[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5347":3,"related-5347":55},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":36,"classification":37,"search_phrases":49,"slug":52,"view_count":53,"doi":8,"paper":8,"created_at":54},5347,"广东农科院揭示褪黑素介导水稻铜胁迫响应的新机制 Journal of Pineal Research","https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F573085.shtm","广东省农业科学院农业质量标准与监测技术研究所研究员陈光团队联合郑州大学农业与生物制造学院副研究员韩慧敏等,研究揭示了褪黑素介导水稻铜胁迫响应的新机制,相关成果发表于 Journal of Pineal Research。研究发现 OsCSA 直接结合到 OsCOMT12 的启动子区域并上调其表达,确立了 OsCSA-OsCOMT12 调控模块作为褪黑素介导的水稻 Cu 胁迫响应新机制,为耐 Cu 作物遗传改良提供了靶标。论文 DOI: 10.1111\u002Fjpi.70187。",null,"[![Image 1: 科学网新闻频道](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnews.jpg)](https:\u002F\u002Fnews.sciencenet.cn\u002F)\n\n[生命科学](https:\u002F\u002Fwww.sciencenet.cn\u002Flife\u002F) | [医学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmedicine\u002F) | [化学科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fchemistry\u002F) | [工程材料](https:\u002F\u002Fwww.sciencenet.cn\u002Fmaterial\u002F) | [信息科学](https:\u002F\u002Fwww.sciencenet.cn\u002Finformation\u002F) | [地球科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fearth\u002F) | [数理科学](https:\u002F\u002Fwww.sciencenet.cn\u002Fmathematics\u002F) | [管理综合](https:\u002F\u002Fwww.sciencenet.cn\u002Fpolicy\u002F)[站内规定](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog-45-1064777.html) | [手机版](https:\u002F\u002Fwap.sciencenet.cn\u002F)\n\n[首页](https:\u002F\u002Fwww.sciencenet.cn\u002F) | [新闻](https:\u002F\u002Fnews.sciencenet.cn\u002F) | [博客](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php) | [院士](https:\u002F\u002Fnews.sciencenet.cn\u002Fys\u002F) | [人才](https:\u002F\u002Ftalent.sciencenet.cn\u002F) | [会议](https:\u002F\u002Fmeeting.sciencenet.cn\u002F) | [基金·项目](https:\u002F\u002Ffund.sciencenet.cn\u002F) | [论文](https:\u002F\u002Fpaper.sciencenet.cn\u002F) | [绘图](https:\u002F\u002Fvisual-web.sciencenet.cn\u002F) | [视频·直播](https:\u002F\u002Fblog.sciencenet.cn\u002Fblog.php?mod=video) | [小柯机器人](https:\u002F\u002Fpaper.sciencenet.cn\u002FAInews) | [医学科普](https:\u002F\u002Fyxkp.sciencenet.cn\u002F)\n\n作者：朱汉斌 来源：中国科学报 发布时间：2026\u002F10\u002F17 9:26:33 选择字号：小 中 大 研究揭示褪黑素介导水稻铜胁迫响应的新机制 近日，在国家自然科学基金、广东省基础与应用基础研究基金等项目资助下，广东省农业科学院农业质量标准与监测技术研究所研究员陈光团队联合郑州大学农业与生物制造学院副研究员韩慧敏等，研究揭示了褪黑素介导水稻铜胁迫响应的新机制。相关成果发表于《松果体研究杂志》（_Journal of Pineal Research_）。 褪黑素 作为一种植物次生代谢产物，在环境胁迫应答过程中发挥重要作用，但 褪黑素 缓解水稻过量铜（Cu）毒害的潜在生理和分子机制尚不明确。 该研究发现，与单独Cu处理相比，外源施加 褪黑素 增加了根系和地上部生物量，降低了根系对Cu的吸收能力及植株体内Cu浓度，有效增强了水稻对Cu胁迫的耐受性。通过转录组分析，鉴定到一个响应Cu胁迫的关键转录因子OsCSA。与野生型相比，_OsCSA_ 过表达株系对过量Cu的耐受性显著增强，表现为 褪黑素 含量增加，抗氧化基因 _OsCATC_、_OsAPXs_ 表达以及CAT、SOD和APX活性提高，H 2 O 2 和MDA含量降低。_OsCSA_ 敲除植株则表现出相反结果，且外源施加MT可挽救其过量Cu敏感表型。 进一步研究发现，OsCSA直接结合到 _OsCOMT12_ 的启动子区域并上调其表达。在Cu胁迫下，_OsCOMT12_ 敲除植株中也观察到Cu浓度和ROS积累显著增加，与OsCSA敲除株系中的结果一致。此外，在 _OsCSA_ 过表达水稻背景中敲除 _OsCOMT12_，抵消了OsCSA对Cu胁迫耐受性的正向效应。这些发现确立了OsCSA-OsCOMT12调控模块作为 褪黑素 介导的水稻Cu胁迫响应新机制，为耐Cu作物遗传改良提供了靶标。 相关论文信息：https:\u002F\u002Fdoi.org\u002F10.1111\u002Fjpi.70187 版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。\n\n打印 发E-mail给：\n\n以下评论只代表网友个人观点，不代表科学网观点。