs to the approaches of acceleration measurements as the absolute position is unavailable. Hence, in this paper, an integrated vibration monitoring system that includes a GNSS receiver and 3-axis MEMS accelerometers was developed to obtain the dynamic responses under the thunder loading. First, a new denoising algorithm for thunderstorm-induced vibration data was proposed based on variational mode decomposition (VMD) and the characteristics of white noise, and the low-frequency disturbance was separated from the GNSS displacement time series. Then, a power spectral density (PSD) analysis using data collected by the integrated system was carried out to extract low/high natural frequencies. Finally, field monitoring data collected at Huanghuacheng, Hefangkou, and Qilianguan in Beijing’s Huairou District were used to validate the effectiveness of the integrated system and processing scheme. According to the results, the proposed integrated GNSS/MEMS accelerometer system can not only be used to detect thunder loading events, but also completely extract the natural frequency based on PSD analysis. The high natural frequencies detected from the accelerometer data of the four Great Wall monitoring stations excited by the thunderstorms are 42.12 Hz, 12.94 Hz, 12.58 Hz, and 5.95 Hz, respectively, while the low natural frequencies detected from the GNSS are 0.02 Hz, 0.019 Hz, 0.016 Hz, and 0.014 Hz, respectively. Moreover, thunderstorms can cause the Great Wall to vibrate with a maximum displacement of 14.3 cm.