Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2024-07-22
Abstract： [Background] Solving benchmark problems is a significant step in the validation of numerical simulation programs. The Experimental Breeder Reactor II (EBR-II) is a famous benchmark for sodium-cooled fast reactors (SFR), with a complicated spatial distribution of nuclide density. Therefore, simplification to the spatial distribution of nuclide density was adopted in many studies on EBR-II benchmark calculation. [Purpose] This study aims to contrast the difference between the results of the fine model and the simplified model, evaluating the rationality of the simplification. [Method] In this study, both the fine model and the simplified one were built using LoongSARAX, a neutronic numerical program for fast reactors developed by Xi’an Jiao Tong University. Some approximations were adopted in the models: one-dimensional homogenization was adopted for the half-worth driver assembly to handle its complex radial geometry and the super-assembly method was used in the cross-section generation of poison elements. [Results] The results show that in the simplified model, 1) the spatial distribution of fuel nuclide density presents strong asymmetry and strong non-uniformity, 2) calculation time spent in the simplified model is one-tenth of that in the fine model, 3) the effective multiplication factor (keff) is 1383 pcm lower than in the fine model, 4) the spatial distribution of neutron flux is lower in the center and higher in the outer core, compared to that in the fine model, 5) the maximum relative deviation between neutron flux in two models is 4.25%. [Conclusion] In summary, the simplified model has a much lower calculation cost but limited numerical accuracy in keff and neutron flux, compared to the fine model.
Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2024-07-12
Abstract： [Background] The imperative need for high-performance propulsion systems in deep space exploration missions has led to a focus on improving the design of nuclear thermal propulsion reactors (NTPRs). The existing methods for designing NTPRs have been identified as lacking in systematic and integral approaches. [Purpose] The purpose of this study is to propose a novel multi-objective optimization design method for NTPRs to achieve a core design that offers high thrust, high specific impulse, extended service life, and reduced weight. [Methods] The methodology involves several key steps: Constructing a heat transfer model between assemblies based on their thermal interaction characteristics. Integrating this model with the flight performance model of the nuclear rocket and the two-dimensional criticality model of the assemblies. Proposing a multi-objective parameter screening method that combines the aforementioned models for coupled iterative calculations to optimize the core layout. Ensuring that the design meets comprehensive standards in thermal engineering, flight performance, and neutron physics while minimizing the core mass. Utilizing the open-source Monte Carlo software OpenMC to perform detailed three-dimensional neutronics calculations and conduct a comprehensive assessment of the reactor's criticality, safety, and burnup performance. [Results] The study's findings demonstrate that the low-enriched uranium (LEU) NTPR conceptual design, developed using the proposed method, has preliminarily satisfied the design criteria for high thrust, high specific impulse, long service life, and lightweight. [Conclusions] The results suggest that the proposed method for NTPR core design is effective in meeting the demanding requirements of future manned deep space exploration missions, offering a promising direction for further research and development in this field.
Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2024-06-29
Abstract： [Background]:Neural networks, with their powerful fitting capabilities, can learn the relationships between input and output variables based on large amounts of data, often serving as proxy models for physical programs in the field of engineering calculations, including nuclear engineering calculations. Neutron transport calculations, as one of the core links in neutronics simulations, often suffer from lengthy computational times. However, this issue can also be addressed by utilizing neural network models. Nevertheless, neural network models have a series of hyperparameters that need to be set, but manually adjusting these hyperparameters is laborious, repetitive, and reliant only on experience. Moreover, these hyperparameters are not reusable when solving different problems. [Purpose]: By seeking a surrogate model for VITAS, the research can provide some reference for the application of artificial intelligence in core physics calculation theory.[Methods]:This paper proposes the use of the Bayesian optimization algorithm to adjust neural network hyperparameters, combined with learning rate decay and loss function optimization methods. [Results]: By fitting the key core parameters obtained from VITAS's calculation of the TAKEDA benchmark problem, the results show that the average error of the effective multiplication factor is within 150×10-5, and the average error rate of the regional integral flux on the TAKEDA1 dataset is 1.72%, with a maximum error rate of 7.56%. [Conclusions]: This approach can automatically search for the optimal combination of hyperparameters for different datasets to achieve the best performance, demonstrating high flexibility, efficiency, and strong generalization.
