Patients admitted with prediabetes and atrial fibrillation are at high risk for major adverse cardiac or cerebrovascular events independent of confounding variables.The shared pathophysiology between these three serio...Patients admitted with prediabetes and atrial fibrillation are at high risk for major adverse cardiac or cerebrovascular events independent of confounding variables.The shared pathophysiology between these three serious but common diseases and their association with atherosclerotic cardiovascular risk factors establish a vicious circle culminating in high atherogenicity.Because of that,it is of paramount importance to perform risk stratification of patients with prediabetes to define phenotypes that benefit from various interventions.Furthermore,stress hyperglycemia assessment of hospitalized patients and consensus on the definition of prediabetes is vital.The roles lifestyle and metformin play in prediabetes are well established.However,the role of glucagon-like peptide agonists and metabolic surgery is less clear.Prediabetes is considered an intermediate between normoglycemia and diabetes along the blood glucose continuum.One billion people are expected to suffer from prediabetes by the year 2045.Therefore,realworld randomized controlled trials to assess major adverse cardiac or cerebrovascular event risk reduction and reversal/prevention of type 2 diabetes among patients are needed to determine the proper interventions.展开更多
For fast in-situ assessment of tiller phenotypes in rice breeding,we introduce the TillerPET model,an improved transformer-based deep learning solution that permits phenotyping the number and compactness of rice tille...For fast in-situ assessment of tiller phenotypes in rice breeding,we introduce the TillerPET model,an improved transformer-based deep learning solution that permits phenotyping the number and compactness of rice tillers in images of post-harvest rice stubble.A rice tiller phenotype dataset covering three years of field data and four experimental sites across China was constructed to train and validate the model.TillerPET reports an R2 of 0.941 for counting tiller number,demonstrating state-of-the-art performance on the proposed RTP dataset.Beyond its minimal errors in estimating tiller number,TillerPET also achieves an R2 of 0.978 for characterizing tiller compactness.The two phenotypic parameters exhibit a high degree of consistency with expert breeders,offering reliable phenotypic indicators to guide further breeding.展开更多
Fusarium head blight(FHB)is a serious fungal disease that affect small grain cereals,causing significant wheat(Triticum aestivum L.)yield and quality losses globally.Breeding disease-resistant wheat varieties is key t...Fusarium head blight(FHB)is a serious fungal disease that affect small grain cereals,causing significant wheat(Triticum aestivum L.)yield and quality losses globally.Breeding disease-resistant wheat varieties is key to address FHB-related challenges,but its progress is delayed by traditional methods due to the small-scale,laborious and relatively subjective nature of manual assessment.This study presents a new approach that combines ultralow-altitude drone phenotyping with an optimized You Only Look Once(YOLO)model to examine FHB in wheat,enabling us to perform large-scale and automated symptomatic analysis of this disease.We first established an Open FHB(OFHB)training dataset,consisting of 4867 diseased and 106,801 healthy spikes collected from 132 commercial breeding lines during FHB progression.Then,a deep learning model called YOLOv8-WFD was trained for detecting healthy