Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,an...Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,anti-jamming capabilities,and combat performance,making them critical for future warfare.However,varied perspectives in collaborative combat scenarios pose challenges to object detection,hindering traditional detection algorithms and reducing accuracy.Limited angle-prior data and sparse samples further complicate detection.This paper presents the Multi-View Collaborative Detection System,which tackles the challenges of multi-view object detection in collaborative combat scenarios.The system is designed to enhance multi-view image generation and detection algorithms,thereby improving the accuracy and efficiency of object detection across varying perspectives.First,an observation model for three-dimensional targets through line-of-sight angle transformation is constructed,and a multi-view image generation algorithm based on the Pix2Pix network is designed.For object detection,YOLOX is utilized,and a deep feature extraction network,BA-RepCSPDarknet,is developed to address challenges related to small target scale and feature extraction challenges.Additionally,a feature fusion network NS-PAFPN is developed to mitigate the issue of deep feature map information loss in UAV images.A visual attention module(BAM)is employed to manage appearance differences under varying angles,while a feature mapping module(DFM)prevents fine-grained feature loss.These advancements lead to the development of BA-YOLOX,a multi-view object detection network model suitable for drone platforms,enhancing accuracy and effectively targeting small objects.展开更多
Calcium ions(Ca^(2+))and manganese ions(Mn^(2+))are essential for sustaining life activities and are key monitoring indicators in drinking water.Developing highly sensitive,selective,and portable detection methods for...Calcium ions(Ca^(2+))and manganese ions(Mn^(2+))are essential for sustaining life activities and are key monitoring indicators in drinking water.Developing highly sensitive,selective,and portable detection methods for Ca^(2+)and Mn^(2+)is significant for water quality monitoring and human health.In this paper,blue fluorescent Ti3C2 MXene-based quantum dots(MQDs,λ_(em)=445 nm)are prepared using Ti_(3)C_(2)MXene as the precursor.Through the chelation effect of ethylene diamine tetraacetic acid(EDTA),a blue and red dual-emission fluorescent probe,MQDs-EDTA-Eu^(3+)-DPA,was constructed.Herein,dipicolinic acid(DPA)acts as an absorbing ligand and significantly enhances the red fluorescence of europium ions(Eu^(3+))at 616 nm through the“antenna effect”.The blue fluorescence of MQDs serves as an internal reference signal.High concentrations of Ca^(2+)can quench the red fluorescence of Eu^(3+)-DPA;Mn^(2+)can be excited to emit purple fluorescence at 380 nm after coordinating with DPA,red fluorescence of Eu^(3+)-DPA serves as the internal reference signal.Based on the above two fluorescence intensity changes,ratiometric fluorescence detection methods for Ca^(2+)and Mn^(2+)are established.The