Sex-specific determinants of serum 25-hydroxyvitamin D3 concentrations in an elderly German cohort : a cross-sectional study

BACKGROUND: Considering the suggested link between vitamin D insufficiency and several chronic diseases, attention should be given to approaches for improving vitamin D status. Elderly subjects are regarded as a high-risk group for developing an insufficient vitamin D status. Socioeconomic, dietary, lifestyle and environmental factors are considered as influencing factors, whereupon sex differences in predictors of vitamin D status are rarely investigated. The purpose of this study is to identify the main predictors of serum 25-hydroxyvitamin D3 [25(OH)D3] concentrations in elderly subjects by taking into account potential sex differences. METHODS: This is a cross-sectional study in 162 independently living German elderly aged 66 to 96years. Serum 25(OH)D3 concentrations were assessed by an electrochemiluminescence immunoassay. Multiple regression analyses were performed to identify predictors of 25(OH)D3 concentrations stratified by sex. RESULTS:Median 25(OH)D3 concentration was 64nmol/L and none of the subjects had 25(OH)D3 concentrations<25nmol/L. In women, intact parathyroid hormone (iPTH) (beta=-0.323), % total body fat (beta=-0.208), time spent outdoors (beta=0.328), month of blood sampling (beta=0.229) and intake of vitamin D supplements (beta=0.172) were the predominant predictors of 25(OH)D3, whereas in men, iPTH (beta=-0.254), smoking (beta=-0.282), physical activity (beta=0.336) and monthly household net income (beta=0.302) predicted 25(OH)D3 concentrations. The final regression models accounted for 30% and 32% of the variance in 25(OH)D3 concentrations in women and men, respectively. CONCLUSION: The findings indicate that 25(OH)D3 concentrations are influenced by body composition, month of blood sampling, economic factors, lifestyle, supplement intake and iPTH, but may not be associated with age, sex, dietary factors, kidney function and presence of selected chronic diseases in community-dwelling elderly. Furthermore, our results provide evidence for sex-specific determinants of the vitamin D status, which ought to be considered for preventive strategies

Bayesian geoacoustic inversion using mixture density network

Bayesian geoacoustic inversion problems are conventionally solved by Markov chain Monte Carlo methods or its variants, which are computationally expensive. This paper extends the classic Bayesian geoacoustic inversion framework by deriving important geoacoustic statistics of Bayesian geoacoustic inversion from the multidimensional posterior probability density (PPD) using the mixture density network (MDN) theory. These statistics make it convenient to train the network directly on the whole parameter space and get the multidimensional PPD of model parameters. The present approach provides a much more efficient way to solve geoacoustic inversion problems in Bayesian inference framework. The network is trained on a simulated dataset of surface-wave dispersion curves with shear-wave velocities as labels and tested on both synthetic and real data cases. The results show that the network gives reliable predictions and has good generalization performance on unseen data. Once trained, the network can rapidly (within seconds) give a fully probabilistic solution which is comparable to Monte Carlo methods. It provides an promising approach for real-time inversion

Broad-line region configuration of the supermassive binary black hole candidate PG1302-102 in the relativistic Doppler boosting scenario

PG1302-102 is thought to be a supermassive binary black hole (BBH) system according to the periodical variations of its optical and UV photometry, which may be interpreted as being due to the relativistic Doppler boosting of the emission mainly from the disk around the secondary black hole (BH) modulated by its orbital motion. In this paper, we investigate several broad emission lines of PG1302-102 using archived UV spectra obtained by IUE, GALEX, and Hubble, to reveal the broad-line region (BLR) emission properties of this BBH system under the Doppler boosting scenario. We find that the broad lines Ly$\alpha$, NV, CIV, and CIII] all show Gaussian profiles, and none of these lines exhibits obvious periodical variation. Adopting a simple model for the BLR, we perform Markov chain Monte Carlo fittings to these broad lines, and find that the BLR must be viewed at an orientation angle of $\sim33^{\circ}$, close to face-on. If the Doppler boosting interpretation is correct, then the BLR is misaligned with the BBH orbital plane by an angle of $\sim51^\circ$, which suggests that the Doppler boosted continuum variation has little effect on the broad-line emission and thus does not lead to periodical line variation. We further discuss the possible implications for such a BLR configuration with respect to the BBH orbital plane.Comment: 9 pages, 6 figures, matches A&A version (only minor changes

Long-Term Prediction Accuracy Improvement of Data-Driven Medium-Range Global Weather Forecast

Long-term stability stands as a crucial requirement in data-driven medium-range global weather forecasting. Spectral bias is recognized as the primary contributor to instabilities, as data-driven methods difficult to learn small-scale dynamics. In this paper, we reveal that the universal mechanism for these instabilities is not only related to spectral bias but also to distortions brought by processing spherical data using conventional convolution. These distortions lead to a rapid amplification of errors over successive long-term iterations, resulting in a significant decline in forecast accuracy. To address this issue, a universal neural operator called the Spherical Harmonic Neural Operator (SHNO) is introduced to improve long-term iterative forecasts. SHNO uses the spherical harmonic basis to mitigate distortions for spherical data and uses gated residual spectral attention (GRSA) to correct spectral bias caused by spurious correlations across different scales. The effectiveness and merit of the proposed method have been validated through its application for spherical Shallow Water Equations (SWEs) and medium-range global weather forecasting. Our findings highlight the benefits and potential of SHNO to improve the accuracy of long-term prediction