and diseased spikes,followed by an adaptive Excess Green method to identify symptomatic regions and thus FHBrelated traits on spikes.To study resistance levels,we employed an unsupervised SHapley Additive exPlanations(SHAP)method to pinpoint key traits between 10 and 20 d after inoculation(DAIs),resulting in the classification of 423 varieties trialed during the 2023–2024 growing seasons into four resistance levels(i.e.,highly and moderately susceptible,and moderately and highly resistant),which were highly correlated with field specialists’evaluations.Finally,we derived disease developmental curves based on measures of key traits during 10–20 DAI,quantifying varietal disease progression patterns over time.To our knowledge,this work represents a significant advancement in large-scale disease phenotyping and automated analysis of FHB in wheat,providing a valuable toolkit for breeders and plant researchers to assess resistance levels,select disease-resistant varieties,and understand dynamics of the fungal disease.展开更多
The effects of climate change are becoming more evident nowadays,and the environmental stress imposed on crops has become more severe.Farmers around the globe continually seek ways to gain insights into crop health an...The effects of climate change are becoming more evident nowadays,and the environmental stress imposed on crops has become more severe.Farmers around the globe continually seek ways to gain insights into crop health and provide mitigation as early as possible.Phenotyping is a non-destructive method for assessing crop responses to environmental stresses and can be performed using airborne systems.Unmanned Aerial Systems(UAS)have significantly contributed to high-throughput phenotyping andmade the process rapid,efficient,and non-invasive for collecting large-scale agronomic data.Because of the high complexity and cost of specialized equipment used in aerial phenotyping,such as multispectral and hyperspectral cameras as well as lidar,this study proposes a framework for implementing aerial phenotyping where chlorophyll estimation,leaf count,and coverage are determined using the RGB(Red,Green and Blue)camera native to a UAS.Thestudy proposes the Dynamic Coefficient Triangular Greenness Index(DCTGI)for aerial phenotyping.Evaluation of the proposed DCTGI includes the correlation with chlorophyll content estimated using a Soil Plant Analysis Development(SPAD)chlorophyll meter on randomly sampled Liberica coffee seedlings.Analysis revealed a strong relationship between DCTGI values and chlorophyll estimates derived from SPAD measurements,with a Pearson’s correlation coefficient of 0.912.However,the study didn’t implement tissue-level validation and field-scale temporal analysis to assess seasonal variability.In addition,the SPAD meter provided the approximate nitrogen content together with the chlorohyll estimate.展开更多
面向作物表型组大数据获取解析、作物种质资源表型鉴定等亟需高效率、智能化和低成本技术、装备及系统的问题,在系统梳理分析国内外农作物高通量表型平台相关技术产品研究现状的基础上,通过组织多学科的协同技术攻关,突破了作物表型组...面向作物表型组大数据获取解析、作物种质资源表型鉴定等亟需高效率、智能化和低成本技术、装备及系统的问题,在系统梳理分析国内外农作物高通量表型平台相关技术产品研究现状的基础上,通过组织多学科的协同技术攻关,突破了作物表型组大数据高通量获取和智能化解析中的关键技术难题,设计了具有自主知识产权的轻小敏捷型多传感器阵列、通用化成像单元和适用于多生境的固定式、移动式高通量表型平台装备,以及配套算法和软件平台,构建了农作物表型组大数据工厂成套技术装备体系。该体系由大田和设施作物高通量自主作业表型平台、室内器官和显微表型平台、大田和设施环境自动化种植管控设备、作物模型系统、数字孪生智慧管控平台和大数据计算服务中心等构成,可实现多生境、自动化、高通量、高效率、高精度的多源作物表型-环境数据协同采集,涵盖农作物群体、个体、器官和显微多重尺度,能够重建农林作物的三维形态结构并精准解析株型、产品、品质、抗性等表型组指标,是发展数字育种和智慧栽培的新一代信息化基础设施。农作物表型组大数据工厂技术装备体系创新了作物表型组大数据的产生、处理和服务模式,可为作物表型组理论技术的发展、基于AI for Science的平台化科研和工厂化的作物种质资源表型鉴定等提供体系化的技术装备支撑。展开更多