fluorescence intensity ratio(IF_(616)/IF_(445))exhibits a linear relationship with Ca^(2+)in the range of 35-120μmol/L,with a detection limit of 5.98μmol/L.The fluorescence intensity ratio(IF_(380)/IF_(616))shows good linearity with Mn^(2+)in the range of 0-14μmol/L,with a detection limit of 28.6 nmol/L.This method was successfully applied to the quantitative analysis of Ca^(2+)and Mn^(2+)in commercially available mineral water(Nongfu Spring,Ganten,and Evergrande),with recovery rates of 80.6%-117%and relative standard deviations(RSD)of 0.76%-4.6%.Additionally,by preparing MQD-based fluorescent test strips,visual detections of Ca^(2+)and Mn^(2+)are achieved.This work demonstrates the application potential of MQDs in the field of visual fluorescence sensing of ions in water quality.展开更多
乳腺癌是中国女性常见的恶性肿瘤之一,其中遗传性乳腺癌占5%~10%,BRCA1/2基因突变是最主要的遗传易感因素。近年来,尽管多腺苷二磷酸核糖聚合酶[poly(ADP-ribose)polymerase,PARP]抑制剂等靶向药物的应用改善了BRCA突变乳腺癌患者的预后...乳腺癌是中国女性常见的恶性肿瘤之一,其中遗传性乳腺癌占5%~10%,BRCA1/2基因突变是最主要的遗传易感因素。近年来,尽管多腺苷二磷酸核糖聚合酶[poly(ADP-ribose)polymerase,PARP]抑制剂等靶向药物的应用改善了BRCA突变乳腺癌患者的预后,但在临床实践中仍存在诸多亟待解决的问题,包括突变检测的规范化、精准治疗策略的优化及长期管理的完善等。针对这些临床问题,本共识专家组基于《中国乳腺癌患者BRCA1/2基因检测与临床应用专家共识(2018年版)》及国内外最新循证医学证据,结合中国临床实践特点,对BRCA1/2基因检测的适用人群、检测方法、结果解读、治疗策略和风险管理等关键环节进行了系统评估和深入讨论,最终形成《中国乳腺癌患者BRCA1/2基因检测与临床应用专家共识(2025年版)》。主要更新内容包括:①增加BRCA1/2基因突变与程序性死亡蛋白配体-1(programmed death ligand-1,PD-L1)表达的关系,以及BRCAness类型的相关内容;②规范基因检测的应用,如增加临床检测的意义、时机及样本选择、优化BRCA检测人群;③更新治疗策略,如BRCA1/2基因突变的非药物治疗、BRCA1/2基因突变三阴性乳腺癌(triple-negative breast cancer,TNBC)患者的治疗、BRCA1/2基因突变激素受体(hormone receptor,HR)+/人表皮生长因子受体2(human epidermal growth factor receptor 2,HER2)-乳腺癌患者的治疗决策、PARP抑制剂的临床使用及不良反应管理;④增加长期风险管理的相关内容,如涵盖随访管理、预防性手术指征、新增基因检测的质量控制与要求、更新基因检测流程、报告内容及解读等。本共识旨在为临床医师提供规范化的诊疗指导,推动BRCA基因突变乳腺癌的精准医疗发展,最终改善患者生存及预后。随着研究的深入,本共识今后将持续更新以纳入最新的循证医学证据。本共识已在国际实践指南注册平台(Practice guideline REgistration for transPAREncy,PREPARE)注册,注册号为PREPARE-2025CN1085。展开更多
Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and...Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and time-consuming;thus,an efficient and accurate measurement method is needed.In recent years,classification-based deep learning and computer vision have shown promise in solving various classification tasks.Results In this study,we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning.The model is deployed on a lint percentage detection instrument,which can rapidly and accurately determine the lint percentage of seed cotton.We evaluated the performance of the proposed approach using a dataset comprising 66924 seed cotton images from different regions of China.The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%,with an average precision of 94.97%,an average recall of 95.26%,and an average F1-score of 95.20%.Furthermore,the proposed classification model achieved an average accuracy of 97.22%in calculating the lint percentage,showing no significant difference from the performance of experts(independent-sample t-test,t=0.019,P=0.860).Conclusion This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton.The proposed approach is a promising alternative to traditional methods,providing a rapid and accurate solution for the industry.展开更多
