Single-cell biomechanics and electrophysiology measuring tools have transformed biological research over the last few decades,which enabling a comprehensive and nuanced understanding of cellular behavior and function....Single-cell biomechanics and electrophysiology measuring tools have transformed biological research over the last few decades,which enabling a comprehensive and nuanced understanding of cellular behavior and function.Despite their high-quality information content,these single-cell measuring techniques suffer from laborious manual processing by highly skilled workers and extremely low throughput(tens of cells per day).Recently,numerous researchers have automated the measurement of cell mechanical and electrical signals through robotic localization and control processes.While these efforts have demonstrated promising progress,critical challenges persist,including human dependency,learning complexity,in-situ measurement,and multidimensional signal acquisition.To identify key limitations and highlight emerging opportunities for innovation,in this review,we comprehensively summarize the key steps of robotic technologies in single-cell biomechanics and electrophysiology.We also discussed the prospects and challenges of robotics and automation in biological research.By bridging gaps between engineering,biology,and data science,this work aims to stimulate interdisciplinary research and accelerate the translation of robotic single-cell technologies into practical applications in the life sciences and medical fields.展开更多
Spatial angle measurement,especially the measurement of horizontal and vertical angle,is a basic method used for industrial large-scale coordinate measurement.As main equipments in use,both theodolites and laser track...Spatial angle measurement,especially the measurement of horizontal and vertical angle,is a basic method used for industrial large-scale coordinate measurement.As main equipments in use,both theodolites and laser trackers can provide very high accuracy for spatial angle measurement.However,their industrial applications are limited by low level of automation and poor parallelism.For the purpose of improving measurement efficiency,a lot of studies have been conducted and several alternative methods have been proposed.Unfortunately,all these means are either low precision or too expensive.In this paper,a novel method of spatial angle measurement based on two rotating planar laser beams is proposed and demonstrated.Photoelectric receivers placed on measured points are used to receive the rotating planner laser signals transmitted by laser transmitters.The scanning time intervals of laser planes were measured,and then measured point's horizontal/vertical angles can be calculated.Laser plane's angle parameters are utilized to establish the abstract geometric model of transmitter.Calculating formulas of receiver's horizontal/vertical angles have been derived.Measurement equations'solvability conditions and judgment method of imaginary solutions are also presented after analyzing.Proposed method for spatial angle measurement is experimentally verified through a platform consisting of one laser transmitter and one optical receiver.The transmitters used in new method are only responsible for providing rotating light plane signals carrying angle information.Receivers automatically measure scanning time of laser planes and upload data to the workstation to calculate horizontal angle and vertical angle.Simultaneous measurement of multiple receivers can be realized since there is no human intervention in measurement process.Spatial angle measurement result indicates that the repeatable accuracy of new method is better than 10".Proposed method can improve measurement's automation degree and speed while ensuring measurement accuracy.展开更多
Radicle length is a critical indicator of seed vigor,germination capacity,and seedling growth potential.However,existing measurement methods face challenges in automation,efficiency,and generalizability,often requirin...Radicle length is a critical indicator of seed vigor,germination capacity,and seedling growth potential.However,existing measurement methods face challenges in automation,efficiency,and generalizability,often requiring manual intervention or re-annotation for different seed types.To address these limitations,this paper proposes an automated method,LenRuler,with a primary focus on rice seeds and validation in multiple crops.The method leverages the Segment Anything Model(SAM)as the foundational segmentation model and employs a coarse-to-fine segmentation strategy combined with Gaussian-based classification to automatically generate bounding boxes and centroids,which are then fed into SAM for precise segmentation of the seed coat and radicle.The radicle length is subsequently computed by converting the geodesic distance between the radicle skeleton's farthest endpoint and its nearest intersection with the seed coat skeleton into the true length.Experiments on the Riceseed1 dataset show that the proposed method achieves a Dice coefficient of 0.955 and a Pixel Accuracy of 0.944,demonstrating excellent segmentation performance.Radicle length measurement experiments on the Riceseed2 test set show that the Mean Absolute Error(MAE)was 0.273 mm and the coefficient of determination(R^(2))was 0.982,confirming the method's high precision for rice.On the Otherseed dataset,the predicted radicle lengths for maize(Zea mays),pearl millet(Pennisetum glaucum),and rye(Secale cereale)are consistent with the observed radicle length distributions,demonstrating strong cross-species performance.These results establish LenRuler as an accurate and automated solution for radicle length measurement in rice,with validated appli-cability to other crop species.展开更多