Fractional Variational Iteration Method versus Adomian’s Decomposition Method in Some Fractional Partial Differential Equations

A comparative study is presented about the Adomian’s decomposition method (ADM), variational iteration method (VIM), and fractional variational iteration method (FVIM) in dealing with fractional partial differential equations (FPDEs). The study outlines the significant features of the ADM and FVIM methods. It is found that FVIM is identical to ADM in certain scenarios. Numerical results from three examples demonstrate that FVIM has similar efficiency, convenience, and accuracy like ADM. Moreover, the approximate series are also part of the exact solution while not requiring the evaluation of the Adomian’s polynomials

A hybrid data assimilation system based on machine learning

In the earth sciences, numerical weather prediction (NWP) is the primary method of predicting future weather conditions, and its accuracy is affected by the initial conditions. Data assimilation (DA) can provide high-precision initial conditions for NWP. The hybrid 4DVar-EnKF is currently an advanced DA method used by many operational NWP centres. However, it has two major shortcomings: The complex development and maintenance of the tangent linear and adjoint models and the empirical combination of the results of 4DVar and EnKF. In this paper, a new hybrid DA method based on machine learning (HDA-ML) is presented to overcome these drawbacks. In the new method, the tangent linear and adjoint models in the 4DVar part of the hybrid algorithm can be easily obtained by using a bilinear neural network to replace the forecast model, and a CNN model is adopted to fuse the analysis of 4DVar and EnKF to adaptively obtain the optimal coefficient of combination rather than the empirical coefficient as in the traditional hybrid DA method. The hybrid DA methods are compared with the Lorenz-96 model using the true values as labels. The experimental results show that HDA-ML improves the assimilation performance and significantly reduces the time cost. Furthermore, using observations instead of the true values as labels in the training system is more realistic. The results show comparable assimilation performance to that in the experiments with the true values used as the labels. The experimental results show that the new method has great potential for application to operational NWP systems

Cytotoxic necrotizing factor 1 promotes bladder cancer angiogenesis through activating RhoC

Uropathogenic Escherichia coli (UPEC), a leading cause of urinary tract infections, is associated with prostate and bladder cancers. Cytotoxic necrotizing factor 1 (CNF1) is a key UPEC toxin; however, its role in bladder cancer is unknown. In the present study, we found CNF1 induced bladder cancer cells to secrete vascular endothelial growth factor (VEGF) through activating Ras homolog family member C (RhoC), leading to subsequent angiogenesis in the bladder cancer microenvironment. We then investigated that CNF1- mediated RhoC activation modulated the stabilization of hypoxia- inducible factor 1α (HIF1α) to upregulate the VEGF. We demonstrated in vitro that active RhoC increased heat shock factor 1 (HSF1) phosphorylation, which induced the heat shock protein 90α (HSP90α) expression, leading to stabilization of HIF1α. Active RhoC elevated HSP90α, HIF1α, VEGF expression, and angiogenesis in the human bladder cancer xenografts. In addition, HSP90α, HIF1α, and VEGF expression were also found positively correlated with the human bladder cancer development. These results provide a potential mechanism through which UPEC contributes to bladder cancer progression, and may provide potential therapeutic targets for bladder cancer.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155984/1/fsb220522.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155984/2/fsb220522-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155984/3/fsb220522_am.pd

High-performance time-series quantitative retrieval from satellite images on a GPU cluster

The quality and accuracy of remote sensing instruments continue to increase, allowing geoscientists to perform various quantitative retrieval applications to observe the geophysical variables of land, atmosphere, ocean, etc. The explosive growth of time-series remote sensing (RS) data over large-scales poses great challenges on managing, processing, and interpreting RS ‘‘Big Data.’’ To explore these time-series RS data efficiently, in this paper, we design and implement a high-performance framework to address the time-consuming time-series quantitative retrieval issue on a graphics processing unit cluster, taking the aerosol optical depth (AOD) retrieval from satellite images as a study case. The presented framework exploits the multilevel parallelism for time-series quantitative RS retrieval to promote efficiency. At the coarse-grained level of parallelism, the AOD time-series retrieval is represented as multidirected acyclic graph workflows and scheduled based on a list-based heuristic algorithm, heterogeneous earliest finish time, taking the idle slot and priorities of retrieval jobs into account. At the fine-grained level, the parallel strategies for the major remote sensing image processing algorithms divided into three categories, i.e., the point or pixel-based operations, the local operations, and the global or irregular operations have been summarized. The parallel framework was implemented with message passing interface and compute unified device architecture, and experimental results with the AOD retrieval case verify the effectiveness of the presented framework.N/