Subjects: Nuclear Science and Technology >> Other Disciplines of Nuclear Science submitted time 2024-06-27
Abstract： Abstract Background : The operation of marine reactors and floating nuclear power plants is challenged by the thermal fluctuation of heat transfer surfaces caused by oceanic motion. The pyroelectric effect of lithium tantalate (LT), a material that changes its spontaneous polarization with temperature variations, has the potential to influence the wettability of surfaces, thereby improving heat transfer performance. Purpose : This study aims to prepare LT coatings with controlled pyroelectric properties and to investigate the mechanism by which these properties can regulate the wettability of surfaces, thereby enhancing heat transfer efficiency in two-phase systems. Methods : The sol-gel method was used to achieve the controlled synthesis of LT, and the pyroelectric effect and mechanism of wettability modulation were investigated by the change of hydroxyl radical. Results : The results indicate that the crystallinity, particle size, and thickness of the coatings can be regulated by adjusting the synthesis parameters and the particle size of LT increases with increasing annealing temperature. The pyroelectric characteristics are significantly influenced by the thickness and particle size of the coatings, with increased thickness and decreased particle size both contributing to enhanced pyroelectric performance. Fluorescence spectroscopy analysis shows that the hydroxyl radical concentration of LT increases during the heating process, and it also confirms that LT has the ability to regulate the hydroxyl radical concentration when undergoing heating and cooling cycles. Conclusions : In conclusion, LT coatings have temperature-dependent wettability in two-phase system with variable temperature.
Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2024-06-16
Abstract： [Background]:The elastic properties of dispersed fuel serve as crucial parameters in the safety analysis of reactors and the performance assessment of fuel components.[Purpose]:This study considers dispersed nuclear fuel elements as a special type of particulate composite material and employs micromechanics methods to calculate the equivalent elastic properties of the fuel element.[Methods]:Using the universal finite element software ABAQUS and user-defined subroutines, assuming the periodic distribution of fuel particles in the core, a finite element calculation model is established. A representative volume element was selected as the research object, and a thermal mechanical fission gas migration coupling analysis method was established to calculate the equivalent elastic performance of the core. [Results]: The equivalent elastic properties of the fuel element were determined. The effects of particle volume content, particle size, and burnup on the equivalent elastic properties of dispersed nuclear fuel were analyzed and compared. [Conclusions]: The results indicate that the main factors influencing the equivalent elastic properties of the fuel element are particle volume and burnup.
Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2024-06-05
Abstract： [Background]:The elastic properties of dispersed fuel serve as crucial parameters in the safety analysis of reactors and the performance assessment of fuel components.[Purpose]:This study considers dispersed nuclear fuel elements as a special type of particulate composite material and employs micromechanics methods to calculate the equivalent elastic properties of the fuel element.[Methods]:Using the universal finite element software ABAQUS and user-defined subroutines, assuming the periodic distribution of fuel particles in the core, a finite element calculation model is established. A representative volume element was selected as the research object, and a thermal mechanical fission gas migration coupling analysis method was established to calculate the equivalent elastic performance of the core. [Results]: The equivalent elastic properties of the fuel element were determined. The effects of particle volume content, particle size, and burnup on the equivalent elastic properties of dispersed nuclear fuel were analyzed and compared. [Conclusions]: The results indicate that the main factors influencing the equivalent elastic properties of the fuel element are particle volume and burnup.
Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2024-06-04
Abstract： [Background]:Neural networks, with their powerful fitting capabilities, can learn the relationships between input and output variables based on large amounts of data, often serving as proxy models for physical programs in the field of engineering calculations, including nuclear engineering calculations. Neutron transport calculations, as one of the core links in neutronics simulations, often suffer from lengthy computational times. However, this issue can also be addressed by utilizing neural network models. Nevertheless, neural network models have a series of hyperparameters that need to be set, but manually adjusting these hyperparameters is laborious, repetitive, and reliant only on experience. Moreover, these hyperparameters are not reusable when solving different problems. [Purpose]: By seeking a surrogate model for VITAS, the research can provide some reference for the application of artificial intelligence in core physics calculation theory.[Methods]:This paper proposes the use of the Bayesian optimization algorithm to adjust neural network hyperparameters, combined with learning rate decay and loss function optimization methods. [Results]: By fitting the key core parameters obtained from VITAS's calculation of the TAKEDA benchmark problem, the results show that the average error of the effective multiplication factor is within 150×10-5, and the average error rate of the regional integral flux on the TAKEDA1 dataset is 1.72%, with a maximum error rate of 7.56%. [Conclusions]: This approach can automatically search for the optimal combination of hyperparameters for different datasets to achieve the best performance, demonstrating high flexibility, efficiency, and strong generalization.
Subjects: Nuclear Science and Technology >> Nuclear Detection Technology and Nuclear Electronics submitted time 2024-06-03
Abstract： [Background] As one of the main radionuclides released into the environment by the operation of reactors and accelerators, the measurement of 41Ar activity concentration is of great significance for ensuring public health. In the field of radioactive gas measurement, the β-γ coincidence method is widely used because it can significantly reduce the background and improve the sensitivity of the detector. However, at present, there is little study on β-γ coincidence detectors for 41Ar measurement. [Purpose] In order to realize high sensitivity measurement of 41Ar, a detector composed of plastic scintillator and CsI(Tl) scintillator is designed, and an optimization method of detector structure based on minimum detectable activity concentration (MDC) is proposed. [Methods] The optimization process of the detector is realized based on Geant4 simulation. Firstly, the energy deposition of β-rays in CsI(Tl) with different thicknesses of BC404 was simulated. Secondly, the peak efficiency of 1293.6 keV γ-ray in CsI(Tl) scintillator with different thicknesses was simulated. Thirdly, assuming that the sampling time of argon is proportional to the volume of the detector’s gas chamber, by simulating with different gas chamber volume, the comprehensive effects of β detection efficiency, γ peak efficiency, gas chamber volume and sampling time on MDC were analyzed. Finally, the influence of measurement time on MDC under different background counting rates was analyzed. [Results] It is concluded that the particle number percentage of the energy deposited by 41Ar decay β-rays in CsI(Tl) is about 0.74% when the thickness of BC404 is 3 mm. The γ peak efficiency increases with the increase of CsI(Tl) scintillator thickness. With the argon sampling rate of 600 mL/h, the optimal detector size parameters that minimize MDC are completely determined. When the background count rate is 1×10-3 ~1 cps, the recommended measurement time for 41Ar is about 200 minutes. [Conclusions] When the measurement time is 200 minutes, the sample cooling time is 30 minutes and the background count rate is 5×10-3 cps, the MDC of the optimized β-γ coincidence detector for 41Ar measurement is about 1.7 Bq/m3.
Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2024-05-21
Abstract： [Background]:Neural networks, with their powerful fitting capabilities, can learn the relationships between input and output variables based on large amounts of data, often serving as proxy models for physical programs in the field of engineering calculations, including nuclear engineering calculations. Neutron transport calculations, as one of the core links in neutronics simulations, often suffer from lengthy computational times. However, this issue can also be addressed by utilizing neural network models. Nevertheless, neural network models have a series of hyperparameters that need to be set, but manually adjusting these hyperparameters is laborious, repetitive, and reliant only on experience. Moreover, these hyperparameters are not reusable when solving different problems. [Purpose]: By seeking a surrogate model for VITAS, the research can provide some reference for the application of artificial intelligence in core physics calculation theory.[Methods]:This paper proposes the use of the bayesian optimization algorithm to adjust neural network hyperparameters, combined with learning rate decay and loss function optimization methods. [Results]: By fitting the key core parameters obtained from VITAS's calculation of the TAKEDA benchmark problem, the results show that the average error of the effective multiplication factor is within 150pcm, and the average error rate of the regional integral flux on the TAKEDA1 dataset is 1.72%, with a maximum error rate of 7.56%. [Conclusions]: This approach can automatically search for the optimal combination of hyperparameters for different datasets to achieve the best performance, demonstrating high flexibility, efficiency, and strong generalization.
Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2024-05-10
Abstract： Under high load conditions, the speed regulation system of the feedwater pump and the main/bypass valve regulation system of nuclear power units are coupled, resulting in control system oscillation, which poses a challenge to the reliability and durability of the actuator, and also affects the safe and stable operation of the nuclear power unit. Therefore, it is crucial to solve the coupling oscillation problem of control systems under high load conditions. Through in-depth analysis of the multi base fluctuation curve, it was found that the slow response characteristics of the executing mechanism and the inability to follow the response speed of the control system in a timely manner are the main reasons for the adjustment fluctuation. By evaluating the response characteristic curve of the actuator, optimizing the parameters of the valve actuator, implementing matching settings for proportional gain, speed gain, and minimum loop gain, optimizing the response characteristics in three aspects: good small signal follow-up, low large signal overshoot, and fast time response. And it improve the response and control characteristics of the actuator when it has both small and large signals. By adjusting the characteristics of the actuator, the fluctuation amplitude of the steam generator water level under high load was significantly reduced.
Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2024-04-27
Abstract： [background] This study focuses on the phenomenon of bubbling in plate-type fuel assemblies within nuclear reactors. The study uses Fluent software for numerical simulation research and compares the differences between fission gas bubbles and solid bubbles, which have been previously studied. [Purpose] The purpose of the study is to investigate the effects of bubbling on temperature distribution, heat flux, and heat transfer capability in plate-type fuel assemblies. [Methods]The study employs Fluent software for numerical simulation to analyze the effects of both fission gas bubbles and solid bubbles on the thermal-hydraulic characteristics of plate-type fuel assemblies. [Results] The findings reveal that gas bubbles cause a local increase in temperature, with the heat flux around the bubbles tripling, though the overall heat flux of the fuel plate remains largely unchanged. The formation of bubbles locally enhances heat transfer capability by approximately 10%, with a 4% increase in heat flux on the bubble side. Under conditions of high flow rates, the presence of bubbles leads to a significant pressure difference across the fuel plate, causing deformation of the fuel plate and potentially leading to the blockage of the flow channel. [Conclusions] These discoveries provide significant references for the design and safety assessment of nuclear fuel plates, highlighting the importance of considering the effects of gas bubbling on thermal-hydraulic characteristics in the design and operation of nuclear reactors.
Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2024-04-15
Abstract： Background Reactor activation-burnup calculation is aan important crucial part component of reactor analysis, which is the iterative calculation by coupling the criticality program and point burnup programinvolving an iterative process that combines criticality programs with point burnup programs. Purposes LightAB (Light Activation and Burnup) is a novelnew lightweight, general-purposelight general-purpose activation-burnup program designed for this purpose. Methods LightAB used utilized burnup databases based on ORIGEN-2 and ORIGEN-S, the ORIGEN-2 and ORIGEN-S-based burnup databases, implementingand employed a Chebyshev rational approximation (CRAM) algorithm for rigid accurate burnup systems. LightAB supporteded point burnup calculation in decay mode, constant flux mode and constant power mode. MoreoverThe programs was well-structured, LightAB was highly packaged and divided intoconsisting of a solver module, an I/O module, and a burnup chain module. LightAB taked used nuclide as the basic storagefundamental unit of storage, and the input module of LightAB specifiedd physical quantities such as burnup database path and sub-burnup step division. Results The 237Np decay problem and the fixed-flux irradiation problem of Zr are calculated to verify the correctness of LightAB, where its results is consistent with that of ORIGEN2.1. LightAB and RMC programs are coupled and used to calculate the Pressurized Water Reactor (PWR) cell burnup model, the PWR assembly burnup model, and the OECD/Nuclear Energy Agency (OECD/NEA) fast reactor burnup model, and the results are consistent with that of RMCLightAB’s accuracy has been demonstrated through the successful calculation of the decay of 237Np and the irradiation of Zr under fixed-flux conditions, yielding results consistent with ORIGEN 2.1. By coupling with RMC programs, LightAB has been utilized in calculating various reactor burnup models, including Pressurized Water Reactor(PWR) cell, PWR assembly, and OECD/Nuclear Energy Agency(OECD/NEA) fast reactor models, with results being consistent with RMC calculations. Conclusions LightAB can be used in the simulation of radiation production of transplutonium isotopes.has shown promising application prospects in the irradiation production of transplutonium isotopes compared with RMC simulation calculations.
Peer Review Status:
Awaiting Review
Subjects: Nuclear Science and Technology >> Nuclear Science and Technology submitted time 2024-03-21
Abstract： [background] This study focuses on the phenomenon of bubbling in plate-type fuel assemblies within nuclear reactors. The study uses Fluent software for numerical simulation research and compares the differences between fission gas bubbles and solid bubbles, which have been previously studied. [Purpose] The purpose of the study is to investigate the effects of bubbling on temperature distribution, heat flux, and heat transfer capability in plate-type fuel assemblies. [Methods]The study employs Fluent software for numerical simulation to analyze the effects of both fission gas bubbles and solid bubbles on the thermal-hydraulic characteristics of plate-type fuel assemblies. [Results] The findings reveal that gas bubbles cause a local increase in temperature, with the heat flux around the bubbles tripling, though the overall heat flux of the fuel plate remains largely unchanged. The formation of bubbles locally enhances heat transfer capability by approximately 10%, with a 4% increase in heat flux on the bubble side. Under conditions of high flow rates, the presence of bubbles leads to a significant pressure difference across the fuel plate, causing deformation of the fuel plate and potentially leading to the blockage of the flow channel. [Conclusions] These discoveries provide significant references for the design and safety assessment of nuclear fuel plates, highlighting the importance of considering the effects of gas bubbling on thermal-hydraulic characteristics in the design and operation of nuclear reactors.
Subjects: Linguistics and Applied Linguistics >> Linguistics and Applied Linguistics submitted time 2024-01-05
Abstract： Generally, a word’s meaning consists of at least two components. The first is denotative meaning, representing the definitional meaning found in dictionaries and serving as the word’s fundamental meaning. The second component involves semantics that a word “absorbs” from its linguistic context, not constrained by definitions; this is known as semantic prosody, described as “a consistent aura of meaning with which a form is imbued by its collocates” (Louw, 1993, p. 157). While theories and empirical studies have shed light on mechanisms supporting the acquisition of the first word meaning component, the acquisition of the connotative meaning engendered by semantic prosody has been overlooked. It remains unclear whether readers can unconsciously acquire the semantic prosody (or emotional connotations) of a novel word after encountering it consistently in a context with a strong emotional polarity.
