Comparison of Analysis and Spectral Nudging Techniques for Dynamical Downscaling with the WRF Model over China

To overcome the problem that the horizontal resolution of global climate models may be too low to resolve features which are important at the regional or local scales, dynamical downscaling has been extensively used. However, dynamical downscaling results generally drift away from large-scale driving fields. The nudging technique can be used to balance the performance of dynamical downscaling at large and small scales, but the performances of the two nudging techniques (analysis nudging and spectral nudging) are debated. Moreover, dynamical downscaling is now performed at the convection-permitting scale to reduce the parameterization uncertainty and obtain the finer resolution. To compare the performances of the two nudging techniques in this study, three sensitivity experiments (with no nudging, analysis nudging, and spectral nudging) covering a period of two months with a grid spacing of 6 km over continental China are conducted to downscale the 1-degree National Centers for Environmental Prediction (NCEP) dataset with the Weather Research and Forecasting (WRF) model. Compared with observations, the results show that both of the nudging experiments decrease the bias of conventional meteorological elements near the surface and at different heights during the process of dynamical downscaling. However, spectral nudging outperforms analysis nudging for predicting precipitation, and analysis nudging outperforms spectral nudging for the simulation of air humidity and wind speed

A study on reducing update frequency of the forecast samples in the ensemble-based 4DVar data assimilation method

In the ensemble-based four dimensional variational assimilation method (SVD-En4DVar), a singular value decomposition (SVD) technique is used to select the leading eigenvectors and the analysis variables are expressed as the orthogonal bases expansion of the eigenvectors. The experiments with a two-dimensional shallow-water equation model and simulated observations show that the truncation error and rejection of observed signals due to the reduced-dimensional reconstruction of the analysis variable are the major factors that damage the analysis when the ensemble size is not large enough. However, a larger-sized ensemble is daunting computational burden. Experiments with a shallow-water equation model also show that the forecast error covariances remain relatively constant over time. For that reason, we propose an approach that increases the members of the forecast ensemble while reducing the update frequency of the forecast error covariance in order to increase analysis accuracy and to reduce the computational cost. A series of experiments were conducted with the shallow-water equation model to test the efficiency of this approach. The experimental results indicate that this approach is promising. Further experiments with the WRF model show that this approach is also suitable for the real atmospheric data assimilation problem, but the update frequency of the forecast error covariances should not be too low

Retrieving 3D Wind Field from Phased Array Radar Rapid Scans

The previous two-dimensional simple adjoint method for retrieving horizontal wind field from a time sequence of single-Doppler scans of reflectivity and/or radial velocity is further developed into a new method to retrieve both horizontal and vertical winds at high temporal and spatial resolutions. This new method performs two steps. First, the horizontal wind field is retrieved on the conical surface at each tilt (elevation angle) of radar scan. Second, the vertical velocity field is retrieved in a vertical cross-section along the radar beam with the horizontal velocity given from the first step. The method is applied to phased array radar (PAR) rapid scans of the storm winds and reflectivity in a strong microburst event and is shown to be able to retrieve the three-dimensional wind field around a targeted downdraft within the storm that subsequently produced a damaging microburst. The method is computationally very efficient and can be used for real-time applications with PAR rapid scans

Oroxylin A inhibits hemolysis via hindering the self-assembly of α-hemolysin heptameric transmembrane pore.

Alpha-hemolysin (α-HL) is a self-assembling, channel-forming toxin produced by most Staphylococcus aureus strains as a 33.2-kDa soluble monomer. Upon binding to a susceptible cell membrane, the monomer self-assembles to form a 232.4-kDa heptamer that ultimately causes host cell lysis and death. Consequently, α-HL plays a significant role in the pathogenesis of S. aureus infections, such as pneumonia, mastitis, keratitis and arthritis. In this paper, experimental studies show that oroxylin A (ORO), a natural compound without anti-S. aureus activity, can inhibit the hemolytic activity of α-HL. Molecular dynamics simulations, free energy calculations, and mutagenesis assays were performed to understand the formation of the α-HL-ORO complex. This combined approach revealed that the catalytic mechanism of inhibition involves the direct binding of ORO to α-HL, which blocks the conformational transition of the critical "Loop" region of the α-HL protein thereby inhibiting its hemolytic activity. This mechanism was confirmed by experimental data obtained from a deoxycholate-induced oligomerization assay. It was also found that, in a co-culture system with S. aureus and human alveolar epithelial (A549) cells, ORO could protect against α-HL-mediated injury. These findings indicate that ORO hinders the lytic activity of α-HL through a novel mechanism, which should facilitate the design of new and more effective antibacterial agents against S. aureus

Genetic overlap between schizophrenia and cognitive performance

Abstract Schizophrenia (SCZ), a highly heritable mental disorder, is characterized by cognitive impairment, yet the extent of the shared genetic basis between schizophrenia and cognitive performance (CP) remains poorly understood. Therefore, we aimed to explore the polygenic overlap between SCZ and CP. Specifically, the bivariate causal mixture model (MiXeR) was employed to estimate the extent of genetic overlap between SCZ (n = 130,644) and CP (n = 257,841), and conjunctional false discovery rate (conjFDR) approach was used to identify shared genetic loci. Subsequently, functional annotation and enrichment analysis were carried out on the identified genomic loci. The MiXeR analyses revealed that 9.6 K genetic variants are associated with SCZ and 10.9 K genetic variants for CP, of which 9.5 K variants are shared between these two traits (Dice coefficient = 92.8%). By employing conjFDR, 236 loci were identified jointly associated with SCZ and CP, of which 139 were novel for the two traits. Within these shared loci, 60 exhibited consistent effect directions, while 176 had opposite effect directions. Functional annotation analysis indicated that the shared genetic loci were mainly located in intronic and intergenic regions, and were found to be involved in relevant biological processes such as nervous system development, multicellular organism development, and generation of neurons. Together, our findings provide insights into the shared genetic architecture between SCZ and CP, suggesting common pathways and mechanisms contributing to both traits

RMS fluctuations of the residues around the binding sites of the α-HL –ORO complex and free α-HL.

<p>RMS fluctuations are defined as RMS deviations of the structure at a given time from the average structure found using the MD simulation. Only mobile atoms of the simulation were considered for the RMS fluctuations.</p

ORO prevents the deoxycholate-induced oligomerization of α-HL.

<p>α-HL was treated with 5 mM deoxycholate in the presence or absence of ORO. Following sodium dodecyl sulfate (SDS)-polyacrylamide gel electrophoresis (PAGE) analysis, the proteins were detected by silver staining. Lane 1, WT-HL; lane 2, T12A-HL; lane 3, I14A-HL; lane 4, WT-HL plus 8 µg/ml of ORO; lane 5, I14A-HL plus 8 µg/ml of ORO; lane 6, T12A-HL plus 8 µg/ml of ORO.</p

ORO protects A549 cells from α-HL mediated cell injury.

<p>Live (green)/dead (red) stained A549 cells were captured by confocal laser scanning microscopy after infection with <i>S. aureus</i> 8325-4 or DU 1090. Uninfected cells (A); cells co-cultured with <i>S. aureus</i> 8325-4 in the absence of ORO (B); cells co-cultured with <i>S. aureus</i> 8325-4 in the presence of 8 µg/ml of ORO (C); cells infected with DU1090 (D); (E) LDH release by A549 cells when co-cultured with <i>S. aureus</i> 8325-4 and the indicated concentrations of ORO. All data represent the mean and standard error of three independent experiments. * indicates P<0.05 and ** indicates P<0.01 compared to the drug-free culture.</p