A theoretical and empirical integrated method to select the optimal combined signals for geometry-free and geometry-based three-carrier ambiguity resolution

12 GPS Block IIF satellites, out of the current constellation, can transmit on three-frequency signals (L1, L2, L5). Taking advantages of these signals, Three-Carrier Ambiguity Resolution (TCAR) is expected to bring much benefit for ambiguity resolution. One of the research areas is to find the optimal combined signals for a better ambiguity resolution in geometry-free (GF) and geometry-based (GB) mode. However, the existing researches select the signals through either pure theoretical analysis or testing with simulated data, which might be biased as the real observation condition could be different from theoretical prediction or simulation. In this paper, we propose a theoretical and empirical integrated method, which first selects the possible optimal combined signals in theory and then refines these signals with real triple-frequency GPS data, observed at eleven baselines of different lengths. An interpolation technique is also adopted in order to show changes of the AR performance with the increase in baseline length. The results show that the AR success rate can be improved by 3% in GF mode and 8% in GB mode at certain intervals of the baseline length. Therefore, the TCAR can perform better by adopting the combined signals proposed in this paper when the baseline meets the length condition

Uremia toxin helps to induce inflammation in intestines by activating the ATM/NEMO/ NF-B signalling pathway in human intestinal epithelial cells

638-642During progressive chronic kidney disease, toxic substances known as uremic toxins accumulate in body fluids. Uremia toxin has been documented to be involved in most inflammatory reactions, and indoxyl-sulfate (IS) a major serum metabolite of uremia is a key player in this. The mechanism by which uremia toxin establishes it inflammatory activity is scarcely known; however, researchers believes that a clear understanding of this process can serve as a guide to combat the situation. The study was designed to investigate the role played by uremia toxin in intestinal inflammation. SW480 was used as cell lines for this study. Luciferase assay was used to detect the cell viability of different concentrations of IS. RT-qPCR was used to detect the effect of IS on the expression of inflammatory factors. The comet assay was used as a tool to detect DNA damage. Western blot was used to detect the phosphorylation level of ATM/NEMO/NF-kB protein. The IS of 0.09 nM was determined to be the best experimental concentration by luciferase assay. Result showed that IS promotes the expression of inflammatory factors TNF-α and IL-6. In addition, IS led to enhanced DNA damage in cells. IS promoted ATM phosphorylation leading to phosphorylation of NEMO to activate the NF-kB signalling pathway. In conclusion, uremia toxin facilitates inflammation in intestines by activating the ATM/NEMO/ NF-kB signalling pathway in human intestinal epithelial cells

Uremia toxin helps to induce inflammation in intestines by activating the ATM/NEMO/NF-kB signalling pathway in human intestinal epithelial cells

During progressive chronic kidney disease, toxic substances known as uremic toxins accumulate in body fluids. Uremia toxin has been documented to be involved in most inflammatory reactions, and indoxyl-sulfate (IS) a major serum metabolite of uremia is a key player in this. The mechanism by which uremia toxin establishes it inflammatory activity is scarcely known; however, researchers believes that a clear understanding of this process can serve as a guide to combat the situation. The study was designed to investigate the role played by uremia toxin in intestinal inflammation. SW480 was used as cell lines for this study. Luciferase assay was used to detect the cell viability of different concentrations of IS. RT-qPCR was used to detect the effect of IS on the expression of inflammatory factors. The comet assay was used as a tool to detect DNA damage. Western blot was used to detect the phosphorylation level of ATM/NEMO/NF-kB protein. The IS of 0.09 nM was determined to be the best experimental concentration by luciferase assay. Result showed that IS promotes the expression of inflammatory factors TNF-α and IL-6. In addition, IS led to enhanced DNA damage in cells. IS promoted ATM phosphorylation leading to phosphorylation of NEMO to activate the NF-kB signalling pathway. In conclusion, uremia toxin facilitates inflammation in intestines by activating the ATM/NEMO/ NF-kB signalling pathway in human intestinal epithelial cells

Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy. Objective: We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures. Methods: We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65 ± 11 years). Custom DL architectures were trained to identify AF using N = 29,340 unipolar and N = 23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate and timing on AF/AT classification in 246,067 EGMs reconstructed from clinical data. Results: DL identified AF with AUC of 0.97 ± 0.04 (unipolar) and 0.92 ± 0.09 (bipolar). Rule-based classifiers misclassified ∼10-12% of cases. DL classification was explained by regularity in EGM shape (13%) or timing (26%), and rate (60%; p 15% timing variation, <0.48 correlation in beat-to-beat EGM shapes and CL < 190 ms (p < 0.001). Conclusions: Deep learning of intracardiac EGMs can identify AF or AT via signatures of rate, regularity in timing or shape, and specific EGM shapes. Future work should examine if these signatures differ between different clinical subpopulations with AF