\n\n[![Image 2](https:\u002F\u002Fnews.sciencenet.cn\u002Fimages\u002Fnewcomm.gif)](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtml\u002Fcomment.aspx?id=573085)\n\n相关新闻 相关论文\n*   1\n*   [植物星球计划：为“植物生命之树”绘制全景图](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F573066.shtm)\n\n*   2\n*   [李德生：矢志为国探油，漫漫征程藏于子女之名](https:\u002F\u002Fnews.sciencenet.cn\u002Fhtmlnews\u002F2026\u002F10\u002F573065.shtm)\n\n*   3\n*   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_OsCOMT12_，抵消了OsCSA对Cu胁迫耐受性的正向效应。这些发现确立了OsCSA-OsCOMT12调控模块作为 褪黑素 介导的水稻Cu胁迫响应新机制，为耐Cu作物遗传改良提供了靶标。\n\n相关论文信息：https:\u002F\u002Fdoi.org\u002F10.1111\u002Fjpi.70187","中国科学报 2026-10-17","2026-10-17T00:00:00Z","报道",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,7,1,"省级科研机构在植物逆境生理领域取得机制性突破，结论可靠且为耐铜作物遗传改良提供靶标，具备专业参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"水稻","褪黑素","重金属","铜胁迫","耐逆育种",{"code":32,"slug":33,"name":26,"path":34,"parent_code":35},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43],{"code":32,"score":44,"supported":45,"title":46,"abstract":47,"tags":48},12,true,[26],[26],[26],[50,51],"广东农科院 水稻 褪黑素","OsCSA OsCOMT12 铜胁迫","广东农科院水稻褪黑素-5347",0,"2026-10-20T02:47:44.242122Z",{"total":56,"page":21,"page_size":56,"items":57},6,[58,112,177,218,270,351],{"id":59,"title":60,"url":61,"summary":62,"summary_zh":8,"content":63,"source_name":64,"source_url":8,"published_at":65,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":66,"score_detail":67,"sources":73,"tags":75,"primary_topic":80,"secondary_topics":81,"classification":82,"search_phrases":107,"slug":110,"view_count":53,"doi":8,"paper":8,"created_at":111},5449,"安徽省农科院专家在淮南潘集「绿色吨半粮」基地开展水稻良种与高效肥测产观摩","https:\u002F\u002Fwww.panji.gov.cn\u002Fpjzx\u002Fjcgz\u002F11259072035.html","10 月 14 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年实现头季、再生季两季亩产突破「吨粮」。","广东省农业技术推广中心种植业技术与种业推广部部长罗学梅在总结中充分肯定再生稻项目取得的技术突破和示范成效，并就下一阶段强化技术指导、完善示范推广体系、推动再生稻扩大应用等工作作出部署。他指出，要进一步加强科研、推广、种业和种植主体协同联动，加快适宜品种、先进技术和标准模式落地，为稳定粮食生产提供有力支撑。\n\n随后，与会人员前往始兴县澄江镇善亨村750亩再生稻示范基地开展现场观摩。该基地在广东省农业科学院水稻研究所、广东省农业技术推广中心以及广东省水稻产业技术体系的技术支撑下实行标准化种植，集中展示多个优质稻、杂交稻品种，并开展品种对比试验。技术人员走进田间，结合不同品种长势，围绕品种筛选、栽培管理、促芽肥施用、病虫害防控和机械化收割等关键环节进行讲解，并面对面解答参会人员提出的生产问题。\n\n参训人员沿着田埂仔细察看苗情和穗型，认真听取田间讲解，踊跃交流技术要点，现场学习氛围浓厚。此次活动将室内授课与田间教学有机结合，进一步提升了基层农技人员和种植主体对再生稻技术的认识，增强了推广应用信心。与会人员表示，将结合本地生产实际，把学习成果转化为田间管理措施，积极提升再生稻种植水平。\n\n版权声明：凡本网注明“来源：中国科学报、科学网、科学新闻杂志”的所有作品，网站转载，请在正文上方注明来源和作者，且不得对内容作实质性改动；微信公众号、头条号等新媒体平台，转载请联系授权。邮箱：shouquan@stimes.cn。","科学网 2026-10-17",58,{"impact":186,"substance":19,"depth":69,"authority":44,"freshness":56,"relevant":21,"comment":227},"省级科研机构主办的再生稻技术培训与田间观摩，有示范基地和品种对比等实质内容，但属常规推广活动，影响力限于区域。",[229],{"name":224,"url":221},[231,26,232,233,234],"广东","再生稻","农技推广","粮食生产",{"code":32,"slug":33,"name":26,"path":34,"parent_code":35},[237],{"code":142,"slug":238,"name":239,"path":240,"parent_code":241},"extension-transfer","农技推广与成果转化","\u002Ftopic\u002Frural-services\u002Fextension-transfer","G",{"status":38,"locked":14,"version":39,"method":40,"evidence":243},{"candidates":244,"agricultural_evidence":45},[245,250,256,261],{"code":32,"score":246,"supported":45,"title":247,"abstract":248,"tags":249},27,[26,232],[26,202,232],[232,26],{"code":142,"score":251,"supported":45,"title":252,"abstract":254,"tags":255},15,[253],"技术培训",[233,253],[233],{"code":97,"score":98,"supported":45,"title":257,"abstract":258,"tags":260},[],[259],"粮食产能",[234],{"code":35,"score":21,"supported":14,"title":262,"abstract":263,"tags":264},[],[],[234],[266,267],"广东农科院 