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Accurate water vapor information is crucial for improving the quality of numerical weather forecasting. Previous studies have incorporated tropospheric water vapor data obtained from a global navigation satellite system (GNSS) into numerical weather models to enhance the accuracy and reliability of rainfall forecasts. However, research on evaluating forecast accuracy for different rainfall levels and the development of corresponding forecasting platforms is lacking. This study develops and establishes a rainfall forecasting platform supported by the GNSS-assisted weather research and forecasting (WRF) model, quantitatively assessing the effect of GNSS precipitable water vapor (PWV) on the accuracy of WRF model forecasts for light rain (LR), moderate rain (MR), heavy rain (HR), and torrential rain (TR). Three schemes are designed and tested using data from seven ground meteorological stations in Xi'an City, China, in 2021. The results show that assimilating GNSS PWV significantly improves the forecast accuracy of the WRF model for different rainfall levels, with the root mean square error (RMSE) improvement rates of 8%, 15%, 19%, and 25% for LR, MR, HR, and TR, respectively. Additionally, the RMSE of rainfall forecasts demonstrates a decreasing trend with increasing magnitudes of assimilated PWV, particularly effective in the range of [50, 55) mm where the lowest RMSE is 3.58 mm. Moreover, GNSS-assisted numerical weather model shows improvements in statistical forecasting indexes such as probability of detection (POD), false alarm rate (FAR), threat score (TS), and equitable threat score (ETS) across all rainfall intensities, with notable improvements in the forecasts of HR and TR. These results confirm the high precision, visualization capabilities, and robustness of the developed rainfall forecasting platform. [ABSTRACT FROM AUTHOR]

Metastasis is the leading cause for the high mortality of lung cancer, however, effective anti‐metastatic drugs are still limited. Here it is reported that the RNA‐binding protein RBMS1 is positively associated with increased lymph node metastasis in non‐small cell lung cancer (NSCLC). Depletion of RBMS1 suppresses cancer cell migration and invasion in vitro and inhibits cancer cell metastasis in vivo. Mechanistically, RBMS1 interacts with YTHDF1 to promote the translation of S100P, thereby accelerating NSCLC cell metastasis. The RRM2 motif of RBMS1 and the YTH domain of YTHDF1 are required for the binding of RBMS1 and YTHDF1. RBMS1 ablation inhibits the translation of S100P and suppresses tumor metastasis. Targeting RBMS1 with NTP, a small molecular chemical inhibitor of RBMS1, attenuates tumor metastasis in a mouse lung metastasis model. Correlation studies in lung cancer patients further validate the clinical relevance of the findings. Collectively, the study provides insight into the molecular mechanism by which RBMS1 promotes NSCLC metastasis and offers a therapeutic strategy for metastatic NSCLC. [ABSTRACT FROM AUTHOR]

Ferroptosis, an iron-dependent nonapoptotic cell death, is a highly regulated tumor suppressing process. However, functions and mechanisms of RNA-binding proteins in regulation of evasion of ferroptosis during lung cancer progression are still largely unknown. Here, we report that the RNA-binding protein RBMS1 participates in lung cancer development via mediating ferroptosis evasion. Through an shRNA-mediated systematic screen, we discovered that RBMS1 is a key ferroptosis regulator. Clinically, RBMS1 was elevated in lung cancer and its high expression was associated with reduced patient survival. Conversely, depletion of RBMS1 inhibited lung cancer progression both in vivo and in vitro. Mechanistically, RBMS1 interacted with the translation initiation factor eIF3d directly to bridge the 3'- and 5'-UTR of SLC7A11. RBMS1 ablation inhibited the translation of SLC7A11, reduced SLC7A11-mediated cystine uptake, and promoted ferroptosis. In a drug screen that targeted RBMS1, we further uncovered that nortriptyline hydrochloride decreased the level of RBMS1, thereby promoting ferroptosis. Importantly, RBMS1 depletion or inhibition by nortriptyline hydrochloride sensitized radioresistant lung cancer cells to radiotherapy. Our findings established RBMS1 as a translational regulator of ferroptosis and a prognostic factor with therapeutic potential and clinical value., Introduction Ferroptosis, an iron-dependent form of nonapoptotic cell death, is induced by cystine depletion and massive lipid peroxidation-controlled membrane damage (1-4). Dysregulation of ferroptosis has been closely associated with human [...]

Carrier Smoothing Code (CSC), as a low-pass filter, has been widely used in GNSS positioning processing to reduce pseudorange noise via carrier phases. However, current CSC methods do not consider the systematic bias between the code and carrier phase observation, also known as Satellite-induced Code Bias (SICB). SICB has been identified in the BDS-2 and the bias will reduce the accuracy or reliability of the CSC. To confront bias, an improved CSC algorithm is proposed by considering SICB for GEO, IGSO, and MEO satellites in BDS constellations. The correction model of SICB for IGSO/MEO satellites is established by using a 0.1-degree interval piecewise weighted least squares Third-order Curve Fitting Method (TOCFM). The Variational Mode Decomposition combined with Wavelet Transform (VMD-WT) is proposed to establish the correction model of SICB for the GEO satellite. To verify the proposed method, the SICB model was established by collecting 30 Multi-GNSS Experiment (MGEX) BDS stations in different seasons of a year, in which the BDS data of ALIC, KRGG, KOUR, GCGO, GAMG, and SGOC stations were selected for 11 consecutive days to verify the effectiveness of the algorithm. The results show that there is obvious SICB in the BDS-2 Multipath (MP) combination, but the SICB in the BDS-3 MP is smaller and can be ignored. Compared with the modeling in the references, TOCFM is more suitable for IGSO/MEO SICB modeling, especially for the SICB correction at low elevation angles. After the VMD-WT correction, the Root Mean Square Error (RMSE) of SICB of B1I, B2I, and B3I in GEO satellites is reduced by 53.35%, 63.50%, and 64.71% respectively. Moreover, we carried out ionosphere-free Single Point Positioning (IF SPP), Ionosphere-free CSC SPP (IF CSC SPP), CSC single point positioning with the IGSO/MEO SICB Correction based on the TOCFA Method (IGSO/MEO SICB CSC), and CSC single point positioning with the IGSO/MEO/GEO SICB correction based on VMD-WT and TOCFA (IGSO/MEO/GEO SICB CSC), respectively. Compared to IF SPP, the average improvement of the IGSO/MEO/GEO SICB CSC algorithm in the north, east, and up directions was 24.42%, 27.94%, and 24.98%, respectively, and the average reduction in 3D RMSE is 24.54%. Compared with IF CSC SPP, the average improvement of IGSO/MEO/GEO SICB CSC is 7.03%, 6.50%, and 10.48% in the north, east, and up directions, respectively, while the average reduction in 3D RMSE was 9.86%. IGSO/MEO SICB mainly improves the U direction positioning accuracy, and GEO SICB mainly improves the E and U direction positioning accuracy. After the IGSO/MEO/GEO SICB correction, the overall improvement was about 10% and positioning improved to a certain extent. [ABSTRACT FROM AUTHOR]