[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-350":3,"related-350":67},{"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":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":43,"search_phrases":61,"slug":64,"view_count":65,"doi":8,"paper":8,"created_at":66},350,"解码棉纤维密码:从绘制'地图'到锻造'芯片'——中国农科院棉花所杨作仁团队构建油菜素内酯调控网络","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002Fb894e7ba175c44faad6347b7fe003936.htm","中国农科院棉花所杨作仁团队创制棉花首个油菜素内酯缺陷型突变体,证实内源油菜素内酯是棉花纤维伸长的中枢调控因子;首次发现油菜素内酯通过促进超长链脂肪酸合成推动纤维细胞发育,系统解析调控网络并构建发育模型。团队还建立基于茎尖分生组织的不依赖基因型的遗传转化体系,将棉花遗传转化周期从7~8个月缩短至2~3个月。基于此创制出自然界不存在的粉红色纤维,以及高效合成虾青素的功能型棉花新材料。目前纤维改良新材料已发放给全国13家育种单位,创制优异种质500余份,15份材料进入转基因安全评价程序,授权发明专利6项,获新疆自然科学奖一等奖。",null,"2025年6月，团队联合中国农科院生物技术所成功创制高效合成虾青素的功能型棉花新材料，为开发“非纤维经济”提供全新范式。\n\n“种子是农业的芯片。”目前，团队创制的纤维改良新材料已发放给全国13家育种单位，包括新疆农科院、新疆农垦科学院、石河子农科院、塔河种业等7家新疆单位，开展联合育种，创制优异种质500余份，15份材料进入转基因安全评价程序，授权发明专利6项。\n\n从2008年在中棉所研究员李付广指导下涉足棉花生物技术，到2020年援疆，再到扎根新疆，杨作仁在这个领域已深耕18年。他说：“老一辈用锄头在新疆戈壁开垦棉田，我们新时代科研工作者要用尖端技术助力产业高质量发展，把基础研究的根扎深，让科技大树结出更好种子。”","中国科学报 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种质创新","中国农科院种质创新杨作仁棉花-350",5,"2026-08-10T23:54:42.347239Z",{"total":68,"page":22,"page_size":68,"items":69},6,[70,131,166,204,280,314],{"id":71,"title":72,"url":73,"summary":74,"summary_zh":8,"content":75,"source_name":76,"source_url":8,"published_at":77,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":78,"score_detail":79,"sources":83,"tags":85,"primary_topic":90,"secondary_topics":91,"classification":93,"search_phrases":126,"slug":129,"view_count":21,"doi":8,"paper":8,"created_at":130},4935,"中国农科院棉花所揭示角鲨烯合成协同促进棉花生长发育与抗逆性","https:\u002F\u002Fcricaas.com.cn\u002Fsylm\u002Fkyjz\u002Fc44ab2cf89bc44c682ab8105af99fbaf.htm","中国农业科学院棉花研究所棉花分子遗传改良创新团队在棉花角鲨烯生物合成及功能研究方面取得新进展,发现棉花角鲨烯合成关键基因GhSQS1能够提高棉花组织中的角鲨烯积累水平,并促进棉花生长发育,改善干旱和碱胁迫耐受性。相关成果发表于《实验植物学杂志》。过表达GhSQS1的代表性株系OE2叶片角鲨烯含量达989.1 mg\u002Fkg干重,较野生型提高约70%,叶片增大、根系发育改善、纤维伸长和种子增大。","### 角鲨烯合成促进棉花生长发育与抗逆性\n\n发布时间:2026-10-14 来源 ：**棉花分子遗传改良创新团队**作者：**徐雅梦**\n\n近日，中国农业科学院棉花研究所棉花分子遗传改良创新团队在棉花角鲨烯生物合成及功能研究方面取得新进展，发现棉花角鲨烯合成关键基因 _GhSQS1_ 能够提高棉花组织中的角鲨烯积累水平，并促进棉花生长发育，改善干旱和碱胁迫耐受性，为棉花遗传改良与副产物高值化利用提供了新思路。相关成果以“GhSQS1-mediated squalene accumulation coordinates cotton development and stress resilience”为题，发表于国际植物科学期刊《实验植物学杂志（Journal of Experimental Botany）》上。\n\n棉花是重要的天然纤维作物，其棉叶、棉籽等副产物也具有较高的开发利用潜力。角鲨烯是一种具有较高应用价值的三萜类化合物，在化妆品、营养保健等领域具有广泛应用，同时也是植物甾醇及其他三萜类化合物生物合成的重要前体。然而，角鲨烯生物合成与棉花生长发育及逆境适应之间的关系尚不明确，其潜在调控机制仍有待深入研究。\n\n该研究发现适宜浓度的角鲨烯可促进棉花主根伸长和离体胚珠发育，并鉴定出棉花中角鲨烯合成关键基因 _GhSQS1_。进一步研究发现，过表达该基因伴随油菜素内酯相关代谢及信号通路变化，且显著提高棉花叶片角鲨烯含量，其中代表性株系OE2达到989.1 mg\u002Fkg干重，较野生型提高约70%，同时表现出叶片增大、根系发育改善、纤维伸长和种子增大等性状。在模拟干旱和碱胁迫条件下，过表达该基因的植株表现出较强的耐受能力，并伴随抗氧化能力增强和膜脂过氧化程度降低。此外，异源表达真菌来源角鲨烯合酶也获得了类似的促生长表型，表明增强角鲨烯合成有助于改善棉花发育。研究结果表明，增强角鲨烯生物合成与棉花生长发育及逆境适应能力改善之间的联系，为通过代谢工程协同改良作物性状与提高功能性天然产物积累提供了新思路，也为棉花副产物高值化利用提供了研究基础。