Effect of the atmospheric heat source on the development and eastward movement of the Tibetan Plateau vortices

Based on the final analyses data (FNL) of the Global Forecasting System of the National Centers for Environment Prediction (NCEP) and the radiosonde data over the Tibetan Plateau, evolutions of two types of the Tibetan Plateau vortices, moving-off the plateau (Type A) and dying-out on the plateau (Type B), are investigated respectively. Compared to Type B vortices, the large-scale circulations associated with Type A vortices show stronger ridge to the north of the plateau and deeper trough near the Bay of Bengal at 500 hPa, and the southwesterly flow from the trough and the northwesterly flow from the ridge converge more intensively to the east of Type A vortices. Meanwhile, at 200 hPa the divergence on the right-hand side of the upper westerly jet is just over the vortices. The convergence at 500 hPa and divergence at 200 hPa provide favourable conditions for the development and eastward motion of the vortices. The diagnoses of the potential vorticity (PV) budgets reveal that in the developing stages of the two types of vortices, the vertical distribution of the atmospheric heat source determines both their intensity and the moving direction. In the decaying stage, the maintenance and eastward movement for Type A vortices mainly depend on the convergence of the strong northwesterly and southwesterly to the east of the vortices. For Type B vortices, the vertical PV flux divergence caused by the ascending motion around the vortices reduces the intensity of the vortices and is unfavourable for their eastward motion

PCM and TAT co-modified liposome with improved myocardium delivery: in vitro and in vivo evaluations

In this study, PCM and TAT co-modified liposome was developed as a novel drug carrier for myocardium delivery with evaluation of its in vitro and in vivo properties. Liposomes containing fluorescent probe coumarin-6 were prepared by thin-film hydration. The PCM ligands specifically bind to the PCM receptors in the extracellular connective tissue of primary myocardium cells (MCs), while the TAT ligands functioned as a classical cell penetrating peptide to make liposomes internalized by MCs. The unmodified liposome (L), PCM-modified liposome (PL), TAT-modified liposome (TL) and PCM and TAT co-modified liposome (PTL) were prepared and characterized. The cellular uptake and intracellular distribution of various liposomes by MCs demonstrated that PTL had the best delivery capability. Peptide inhibition assay indicated that the uptake of PL could be inhibited by PCM. However, TAT could almost not suppress the uptake of TL. In addition, the CCK-8 experiments showed that liposomes had low cytotoxicity. In vivo fluorescent images of frozen sections and HPLC-fluorescence analysis further demonstrated that PTL had highest myocardium distribution. The results of this study demonstrated that PCM and TAT co-modifying could improve the myocardial targeting ability of liposome

Spatiotemporal Analysis of Regional Ionospheric TEC Prediction Using Multi-Factor NeuralProphet Model under Disturbed Conditions

The ionospheric total electron content (TEC) is susceptible to factors, such as solar and geomagnetic activities, resulting in the enhancement of its non-stationarity and nonlinear characteristics, which aggravate the impact on radio communications. In this study, based on the NeuralProphet hybrid prediction framework, a regional ionospheric TEC prediction model (multi-factor NeuralProphet model, MF-NPM) considering multiple factors was constructed by taking solar activity index, geomagnetic activity index, geographic coordinates, and IGS GIM data as input parameters. Data from 2009 to 2013 were used to train the model to achieve forecasts of regional ionospheric TEC at different latitudes during the solar maximum phase (2014) and geomagnetic storms by sliding 1 day. In order to verify the prediction performance of the MF-NPM, the multi-factor long short-term memory neural network (LSTMNN) model was also constructed for comparative analysis. At the same time, the TEC prediction results of the two models were compared with the IGS GIM and CODE 1-day predicted GIM products (COPG_P1). The results show that the MF-NPM achieves good prediction performance effectively. The RMSE and relative accuracy (RA) of MF-NPM are 2.33 TECU and 93.75%, respectively, which are 0.77 and 1.87 TECU and 1.91% and 6.68% better than LSTMNN and COPG_P1 in the solar maximum phase (2014). During the geomagnetic storm, the RMSE and RA of TEC prediction results based on the MF-NPM are 3.12 TECU and 92.86%, respectively, which are improved by 1.25 and 2.30 TECU and 2.38% and 7.24% compared with LSTMNN and COPG_P1. Furthermore, the MF-NPM also achieves better performance in low–mid latitudes

Serum CCL21 as a Potential Biomarker for Cognitive Impairment in Spinal Cord Injury

Objective. Cognitive impairment is considered to be an important complication of spinal cord injury (SCI), but its underlying mechanism remains unclear. The purpose of this study is to explore whether serum CCL21 can be used as a potential biomarker of cognitive impairment in SCI. Methods. In Neck-Shoulder and Lumbocrural Pain Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, hospitalized or treated acute SCI patients were included in the study as the SCI group (SCI). At the same time, a normal control group (NC) matching the age and sex of the SCI group was recruited in the outpatient clinic. Once the two groups were enrolled, their demographics and clinical characteristics were collected immediately. Enzyme-linked immunosorbent assay (ELISA) was used to detect serum CCL21 levels within 24 hours of admission. Three months later, the Montreal Cognitive Assessment (MoCA) was used to test the cognitive function of the population. Results. A total of 84 SCI patients and 49 NC populations were eligible for inclusion in the study. There was no significant statistical difference in the demographics and clinical characteristics (age, gender, BMI, TG, LDL-C, FBG, SBP, and DBP) between the two groups (p>0.05). Compared with the NC group, the SCI group had a higher serum CCL21 level (p<0.001) and a lower MoCA score (p<0.001). Serum CCL21 level in SCI was negatively correlated with MoCA score (p=0.023). Multivariable analyses showed that serum CCL21 level is an independent prognostic factor of cognitive impairment in SCI. Conclusions. MoCA score has a linear relationship with serum CCL21 quartile, and SCI cognitive function has a negative correlation with serum CCL21. Serum CCL21 is an independent risk factor for cognitive impairment after SCI

Spatial Estimation of Regional PM_2.5 Concentrations with GWR Models Using PCA and RBF Interpolation Optimization

In recent years, geographically weighted regression (GWR) models have been widely used to address the spatial heterogeneity and spatial autocorrelation of PM2.5, but these studies have not fully considered the effects of all potential variables on PM2.5 variation and have rarely optimized the models for residuals. Therefore, we first propose a modified GWR model based on principal component analysis (PCA-GWR), then introduce five different spatial interpolation methods of radial basis functions to correct the residuals of the PCA-GWR model, and finally construct five combinations of residual correction models to estimate regional PM2.5 concentrations. The results show that (1) the PCA-GWR model can fully consider the contributions of all potential explanatory variables to estimate PM2.5 concentrations and minimize the multicollinearity among explanatory variables, and the PM2.5 estimation accuracy and the fitting effect of the PCA-GWR model are better than the original GWR model. (2) All five residual correction combination models can better achieve the residual correction optimization of the PCA-GWR model, among which the PCA-GWR model corrected by Multiquadric Spline (MS) residual interpolation (PCA-GWRMS) has the most obvious accuracy improvement and more stable generalizability at different time scales. Therefore, the residual correction of PCA-GWR models using spatial interpolation methods is effective and feasible, and the results can provide references for regional PM2.5 spatial estimation and spatiotemporal mapping. (3) The PM2.5 concentrations in the study area are high in winter months (January, February, December) and low in summer months (June, July, August), and spatially, PM2.5 concentrations show a distribution of high north and low south