为建立花生致敏蛋白Ara h 2双抗夹心酶联免疫吸附试验(ELISA)检测方法,本研究基于所制备的抗体,采用双抗体夹心检测模式,以Ara h 2鼠源单克隆抗体作为捕获抗体、兔源多克隆抗体作为检测抗体,并通过棋盘法优化抗体工作浓度,对该方法的灵...为建立花生致敏蛋白Ara h 2双抗夹心酶联免疫吸附试验(ELISA)检测方法,本研究基于所制备的抗体,采用双抗体夹心检测模式,以Ara h 2鼠源单克隆抗体作为捕获抗体、兔源多克隆抗体作为检测抗体,并通过棋盘法优化抗体工作浓度,对该方法的灵敏度、准确度、精密度和特异性进行鉴定。结果表明,建立的双抗夹心ELISA检测方法对Ara h 2的检出限为5.04 ng·mL^(-1),线性范围为15.63~1 000 ng·mL^(-1),添加回收率为80.12%~96.03%,批内和批间变异系数均小于10%,且特异性良好、与其他常见食物过敏原无交叉反应。本研究可为致敏蛋白Ara h 2检测提供一种快速高效的方法。展开更多
针对不同陆地生态系统中净生态系统CO_(2)交换量(Net ecosystem exchange,NEE)数据的长期连续测量中存在的数据差异问题,以中国气象局青海高寒生态气象野外科学试验基地野牛沟试验站为研究对象,利用涡动协方差技术获取高寒湿地生态系统...针对不同陆地生态系统中净生态系统CO_(2)交换量(Net ecosystem exchange,NEE)数据的长期连续测量中存在的数据差异问题,以中国气象局青海高寒生态气象野外科学试验基地野牛沟试验站为研究对象,利用涡动协方差技术获取高寒湿地生态系统水平上的NEE数据。通过对比机器学习算法和通量数据后处理算法(Reddyproc)两种数据填充方法,提出了一种结合机器学习与时序异常检测(Time series anomaly detection,TAD)的新框架,用于NEE数据的空白填补。研究结果表明:1)Reddyproc算法在剔除异常值后,NEE插补决定系数(R^(2))达到0.67,数据离散度显著降低,数据质量提升;2)八种机器学习模型中,随机森林(Random Forest,RF)模型表现最优,其决定系数(Coefficient of determination,R^(2))为0.63,均方根误差(Root mean square error,RMSE)为2.17μmol s^(-1)m^(-2),且经过时序异常检测后,估算精度提升了17%;3)Reddyproc和RF估算的CO_(2)通量存在季节性差异,冷季(1—3月和10—12月)Reddyproc估算值低于RF,而暖季(4—9月)则高于RF,表明冬季Reddyproc低估了CO_(2)释放,夏季则低估了CO_(2)吸收。该新框架有效解决了数据采集不确定性和缺失导致的二氧化碳通量计算准确率问题,为研究高寒湿地生态系统的碳固持能力、对气候变化的响应以及极端事件的影响提供了关键数据支持。未来研究应进一步探索新方法的适用性、改进和优化方向,以实现更准确、可靠且适用于不同生态系统的填补模型,为生态系统建模和预测提供强大工具。展开更多
基金supported by the Natural Science Foundation of China,Grant No.62103052.
文摘Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,anti-jamming capabilities,and combat performance,making them critical for future warfare.However,varied perspectives in collaborative combat scenarios pose challenges to object detection,hindering traditional detection algorithms and reducing accuracy.Limited angle-prior data and sparse samples further complicate detection.This paper presents the Multi-View Collaborative Detection System,which tackles the challenges of multi-view object detection in collaborative combat scenarios.The system is designed to enhance multi-view image generation and detection algorithms,thereby improving the accuracy and efficiency of object detection across varying perspectives.First,an observation model for three-dimensional targets through line-of-sight angle transformation is constructed,and a multi-view image generation algorithm based on the Pix2Pix network is designed.For object detection,YOLOX is utilized,and a deep feature extraction network,BA-RepCSPDarknet,is developed to address challenges related to small target scale and feature extraction challenges.Additionally,a feature fusion network NS-PAFPN is developed to mitigate the issue of deep feature map information loss in UAV images.A visual attention module(BAM)is employed to manage appearance differences under varying angles,while a feature mapping module(DFM)prevents fine-grained feature loss.These advancements lead to the development of BA-YOLOX,a multi-view object detection network model suitable for drone platforms,enhancing accuracy and effectively targeting small objects.
