No abstract

The concept of stochastic parameterization provides an opportunity to represent spatiotemporal errors caused by microphysics schemes that play important roles in supercell simulations. In this study, two stochastic methods, the stochastically perturbed temperature tendency from microphysics (SPTTM) method and the stochastically perturbed intercept parameters of microphysics (SPIPM) method, are implemented within the Lin scheme, which is based on the Advanced Regional Prediction System (ARPS) model, and are tested using an idealized supercell case. The SPTTM and SPIPM methods perturb the temperature tendency and the intercept parameters (IPs), respectively. Both methods use recursive filters to generate horizontally smooth perturbations and adopt the barotropic structure for the perturbation r, which is multiplied by tendencies or parameters from this parameterization. A double-moment microphysics scheme is used for the truth run. Compared to the multiparameter method, which uses randomly perturbed prescribed parameters, stochastic methods often produce larger ensemble spreads and better forecast the intensity of updraft helicity (UH). The SPTTM method better predicts the intensity by intensifying the midlevel heating with its positive perturbation r, whereas it performs worse in the presence of negative perturbation. In contrast, the SPIPM method can increase the intensity of UH by either positive or negative perturbation, which increases the likelihood for members to predict strong UH.

Objective Autophagy has been reported to be involved in the development of various disorders such as neurodegenerative and metabolic diseases and tumors. Autophagy activators and inhibitors are also potential therapeutics for these diseases. However, the mechanism of autophagic involvement in different diseases is not the same, and the role of autophagy in endometriosis (EM) has not yet been elucidated. This research investigated the mechanism by which autophagy acts in EM, with the aim of establishing a theoretical basis for its prevention and treatment through the targeted interference with autophagy. Methods We used an RNA interference fragment targeting ATG5, the autophagy activator rapamycin, and the autophagy inhibitor 3-MA or overexpression of filopodia-related protein fascin-1, in conjunction with clonogenic assays, growth curves, and scratch assay to investigate the influence of autophagy on cellular growth, proliferation, and invasiveness. We collected specimens from 20 clinical cases of EM and investigated the protein expression of the autophagic marker LC3-II, the autophagic substrate p62, and fascin-1. Results Rapamycin was able to inhibit the proliferation and colony formation of the endometriotic cell line CRL-7566, whereas the autophagy inhibitor 3-MA as well as the interference with the autophagy-related gene ATG5 had the opposite effect. More importantly, the autophagy activator rapamycin was able to inhibit the growth of filopodia in the endometriotic cells, and the overexpression of the fascin-1 restored the rapamycin-induced decrease of invasiveness. We found that the expression of the autophagy marker LC3-II was significantly reduced among the clinical EM specimens compared to the control group, while the expressions of fascin-1 and autophagic substrate p62 were increased. Conclusion Our results indicate that the inhibition of autophagy and exogenous expression of fascin-1 may promote the invasiveness of endometrial cells. As a corollary, autophagy represents a potential target for the treatment of EM.

A four-dimensional ensemble square-root filter algorithm (4DEnSRF) is designed to assimilate high-frequency asynchronous observations distributed over time. Given the serial nature of the EnSRF, the 4DEnSRF algorithm pre-calculates observation priors from ensemble model states at observation times and updates the observation priors at asynchronous observational times using the filter. These updated observation posteriors are used to update model state variables at the analysis time. Such an algorithm is able to utilize more observations collected over time with fewer analysis cycles, thereby reducing computational costs and potentially improving filter performance. The 4DEnSRF algorithm is tested using simulated Doppler radar data for a convective storm. The radar data are simulated elevation-by-elevation, grouped into batches with different time intervals and then assimilated with analysis cycles of the same lengths. Parallel sets of experiments using 4DEnSRF and the regular EnSRF are performed for comparison, with varying data batch or cycle lengths of 1 to 20 min. For longer time intervals, EnSRF either assumes that all data collected within the time window are valid at the same analysis time, or uses only elevations collected within a shorter time interval centered at the analysis time. Results show that 4DEnSRF outperforms EnSRF when the cycle length is more than 1 min. Observation timing error is the main cause of the performance degradation with EnSRF for both analysis and forecast; the longer the cycle length, the worse the degradation. For long cycle lengths, 4DEnSRF improves the analysis by utilizing more data whereas the EnSRF performs well only when data far away from the analysis time are discarded. Assimilating only a couple of scan elevations at a time using EnSRF with very short cycles can introduce imbalances into the model state that degrades the subsequent analyses and forecasts.