目的:构建BNN6@PEG-HCuSNPs纳米粒子,同时观察其小鼠体内外抗结肠癌效果。方法:水热反应构建BNN6@PEG-HCuSNPs纳米粒子,体外观察BNN6@PEG-HCuSNPs的光热、光动力以及气体治疗效果,选取CT26细胞作为研究对象,共聚焦显微镜下观察CT26细胞...目的:构建BNN6@PEG-HCuSNPs纳米粒子,同时观察其小鼠体内外抗结肠癌效果。方法:水热反应构建BNN6@PEG-HCuSNPs纳米粒子,体外观察BNN6@PEG-HCuSNPs的光热、光动力以及气体治疗效果,选取CT26细胞作为研究对象,共聚焦显微镜下观察CT26细胞对BNN6@PEG-HCuSNPs的摄取,DCFH-DA荧光探针检测BNN6@PEG-HCuSNPs对CT26细胞释放ROS的影响,DAF-FMDA荧光探针检测BNN6@PEG-HCuSNPs对CT26细胞释放NO的影响,BBoxiProbe荧光探针检测BNN6@PEG-HCuSNPs对CT26细胞释放ONOO-的影响,Calcein-AM和PI检测BNN6@PEG-HCuSNPs对CT26细胞存活的影响。取雌性Balb/c小鼠肿瘤建模,当肿瘤体积生长到约150 mm 3后,将荷瘤小鼠随机分成6组,每组5只:对照组、Laser组、PEG-HCuSNPs组、PEG-HCuSNPs+Laser组、BNN6@PEG-HCuSNPs组、BNN6@PEG-HCuSNPs+Laser组。在治疗过程中,通过尾静脉分别在第0和3天向对应的组注入生理盐水或20 mg/kg的BNN6@PEG-HCuSNPs纳米粒子,观察各组肿瘤体积和重量,HE染色分析细胞结构、TUNEL分析细胞DNA碎片和Ki-67抗原染色。结果:透射电镜、扫描电镜、核磁氢谱显示成功合成了BNN6@PEG-HCuSNPs纳米粒子。BNN6@PEG-HCuSNPs能够被CT26细胞有效内吞,BNN6@PEG-HCuSNPs在联合NIR-Ⅰ照射之后,在细胞水平能够稳定且有效的产生ROS和NO,并且二者经过进一步氧化反应生成更具有细胞毒性的活性氮成分ONOO-。Calcein-AM和PI结果显示BNN6@PEG-HCuSNPs在NIR-Ⅰ照射下能够引起肿瘤细胞CT26的凋亡进而达到治疗作用,且不会对HUVECs细胞和CT26细胞产生明显的毒性。光声成像监测显示BNN6@PEG-HCuSNPs能聚集在肿瘤部位,BNN6@PEG-HCuSNPs联合NIR-Ⅰ所产生的光热/光动力/气体治疗对肿瘤抑制作用优于其他组。TUNEL结果显示BNN6@PEG-HCuSNPs+Laser组所引起的大量肿瘤细胞凋亡而出现较其他组更为明显的绿色荧光;Ki-67抗原染色结果显示相比于对照组、Laser组、PEG-HCuSNPs组、BNN6@PEG-HCuSNPs组和PEG-HCuSNPs+Laser组,BNN6@PEG-HCuSNPs+Laser组的中阳性核数量减少最为明显。结论:BNN6@PEG-HCuSNPs纳米粒子小鼠体内外抗结肠癌效果显著。展开更多
Tri-flo cyclone,as a dense-medium separation device,is one of the most typical environmentally friendly industrial techniques in the coal washery plants.Surprisingly,no detailed investigation has been conducted to exp...Tri-flo cyclone,as a dense-medium separation device,is one of the most typical environmentally friendly industrial techniques in the coal washery plants.Surprisingly,no detailed investigation has been conducted to explore the effectiveness of tri-flo cyclone operating parameters on their representative metallurgical responses(yield and recovery).To fill this gap,this work for the first time in the coal processing sector is going to introduce a type of advanced intelligent method(boosted-neural network"BNN")which is able to linearly and nonlinearly assess multivariable correlations among all variables,rank them based on their effectiveness and model their produced responses.These assessments and modeling were considered a new concept called"Conscious Laboratory(CL)".CL can markedly decrease the number of laboratory experiments,reduce cost,save time,remove scaling up risks,expand maintaining processes,and significantly improve our knowledge about the modeled system.In this study,a robust monitoring database from the Tabas coal plant was prepared to cover various conditions for building a CL for coal tri-flo separators.Well-known machine learning methods,random forest,and support vector regression were developed to validate BNN outcomes.The comparisons indicated the accuracy and strength of BNN over the examined traditional modeling methods.In a sentence,generating a novel BNN within the CL concept can apply in various energy and coal processing areas,fill gaps in our knowledge about possible interactions,and open a new window for plants’fully automotive process.展开更多
Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural N...Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.展开更多
