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| 1 | Molecular cloning and expression patterns of the cholesterol side chain cleavage enzyme(CYP11A1) gene during the reproductive cycle in goose(Anas cygnoides)显示文摘Background: CYP11A1, a gene belonging to the family 11 of cytochrome P450, encodes a crucial steroidogenic enzyme that catalyzes the initial step in the production of all classes of steroids. Many studies show that CYP11A1 plays a role in ovary function. However, the role of CYP11A1 in goose reproductive cycle remains largely unknown.Results: In this study, full-length CYP11A1 c DNA of Zhedong goose was obtained using reverse transcription polymerase chain reaction(RT-PCR) and rapid amplification of c DNA ends(RACE). The c DNA consisted of a 96-base pair(bp) 5′untranslated region(UTR), a 179-bp 3′UTR and a 1509-bp open reading frame. The open reading frame encodes a putative 503 amino acid protein that shares high homology with CYP11A1 of other birds. The amino acid sequence possesses conserved domains of the P450 superfamily, which include the steroid-binding domain and the heme-binding region. Real-time quantitative polymerase chain reaction(q PCR) analysis revealed CYP11A1 mR NA was expressed ubiquitously in every Zhedong goose tissue analyzed, including the heart, liver, glandular stomach,lung, spleen, kidney, intestinum tenue, intestinum crassum, cerebrum, cerebellum, muscle, oviduct, pituitary,hypothalamus and ovary.. The relatively low levels of CYP11A1 m RNA were detected in pituitary, ovary and oviduct tissues at ovulation when compared with levels at oviposition. Interestingly, higher expression was observed in ovary and oviduct tissues during brooding. Lastly, higher m RNA expression of Yangzhou geese was detected during the ovulation period than that of Zhedong geese.Conclusions: Our findings reveal the sequence characterization and expression patterns of the CYP11A1 gene during the goose reproductive cycle, which may provides correlative evidence that CYP11A1 expression is important in reproduction activity. | Qi Xu Yadong Song Yang Chen Ran Liu Yang Zhang Yang Li Zhengyang Huang Wenming Zhao Guobin Chang Guohong Chen | 2016 | Journal of Animal Science and Biotechnology2016，7，2： | 2 |
| 2 | Assessment of strong earthquake risk in the Chinese mainland from 2021 to 2030显示文摘The long-term earthquake prediction from 2021 to 2030 is carried out by researching the active tectonic block boundary zones in the Chinese mainland.Based on the strong earthquake recurrence model,the cumulative probability of each target fault in the next 10 years is given by the recurrence period and elapsed time of each fault,which are adopted from relevant studies such as seismological geology,geodesy,and historical earthquake records.Based on the long-term predictions of large earthquakes throughout the world,this paper proposes a comprehensive judgment scheme based on the fault segments with the seismic gap,motion strongly locked,sparse small-moderate earthquakes,and apparent Coulomb stress increase.This paper presents a comprehensive analysis of the relative risk for strong earthquakes that may occur in the coming 10 years on the major faults in the active tectonic block boundary zones in the Chinese mainland.The present loading rate of each fault is first constrained by geodetic observations;the cumulative displacement of each fault is then estimated by the elapsed time since the most recent strong earthquake. | Zhigang Shao Yanqiang Wu Lingyun Ji Faqi Diao Fuqiang Shi Yujiang Li Feng Long Hui Zhang Wuxing Wang Wenxin Wei Peng Wang Xiaoxia Liu Qi Liu Zhengyang Pan Xiaofei Yin Yue Liu Wei Feng Zhenyu Zou Jia Cheng Renqi Lu Yueren Xu Xi Li | 2023 | Earthquake Research Advances2023，3，1： | 2 |
| 3 | Newton design:designing CNNs with the family of Newton's methods显示文摘Nowadays,convolutional neural networks(CNNs)have led the developments of machine learning.However,most CNN architectures are obtained by manual design,which is empirical,time-consuming,and non-transparent.In this paper,we aim at offering better insight into CNN models from the perspective of optimization theory.We propose a unified framework for understanding and designing CNN architectures with the family of Newton’s methods,which is referred to as Newton design.Specifically,we observe that the standard feedforward CNN model(PlainNet)solves an optimization problem via a kind of quasi-Newton method.Interestingly,residual network(ResNet)can also be derived if we use a more general quasi-Newton method to solve this problem.Based on the above observations,we solve this problem via a better method,the Newton-conjugate-gradient(Newton-CG)method,which inspires Newton-CGNet.In the network design,we translate binary-value terms in the optimization schemes to dropout layers,so dropout modules naturally appear in the derived CNN structures with specific locations,rather than being an empirical training strategy.Extensive experiments on image classification and text categorization tasks verify that Newton-CGNets perform very competitively.Particularly,Newton-CGNets surpass their counterparts ResNets by over 4%on CIFAR-10 and over 10%on CIFAR-100,respectively. | Zhengyang SHEN Yibo YANG Qi SHE Changhu WANG Jinwen MA Zhouchen LIN | 2023 | Science China(Information Sciences)2023，66，6： | 0 |
| 4 | Genetic interrogation of phenotypic plasticity informs genome-enabled breeding in cotton显示文摘Phenotypic plasticity, or the ability to adapt to and thrive in changing climates and variable environments, is essential for developmental programs in plants. Despite its importance, the genetic underpinnings of phenotypic plasticity for key agronomic traits remain poorly understood in many crops. In this study, we aim to fill this gap by using genome-wide association studies to identify genetic variations associated with phenotypic plasticity in upland cotton (Gossypium hirsutum L.). We identified 73 additive quantitative trait loci (QTLs), 32 dominant QTLs, and 6799 epistatic QTLs associated with 20 traits. We also identified 117 additive QTLs, 28 dominant QTLs, and 4691 epistatic QTLs associated with phenotypic plasticity in 19 traits. Our findings reveal new genetic factors, including additive, dominant, and epistatic QTLs, that are linked to phenotypic plasticity and agronomic traits. Meanwhile, we find that the genetic factors controlling the mean phenotype and phenotypic plasticity are largely independent in upland cotton, indicating the potential for simultaneous improvement. Additionally, we envision a genomic design strategy by utilizing the identified QTLs to facilitate cotton breeding. Taken together, our study provides new insights into the genetic basis of phenotypic plasticity in cotton, which should be valuable for future breeding. | Yuefan Huang Zhengyang Qi Jianying Li Jiaqi You Xianlong Zhang Maojun Wang | 2023 | Journal of Genetics and Genomics2023，50，12： | 0 |
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