Against this backdrop, we conducted a word learning experiment, manipulating context emotionality (negative vs. neutral vs. positive) and context variability (same-repeated vs. varied contexts) as crucial contextual variables. This aimed to address two understudied questions in vocabulary acquisition: (1) Does transfer of affect to a word from its linguistic context take place through reading exposures, facilitating the acquisition of semantic prosody for the word? If so, is such transfer influenced by context variability? (2) Does the acquired semantic prosody for words affect the acquisition of word forms and meanings, and is this acquisition modulated by context variability? This experiment involved two sessions: a reading-and-learning phase and a testing phase. During the reading-and-learning session, participants read emotionally charged passages, simultaneously learning embedded target words. The testing session included an immediate posttest, incorporating four vocabulary tests—valence rating, orthographic choice, definition matching, and definition generation. A total of 196 Chinese speakers participated in the experiment.
Mixed-effects models were utilized to analyze data from the valence rating task and the other three vocabulary knowledge tests. The findings revealed that, within the same-repeated context, manipulating context emotionality (positive vs. neutral vs. negative) significantly influenced valence ratings, showing significantly higher ratings in the positive condition compared to neutral and negative conditions. Conversely, in the varied context, no significant differences in valence ratings were observed. This result supports the hypothesis of the “Double-Jujube Tree” effect, emphasizing the effect of repetitive texts compared to multiple texts. However, in the varied context, valence ratings played a role in influencing participants’ performances in the vocabulary tests, leading to better outcomes as valence ratings increased. In the same-repeated context, valence ratings had minimal effect on accuracy in the orthographic choice test and the definition prompting test.
We posit that the effective mechanism for learning the semantic-prosody-engendered connotations of words involves the transfer of affect from their collocations. However, this transfer seems to be contingent on context variability, occurring only in the same-repeated context and not in the varied context. Furthermore, we illustrate that the emotionality of context influences the quality of both orthographic and semantic word learning, with words being better learned in positive contexts as opposed to negative or neutral ones.
Subjects: Digital Publishing >> New Media submitted time 2023-10-08 Cooperative journals: 《中国传媒科技》
Abstract：21世纪的大数据技术在众多领域有着广泛的应用，其中在博物馆的智能媒体化建设、信息化发展中起着举足轻重的作用。同时，博物馆文创产业链的发展也越来越依赖大数据。国内博物馆紧跟时代发展的大趋势，加大大数据在智能媒体方面的使用和渗透比重。目前博物馆文创产业链的大数据主要运用在数据采集展示和管理藏品、综合数据库促进产品开发、数据挖掘推动产品O2O精准营销三个方面。为了借助大数据形成更高效的发展模式，各大博物馆未来应提高数据采集能力，搭建数据网络平台；建立数据管理系统，提供精准决策支持；构建“大数据库”，助力行业合作，完善产业链。
Subjects: Digital Publishing >> New Media submitted time 2023-10-08 Cooperative journals: 《中国传媒科技》
Abstract：人工智能的发展给智能生活带来便利的同时，用户可能会因算法自动处理的个人数据而受到不公平地对待，由此产生了数字歧视这一新型不平等现象。数字歧视是算法通过继承先前决策者的偏见或复制现实世界中的歧视实例进行计算，可能导致之前处于弱势的群体受到更不公正的待遇，从而加剧现有的不平等。本文以红色药丸论坛为例，运用语言偏见可视化工具DADD分析其性别不平等程度，并就数字歧视这一问题提出思考。
Subjects: Digital Publishing >> New Media submitted time 2023-10-08 Cooperative journals: 《中国传媒科技》
Abstract：过去二十几年中,我国传媒业不断发展,为了更好地开拓媒体产业的市场,营销机制的创新也成为传媒业发展的重要一环。但是,随着新媒体的出现,使传统媒体行业发生了一些变化和新挑战。
Subjects: Computer Science >> Other Disciplines of Computer Science submitted time 2022-06-28
Abstract：
Data processing of the Square Kilometre Array (SKA) is carried out in pipeline mode, and the execution efficiency of pipeline is an important factor to be considered in SKA data processing. Continuum imaging is one of the main observation modes of SKA and is a prerequisite for many other scientific works. In this paper, we take the imaging pipeline of SKA low-frequency precursor Murchison Widefield Array (MWA), as an example and optimize the parallel processing pipeline on the China SKA Regional Centre prototype (CSRC-P). Previous optimization schemes have focused on a few performance hotspots and lacked systematic optimization of the overall pipeline, resulting in a relatively poor overall speedup ratio. In this paper, we propose a global optimization scheme that combines C++ multi-threading, Python multi-processing, and Shell multi-tasking parallelism for pipelines using multiple programming languages and image datasets that can be processed independently, and verify the accuracy of the optimization results. Experiments show taht the optimized pipeline achieves an overall speedup of 2.7 and 2.4 times on the x86 and ARM nodes of CSRC-P, respectively, and the ARM compute nodes shows good adaptability to SKA applications. The optimization strategies and methods in this paper are also applicable to other SKA applications and will be useful for the scientific operation and future operation of the SKA precursor telescope.