再生稻 始兴","再生稻 技术培训 观摩会","广东农科院再生稻始兴-5432","2026-10-21T00:10:01.007510Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":275,"content":8,"source_name":276,"source_url":273,"published_at":277,"category":278,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":279,"sources":281,"tags":283,"primary_topic":288,"secondary_topics":289,"classification":296,"search_phrases":330,"slug":333,"view_count":53,"doi":334,"paper":335,"created_at":350},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z","论文",{"impact":17,"substance":18,"depth":17,"authority":44,"freshness":70,"relevant":21,"comment":280},"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[282],{"name":276,"url":273},[284,285,26,286,287],"智慧农业","农业人工智能","产量预测","遥感",{"code":32,"slug":33,"name":26,"path":34,"parent_code":35},[290],{"code":291,"slug":292,"name":293,"path":294,"parent_code":295},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":38,"locked":14,"version":39,"method":40,"evidence":297},{"candidates":298,"agricultural_evidence":45},[299,303,308,314,320,325],{"code":32,"score":251,"supported":45,"title":300,"abstract":301,"tags":302},[33],[26,33],[26],{"code":291,"score":70,"supported":45,"title":304,"abstract":305,"tags":307},[],[287,306],"remote sensing",[286,287],{"code":97,"score":56,"supported":45,"title":309,"abstract":310,"tags":313},[],[311,312],"粮食安全","food security",[],{"code":315,"score":147,"supported":45,"title":316,"abstract":317,"tags":319},"J1",[],[318],"农业规划",[],{"code":321,"score":21,"supported":14,"title":322,"abstract":323,"tags":324},"I1",[],[],[284],{"code":326,"score":21,"supported":14,"title":327,"abstract":328,"tags":329},"I2",[],[],[285],[331,332],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":334,"openalex_id":336,"authors":337,"venue":276,"cited_by_count":53,"oa_url":273,"card":343,"direction":347,"ingested_from":349},"W7222942064",[338,340],{"name":339,"orcid":8},"Nayana Venkatesh",{"name":341,"orcid":342},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":344,"method":345,"finding":346,"direction":347,"opportunity":348},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","农业遥感与作物表型","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","openalex","2026-10-20T23:30:19.292634Z",{"id":352,"title":353,"url":354,"summary":355,"summary_zh":356,"content":8,"source_name":357,"source_url":354,"published_at":358,"category":278,"cover_url":8,"hotness":13,"is_selected":14,"score":359,"score_detail":360,"sources":363,"tags":365,"primary_topic":368,"secondary_topics":369,"classification":370,"search_phrases":411,"slug":414,"view_count":53,"doi":415,"paper":416,"created_at":430},5388,"A digital twin framework for proactive disease management in paddy cultivation using spatio-temporal graph neural networks","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20421338.2026.2707951","Rice production is highly affected by disease outbreaks, particularly in Asian agricultural systems, resulting in major yield losses and excessive pesticide usage. This study proposes a digital twin framework for paddy cultivation that integrates predictive