Single-cell analysis is crucial for deciphering cellular heterogeneity and understanding complex biological systems.However,most existing single-cell sample manipulation(SCSM)systems suffer from various drawbacks such...Single-cell analysis is crucial for deciphering cellular heterogeneity and understanding complex biological systems.However,most existing single-cell sample manipulation(SCSM)systems suffer from various drawbacks such as high cost,low throughput,and heavy reliance on human interventions.Currently,large language models(LLMs)have been used in robotic platforms,but a limited number of studies have reported the application of LLMs in the field of lab-ona-chip automation.Consequently,we have developed an active-matrix digital microfluidic(AM-DMF)platform that realizes fully automated biological procedures for intelligent SCSM.By combining this with a fully programmable labon-a-chip system,we present a breakthrough for SCSM by combining LLMs and object detection technologies.With the proposed platform,the single-cell sample generation rate and identification precision reach up to 25%and 98%,respectively,which are much higher than the existing platforms in terms of SCSM efficiency and performance.Furthermore,a three-class detection method considering droplet edges is implemented to realize the automatic identification of cells and oil bubbles.This method achieves a 1.0%improvement in cell recognition accuracy according to the AP_(75)^(test)metric,while efficiently distinguishing obscured cells at droplet edges,where approximately 20%of all droplets contain cells at their edges.More importantly,as the first attempt,a ubiquitous tool for automatic SCSM workflow generation is developed based on the LLMs,thus advancing the development and progression of the field of single-cell analysis in the life sciences.展开更多
An active-matrix electrowetting-on-dielectric(AM-EWOD)system integrates hundreds of thousands of active electrodes for sample droplet manipulation,which can enable simultaneous,automatic,and parallel on-chip biochemic...An active-matrix electrowetting-on-dielectric(AM-EWOD)system integrates hundreds of thousands of active electrodes for sample droplet manipulation,which can enable simultaneous,automatic,and parallel on-chip biochemical reactions.A smart detection system is essential for ensuring a fully automatic workflow and online programming for the subsequent experimental steps.In this work,we demonstrated an artificial intelligence(Al)-enabled multipurpose smart detection method in an AM-EWOD system for different tasks.We employed the U-Net model to quantitatively evaluate the uniformity of the applied droplet-splitting methods.We used the YOLOv8 model to monitor the droplet-splitting process online.A 97.76% splitting success rate was observed with 18 different AM-EWOD chips.A 99.982% model precision rate and a 99.980%model recall rate were manually verified. We employed an improved YOLOv8 model to detect single-cell samples in nanolitre droplets.Compared with manual verification,the model achieved 99.260%and 99.193%precision and recall rates,respectively.In addition,single-cell droplet sorting and routing experiments were demonstrated.With an Al-based smart detection system,AM-EWOD has shown great potential for use as a ubiquitous platform for implementing true lab-on-a-chip applications.展开更多
A novel adaptive sliding mode control algo- rithm is derived to deal with seam tracking control problem of welding robotic manipulator, during the process of large-scale structure component welding. The proposed algor...A novel adaptive sliding mode control algo- rithm is derived to deal with seam tracking control problem of welding robotic manipulator, during the process of large-scale structure component welding. The proposed algorithm does not require the precise dynamic model, and is more practical. Its robustness is verified by the Lyapunov stability theory. The analytical results show that the proposed algorithm enables better high-precision tracking performance with chattering-free than traditional sliding mode control algorithm under various disturbances.展开更多
基金the National Key R&D Program of China(2023YFF0721500)The National Natural Science Foundation of China(Nos.62374102,82172077,22201298,and 62027825)+3 种基金The Innovation and Entrepreneurship Team of Jiangsu Province(JSSCTD202145)The Science and Technology Innovation Project of Foshan,Guangdong Province,China(No.1920001000047)The Science and Technology Development Project of Jilin Province(No.20210204110YY and 20250204092YY)The Suzhou Basic Research Project(SSD2023013).
文摘Single-cell analysis is crucial for deciphering cellular heterogeneity and understanding complex biological systems.However,most existing single-cell sample manipulation(SCSM)systems suffer from various drawbacks such as high cost,low throughput,and heavy reliance on human interventions.Currently,large language models(LLMs)have been used in robotic platforms,but a limited number of studies have reported the application of LLMs in the field of lab-ona-chip automation.Consequently,we have developed an active-matrix digital microfluidic(AM-DMF)platform that realizes fully automated biological procedures for intelligent SCSM.By combining this with a fully programmable labon-a-chip system,we present a breakthrough for SCSM by combining LLMs and object detection technologies.With the proposed platform,the single-cell sample generation rate and identification precision reach up to 25%and 98%,respectively,which are much higher than the existing platforms in terms of SCSM efficiency and performance.Furthermore,a three-class detection method considering droplet edges is implemented to realize the automatic identification of cells and oil bubbles.This method achieves a 1.0%improvement in cell recognition accuracy according to the AP_(75)^(test)metric,while efficiently distinguishing obscured cells at droplet edges,where approximately 20%of all droplets contain cells at their edges.More importantly,as the first attempt,a ubiquitous tool for automatic SCSM workflow generation is developed based on the LLMs,thus advancing the development and progression of the field of single-cell analysis in the life sciences.
基金This research was funded by:The National Key R&D Program of China(2023YFF0721500)The National Natural Science Foundation of China(No.62374102,82172077,22201298,and 62027825)+2 种基金The Science and Technology Innovation Project of Foshan,Guangdong Province,China(No.1920001000047)The Science and Technology Development Project of jilin Province(No.20210204110YY)The Suzhou Basic Research Project.(SSD2023013).
文摘An active-matrix electrowetting-on-dielectric(AM-EWOD)system integrates hundreds of thousands of active electrodes for sample droplet manipulation,which can enable simultaneous,automatic,and parallel on-chip biochemical reactions.A smart detection system is essential for ensuring a fully automatic workflow and online programming for the subsequent experimental steps.In this work,we demonstrated an artificial intelligence(Al)-enabled multipurpose smart detection method in an AM-EWOD system for different tasks.We employed the U-Net model to quantitatively evaluate the uniformity of the applied droplet-splitting methods.We used the YOLOv8 model to monitor the droplet-splitting process online.A 97.76% splitting success rate was observed with 18 different AM-EWOD chips.A 99.982% model precision rate and a 99.980%model recall rate were manually verified. We employed an improved YOLOv8 model to detect single-cell samples in nanolitre droplets.Compared with manual verification,the model achieved 99.260%and 99.193%precision and recall rates,respectively.In addition,single-cell droplet sorting and routing experiments were demonstrated.With an Al-based smart detection system,AM-EWOD has shown great potential for use as a ubiquitous platform for implementing true lab-on-a-chip applications.
基金The work here was supported by the National Science and Technology Supporting Plan （Crrant No. 2015BAF01B04）, Collaborative Innovation Center of High-End Manufacturing Equipment, the National Key Basic Research Program of China （Grant No. 2011 CB706803）, the National Natural Science Foundation of China （Grant No. 51175208）, the Funda- mental Research Funds for the Central Universities （Grant Nos. 2013ZZGH001 and 2014CG006）.
文摘A novel adaptive sliding mode control algo- rithm is derived to deal with seam tracking control problem of welding robotic manipulator, during the process of large-scale structure component welding. The proposed algorithm does not require the precise dynamic model, and is more practical. Its robustness is verified by the Lyapunov stability theory. The analytical results show that the proposed algorithm enables better high-precision tracking performance with chattering-free than traditional sliding mode control algorithm under various disturbances.