Peer Review Status:Awaiting Review
Subjects: Biology >> Bioengineering submitted time 2017-07-24 Cooperative journals: 《中国生物工程杂志》
Abstract：目的：建立谷氨酸依赖型氨基转移酶-谷氨酸脱氢酶偶联反应的96孔板高通量筛选方法，并用于大肠杆菌氨基转移酶WecE突变库的筛选。方法：通过优化偶联指示酶-谷氨酸脱氢酶、信号分子NADH浓度及双酶偶联反应时间，建立了光学法测定氨基转移酶活性的氨基转移酶-谷氨酸脱氢酶偶联反应方法；通过定点饱和突变技术构建了大肠杆菌氨基转移酶WecE的突变库；采用96孔板高通量初筛、摇瓶复筛获得了高活性的转氨酶突变体，并对纯化的突变体进行催化活力分析。结果：建立了谷氨酸依赖型氨基转移酶目标反应与0.5 U/mL L-谷氨酸脱氢酶和0.4 mmol/L NADH信号指示反应相偶联的筛选方法；构建了氨基转移酶WecE Tyr 321饱和突变库，通过96孔板高通量筛选，获得了催化活性比野生型提高3.4倍的突变体Y321F。结论：本研究建立了高通量筛选方法，背景干扰小，准确性高，为谷氨酸依赖型氨基转移酶分子进化提供了可行性方案。
Subjects: Other Disciplines >> Synthetic discipline submitted time 2017-03-22 Cooperative journals: 《中国科学院院刊》
Abstract：人体是一个非常复杂的生态系统。在人的身体内外生活的微生物的细胞数量有百万亿之多，编码的基因数量可达人的基因数目100倍，统称为人体微生物组（HumanMicrobiome）。特别是超过90%的共生微生物生活在人的消化道里，称为“肠道菌群”，对人体健康起着不可或缺的作用，其结构的失调与多种慢性疾病的发生发展关系密切。因此，对肠道菌群为代表的人体微生物组与健康和疾病关系的研究已经成为国际学术前沿的重大科学问题。中华民族是有文字记载的历史最悠久的民族之一，更是地球上人口最为众多、分布也最为广泛的民族，随着经济发展和社会变化，海内外华族的疾病谱也在发生着急剧的变化，糖尿病、癌症等慢性病成为民众健康的最大威胁。“国际华族微生物组研究计划”希望通过对海内外华人的肠道微生物组结构变化与健康关系的系统研究，深入理解在遗传背景相对稳定、而饮食结构和生活方式发生快速变化时，菌群结构的改变在疾病谱的变化中的地位和作用，从而有助于深入揭示慢性病的发病新机制；同时，借助中医药和中国传统养生经验，开发以肠道菌群为靶点的新的医药、食品和保健品，并通过海外华人群体的使用，逐步在各国推广，有可能是中国传统医药产业走向现代化、走向世界的难得机会，更可能成为中华民族为世界人类健康作出重大贡献的难得机遇。
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