\n\n该研究得到中国农业科学院青年创新专项、河南省自然科学基金、新疆维吾尔自治区重点研发计划、国家自然科学基金、国家现代农业产业技术体系等项目支持。中棉所徐雅梦、张志甜、刘乐和贾舒元为论文共同第一作者，李付广研究员和杨作仁研究员为共同通讯作者。\n\n原文链接：https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjxb\u002Ferag424\n\n![Image 1: 未标题-1xiu.jpg](https:\u002F\u002Fcricaas.com.cn\u002Fimages\u002F2026-10\u002F790d9cb6eae848e29b5c94cf46779bc5.jpg)\n\n角鲨烯合成增强协同促进棉花生长发育、抗逆性及生物质高值化利用示意图\n\n打印本页\n\n关闭本页","中国农业科学院棉花研究所 2026 年 10 月 14 日","2026-10-14T00:00:00Z",87,{"impact":18,"substance":18,"depth":80,"authority":81,"freshness":13,"relevant":22,"comment":82},18,15,"国家级科研机构在棉花角鲨烯合成基因GhSQS1上的新发现，兼具生长发育促进与抗旱耐碱改良价值，数据具体、期刊权威，值得进入每日精选。",[84],{"name":76,"url":73},[86,87,29,88,89],"种业振兴","生物育种","功能成分","抗旱耐盐",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[92],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":94},{"candidates":95,"agricultural_evidence":51},[96,100,105,111,116,121],{"code":32,"score":50,"supported":51,"title":97,"abstract":98,"tags":99},[29],[29],[29],{"code":39,"score":56,"supported":51,"title":101,"abstract":103,"tags":104},[102],"花生",[102],[],{"code":106,"score":21,"supported":51,"title":107,"abstract":108,"tags":110},"H7",[],[109],"干旱",[],{"code":112,"score":22,"supported":14,"title":113,"abstract":114,"tags":115},"E2",[],[],[88],{"code":117,"score":22,"supported":14,"title":118,"abstract":119,"tags":120},"F1",[],[],[86],{"code":122,"score":22,"supported":14,"title":123,"abstract":124,"tags":125},"F2",[],[],[87],[127,128],"中国农科院棉花所 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2026-07-28","2026-07-28T01:00:00Z",{"impact":17,"substance":18,"depth":80,"authority":81,"freshness":139,"relevant":22,"comment":140},7,"国家级科研机构发布棉花生长-免疫权衡机制突破，影响全国棉花抗逆育种，信息增量大，专业深度高，但时效性稍差。",[142],{"name":136,"url":134},[27,29,144,145],"叶绿体","免疫",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[148],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":150},{"candidates":151,"agricultural_evidence":51},[152,156],{"code":32,"score":50,"supported":51,"title":153,"abstract":154,"tags":155},[29],[29],[29],{"code":39,"score":56,"supported":51,"title":157,"abstract":158,"tags":159},[102],[102],[],[161,162],"中国农科院 叶绿体 免疫 棉花","中国农科院 叶绿体","中国农科院叶绿体免疫棉花-97",9,"2026-07-31T00:00:54.853937Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":8,"content":171,"source_name":172,"source_url":8,"published_at":173,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":174,"score_detail":175,"sources":178,"tags":180,"primary_topic":184,"secondary_topics":185,"classification":186,"search_phrases":198,"slug":201,"view_count":202,"doi":8,"paper":8,"created_at":203},4766,"棉花关键技术集成助力高温逆境下产量品质效益三丰收","https:\u002F\u002Fwap.sciencenet.cn\u002Fmobile.php?cat=T&id=572667","中国农业科学院西部农业研究中心在昌吉州老龙河综合试验基地举办棉花优质高产关键技术集成示范观摩会。