Correction:aBIOTECH https://doi.org/10.1007/s42994-025-00245-0 In this article the author“Yongqing Suo”should read“Yongqiang Suo”.The original article has been corrected.Open Access This article is licensed under ...Correction:aBIOTECH https://doi.org/10.1007/s42994-025-00245-0 In this article the author“Yongqing Suo”should read“Yongqiang Suo”.The original article has been corrected.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use,sharing,adaptation,distribution and reproduction in any medium or format,as long as you give appropriate credit to the original author(s)and the source,provide a link to the Creative Commons licence,and indicate if changes were made.The images or other third party material in this article are included in the article’s Creative Commons licence,unless indicated otherwise in a credit line to the material.展开更多
Efficient traffic signal system management plays a pivotal role in reducing traffic conges-tion and improving traffic mobility on urban roads.The applications of the automated traf-fic signal performance measures(ATSP...Efficient traffic signal system management plays a pivotal role in reducing traffic conges-tion and improving traffic mobility on urban roads.The applications of the automated traf-fic signal performance measures(ATSPMs)revolutionize the way of proactively managing and evaluating traffic signal systems through a suite of performance measures.The percent arrival on red(PAoR)is one of the commonly used progression performance measures in the ATSPMs to characterize vehicle arrivals at the intersection.However,the accuracy of PAoR to assess arterial signal coordination is restricted by configuration limitations of advance detectors and remains to be further explored.To address this problem,this research proposes an easy-to-use trajectory-based performance measure,i.e.,arterial through percent arrival on red(ATPAoR),for arterial signal coordination performance eval-uation and presents the general procedures to calculate ATPAoRs from connected vehicle data.A case study is carried out to implement the proposed ATPAoR and investigate the relationship between the ATPAoR and the PAoR.It is found that the combination of the time-space diagram(TSD)and arterial through-vehicle trajectories is effective in the actual arterial signal coordination performance visualization,ATPAoR result interpretation,and potential timing improvement recommendations.The PAoRs are found to be greater than the ATPAoRs in undersaturated conditions,and the PAoRs above 60%are recommended to identify poor arterial signal coordination design.The historical TSD can be utilized to verify the accuracy of PAoR to evaluate the actual arterial signal coordination when vehicle tra-jectory data are unavailable.展开更多
基金the National Natural Science Foundation of China[62525301,62127811,62433019]the New Cornerstone Science Foundation through the XPLORER PRIZEthe financial support by the China Postdoctoral Science Foundation[GZB20240797].
文摘Single-cell biomechanics and electrophysiology measuring tools have transformed biological research over the last few decades,which enabling a comprehensive and nuanced understanding of cellular behavior and function.Despite their high-quality information content,these single-cell measuring techniques suffer from laborious manual processing by highly skilled workers and extremely low throughput(tens of cells per day).Recently,numerous researchers have automated the measurement of cell mechanical and electrical signals through robotic localization and control processes.While these efforts have demonstrated promising progress,critical challenges persist,including human dependency,learning complexity,in-situ measurement,and multidimensional signal acquisition.To identify key limitations and highlight emerging opportunities for innovation,in this review,we comprehensively summarize the key steps of robotic technologies in single-cell biomechanics and electrophysiology.We also discussed the prospects and challenges of robotics and automation in biological research.By bridging gaps between engineering,biology,and data science,this work aims to stimulate interdisciplinary research and accelerate the translation of robotic single-cell technologies into practical applications in the life sciences and medical fields.
