Simulation of Wind Speed Based on Different Driving Datasets and Parameterization Schemes Near Dunhuang Wind Farms in Northwest of China

In this study, we evaluate the impacts of different datasets (e.g., NCEP global forecast system (GFS) and ERA5) that are used to derive the initial and boundary conditions, various planetary parameterization boundary layer (PBL) schemes and radiation parameterization schemes on wind speed simulations over wind farms near Dunhuang in Northwest of China. The mesoscale community Weather Research and Forecasting (WRF) model is employed to simulate the wind speeds in March of 2014. The sensitivity of numerical simulations to different PBL schemes, including the Yonsei University (YSU), the Asymmetric Convective Model (ACM2) and the Mellor–Yamada–Janjic (MYJ) scheme are examined. Besides, simulations with different radiation parameterization schemes, including the Rapid Radiative Transfer Model for general circulation model (GCM) applications (RRTMG) and the Fu–Liou–Gu radiative transfer scheme (FLG), are compared. Based on hourly observation data from three national basic meteorological observing stations and an anemometer tower in Dunhuang, the simulation results are evaluated. Results show that, using the GFS data as the initial data, the simulation error of 10-m wind speed is rather smaller under the combination of the YSU and FLG. When using the ERA5 data as the initial data, the error of the 2-m temperature simulation is smaller, and it is also less than that of the 10-m wind speed simulation. The simulation results show significant differences at different altitudes. The relative error of wind speed is larger at higher altitude. In the vertical direction, the wind speed is smaller at a lower height and so is the simulation error. In terms of wind speed from the anemometer tower, the error of the wind speed is related to the magnitude of the observed wind speed. Therefore, according to specific conditions of the simulated area, selecting an appropriate combination of initial data and parameterization schemes can effectively reduce the errors of simulated wind speed

Simultaneous Determination of Cortisol, Cortisone, and Multiple Illicit Drugs in Hair among Female Drug Addicts with LC-MS/MS

Long-term dependence of illicit drugs impairs the function of the hypothalamic-pituitary-adrenal (HPA) axis, which regulates the secretion of endogenous steroids, cortisol, and cortisone. Thus, the present study aimed to develop a sensitive method for simultaneous determination of the multiple illicit drugs and two steroids in hair to monitor the status of illicit drug exposure and the physiological and psychological health of drug addicts. The target analytes were extracted from hair by incubation with 1 mL methanol for 24 h at 40 °C and then determined with LC-APCI+-MS/MS. The validated method showed acceptable linearity (R2 > 0.99) in the range of 1.25–250 pg/mg for cortisol and cortisone, 2.5–125 pg/mg for heroin, 2.5–1250 pg/mg for ketamine, 2.5–5000 pg/mg for methamphetamine (MAM), 2.5–250 pg/mg for 3, 4-methylenedioxymethamphetamine (MDMA), morphine, and 6-monoacetylmorphine (6-AM). Limits of quantification were 1.6, 1.2, 1.6, 1.0, 1.4, 0.3, 2.1, and 1.2 pg/mg for cortisol, cortisone, heroin, ketamine, MAM, MDMA, morphine, and 6-AM, respectively. Method recoveries were from 90–115% for all analytes. Inter-day and intra-day coefficients of variation were within 10%. Finally, this method was successfully applied to detect the aforementioned analytes in hair among female drug addicts who self-reported to be MAM abuser, heroin abuser, ketamine abuser, and abuser of mixture drugs of MAM and heroin. MAM abusers with current MAM use showed significantly higher concentrations of cortisol, MAM, and MDMA than controls with drug withdrawal

DataSheet_1_Delta radiomics model for the prediction of progression-free survival time in advanced non-small-cell lung cancer patients after immunotherapy.docx

ObjectiveTo assess the validity of pre- and posttreatment computed tomography (CT)-based radiomics signatures and delta radiomics signatures for predicting progression-free survival (PFS) in stage III-IV non-small-cell lung cancer (NSCLC) patients after immune checkpoint inhibitor (ICI) therapy.MethodsQuantitative image features of the largest primary lung tumours were extracted on CT-enhanced imaging at baseline (time point 0, TP0) and after the 2nd-3rd immunotherapy cycles (time point 1, TP1). The critical features were selected to construct TP0, TP1 and delta radiomics signatures for the risk stratification of patient survival after ICI treatment. In addition, a prediction model integrating the clinicopathologic risk characteristics and phenotypic signature was developed for the prediction of PFS.ResultsThe C-index of TP0, TP1 and delta radiomics models in the training and validation cohort were 0.64, 0.75, 0.80, and 0.61, 0.68, 0.78, respectively. The delta radiomics score exhibited good accuracy for distinguishing patients with slow and rapid progression to ICI treatment. The predictive accuracy of the combined prediction model was higher than that of the clinical prediction model in both training and validation sets (PConclusionsThe combined prediction model including clinicopathologic characteristics (tumour anatomical classification and brain metastasis) and the delta radiomics signature could achieve the individualized prediction of PFS in ICIs-treated NSCLC patients.</p