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

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

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/

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

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

Ocean response offshore of Taiwan to super typhoon Nepartak (2016) based on multiple satellite and buoy observations

Multi-satellite and buoy observation data were used to systematically analyze the ocean response offshore of Taiwan to Super Typhoon Nepartak in 2016. The satellite data showed that a high sea surface temperature combined with a thick warm water layer and deep mixed layer provided a good thermal environment for continuous intensification of the typhoon. Two high-resolution buoys (NTU1 and NTU2) moored 375 and 175 km offshore of southeastern Taiwan were used to clarify the typhoon–ocean interaction as the typhoon approached Taiwan. The ocean conditions were similar at the two buoys before the typhoon, and both buoys were on the left side of the typhoon track and suffered similar typhoon factors (e.g., typhoon intensity and translation speed) during its passage. However, the ocean response differed significantly at the two buoys. During the forced period, the entire upper ocean was cooled at NTU1. In contrast, there was a clear three-layer vertical structure at NTU2 consisting of cool surface and deep layers with a warmer layer between the two cool layers. These responses can be attributed to strong upwelling of a cold eddy at NTU1 and vertical mixing at NTU2. These results indicate that, under similar preexisting conditions and typhoon factors, the movement of ocean eddies under typhoon forcing is an unexpected mechanism that results in upwelling and thus needs to be considered when predicting changes in the ocean environment and typhoon intensity

Pollen tube emergence is mediated by ovary-expressed ALCATRAZ in cucumber

Pollen tube guidance within female tissues of flowering plants can be divided into preovular guidance, ovular guidance and a connecting stage called pollen tube emergence. As yet, no female factor has been identified to positively regulate this transition process. In this study, we show that an ovary-expressed bHLH transcription factor Cucumis sativus ALCATRAZ (CsALC) functions in pollen tube emergence in cucumber. CsALC knockout mutants showed diminished pollen tube emergence, extremely reduced entry into ovules, and a 95% reduction in female fertility. Further examination showed two rapid alkalinization factors CsRALF4 and CsRALF19 were less expressed in Csalc ovaries compared to WT. Besides the loss of male fertility derived from precocious pollen tube rupture as in Arabidopsis, Csralf4 Csralf19 double mutants exhibited a 60% decrease in female fertility due to reduced pollen tube distribution and decreased ovule targeting efficiency. In brief, CsALC regulates female fertility and promotes CsRALF4/19 expression in the ovary during pollen tube guidance in cucumber. Pollen tube growth is guided towards ovules. Here the authors show that a bHLH transcriptional factor CsALC functions in pollen tube emergence towards ovules to regulate female fertility in cucumber and promotes the expression of two rapid alkalinization factors CsRALF4/19 in the ovary