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Genes & Diseases 2024年第1期11卷 19-22页

作者： zhenhai li Wenchao Xu Feng Chen Jun Zhang Wei-Guo Zhu Guangdong Key Laboratory of Genome Instability and Human Disease Prevention Department of Biochemistry and Molecular BiologyShenzhen University School of MedicineShenzhenGuangdong 518055China School of Pharmaceutical Sciences Shenzhen University Health Science CenterShenzhenGuangdong 518055China Base for International Science and Technology Cooperation:Carson Cancer Stem Cell Vaccines R&D Center International Cancer CenterShenzhen University Health Science CenterShenzhenGuangdong 518055China Shenzhen Bay Laboratory Shenzhen University School of MedicineShenzhenGuangdong 518055China Marshall Laboratory of Biomedical Engineering Shenzhen University School of MedicineShenzhenGuangdong 518055China

Etoposide is widely used for cancer chemotherapy in the clinic.However,long-term etoposide treatment can lead to adverse effects or drug resistance.To improve the situation,we evaluated the therapeutic efficiency of etoposide combined with inhibitors of bromodomain and extraterminal(BET)family proteins,which have recently emerged as novel anti-cancer targets due to their critical roles in cancer development.Firstly,we showed BRD4,one of the main targets of BET inhibitors,was involved in DNA damage response(DDR)via the homologous recombination(HR)repair pathway.

关键词： etoposide damage cancer

Remote sensing of quality traits in cereal and arable production systems:A review

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The Crop Journal 2024年第1期12卷 45-57页

作者： zhenhai li Chengzhi Fan Yu Zhao Xiuliang Jin Raffaele Casa Wenjiang Huang Xiaoyu Song Gerald Blasch Guijun Yang James Taylor Zhenhong li College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdao 266590ShandongChina Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs Information Technology Research CenterBeijing Academy of Agriculture and Forestry SciencesBeijing 100097China Institute of Crop Sciences Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and EcologyMinistry of Agriculture and Rural AffairsBeijing 100081China DAFNE Universitàdella TusciaVia San Camillo de Lellis01100 ViterboItaly Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China International Maize and Wheat Improvement Center(CIMMYT) PO Box 5689Addis AbabaEthiopia ITAP Univ.MontpellierINRAEInstitut AgroMontpellier 34000France College of Geological Engineering and Geomatics Chang’an UniversityXi’an 710054ShaanxiChina

Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy planning.The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits.Many studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing data.In this paper,the key quality traits that are of interest to producers and consumers are introduced.The literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality prediction.This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

关键词： Remote sensing Quality traits Grain protein Cereal

Differential pressure reset strategy based on reinforcement learning for chilled water systems

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Building Simulation 2022年第2期15卷 233-248页

作者： Xinfang Zhang zhenhai li Zhengwei li Shunian Qiu Hai Wang School of Mechanical Engineering Tongji UniversityShanghaiChina Key Laboratory of Performance Evolution and Control for Engineering Structures of Ministry of Education Tongji UniversityShanghaiChina

Air conditioning water systems account for a large proportion of building energy consumption.In a pressure-controlled water system,one of the key measures to save energy is to adjust the differential pressure setpoints during operation.Typically,such adjustments are based either on certain rules,which rely on operator experience,or on complicated models that are not easy to calibrate.In this paper,a data-driven control method based on reinforcement learning is proposed.The main idea is to construct an agent model that adapts to the researched problem.Instead of directly being told how to react,the agent must rely on its own experiences to learn.Compared with traditional control strategies,reinforcement learning control(RLC)exhibits more accurate and steady performances while maintaining indoor air temperature within a limited range.A case study shows that the RLC strategy is able to save substantial amounts of energy.

关键词： water system differential pressure reset reinforcement learning control energy saving

Should phenological information be applied to predict agronomic traits across growth stages of winter wheat?