disease modelling with real-time crop phenology simulation to support proactive disease management. The framework utilizes historical phenological records, IoT-based microclimatic sensor data, irrigation information, and field management records collected from 150 hectares of paddy fields in Maharashtra, India. A Spatio-Temporal Graph Neural Network (ST-GNN), integrating Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks, is employed to model spatial disease propagation and temporal crop dynamics for predicting blast, brown planthopper, and bacterial leaf blight outbreaks up to 14 days in advance. Experimental results demonstrate 87.3% prediction accuracy, 23.7% reduction in pesticide usage, and 31.2% reduction in crop losses, while outperforming traditional Random Forest and SVM models in F1-score performance. The proposed framework enables real-time decision support for sustainable precision agriculture and optimized resource management.","水稻生产极易受到病害暴发的影响，在亚洲农业体系中尤为突出，造成重大产量损失和农药过度使用。本研究提出一个水稻栽培数字孪生（digital twin）框架，将预测性病害建模与实时作物物候模拟相结合，以支持主动式病害管理。该框架利用从印度马哈拉施特拉邦150公顷稻田采集的历史物候记录、基于物联网的微气候传感器数据、灌溉信息及田间管理记录。采用融合图卷积网络（Graph Convolutional Networks, GCN）与长短期记忆网络（Long Short-Term Memory, LSTM）的时空图神经网络（Spatio-Temporal Graph Neural Network, ST-GNN），对病害空间传播与作物时序动态进行建模，以提前14天预测稻瘟病、褐飞虱和白叶枯病的暴发。实验结果表明，预测准确率达87.3%，农药使用量减少23.7%，作物损失减少31.2%，且在F1分数上优于传统随机森林（Random Forest）和支持向量机（SVM）模型。所提框架可为可持续精准农业和优化资源管理提供实时决策支持。","African Journal of Science Technology Innovation and Development","2026-10-19T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":361,"relevant":21,"comment":362},9,"方法新颖、数据规模明确且量化效果显著，属智慧农业细分领域实质进展，值得进入每日精选。",[364],{"name":357,"url":354},[284,285,26,366,367],"数字孪生","病虫害预警",{"code":32,"slug":33,"name":26,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":371},{"candidates":372,"agricultural_evidence":45},[373,381,386,392,399,406],{"code":321,"score":374,"supported":45,"title":375,"abstract":377,"tags":380},19,[376],"digital twin",[378,366,379,376],"精准农业","precision agriculture",[366,284],{"code":32,"score":17,"supported":45,"title":382,"abstract":384,"tags":385},[383],"paddy",[26,139,33,383],[26],{"code":152,"score":56,"supported":45,"title":387,"abstract":388,"tags":391},[],[389,390],"农药","pesticide",[],{"code":393,"score":56,"supported":45,"title":394,"abstract":395,"tags":398},"H3",[],[396,397],"灌溉","irrigation",[],{"code":400,"score":56,"supported":45,"title":401,"abstract":402,"tags":405},"I5",[],[403,404],"物联网","IoT",[],{"code":326,"score":98,"supported":45,"title":407,"abstract":408,"tags":410},[],[409],"decision support",[285],[412,413],"水稻 病害预测 数字孪生","ST-GNN 水稻 病虫害","水稻病害预测数字孪生-5388","10.1080\u002F20421338.2026.2707951",{"doi":415,"openalex_id":417,"authors":418,"venue":357,"cited_by_count":53,"oa_url":8,"card":423,"direction":429,"ingested_from":349},"W7222969403",[419,421],{"name":420,"orcid":8},"Jyoti Rani",{"name":422,"orcid":8},"Dr.  Narendra S.  Jadhav",{"tldr":424,"method":425,"finding":426,"direction":427,"opportunity":428},"构建水稻数字孪生框架，用时空图神经网络提前14天预测病害。","融合物联网微气候、物候与管理数据，用GCN+LSTM的ST-GNN建模。","预测准确率87.3%，农药减23.7%、损失减31.2%，优于RF与SVM。","农业人工智能与决策模型","可探索多尺度时空图建模与数字孪生闭环决策，并迁移至其他作物与区域。","智慧农业 \u002F 农业物联网","2026-10-20T23:30:12.636299Z"]