背景:细胞衰老引起机体生理性衰退的老化过程,与骨质疏松密切相关。随着衰老研究的发展,靶向清除衰老细胞研究取得显著进展,基于此的衰老细胞疗法治疗骨质疏松症日益受到关注。当前衰老与骨质疏松的关联研究呈现多学科交叉特征,但现有...背景:细胞衰老引起机体生理性衰退的老化过程,与骨质疏松密切相关。随着衰老研究的发展,靶向清除衰老细胞研究取得显著进展,基于此的衰老细胞疗法治疗骨质疏松症日益受到关注。当前衰老与骨质疏松的关联研究呈现多学科交叉特征,但现有综述多基于单一数据库(如PubMed)且缺乏中英文文献的系统对比分析。因此,整合多数据库资源,系统揭示衰老在骨质疏松研究领域的研究现状及热点趋势,具有重要的学术价值。目的:总结近20年来衰老在骨质疏松领域研究的发展历程、研究现状、热点和未来趋势,以期为今后相关研究提供参考。方法:在中国知网、万方知识平台、维普网与Web of Science核心数据库中检索衰老与骨质疏松相关性的研究文献,检索时间跨度为2004-08-01/2024-09-24,使用文献管理软件NoteExpress 4.0进行数据清洗,CiteSpace 6.3R1(Advanced)、Excel(2024)进行文献分析。结果与结论:自2004年以来,衰老与骨质疏松相关性的研究呈现显著增长态势。文献计量分析显示(截至2024年9月),英文文献发表1 275篇,中文文献发表151篇,其中以中国和美国的发文数量最多。从发文机构来看,梅奥医学中心居于榜首,其次是上海交通大学、加州大学系统。从核心作者来看,发文和被引最多的作者是Sundeep Khosla与Joshua Nicholas Farr,中文文献中裴凌鹏和惠伯棣最值得关注。通过排除关键词中“衰老”与“骨质疏松”及同义词后,发现细胞衰老、衰老相关分泌表型、信号通路、靶向衰老细胞清除治疗是当下的研究热点和理论前沿。展开更多
【目的】研究水稻防御反应和叶片早衰相关基因的分子机理。【方法】从粳稻武运粳7号诱变库中筛选出一个类病变早衰突变体lmes7(lesion mimic and early senescence 7)。调查统计了该突变体的主要农艺性状,对其叶片中的光合色素含量进行...【目的】研究水稻防御反应和叶片早衰相关基因的分子机理。【方法】从粳稻武运粳7号诱变库中筛选出一个类病变早衰突变体lmes7(lesion mimic and early senescence 7)。调查统计了该突变体的主要农艺性状,对其叶片中的光合色素含量进行测定,利用图位克隆技术对目的基因进行精细定位和测序鉴定,并对其编码蛋白进行分析与序列比对。【结果】突变体叶片中的光合色素含量比WT极显著降低,且细胞中存在活性氧(reactive oxygen species,ROS)的过量积累;突变体植株的生长发育受到严重影响,有效分蘖数、株高、剑叶长、剑叶宽、穗长、每穗粒数、结实率及单株产量等均显著下降,但粒厚显著增加;遗传分析表明,lmes7的突变表型受单隐性核基因控制;利用图位克隆技术将目的基因精细定位在水稻12号染色体上RM28486和RM28489之间的90 kb区域内;PCR测序分析表明,lmes7突变体中一个编码ATP-柠檬酸裂解酶的目标基因Os ACL-A2发生了7个碱基缺失,导致该基因发生移码突变,蛋白翻译提前终止;对水稻OsACL-A2蛋白的进化分析与序列比对可知,lmes7突变位点发生在琥珀酰辅酶A合成酶结构域,lmes7是OsACL-A2新的等位变异。展开更多
文摘Patients admitted with prediabetes and atrial fibrillation are at high risk for major adverse cardiac or cerebrovascular events independent of confounding variables.The shared pathophysiology between these three serious but common diseases and their association with atherosclerotic cardiovascular risk factors establish a vicious circle culminating in high atherogenicity.Because of that,it is of paramount importance to perform risk stratification of patients with prediabetes to define phenotypes that benefit from various interventions.Furthermore,stress hyperglycemia assessment of hospitalized patients and consensus on the definition of prediabetes is vital.The roles lifestyle and metformin play in prediabetes are well established.However,the role of glucagon-like peptide agonists and metabolic surgery is less clear.Prediabetes is considered an intermediate between normoglycemia and diabetes along the blood glucose continuum.One billion people are expected to suffer from prediabetes by the year 2045.Therefore,realworld randomized controlled trials to assess major adverse cardiac or cerebrovascular event risk reduction and reversal/prevention of type 2 diabetes among patients are needed to determine the proper interventions.
基金supported by the National Natural Science Foundation of China(32370435,62106080)the Hubei Provincial Natural Science Foundation of China(2024AFB566).
文摘For fast in-situ assessment of tiller phenotypes in rice breeding,we introduce the TillerPET model,an improved transformer-based deep learning solution that permits phenotyping the number and compactness of rice tillers in images of post-harvest rice stubble.A rice tiller phenotype dataset covering three years of field data and four experimental sites across China was constructed to train and validate the model.TillerPET reports an R2 of 0.941 for counting tiller number,demonstrating state-of-the-art performance on the proposed RTP dataset.Beyond its minimal errors in estimating tiller number,TillerPET also achieves an R2 of 0.978 for characterizing tiller compactness.The two phenotypic parameters exhibit a high degree of consistency with expert breeders,offering reliable phenotypic indicators to guide further breeding.