基金The Tertiary Education Scientific Research Project of the Guangzhou Municipal Education Bureau(2024312227)Innovative and Entrepreneurial Projects of Guangzhou University Students(202411078014)+2 种基金Guangzhou University Open Sharing Fund for Instruments and Equipment(2025)National Major Scientific Research Instrument Development Project(22227804)Sub-subject of the National Key Research Project(2023YFB3210100)。
文摘Calcium ions(Ca^(2+))and manganese ions(Mn^(2+))are essential for sustaining life activities and are key monitoring indicators in drinking water.Developing highly sensitive,selective,and portable detection methods for Ca^(2+)and Mn^(2+)is significant for water quality monitoring and human health.In this paper,blue fluorescent Ti3C2 MXene-based quantum dots(MQDs,λ_(em)=445 nm)are prepared using Ti_(3)C_(2)MXene as the precursor.Through the chelation effect of ethylene diamine tetraacetic acid(EDTA),a blue and red dual-emission fluorescent probe,MQDs-EDTA-Eu^(3+)-DPA,was constructed.Herein,dipicolinic acid(DPA)acts as an absorbing ligand and significantly enhances the red fluorescence of europium ions(Eu^(3+))at 616 nm through the“antenna effect”.The blue fluorescence of MQDs serves as an internal reference signal.High concentrations of Ca^(2+)can quench the red fluorescence of Eu^(3+)-DPA;Mn^(2+)can be excited to emit purple fluorescence at 380 nm after coordinating with DPA,red fluorescence of Eu^(3+)-DPA serves as the internal reference signal.Based on the above two fluorescence intensity changes,ratiometric fluorescence detection methods for Ca^(2+)and Mn^(2+)are established.The fluorescence intensity ratio(IF_(616)/IF_(445))exhibits a linear relationship with Ca^(2+)in the range of 35-120μmol/L,with a detection limit of 5.98μmol/L.The fluorescence intensity ratio(IF_(380)/IF_(616))shows good linearity with Mn^(2+)in the range of 0-14μmol/L,with a detection limit of 28.6 nmol/L.This method was successfully applied to the quantitative analysis of Ca^(2+)and Mn^(2+)in commercially available mineral water(Nongfu Spring,Ganten,and Evergrande),with recovery rates of 80.6%-117%and relative standard deviations(RSD)of 0.76%-4.6%.Additionally,by preparing MQD-based fluorescent test strips,visual detections of Ca^(2+)and Mn^(2+)are achieved.This work demonstrates the application potential of MQDs in the field of visual fluorescence sensing of ions in water quality.
文摘乳腺癌是中国女性常见的恶性肿瘤之一,其中遗传性乳腺癌占5%~10%,BRCA1/2基因突变是最主要的遗传易感因素。近年来,尽管多腺苷二磷酸核糖聚合酶[poly(ADP-ribose)polymerase,PARP]抑制剂等靶向药物的应用改善了BRCA突变乳腺癌患者的预后,但在临床实践中仍存在诸多亟待解决的问题,包括突变检测的规范化、精准治疗策略的优化及长期管理的完善等。针对这些临床问题,本共识专家组基于《中国乳腺癌患者BRCA1/2基因检测与临床应用专家共识(2018年版)》及国内外最新循证医学证据,结合中国临床实践特点,对BRCA1/2基因检测的适用人群、检测方法、结果解读、治疗策略和风险管理等关键环节进行了系统评估和深入讨论,最终形成《中国乳腺癌患者BRCA1/2基因检测与临床应用专家共识(2025年版)》。主要更新内容包括:①增加BRCA1/2基因突变与程序性死亡蛋白配体-1(programmed death ligand-1,PD-L1)表达的关系,以及BRCAness类型的相关内容;②规范基因检测的应用,如增加临床检测的意义、时机及样本选择、优化BRCA检测人群;③更新治疗策略,如BRCA1/2基因突变的非药物治疗、BRCA1/2基因突变三阴性乳腺癌(triple-negative breast cancer,TNBC)患者的治疗、BRCA1/2基因突变激素受体(hormone receptor,HR)+/人表皮生长因子受体2(human epidermal growth factor receptor 2,HER2)-乳腺癌患者的治疗决策、PARP抑制剂的临床使用及不良反应管理;④增加长期风险管理的相关内容,如涵盖随访管理、预防性手术指征、新增基因检测的质量控制与要求、更新基因检测流程、报告内容及解读等。本共识旨在为临床医师提供规范化的诊疗指导,推动BRCA基因突变乳腺癌的精准医疗发展,最终改善患者生存及预后。随着研究的深入,本共识今后将持续更新以纳入最新的循证医学证据。本共识已在国际实践指南注册平台(Practice guideline REgistration for transPAREncy,PREPARE)注册,注册号为PREPARE-2025CN1085。
基金National Natural Science Foundation of China(Grant number:11904327,61905223,and 62073299)Training Plan of Young Backbone Teachers in Universities of Henan Province(2023GGJS087)+1 种基金Henan Provincial Science and Technology Research Project(222102110279,222102210085,and 242102210157)Project of Central Plains Science and Technology Innovation Leading Talents(224200510026).