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The Crop Journal 2022年第5期10卷 1346-1352页

作者： Yu Zhao Yang Meng Shaoyu Han Haikuan Feng Guijun Yang zhenhai li Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs Information Technology Research CenterBeijing Academy of Agriculture and Forestry SciencesBeijing 100097China Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry Nanjing Agricultural UniversityNanjing 210095JiangsuChina College of Geomatics Shandong University of Science and TechnologyQingdao 266590ShandongChina

Most existing agronomic trait models of winter wheat vary across growing seasons, and it is an open question whether a unified statistical model can be developed to predict agronomic traits using a vegetation index(VI) across multiple growing seasons. In this study, we constructed a hierarchical linear model(HLM) to automatically adapt the relationship between VIs and agronomic traits across growing seasons and tested the model’s performance by sensitivity analysis. Results demonstrated that(1) optical VIs give poor performance in predicting AGB and PNC across all growth stages, whereas VIs perform well for LAI, LGB, LNC, and SPAD.(2) The sensitivity indices of the phenological information in the AGB and PNC prediction models were 0.81–0.86 and 0.66–0.73, whereas LAI, LGB, LNC, and SPAD prediction models produced sensitivity indexes of 0.01–0.02, 0.01–0.02, 0.01–0.02, and 0.02–0.08, respectively.(3) The AGB and PNC prediction models considering ZS were more accurate than the prediction models based on VI. Whether or not phenological information is used, there was no difference in model accuracy for LGB,LNC, SPAD, and LAI. This study may provide a guideline for deciding whether phenological correction is required for estimation of agronomic traits across multiple growing seasons.

关键词： Agronomic traits Phenological effect Vegetation index Hierarchical linear model Winter wheat

Novel evolutionary insights into nemacheilid cavefi sh: evidence from comparative analysis of mitochondrial genomes

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Journal of Oceanology and limnology 2022年第4期40卷 1640-1653页

作者： Lei ZHOU Shihui HUANG Qing WANG zhenhai li Zongyang li Anyou HE Jiehu CHEN li liU Keshu ZOU Joint Laboratory of Guangdong Province and Hong Kong Region on Marine Bioresource Conservation and Exploitation College of Marine SciencesSouth China Agricultural UniversityGuangzhou 510642China Guangxi Key Laboratory of Aquatic Genetic Breeding and Healthy Aquaculture Guangxi Academy of Fishery SciencesNanning 530021China Science Corporation of Gene Guangzhou 510000China

Cavefi sh can be important models for understanding the relationships among evolution,adaptation,and development in extreme environments.However,cavefi sh remain poorly studied,particularly at the genome level.Here,we sequenced the complete mitogenome of three cavefi sh in the family Nemacheilidae(Paranemachilus pingguoensis,Oreonectes polystigmus,and Heminoemacheilus longibarbatus),which were collected from karst caves in South China.The mitogenomes each contained 37 genes(13 protein coding,22 tRNA,and two rRNA genes)and a single control region,with the same genetic arrangement and distribution as those found in vertebrates.The non-synonymous/synonymous mutation ratios(Ka/Ks)of the mitogenomes indicated that the protein-coding genes(PCGs)of the three cavefi sh evolved under purifying selection.The mitogenomes of the three cavefi sh exhibit nucleotide composition biases for PCGs,tRNAs,rRNAs,and the whole genome,indicating that the mitochondrial DNA might have been subjected to evolutionary selection in response to extreme cave environments.Divergence time and evolutionary history analyses suggested that the speciation and diversifi cation of the cavefi sh coincided with the Miocene uplift of the southern Qinghai-Tibet Plateau,which greatly changed cave habitats.Overall,our study sheds light on the mitogenomes,phylogeny,and evolutionary history of nemacheilid cavefi sh.