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD04025 to Xiu’e Wang)the Seed Industry Revitalization Project of Jiangsu Province(JBGS(2021)006 to Xiu’e Wang)+3 种基金the National Natural Science Foundation of China(32070400 to Ji Zhou)Ji Zhou,Robert Jackson,and Greg Deakin were partially supported by the Allan&Gill Gray Foundation’Sustainable Productivity for Crop Improvement(G118688 to the University of Cambridge and Q-20-0370 to NIAB)Ji Zhou was supported by the United Kingdom Research and Innovation’s(UKRI)Biotechnology and Bio logical Sciences Research Council(BBSRC)AI in Bioscience Grant(BB/Y513969/1 to Ji Zhou)The UK-China research activities were supported by the BBSRC’s International Partnership Grant(BB/Y514081/1 to NIAB)
文摘Fusarium head blight(FHB)is a serious fungal disease that affect small grain cereals,causing significant wheat(Triticum aestivum L.)yield and quality losses globally.Breeding disease-resistant wheat varieties is key to address FHB-related challenges,but its progress is delayed by traditional methods due to the small-scale,laborious and relatively subjective nature of manual assessment.This study presents a new approach that combines ultralow-altitude drone phenotyping with an optimized You Only Look Once(YOLO)model to examine FHB in wheat,enabling us to perform large-scale and automated symptomatic analysis of this disease.We first established an Open FHB(OFHB)training dataset,consisting of 4867 diseased and 106,801 healthy spikes collected from 132 commercial breeding lines during FHB progression.Then,a deep learning model called YOLOv8-WFD was trained for detecting healthy and diseased spikes,followed by an adaptive Excess Green method to identify symptomatic regions and thus FHBrelated traits on spikes.To study resistance levels,we employed an unsupervised SHapley Additive exPlanations(SHAP)method to pinpoint key traits between 10 and 20 d after inoculation(DAIs),resulting in the classification of 423 varieties trialed during the 2023–2024 growing seasons into four resistance levels(i.e.,highly and moderately susceptible,and moderately and highly resistant),which were highly correlated with field specialists’evaluations.Finally,we derived disease developmental curves based on measures of key traits during 10–20 DAI,quantifying varietal disease progression patterns over time.To our knowledge,this work represents a significant advancement in large-scale disease phenotyping and automated analysis of FHB in wheat,providing a valuable toolkit for breeders and plant researchers to assess resistance levels,select disease-resistant varieties,and understand dynamics of the fungal disease.
文摘The effects of climate change are becoming more evident nowadays,and the environmental stress imposed on crops has become more severe.Farmers around the globe continually seek ways to gain insights into crop health and provide mitigation as early as possible.Phenotyping is a non-destructive method for assessing crop responses to environmental stresses and can be performed using airborne systems.Unmanned Aerial Systems(UAS)have significantly contributed to high-throughput phenotyping andmade the process rapid,efficient,and non-invasive for collecting large-scale agronomic data.Because of the high complexity and cost of specialized equipment used in aerial phenotyping,such as multispectral and hyperspectral cameras as well as lidar,this study proposes a framework for implementing aerial phenotyping where chlorophyll estimation,leaf count,and coverage are determined using the RGB(Red,Green and Blue)camera native to a UAS.Thestudy proposes the Dynamic Coefficient Triangular Greenness Index(DCTGI)for aerial phenotyping.Evaluation of the proposed DCTGI includes the correlation with chlorophyll content estimated using a Soil Plant Analysis Development(SPAD)chlorophyll meter on randomly sampled Liberica coffee seedlings.Analysis revealed a strong relationship between DCTGI values and chlorophyll estimates derived from SPAD measurements,with a Pearson’s correlation coefficient of 0.912.However,the study didn’t implement tissue-level validation and field-scale temporal analysis to assess seasonal variability.In addition,the SPAD meter provided the approximate nitrogen content together with the chlorohyll estimate.
