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Li Yue, Family instead of narration: second child, After 37 weeks of pregnancy, Paroxysmal lower abdominal pain 2 hours into...

Tropospheric delay is a major error source that affects the initialization and re-initialization speed of the Global Navigation Satellite System&rsquo, s (GNSS) medium-/long-range baseline in Network Real-Time Kinematic (NRTK) positioning. Fusing the meteorological data from the Numerical Weather Prediction (NWP) model to estimate the zenith tropospheric delay (ZTD) is one of the current research hotspots. However, research has shown that the ZTD derived from NWP models is still not accurate enough for high-precision GNSS positioning applications without the estimation of the residual tropospheric delay. To date, General Regression Neural Network (GRNN) has been applied in many fields. It has a high learning speed and simple structure, and can approximate any function with arbitrary precision. In this study, we developed a regional NWP tropospheric delay inversion method based on a GRNN model to improve the accuracy of the tropospheric delay derived from the NWP model. The accuracy of the tropospheric delays derived from reanalysis data of the European Center for Medium-Range Weather Forecasts (ECMWF) and the US National Centers for Environmental Prediction (NCEP) was assessed through comparisons with the results of the International GPS Service (IGS). The variation characteristics of the residual of the ZTD inverted by NWP data were analyzed considering the factors of temperature, humidity, latitude, and season. To evaluate the performance of this new method, the National Center Atmospheric Research (NCAR) troposphere data of 650 stations in Japan in 2005 were collected as a reference to compare the accuracy of the ZTD before and after using the new method. The experimental results showed that the GRNN model has obvious advantages in fitting the NWP ZTD residual. The mean residual and the root mean square deviation (RMSD) of the ZTD inverted using the method of this study were 9.5 mm and 12.7 mm, respectively, showing reductions of 20.8% and 19.1%, respectively, as compared to the standard NWP model. For long-range baseline (155 km and 207 km), the corrected NWP-constrained RTK showed a reduction of over 43% in the initialization time compared with the standard RTK, and showed a reduction of over 24% in the initialization time compared with the standard NWP-constrained RTK.

For the coefficient matrix of the normal equation is ill-conditioned during inverting deformation model of small baseline subset (SBAS) InSAR technique, a regularization robust method is proposed. Based on Tikhonov regularization theory, this method converts the problem of how to solve the deformation rate into minimization problem. According to L-curve method to choose regularization parameter, considering the relationship between the individual components of least-squares residuals to choose regularization matrix, thus it achieves robust solution of SBAS deformation model inversion. We adopt respectively least-squares estimation, ridge estimation and Tikhonov regularization method to deal with 29 ENVISAT ASAR dataset relevant to the Beijing area, achieving the subsidence rate map of the study area. Through comparative analysis among the mean square error (MSE) of 21 points on behalf of the different subsidence, temporal coherence values and MSE maps of the entire study area, we confirm that Tikhonov regularization robust method in inverting SBAS deformation model can obtain more reliable results of deformation monitoring.

Background It has been reported that the expression of activating transcription factor 3 (ATF3) is closely associated with both microRNA (miRNA) processing and the progress of many cancers. Our study aimed to explore the interaction between ATF3 and miR-488 in tongue squamous cell carcinoma (TSCC). Methods Quantitative real-time PCR was performed to detect the levels of ATF3 and miR-488 in TSCC tissues and cell lines. Cell invasion and epithelial–mesenchymal transition (EMT) were assessed to determine the biological functions of miR-488 and ATF3 in TSCC cells. The mRNA and protein levels of ATF3 were measured using quantitative RT-PCR and western blotting. Luciferase assays were performed to validate ATF3 as an miR-488 target in TSCC cells. Results We found that the level of miR-488 significantly decreased and the expression of ATF3 significantly increased in TSCC tissues and cell lines. A low level of miR-488 was closely associated with increased expression of ATF3 in TSCC tissues. Introducing miR-488 significantly inhibited the invasion and EMT of TSCC cells, and knockdown of miR-488 promoted both processes. The bioinformatics analysis predicted that ATF3 is a potential target gene of miR-488. The luciferase reporter assay showed that miR-488 could directly target ATF3. ATF3 silencing had similar effects to miR-488 overexpression on TSCC cells. Overexpression of ATF3 in TSCC cells partially reversed the inhibitory effects of the miR-488 mimic. Conclusion miR-488 inhibited cell invasion and EMT of TSCC cells by directly downregulating ATF3 expression.