分类： 生物学 >> 植物学 >> 植物细胞学与植物遗传学、植物形态学 提交时间： 2016-05-03
摘要： Biological assay has been based on analysis of all individuals collected from sample populations. Bulked sample analysis (BSA), which works with selected and pooled individuals, has been extensively used in gene mapping through bulked segregant analysis with biparental populations, mapping by sequencing with major gene mutants and pooled genomewide association study using extreme variants. Compared to conventional entire population analysis, BSA significantly reduces the scale and cost by simplifying the procedure. The bulks can be built by selection of extremes or representative samples from any populations and all types of segregants and variants that represent wide ranges of phenotypic variation for the target trait. Methods and procedures for sampling, bulking and multiplexing are described. The samples can be analysed using individual markers, microarrays and high-throughput sequencing at all levels of DNA, RNA and protein. The power of BSA is affected by population size, selection of extreme individuals, sequencing strategies, genetic architecture of the trait and marker density. BSA will facilitate plant breeding through development of diagnostic and constitutive markers, agronomic genomics, marker-assisted selection and selective phenotyping. Applications of BSA in genetics, genomics and crop improvement are discussed with their future perspectives.
分类： 生物学 >> 植物学 >> 植物学研究、实验与植物演化、发展 提交时间： 2016-05-03
摘要： Eukaryotic Argonaute proteins play primary roles in miRNA and siRNA pathways that are essential for numerous developmental and biological processes. However, the functional roles of the four ZmAGO1 genes have not yet been characterized in maize (Zea mays L.). In the present study, ZmAGO1a was identified from four putative ZmAGO1 genes for further characterization. Complementation of the Arabidopsis ago1-27 mutant with ZmAGO1a indicated that constitutive overexpression of ZmAGO1a could restore the smaller rosette, serrated leaves, later flowering and maturation, lower seed set, and darker green leaves at late stages of the mutant to the wild-type phenotype. The expression profiles of ZmAGO1a under five different abiotic stresses indicated that ZmAGO1a shares expression patterns similar to those of Argonaute genes in rice, Arabidopsis, and wheat. Further, variation in ZmAGO1a alleles among diverse maize germplasm that resulted in several amino acid changes revealed genetic diversity at this locus. The present data suggest that ZmAGO1a might be an important AGO1 ortholog in maize. The results presented provide further insight into the function of ZmAGO1a.
分类： 物理学 >> 地球物理学、天文学和天体物理学 提交时间： 2024-01-09 合作期刊: 《Research in Astronomy and Astrophysics》
摘要： A negative correlation was found to exist between the low-energy spectral index and the redshift of gamma-ray bursts (GRBs) by Amati et al. It was later confirmed by Geng & Huang and Gruber et al., but the correlation was also found to be quite dispersive when the sample size was significantly expanded. In this study, we have established two even larger samples of GRBs to further examine the correlation. One of our samples consists of 316 GRBs detected by the Swift satellite, and the other one consists of 80 GRBs detected by the Fermi satellite. It is found that there is no correlation between the two parameters for the Swift sample, but there does exist a weak negative correlation for the Fermi sample. The correlation becomes even more significant when the spectral index at the peak flux is considered. It is argued that the absence of the correlation in the Swift sample may be due to the fact that Swift has a very narrow energy response so that it could not measure the low-energy spectral index accurately enough. Further studies based on even larger GRB samples are solicited.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Regardless of their different types of progenitors and central engines, gamma-ray bursts (GRBs) were always assumed to be standalone systems after they formed. Little attention has been paid to the possibility that a stellar companion can still accompany a GRB itself. This paper investigates such a GRB-involved binary system and studies the effects of the stellar companion on the observed GRB emission when it is located inside the jet opening angle. Assuming a typical emission radius of $\sim10^{15}\,$cm, we show that the blockage by a companion star with a radius of $R_\mathrm{c}\sim67\,\mathrm{R_\odot}$ becomes non-negligible when it is located within a typical GRB jet opening angle (e.g., $\sim10$ degrees) and beyond the GRB emission site. In such a case, an on-axis observer will see a GRB with a similar temporal behavior but 25% dimmer. On the other hand, an off-axis observer outside the jet opening angle (hence missed the original GRB) can see a delayed "reflected" GRB, which is much fainter in brightness, much wider in the temporal profile and slightly softer in energy. Our study can naturally explain the origin of some low-luminosity GRBs. Moreover, we also point out that the companion star may be shocked if it is located inside the GRB emission site, which can give rise to an X-ray transient or a GRB followed by a delayed X-ray bump on top of X-ray afterglows.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Peng Zhang
Bing Li
RenZhou Gui
Shaolin Xiong
Ze-Cheng Zou
Xianggao Wang
Xiaobo Li
Ce Cai
Yi Zhao
Yanqiu Zhang
Wangchen Xue
Chao Zheng
Hongyu Zhao