文摘面向作物表型组大数据获取解析、作物种质资源表型鉴定等亟需高效率、智能化和低成本技术、装备及系统的问题,在系统梳理分析国内外农作物高通量表型平台相关技术产品研究现状的基础上,通过组织多学科的协同技术攻关,突破了作物表型组大数据高通量获取和智能化解析中的关键技术难题,设计了具有自主知识产权的轻小敏捷型多传感器阵列、通用化成像单元和适用于多生境的固定式、移动式高通量表型平台装备,以及配套算法和软件平台,构建了农作物表型组大数据工厂成套技术装备体系。该体系由大田和设施作物高通量自主作业表型平台、室内器官和显微表型平台、大田和设施环境自动化种植管控设备、作物模型系统、数字孪生智慧管控平台和大数据计算服务中心等构成,可实现多生境、自动化、高通量、高效率、高精度的多源作物表型-环境数据协同采集,涵盖农作物群体、个体、器官和显微多重尺度,能够重建农林作物的三维形态结构并精准解析株型、产品、品质、抗性等表型组指标,是发展数字育种和智慧栽培的新一代信息化基础设施。农作物表型组大数据工厂技术装备体系创新了作物表型组大数据的产生、处理和服务模式,可为作物表型组理论技术的发展、基于AI for Science的平台化科研和工厂化的作物种质资源表型鉴定等提供体系化的技术装备支撑。
文摘背景:细胞衰老引起机体生理性衰退的老化过程,与骨质疏松密切相关。随着衰老研究的发展,靶向清除衰老细胞研究取得显著进展,基于此的衰老细胞疗法治疗骨质疏松症日益受到关注。当前衰老与骨质疏松的关联研究呈现多学科交叉特征,但现有综述多基于单一数据库(如PubMed)且缺乏中英文文献的系统对比分析。因此,整合多数据库资源,系统揭示衰老在骨质疏松研究领域的研究现状及热点趋势,具有重要的学术价值。目的:总结近20年来衰老在骨质疏松领域研究的发展历程、研究现状、热点和未来趋势,以期为今后相关研究提供参考。方法:在中国知网、万方知识平台、维普网与Web of Science核心数据库中检索衰老与骨质疏松相关性的研究文献,检索时间跨度为2004-08-01/2024-09-24,使用文献管理软件NoteExpress 4.0进行数据清洗,CiteSpace 6.3R1(Advanced)、Excel(2024)进行文献分析。结果与结论:自2004年以来,衰老与骨质疏松相关性的研究呈现显著增长态势。文献计量分析显示(截至2024年9月),英文文献发表1 275篇,中文文献发表151篇,其中以中国和美国的发文数量最多。从发文机构来看,梅奥医学中心居于榜首,其次是上海交通大学、加州大学系统。从核心作者来看,发文和被引最多的作者是Sundeep Khosla与Joshua Nicholas Farr,中文文献中裴凌鹏和惠伯棣最值得关注。通过排除关键词中“衰老”与“骨质疏松”及同义词后,发现细胞衰老、衰老相关分泌表型、信号通路、靶向衰老细胞清除治疗是当下的研究热点和理论前沿。
文摘【目的】研究水稻防御反应和叶片早衰相关基因的分子机理。【方法】从粳稻武运粳7号诱变库中筛选出一个类病变早衰突变体lmes7(lesion mimic and early senescence 7)。调查统计了该突变体的主要农艺性状,对其叶片中的光合色素含量进行测定,利用图位克隆技术对目的基因进行精细定位和测序鉴定,并对其编码蛋白进行分析与序列比对。【结果】突变体叶片中的光合色素含量比WT极显著降低,且细胞中存在活性氧(reactive oxygen species,ROS)的过量积累;突变体植株的生长发育受到严重影响,有效分蘖数、株高、剑叶长、剑叶宽、穗长、每穗粒数、结实率及单株产量等均显著下降,但粒厚显著增加;遗传分析表明,lmes7的突变表型受单隐性核基因控制;利用图位克隆技术将目的基因精细定位在水稻12号染色体上RM28486和RM28489之间的90 kb区域内;PCR测序分析表明,lmes7突变体中一个编码ATP-柠檬酸裂解酶的目标基因Os ACL-A2发生了7个碱基缺失,导致该基因发生移码突变,蛋白翻译提前终止;对水稻OsACL-A2蛋白的进化分析与序列比对可知,lmes7突变位点发生在琥珀酰辅酶A合成酶结构域,lmes7是OsACL-A2新的等位变异。