文摘目的:构建BNN6@PEG-HCuSNPs纳米粒子,同时观察其小鼠体内外抗结肠癌效果。方法:水热反应构建BNN6@PEG-HCuSNPs纳米粒子,体外观察BNN6@PEG-HCuSNPs的光热、光动力以及气体治疗效果,选取CT26细胞作为研究对象,共聚焦显微镜下观察CT26细胞对BNN6@PEG-HCuSNPs的摄取,DCFH-DA荧光探针检测BNN6@PEG-HCuSNPs对CT26细胞释放ROS的影响,DAF-FMDA荧光探针检测BNN6@PEG-HCuSNPs对CT26细胞释放NO的影响,BBoxiProbe荧光探针检测BNN6@PEG-HCuSNPs对CT26细胞释放ONOO-的影响,Calcein-AM和PI检测BNN6@PEG-HCuSNPs对CT26细胞存活的影响。取雌性Balb/c小鼠肿瘤建模,当肿瘤体积生长到约150 mm 3后,将荷瘤小鼠随机分成6组,每组5只:对照组、Laser组、PEG-HCuSNPs组、PEG-HCuSNPs+Laser组、BNN6@PEG-HCuSNPs组、BNN6@PEG-HCuSNPs+Laser组。在治疗过程中,通过尾静脉分别在第0和3天向对应的组注入生理盐水或20 mg/kg的BNN6@PEG-HCuSNPs纳米粒子,观察各组肿瘤体积和重量,HE染色分析细胞结构、TUNEL分析细胞DNA碎片和Ki-67抗原染色。结果:透射电镜、扫描电镜、核磁氢谱显示成功合成了BNN6@PEG-HCuSNPs纳米粒子。BNN6@PEG-HCuSNPs能够被CT26细胞有效内吞,BNN6@PEG-HCuSNPs在联合NIR-Ⅰ照射之后,在细胞水平能够稳定且有效的产生ROS和NO,并且二者经过进一步氧化反应生成更具有细胞毒性的活性氮成分ONOO-。Calcein-AM和PI结果显示BNN6@PEG-HCuSNPs在NIR-Ⅰ照射下能够引起肿瘤细胞CT26的凋亡进而达到治疗作用,且不会对HUVECs细胞和CT26细胞产生明显的毒性。光声成像监测显示BNN6@PEG-HCuSNPs能聚集在肿瘤部位,BNN6@PEG-HCuSNPs联合NIR-Ⅰ所产生的光热/光动力/气体治疗对肿瘤抑制作用优于其他组。TUNEL结果显示BNN6@PEG-HCuSNPs+Laser组所引起的大量肿瘤细胞凋亡而出现较其他组更为明显的绿色荧光;Ki-67抗原染色结果显示相比于对照组、Laser组、PEG-HCuSNPs组、BNN6@PEG-HCuSNPs组和PEG-HCuSNPs+Laser组,BNN6@PEG-HCuSNPs+Laser组的中阳性核数量减少最为明显。结论:BNN6@PEG-HCuSNPs纳米粒子小鼠体内外抗结肠癌效果显著。
文摘Tri-flo cyclone,as a dense-medium separation device,is one of the most typical environmentally friendly industrial techniques in the coal washery plants.Surprisingly,no detailed investigation has been conducted to explore the effectiveness of tri-flo cyclone operating parameters on their representative metallurgical responses(yield and recovery).To fill this gap,this work for the first time in the coal processing sector is going to introduce a type of advanced intelligent method(boosted-neural network"BNN")which is able to linearly and nonlinearly assess multivariable correlations among all variables,rank them based on their effectiveness and model their produced responses.These assessments and modeling were considered a new concept called"Conscious Laboratory(CL)".CL can markedly decrease the number of laboratory experiments,reduce cost,save time,remove scaling up risks,expand maintaining processes,and significantly improve our knowledge about the modeled system.In this study,a robust monitoring database from the Tabas coal plant was prepared to cover various conditions for building a CL for coal tri-flo separators.Well-known machine learning methods,random forest,and support vector regression were developed to validate BNN outcomes.The comparisons indicated the accuracy and strength of BNN over the examined traditional modeling methods.In a sentence,generating a novel BNN within the CL concept can apply in various energy and coal processing areas,fill gaps in our knowledge about possible interactions,and open a new window for plants’fully automotive process.
基金supported by the National Natural Science Foundation of China(61170147)Scientific Research Project of Zhejiang Provincial Department of Education in China(Y202146796)+2 种基金Natural Science Foundation of Zhejiang Province in China(LTY22F020003)Wenzhou Major Scientific and Technological Innovation Project of China(ZG2021029)Scientific and Technological Projects of Henan Province in China(202102210172).
文摘Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.