关键词： cavefish mitochondrial genome evolution Nemacheilidae

Leaf pigment retrieval using the PROSAIL model: Influence of uncertainty in prior canopy-structure information

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The Crop Journal 2022年第5期10卷 1251-1263页

作者： Jia Sun Lunche Wang Shuo Shi zhenhai li Jian Yang Wei Gong Shaoqiang Wang Torbern Tagesson Key Laboratory of Regional Ecology and Environmental Change School of Geography and Information EngineeringChina University of GeoscienceWuhan 430079HubeiChina Department of Physical Geography and Ecosystem Science Lund UniversityLund 117 SE-22100Sweden State Key Laboratory of Information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhan 430079HubeiChina Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs Beijing Research Center for Information Technology in AgricultureBeijing 100097China Department of Geosciences and Natural Resource Management University of CopenhagenCopenhagen 1172Denmark

Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological conditions.Inversion of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from canopy observations,and adding prior information has been effective in alleviating the“ill-posed”problem,a major challenge in model inversion.Canopy structure parameters,such as leaf area index(LAI)and average leaf inclination angle(ALA),can serve as prior information for leaf pigment retrie-val.Using canopy spectra simulated from the PROSAIL model,we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll(C _(ab))and car-otenoid(C_(ar)).The retrieval accuracies of the two pigments were increased by use of the priors of LAI(RMSE of C_(ab) from 7.67 to 6.32μg cm^(-2),C_(ar) from 2.41 to 2.28μg cm^(-2))and ALA(RMSE of C_(ab) from 7.67 to 5.72μg cm^(-2),C_(ar) from 2.41 to 2.23μg cm^(-2)).However,this improvement deteriorated with an increase of additive and multiplicative uncertainties,and when 40% and 20% noise was added to LAI and ALA respectively,these priors ceased to increase retrieval accuracy.Validation using an experimental winter wheat dataset also showed that compared with C_(ar),the estimation accuracy of C_(ab) increased more or deteriorated less with uncertainty in prior canopy structure.This study demonstrates possible limita-tions of using prior information in RTM inversions for retrieval of leaf biochemistry,when large uncer-tainties are present.

关键词： Leaf pigment PROSAIL model Canopy structure Chlorophyll content Leaf area index Leaf angle distribution

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

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The Crop Journal 2020年第1期8卷 87-97页

作者： Xiuliang Jin zhenhai li Haikuan Feng Zhibin Ren Shaokun li Institute of Crop Sciences Chinese Academy of Agricultural SciencesKey Laboratory of Crop Physiology and EcologyMinistry of AgricultureBeijing 100081China Beijing Research Center for Information Technology in Agriculture Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Northeast Institute of Geography and Agroecology Chinese Academy of SciencesChangchun 130102JilinChina

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.

关键词： Biomass estimation Maize Vegetation indices Deep neural network algorithm LAI

用于水和二氧化碳电解的高熵纳米结构电催化剂

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Science China Materials 2024年第6期67卷 1791-1803页

作者：王根香陈俊翔温珍海李景虹 CAS Key Laboratory of Design and Assembly of Functional Nanostructures and Fujian Provincial Key Laboratory of Materials and Techniques toward Hydrogen EnergyFujian Institute of Research on the Structure of MatterChinese Academy of SciencesFuzhou 350002China Key Laboratory of Bioorganic Phosphorus Chemistry&Chemical Biology Tsinghua UniversityBeijing 100084China School of Energy and Power Engineering Jiangsu UniversityZhenjiang 212013China

电催化剂在加速电化学转换中扮演着关键角色,这种转换可以利用日益增长的可再生能源将广泛存在的分子转化为高价值化学品.高熵材料(HEMs),通常为含有至少五种主要元素的固溶体,因其独特的物理化学特性,在电催化领域展现出广阔的应用潜力... 详细信息

电催化剂在加速电化学转换中扮演着关键角色,这种转换可以利用日益增长的可再生能源将广泛存在的分子转化为高价值化学品.高熵材料(HEMs),通常为含有至少五种主要元素的固溶体,因其独特的物理化学特性,在电催化领域展现出广阔的应用潜力.尽管对HEMs的研究兴趣持续增长,但开发适合实际应用的高效HEMs电催化剂及理解其催化机理仍是挑战所在.本论文聚焦分析HEM纳米催化剂在几种关键电化学反应中的前沿应用,首先回顾了常见的HEMs电催化剂制备技术,继而探讨了关于高熵合金在电催化中的理论计算研究,以深化对其电催化应用的理解.此外,文章还概述了HEMs电催化剂在水和二氧化碳电解的关键反应中的应用前景,包括析氢反应、二氧化碳还原反应、析氧反应及其它小分子的氧化反应.通过分析近期的重要成果,本文重点讨论了HEMs电催化剂的结构与电催化性能之间的关系.最后,文章展望了高熵纳米材料在电催化实际应用中面临的关键挑战与未来的机遇.我们期望本论文能够激发更多关于开发和完善HEMs纳米结构电催化剂的研究,探索其可行且可扩展的制备方法,促进其在电催化领域的广泛应用.