千亩示范方实测籽棉平均亩产601.4公斤,集成中棉113优系、中新棉1号、3号等优质多抗新品种,以及高光效群体调控、智慧棉田管理、绿色病虫害防控等成套技术,节药15%以上、人工成本降低30%以上。","科学网—\n\n## [![Image 1: 科学网](https:\u002F\u002Fwap.sciencenet.cn\u002Fstatic\u002Fimage\u002Fcommon\u002Flogo.png)](https:\u002F\u002Fwap.sciencenet.cn\u002F \"科学网\")\n\n*   [要闻](https:\u002F\u002Fwap.sciencenet.cn\u002Fmobile.php?type=news&cat=yaowen&mobile=1)\n*   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[针对无人机电子垃圾危机，天大团队《科学》发文](https:\u002F\u002Fwap.sciencenet.cn\u002Fmobile.php?type=detail&cat=T&id=572765&mobile=1)\n*   [“核聚山城?智驭靶向”：第十八届全国放射性药物与标记化合物学术交流会在重庆举办](https:\u002F\u002Fwap.sciencenet.cn\u002Fmobile.php?type=detail&cat=T&id=572754&mobile=1)\n*   [通往“器官再生之路”](https:\u002F\u002Fwap.sciencenet.cn\u002Fmobile.php?type=detail&cat=T&id=572753&mobile=1)\n*   [AI写的字，开始自带“暗号”](https:\u002F\u002Fwap.sciencenet.cn\u002Fmobile.php?type=detail&cat=T&id=572752&mobile=1)","中国科学报 科学网 2026-10-10","2026-10-10T00:00:00Z",73,{"impact":18,"substance":80,"depth":20,"authority":176,"freshness":68,"relevant":22,"comment":177},13,"报道中国农科院棉花高温逆境关键技术集成取得产量品质效益三丰收，属细分领域实质进展，但时效稍旧、信息增量有限，可入精选备选。",[179],{"name":172,"url":169},[181,27,29,182,183],"智慧农业","技术集成","高温逆境",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":44,"locked":14,"version":45,"method":46,"evidence":187},{"candidates":188,"agricultural_evidence":51},[189,193],{"code":32,"score":50,"supported":51,"title":190,"abstract":191,"tags":192},[29],[29],[29],{"code":194,"score":22,"supported":14,"title":195,"abstract":196,"tags":197},"I1",[],[],[181],[199,200],"中国农科院 棉花 高温逆境","棉花 关键技术集成 产量品质","中国农科院棉花高温逆境-4766",0,"2026-10-13T00:08:35.350655Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":8,"content":209,"source_name":210,"source_url":8,"published_at":211,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":212,"score_detail":213,"sources":217,"tags":219,"primary_topic":222,"secondary_topics":223,"classification":235,"search_phrases":275,"slug":278,"view_count":21,"doi":8,"paper":8,"created_at":279},435,"三节点采样方案实现滴灌棉田温室气体精准采集——中国农科院西部中心与棉花所建立干旱区棉田碳监测技术体系","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F7ef7293fed924affbc040b406faa3a4e.htm","中国农业科学院西部农业研究中心联合棉花研究所揭示了干旱区滴灌棉田温室气体排放变化规律,建立了三节点滴灌棉田温室气体精准采集技术体系。膜下滴灌水肥一体化技术为新疆棉花高产稳产提供了重要技术支撑,但灌溉施肥过程会引起土壤温室气体排放快速波动,传统采样方式难以实现农田温室气体的精准监测。研究发现,灌溉施肥后第1天是温室气体排放峰值形成的关键时期,据此提出了'灌溉前1天—灌溉后1天—灌溉后5天'三节点精准采样方案,相关研究成果发表在《农业水管理》(Agricultural Water Management)上。","近日，中国农业科学院西部农业研究中心联合棉花研究所揭示了干旱区滴灌棉田温室气体排放变化规律，建立了三节点滴灌棉田温室气体精准采集技术体系。相关研究成果发表在《农业水管理（Agricultural Water Management）》上。\n\n膜下滴灌水肥一体化技术为新疆棉花高产稳产提供了重要技术支撑，但灌溉施肥过程会引起土壤温室气体排放快速波动，传统采样方式难以实现农田温室气体的精准监测。\n\n该研究发现，灌溉施肥后第1天是温室气体排放峰值形成的关键时期，据此提出了“灌溉前1天—灌溉后1天—灌溉后5天”三节点精准采样方案。该方案能够在减少采样工作量的同时，保留温室气体排放关键动态信息，保持较高的排放估算精度。