摘要： To research the burst phenomenon of gamma-ray bursts (GRBs) in depth, it is necessary to explore an effective and accurate identification of GRBs. Onboard blind search, ground blind search, and target search method are popular methods in identifying GRBs. However, they undeniably miss GRBs due to the influence of threshold, especially for sub-threshold triggers. We present a new approach to distinguish GRB by using convolutional neural networks (CNNs) to classify count maps that contain bursting information in more dimensions. For comparison, we design three supervised CNN models with different structures. Thirteen years Time-Tagged Event (TTE) format data from Fermi/GBM is employed to construct useful data sets and to train, validate and test these models. We find an optimal model, i.e. the ResNet-CBAM model trained on the 64 ms data set, which contains residual and attention mechanism modules. We track this deep learning model through two visualization analysis methods separately, Gradient-weighted Class Activation Mapping (Grad-CAM) and T-distributed Stochastic Neighbor Embedding (t-SNE) method, and find it focused on the main features of GRBs. By applying it on one-year data, about 96% of GRBs in the Fermi burst catalog were distinguished accurately, six out of ten GRBs of sub-threshold triggers were identified correctly, and meaningfully thousands of new candidates were obtained and listed according to their SNR information. Our study implies that the deep learning method could distinguish GRBs from background-like maps effectively and reliably. In the future, it can be implemented into real-time analysis pipelines to reduce manual inspection and improve accuracy, enabling follow-up observations with multi-band telescopes.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Peng Zhang
Bing Li
RenZhou Gui
Shaolin Xiong
Ze-Cheng Zou
Xianggao Wang
Xiaobo Li
Ce Cai
Yi Zhao
Yanqiu Zhang
Wangchen Xue
Chao Zheng
Hongyu Zhao
摘要： To research the burst phenomenon of gamma-ray bursts (GRBs) in depth, it is necessary to explore an effective and accurate identification of GRBs. Onboard blind search, ground blind search, and target search method are popular methods in identifying GRBs. However, they undeniably miss GRBs due to the influence of threshold, especially for sub-threshold triggers. We present a new approach to distinguish GRB by using convolutional neural networks (CNNs) to classify count maps that contain bursting information in more dimensions. For comparison, we design three supervised CNN models with different structures. Thirteen years Time-Tagged Event (TTE) format data from Fermi/GBM is employed to construct useful data sets and to train, validate and test these models. We find an optimal model, i.e. the ResNet-CBAM model trained on the 64 ms data set, which contains residual and attention mechanism modules. We track this deep learning model through two visualization analysis methods separately, Gradient-weighted Class Activation Mapping (Grad-CAM) and T-distributed Stochastic Neighbor Embedding (t-SNE) method, and find it focused on the main features of GRBs. By applying it on one-year data, about 96% of GRBs in the Fermi burst catalog were distinguished accurately, six out of ten GRBs of sub-threshold triggers were identified correctly, and meaningfully thousands of new candidates were obtained and listed according to their SNR information. Our study implies that the deep learning method could distinguish GRBs from background-like maps effectively and reliably. In the future, it can be implemented into real-time analysis pipelines to reduce manual inspection and improve accuracy, enabling follow-up observations with multi-band telescopes.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Lei Zhang
Zhi-Yu Zhang
James. W. Nightingale
Ze-Cheng Zou
Xiaoyue Cao
Chao-Wei Tsai
Chentao Yang
Yong Shi
Junzhi Wang
Dandan Xu
Ling-Rui Lin
Jing Zhou
Ran Li
摘要： The fast growth of supermassive black holes and their feedback to the host galaxies play an important role in regulating the evolution of galaxies, especially in the early Universe. However, due to cosmological dimming and the limited angular resolution of most observations, it is difficult to resolve the feedback from the active galactic nuclei (AGN) to their host galaxies. Gravitational lensing, for its magnification, provides a powerful tool to spatially differentiate emission originated from AGN and host galaxy at high redshifts. Here we report a discovery of a radio lobe in a strongly lensed starburst quasar, H1413+117 or Cloverleaf at redshift $z= 2.56$, based on observational data at optical, sub-millimetre, and radio wavelengths. With both parametric and non-parametric lens models and with reconstructed images on the source plane, we find a differentially lensed, kpc scaled, single-sided radio lobe, located at ${\sim}1.2\,\mathrm{kpc}$ to the north west of the host galaxy on the source plane. From the spectral energy distribution in radio bands, we find that the radio lobe has an energy turning point residing between 1.5 GHz and 8 GHz, indicating an age of 20--50 Myr. This could indicate a feedback switching of Cloverleaf quasar from the jet mode to the quasar mode.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Lei Zhang
Zhi-Yu Zhang
James. W. Nightingale
Ze-Cheng Zou
Xiaoyue Cao
Chao-Wei Tsai
Chentao Yang
Yong Shi
Junzhi Wang
Dandan Xu
Ling-Rui Lin
Jing Zhou
Ran Li
摘要： The fast growth of supermassive black holes and their feedback to the host galaxies play an important role in regulating the evolution of galaxies, especially in the early Universe. However, due to cosmological dimming and the limited angular resolution of most observations, it is difficult to resolve the feedback from the active galactic nuclei (AGN) to their host galaxies. Gravitational lensing, for its magnification, provides a powerful tool to spatially differentiate emission originated from AGN and host galaxy at high redshifts. Here we report a discovery of a radio lobe in a strongly lensed starburst quasar, H1413+117 or Cloverleaf at redshift $z= 2.56$, based on observational data at optical, sub-millimetre, and radio wavelengths. With both parametric and non-parametric lens models and with reconstructed images on the source plane, we find a differentially lensed, kpc scaled, single-sided radio lobe, located at ${\sim}1.2\,\mathrm{kpc}$ to the north west of the host galaxy on the source plane. From the spectral energy distribution in radio bands, we find that the radio lobe has an energy turning point residing between 1.5 GHz and 8 GHz, indicating an age of 20--50 Myr. This could indicate a feedback switching of Cloverleaf quasar from the jet mode to the quasar mode.
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