关键词：电催化析氢反应二氧化碳还原催化机理电化学反应可再生能源纳米结构析氧反应

基于反向传播神经网络的卤水蒸发速率预测模型

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无机盐工业 2024年第1期56卷 53-58页

作者：李志伟付振海张志宏李生廷中国科学院青海盐湖研究所青海西宁810008 中国科学院盐湖资源综合高效利用重点实验室青海西宁810008 青海省盐湖资源化学重点实验室青海西宁810008 青海盐湖工业股份有限公司青海格尔木816000

卤水的蒸发速率是盐田生产管理中的一个重要技术参数,通过搭建室外卤水蒸发实验装置,分析了辐照强度、风速、环境温度、相对湿度、卤水温度、卤水浓度与卤水蒸发速率的关系。利用反向传播(BP)神经网络,训练构建了卤水蒸发速率预测模型,... 详细信息

卤水的蒸发速率是盐田生产管理中的一个重要技术参数,通过搭建室外卤水蒸发实验装置,分析了辐照强度、风速、环境温度、相对湿度、卤水温度、卤水浓度与卤水蒸发速率的关系。利用反向传播(BP)神经网络,训练构建了卤水蒸发速率预测模型,并与传统的应用回归方法构建的模型进行比较。结果表明,BP神经网络模型和非线性回归模型的决定系数R2分别为0.902和0.884,预测平均相对误差分别为15.723%和18.943%,BP神经网络模型的拟合效果和预测能力均优于非线性回归模型。说明应用BP神经网络构建卤水蒸发速率预测模型是可行的,能够实现蒸发速率的快速估测。

关键词：卤水蒸发速率定量分析非线性回归反向传播神经网络

城市建成环境对共享单车出行影响的时空特征——以深圳市为例

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热带地理 2024年第2期44卷 236-247页

作者：项振海李青洪良盛杰班鹏飞昆明理工大学建筑与城市规划学院昆明650500 广东国地规划科技股份有限公司广州510070

以深圳市为例,利用共享单车OD等多源数据,从工作日、休息日多时段分析共享单车出行时空特征,并构建基于客观物质空间、主观感知体验的建成环境“5Ds”指标,运用多尺度地理加权回归模型(MGWR)解析不同建成环境对共享单车出行流量影响的... 详细信息

以深圳市为例,利用共享单车OD等多源数据,从工作日、休息日多时段分析共享单车出行时空特征,并构建基于客观物质空间、主观感知体验的建成环境“5Ds”指标,运用多尺度地理加权回归模型(MGWR)解析不同建成环境对共享单车出行流量影响的时空异质效应。研究表明:1)时间上,工作日和休息日早、晚高峰的共享单车出行流量较其他时段整体显著,且休息日较工作日的峰值时段具有滞后性;空间上,各峰值时段的出行流量高值区域呈“多核集聚、带状延伸”的分异格局;2)各建成环境要素对共享单车出行流量影响的时段差异显著。就业设施密度、围合度及人口密度在各时段均具有高解释度,其影响力整体呈全局显著特征,其余变量则在不同时段存在局部显著效应;3)各时段影响较为显著的变量在空间作用尺度上存在分异。就业设施密度与围合度的影响力在各时段的空间变化最为稳定,街道绿视率与人口密度在不同时段呈现差异化的空间分布特征。

关键词：共享单车出行建成环境出行流量时空异质性 MGWR模型深圳市