研究构建的关键时期识别—精准采样优化—排放精准估算技术体系，为干旱区滴灌棉田温室气体监测提供了科学方案。","中国农业科学院 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温室气体","中国农科院温室气体西部农业棉花-435","2026-08-12T23:57:22.438495Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":8,"content":285,"source_name":286,"source_url":8,"published_at":287,"category":12,"cover_url":8,"hotness":13,"is_selected":51,"score":288,"score_detail":289,"sources":293,"tags":295,"primary_topic":300,"secondary_topics":301,"classification":302,"search_phrases":309,"slug":312,"view_count":81,"doi":8,"paper":8,"created_at":313},282,"杨作仁团队「棉花纤维品质形成的分子机制解析与种质创制」成果获新疆自然科学奖一等奖——首次绘制棉纤维发育全景调控「地图」","https:\u002F\u002Fcri.caas.cn\u002Fxwdt\u002Fmtbd\u002F2898e511eb9b496882797496f79c1c72.htm","近日,2025年度新疆维吾尔自治区科学技术奖揭晓,由中国农业科学院西部农业研究中心杨作仁研究员牵头,联合中国农科院棉花研究所和新疆农业大学完成的「棉花纤维品质形成的分子机制解析与种质创制」成果荣获新疆自然科学奖一等奖。该成果首次绘制了棉纤维发育的全景调控「地图」,并创制出打破品质瓶颈的育种「芯片」,为棉花产业发展提供科技支撑。团队对421份陆地棉核心种质开展纤维伸长期群体转录组分析,鉴定出5.6万个调控基因表达的eQTL位点;利用纤维突变体构建涵盖转录调控、mRNA修饰、蛋白翻译后修饰及激素等多层级调控网络,并与合作伙伴整合25个棉花基因组、76个组织样本转录组等海量数据,构建了国际领先的棉花功能基因发掘平台——CottonMD,该数据库上线两年多获55个国家超22万次访问,成为全球棉花研究者共享的基础设施。团队首次揭示染色体倒位是导致种质间纤维品质差异的核心遗传因素,开发了130对特异分子标记,建立杂交后代选育方法。","长期以来，棉花纤维品质改良的最大痛点在于“看不见”——关键基因难以精准定位，品质差异的遗传根源不明。杨作仁团队决定先画“地图”。他们对421份陆地棉核心种质开展纤维伸长期群体转录组分析，鉴定出5.6万个调控基因表达的eQTL位点；利用纤维突变体构建涵盖转录调控、mRNA修饰、蛋白翻译后修饰及激素等多层级调控网络，并与合作伙伴整合25个棉花基因组、76个组织样本转录组等海量数据，构建了国际领先的棉花功能基因发掘平台——CottonMD。该数据库上线两年多，获55个国家超22万次访问，成为全球棉花研究者共享的基础设施。\n\n更关键的是，团队首次揭示染色体倒位是导致种质间纤维品质差异的核心遗传因素。据此，团队开发了130对特异分子标记，建立杂交后代选育方法，为纤维品质改良开辟了新路径。","农民日报 2026-08-06","2026-08-06T00:00:00Z",91,{"impact":290,"substance":291,"depth":80,"authority":50,"freshness":164,"relevant":22,"comment":292},28,24,"棉花纤维品质分子机制重大突破，数据规模大，平台国际共享，影响深远。",[294],{"name":286,"url":283},[27,296,30,297,298,299],"新疆一等奖","棉花纤维","eQTL CottonMD","西部农业研究中心",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":44,"locked":14,"version":45,"method":46,"evidence":303},{"candidates":304,"agricultural_evidence":51},[305],{"code":32,"score":56,"supported":51,"title":306,"abstract":307,"tags":308},[29],[29],[],[310,311],"西部农业研究中心 中国农科院 新疆一等奖 棉花纤维","西部农业研究中心 中国农科院","西部农业研究中心中国农科院新疆一等奖棉花纤维-282","2026-08-07T23:57:49.909628Z",{"id":315,"title":316,"url":317,"summary":318,"summary_zh":319,"content":8,"source_name":320,"source_url":317,"published_at":321,"category":322,"cover_url":8,"hotness":13,"is_selected":14,"score":323,"score_detail":324,"sources":327,"tags":329,"primary_topic":333,"secondary_topics":334,"classification":335,"search_phrases":359,"slug":362,"view_count":22,"doi":363,"paper":364,"created_at":375},5233,"Cotton pest and disease multi-class classification with weather and temporal predictors features across Indian agroclimatic zones","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.73s.45","The Pink Bollworm (Pectinophora gossypiella) remains one of the most destructive pests affecting cotton productivity, with infestation levels strongly driven by climatic fluctuations and crop phenology.This study presents a machine learning-based framework for