文摘Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and time-consuming;thus,an efficient and accurate measurement method is needed.In recent years,classification-based deep learning and computer vision have shown promise in solving various classification tasks.Results In this study,we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning.The model is deployed on a lint percentage detection instrument,which can rapidly and accurately determine the lint percentage of seed cotton.We evaluated the performance of the proposed approach using a dataset comprising 66924 seed cotton images from different regions of China.The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%,with an average precision of 94.97%,an average recall of 95.26%,and an average F1-score of 95.20%.Furthermore,the proposed classification model achieved an average accuracy of 97.22%in calculating the lint percentage,showing no significant difference from the performance of experts(independent-sample t-test,t=0.019,P=0.860).Conclusion This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton.The proposed approach is a promising alternative to traditional methods,providing a rapid and accurate solution for the industry.
文摘为建立花生致敏蛋白Ara h 2双抗夹心酶联免疫吸附试验(ELISA)检测方法,本研究基于所制备的抗体,采用双抗体夹心检测模式,以Ara h 2鼠源单克隆抗体作为捕获抗体、兔源多克隆抗体作为检测抗体,并通过棋盘法优化抗体工作浓度,对该方法的灵敏度、准确度、精密度和特异性进行鉴定。结果表明,建立的双抗夹心ELISA检测方法对Ara h 2的检出限为5.04 ng·mL^(-1),线性范围为15.63~1 000 ng·mL^(-1),添加回收率为80.12%~96.03%,批内和批间变异系数均小于10%,且特异性良好、与其他常见食物过敏原无交叉反应。本研究可为致敏蛋白Ara h 2检测提供一种快速高效的方法。
文摘针对不同陆地生态系统中净生态系统CO_(2)交换量(Net ecosystem exchange,NEE)数据的长期连续测量中存在的数据差异问题,以中国气象局青海高寒生态气象野外科学试验基地野牛沟试验站为研究对象,利用涡动协方差技术获取高寒湿地生态系统水平上的NEE数据。通过对比机器学习算法和通量数据后处理算法(Reddyproc)两种数据填充方法,提出了一种结合机器学习与时序异常检测(Time series anomaly detection,TAD)的新框架,用于NEE数据的空白填补。研究结果表明:1)Reddyproc算法在剔除异常值后,NEE插补决定系数(R^(2))达到0.67,数据离散度显著降低,数据质量提升;2)八种机器学习模型中,随机森林(Random Forest,RF)模型表现最优,其决定系数(Coefficient of determination,R^(2))为0.63,均方根误差(Root mean square error,RMSE)为2.17μmol s^(-1)m^(-2),且经过时序异常检测后,估算精度提升了17%;3)Reddyproc和RF估算的CO_(2)通量存在季节性差异,冷季(1—3月和10—12月)Reddyproc估算值低于RF,而暖季(4—9月)则高于RF,表明冬季Reddyproc低估了CO_(2)释放,夏季则低估了CO_(2)吸收。该新框架有效解决了数据采集不确定性和缺失导致的二氧化碳通量计算准确率问题,为研究高寒湿地生态系统的碳固持能力、对气候变化的响应以及极端事件的影响提供了关键数据支持。未来研究应进一步探索新方法的适用性、改进和优化方向,以实现更准确、可靠且适用于不同生态系统的填补模型,为生态系统建模和预测提供强大工具。
