Cold-induced modulation and functional analyses of the DRE-binding transcription factor gene, GmDREB3, in soybean (Glycine max L.)

DREB (dehydration-responsive element-binding protein) transcription factors have important roles in the stress-related regulation network in plants. A DREB orthologue, GmDREB3, belonging to the A-5 subgroup of the DREB subfamily, was isolated from soybean using the RACE (rapid amplification of cDNA ends) method. Northern blot analysis showed that expression of GmDREB3 in soybean seedlings was induced following cold stress treatment for 0.5 h and was not detected after 3 h. However, it was not induced by drought and high salt stresses or by abscisic acid (ABA) treatment. This response was similar to those of members in the A-1 subgroup and different from those of other members in the A-5 subgroup, suggesting that the GmDREB3 gene was involved in an ABA-independent cold stress-responsive signal pathway. Furthermore, analysis of the GmDREB3 promoter elucidated its cold-induced modulation. A promoter fragment containing bases −1058 to −664 was involved in response to cold stress, and its effect was detected for 1 h after treatment, but a transcriptional repressor appeared to impair this response by binding to a cis-element in the region −1403 to −1058 at 24 h after the beginning of cold stress. Moreover, the GmDREB3 protein could specifically bind to the DRE element in vitro, and activated expression of downstream reporter genes in yeast cells. In addition, overexpression of GmDREB3 enhanced tolerance to cold, drought, and high salt stresses in transgenic Arabidopsis. Physiological analyses indicated that the fresh weight and osmolality of GmDREB3 transgenic Arabidopsis under cold stress were higher than those of wild-type controls. GmDREB3 transgenic tobacco accumulated higher levels of free proline under drought stress and retained higher leaf chlorophyll levels under high salt stress than wild-type tobacco. In addition, constitutive expression of GmDREB3 in transgenic Arabidopsis caused growth retardation, whereas its expression under control of the stress-inducible Rd29A promoter minimized negative effects on plant growth under normal growth conditions, indicating that a combination of the Rd29A promoter and GmDREB3 might be useful for improving tolerance to environmental stresses in crop plants

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

A PLS-Based Weighted Artificial Neural Network Approach for Alpha Radioactivity Prediction inside Contaminated Pipes

Long-range alpha detection (LRAD) has been used to measure alpha particles emitting contamination inside decommissioned steel pipes. There exists a complex nonlinear relationship between input parameters and measuring results. The input parameters, for example, pipe diameter, pipe length, distance to radioactive source, radioactive source strength, wind speed, and flux, exhibit different contributions to the measuring results. To reflect these characteristics and estimate alpha radioactivity as exactly as possible, a hybrid partial least square back propagation (PLSBP) neural network approach is presented in this paper. In this model, each node in the input layer is weighted, which indicates that different input nodes have different contributions on the system and this finding has been little reported. The weights are determined by the PLS. After this modification, a variety of normal three-layered BP networks are developed. The comparison of computational results of the proposed approach with traditional BP model and experiments confirms its clear advantage for dealing with this complex nonlinear estimation. Thus, an integrated picture of alpha particle activity inside contaminated pipes can be obtained

A peak shape model with high-energy tailing for high-resolution alpha-particle spectra

The measurement of alpha-particle spectra using semiconductor silicon detectors is an important radioactive analysis method for alpha emitters. It is a common situation that the peaks of alpha-particles with nearby energies are overlapping even with high-resolution alpha-particle spectrometry. In this work, an improved peak shape model named EMG-Landau (Exponentially Modified Gaussian with Landau), taking the high-energy tailing into account, was proposed based on the traditional EMG (Exponentially Modified Gaussian) model. Then the EMG-Landau model was effectively tested by using IAEA reference alpha spectra for unfolding performance and EUROMET reference alpha spectra for estimating the activity ratio. Compared with the traditional EMG model, the EMG-Landau model could be more accurate to fit high-resolution alpha spectra with high-energy tailing

Image Reconstruction Based on Total Variation Minimization for Radioactive Wastes Tomographic Gamma Scanning From Sparse Projections

Tomographic Gamma Scanning (TGS) is one of the most important non-destructive analyzed techniques for radioactive waste drums. By reconstructing the radioactivity distribution image, it can accurately realize the qualitative, quantitative, and positioning analysis of the radionuclides in the drum. However, the time consuming of the scanning is long and the reconstructed image is rough, which limits its good application in the practical assay of the waste drum. In this work, the total variational minimization (TVM) method was applied to improve the iterative process of the conventional algorithms of maximum likelihood expectation maximization (MLEM) and algebraic reconstruction technique (ART), then the MLEM-TVM and ART-TVM reconstruction methods were developed. The transmitted experiments were carried out where four kinds of materials were arranged in a segment whose densities ranging from 1.04 g/cm3 to 2.02 g/cm3 and a 152Eu isotope was set up as a transmission source. Compared with the traditional algorithms MLEM and ART, the MLEM-TVM and the ART-TVM algorithms have a better performance on the accuracy and the signal-to-noise ratio, and the MLEM-TVM algorithm achieves the best results, which means the quality of the reconstructed image is improved. The accuracy and effectiveness of the TVM method used in the TGS image reconstruction are verified in the work, and moreover, it can save the scanning time and enhance the TGS image resolution through sparse projection sampling