基金supported by Key Program of National Natural Science Foundation of China(Grant No.50735003)
文摘Spatial angle measurement,especially the measurement of horizontal and vertical angle,is a basic method used for industrial large-scale coordinate measurement.As main equipments in use,both theodolites and laser trackers can provide very high accuracy for spatial angle measurement.However,their industrial applications are limited by low level of automation and poor parallelism.For the purpose of improving measurement efficiency,a lot of studies have been conducted and several alternative methods have been proposed.Unfortunately,all these means are either low precision or too expensive.In this paper,a novel method of spatial angle measurement based on two rotating planar laser beams is proposed and demonstrated.Photoelectric receivers placed on measured points are used to receive the rotating planner laser signals transmitted by laser transmitters.The scanning time intervals of laser planes were measured,and then measured point's horizontal/vertical angles can be calculated.Laser plane's angle parameters are utilized to establish the abstract geometric model of transmitter.Calculating formulas of receiver's horizontal/vertical angles have been derived.Measurement equations'solvability conditions and judgment method of imaginary solutions are also presented after analyzing.Proposed method for spatial angle measurement is experimentally verified through a platform consisting of one laser transmitter and one optical receiver.The transmitters used in new method are only responsible for providing rotating light plane signals carrying angle information.Receivers automatically measure scanning time of laser planes and upload data to the workstation to calculate horizontal angle and vertical angle.Simultaneous measurement of multiple receivers can be realized since there is no human intervention in measurement process.Spatial angle measurement result indicates that the repeatable accuracy of new method is better than 10".Proposed method can improve measurement's automation degree and speed while ensuring measurement accuracy.
基金This work was supported by the Yuelushan Laboratory Breeding Project(YLS-2025-ZY02006)to X.H.
文摘Radicle length is a critical indicator of seed vigor,germination capacity,and seedling growth potential.However,existing measurement methods face challenges in automation,efficiency,and generalizability,often requiring manual intervention or re-annotation for different seed types.To address these limitations,this paper proposes an automated method,LenRuler,with a primary focus on rice seeds and validation in multiple crops.The method leverages the Segment Anything Model(SAM)as the foundational segmentation model and employs a coarse-to-fine segmentation strategy combined with Gaussian-based classification to automatically generate bounding boxes and centroids,which are then fed into SAM for precise segmentation of the seed coat and radicle.The radicle length is subsequently computed by converting the geodesic distance between the radicle skeleton's farthest endpoint and its nearest intersection with the seed coat skeleton into the true length.Experiments on the Riceseed1 dataset show that the proposed method achieves a Dice coefficient of 0.955 and a Pixel Accuracy of 0.944,demonstrating excellent segmentation performance.Radicle length measurement experiments on the Riceseed2 test set show that the Mean Absolute Error(MAE)was 0.273 mm and the coefficient of determination(R^(2))was 0.982,confirming the method's high precision for rice.On the Otherseed dataset,the predicted radicle lengths for maize(Zea mays),pearl millet(Pennisetum glaucum),and rye(Secale cereale)are consistent with the observed radicle length distributions,demonstrating strong cross-species performance.These results establish LenRuler as an accurate and automated solution for radicle length measurement in rice,with validated appli-cability to other crop species.
文摘Correction:aBIOTECH https://doi.org/10.1007/s42994-025-00245-0 In this article the author“Yongqing Suo”should read“Yongqiang Suo”.The original article has been corrected.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,which permits use,sharing,adaptation,distribution and reproduction in any medium or format,as long as you give appropriate credit to the original author(s)and the source,provide a link to the Creative Commons licence,and indicate if changes were made.The images or other third party material in this article are included in the article’s Creative Commons licence,unless indicated otherwise in a credit line to the material.
文摘Efficient traffic signal system management plays a pivotal role in reducing traffic conges-tion and improving traffic mobility on urban roads.The applications of the automated traf-fic signal performance measures(ATSPMs)revolutionize the way of proactively managing and evaluating traffic signal systems through a suite of performance measures.The percent arrival on red(PAoR)is one of the commonly used progression performance measures in the ATSPMs to characterize vehicle arrivals at the intersection.However,the accuracy of PAoR to assess arterial signal coordination is restricted by configuration limitations of advance detectors and remains to be further explored.To address this problem,this research proposes an easy-to-use trajectory-based performance measure,i.e.,arterial through percent arrival on red(ATPAoR),for arterial signal coordination performance eval-uation and presents the general procedures to calculate ATPAoRs from connected vehicle data.A case study is carried out to implement the proposed ATPAoR and investigate the relationship between the ATPAoR and the PAoR.It is found that the combination of the time-space diagram(TSD)and arterial through-vehicle trajectories is effective in the actual arterial signal coordination performance visualization,ATPAoR result interpretation,and potential timing improvement recommendations.The PAoRs are found to be greater than the ATPAoRs in undersaturated conditions,and the PAoRs above 60%are recommended to identify poor arterial signal coordination design.The historical TSD can be utilized to verify the accuracy of PAoR to evaluate the actual arterial signal coordination when vehicle tra-jectory data are unavailable.