early multi-classification and prediction of Pink Bollworm incidence using agro-meteorological variables.The dataset includes maximum and minimum air temperature, relative humidity, rainfall, sunshine hours, evaporation, and wind speed, all of which influence Pink Bollworm development, migration, and reproductive cycles.After preprocessing, applying cyclic encoding to temporal variables, and generating lagged climatic features, multiple machine learning algorithms-including SVM, Neural Networks, KNN, Regression models, Ensemble methods, and Naïve Bayes-were trained and systematically compared.SVM model demonstrated the best test accuracy, effectively capturing dynamic climate-pest interactions.The resulting MATLAB-based classification tool enables rapid assessment and supports climate-informed pest surveillance.A stratified 80:20 train-test split was employed, with 5-fold cross-validation on the training set for hyperparameter tuning, ensuring no data leakage between partitions.The validation and training accuracies of these models are 89.4% and 96 % respectively.Overall, the model offers a practical and interpretable solution for early detection of Pink Bollworm risk, thereby contributing to informed decision-making in integrated pest management (IPM) and precision agriculture.","棉红铃虫（Pectinophora gossypiella）仍是影响棉花产量最具破坏性的害虫之一，其发生程度受气候波动和作物物候的强烈驱动。本研究提出了一种基于机器学习的框架，利用农业气象变量对棉红铃虫的发生进行早期多分类与预测。数据集包括最高和最低气温、相对湿度、降雨量、日照时数、蒸发量和风速，这些因素均影响棉红铃虫的发育、迁移和繁殖周期。经过预处理、对时间变量应用循环编码并生成滞后气候特征后，训练并系统比较了多种机器学习算法，包括支持向量机（SVM）、神经网络、K近邻（KNN）、回归模型、集成方法和朴素贝叶斯。SVM模型表现出最佳的测试准确率，有效捕捉了气候与害虫之间的动态相互作用。所构建的基于MATLAB的分类工具可实现快速评估，并支持气候信息驱动的害虫监测。采用分层80：20的训练-测试划分，并在训练集上进行5折交叉验证以调优超参数，确保各分区之间无数据泄漏。这些模型的验证准确率和训练准确率分别为89.4%和96%。总体而言，该模型为棉红铃虫风险的早期检测提供了一种实用且可解释的解决方案，从而有助于综合害虫管理（IPM）和精准农业中的科学决策。","International Journal of Drug Delivery Technology","2026-10-17T00:00:00Z","论文",55,{"impact":325,"substance":80,"depth":20,"authority":68,"freshness":164,"relevant":22,"comment":326},8,"研究用多类机器学习模型结合气象与时序特征预测棉铃虫风险，方法扎实但属细分领域应用研究，信源期刊权威性一般，公共影响有限。",[328],{"name":320,"url":317},[181,330,29,331,332],"机器学习","农业气象","病虫害预警",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":44,"locked":14,"version":45,"method":46,"evidence":336},{"candidates":337,"agricultural_evidence":51},[338,343,349,355],{"code":32,"score":81,"supported":51,"title":339,"abstract":341,"tags":342},[340],"cotton",[29,340],[29],{"code":194,"score":139,"supported":51,"title":344,"abstract":345,"tags":348},[],[346,347],"精准农业","precision agriculture",[181],{"code":350,"score":139,"supported":51,"title":351,"abstract":352,"tags":354},"I2",[],[330,353],"machine learning",[330],{"code":106,"score":215,"supported":51,"title":356,"abstract":357,"tags":358},[],[331],[331],[360,361],"棉铃虫 机器学习 预测","棉花 病虫害 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