ABSTRACT:Background:After ischemic stroke,neutrophils hyperactivate,increasing in number and worsening inflammation,causing neural damage.Prior scRNA-seq showed Lrg1 modulates cells subsentence to cerebral ischemiarep...ABSTRACT:Background:After ischemic stroke,neutrophils hyperactivate,increasing in number and worsening inflammation,causing neural damage.Prior scRNA-seq showed Lrg1 modulates cells subsentence to cerebral ischemiareperfusion injury,but its mechanism in regulating neutrophil accumulation/differentiation post-injury is unclear.Methods:Lrg1 knockout impact on neutrophil accumulation was assessed via immunofluorescence and western blot.Three-dimensional reconstruction of immunofluorescent staining analyzed cell-cell interactions among neutrophils and microglia.scRNA-seq of WT and Lrg1^(-/-)mice from GSE245386 and GSE279462 was conducted.Each group conducted oxidative phosphorylation scoring via Gene Set Enrichment Analysis(GSEA),while Metascape was employed to perform GO and KEGG enrichment analyses for elucidating functional mechanisms.CellChat exhibited cell-cell communication.Furthermore,alterations in microglial phagocytic activity were evaluated by immunostaining for CD68,a well-established marker of phagolysosomal activity in phagocytic cells.Brain energy metabolism was evaluated via glutamate dehydrogenase activity and ATP levels with ELISA,and enzyme expression was analyzed by immunofluorescence and western blot.Results:Lrg1 knockout decreased neutrophil accumulation and NET formation in mice.3D immunofluorescence reconstruction confirmed neutrophil co-localization with endothelial cells/microglia.scRNA-seq revealed that the oxidative phosphorylation score was significantly higher in the MCAO/R+WT group compared to both the Sham-operated+WT and Lrg1^(-/-)groups.Notably,the oxidative phosphorylation score was further elevated in the MCAO/R+Lrg1^(-/-)group.Immunostaining showed that Lrg1 knockout elevated CD68+lysosome expression post-MCAO/R,with TMEM119 colocalizing with these lysosomes.MCAO/R raised CD68 expression in ischemic brains,an effect further intensified by Lrg1 knockout.KEGG analysis linked differential genes to oxidative phosphorylation pathways.Validation in MCAO/R vs.sham groups revealed increased ROS production and reduced expression of complex enzymes I-V(NDUFB8,SDHB,UQCRC1,MTCO2,ATP5A1).Lrg1 intervention increased enzyme expression.Immunofluorescence and western blot in brain tissue showed similar patterns in microglia and enzymes I-V.Conclusions:Lrg1 knockout significantly enhances microglial phagocytic activity towards neutrophils subsequent to cerebral ischemia-reperfusion injury,through its regulatory effect on the oxidative phosphorylation pathway.This finding accentuates Lrg1 as a highly potential therapeutic target for intervening in and modulating post-ischemic inflammatory responses.展开更多
Background:Biochanin A is an excellent dietary isoflavone that has the concomitant function of both medicine and foodstuff.The attenuation function of biochanin A on blood-brain barrier(BBB)damage induced by cerebral ...Background:Biochanin A is an excellent dietary isoflavone that has the concomitant function of both medicine and foodstuff.The attenuation function of biochanin A on blood-brain barrier(BBB)damage induced by cerebral ischemia-reperfusion remains unclear.Methods:C57BL/6 mice were subjected to 1 h middle cerebral artery occlusion(MCAO)followed by 24 h reperfusion.The infarct volume of the brain was stained by TTC,while leakage of the brain was quantitatively stained by Evans blue,and the neurologic deficit score was measured.Microglial-induced morphologic changes were observed via immunofluorescence staining,and rolling and adhering leukocytes in venules were observed via two-photon imaging,while the inner fluorescein isothiocyanate-albumin of venules were compared with those of surrounding interstitial area through venular albumin leakage.Results:The attenuation effect of biochanin A on tight junction injury was compared in ischemia-reperfusion mice or conventional knockdown of leucine-richα2-glycoprotein 1(Lrg1)mice.Biochanin A could ameliorate BBB injury in mice with cerebral ischemiareperfusion in a dose-dependent manner by strengthening the immunostaining volume of occludin,claudin-5,and zonula occludens-1.The amoeba morphologic changes of microglial combined with the elevated expression of Lrg1 could be relieved under the treatment of biochanin A.Biochanin A played a countervailing role on the rolling leukocytes in the vessel,while the leakage of blood vessels was reduced.Biochanin A diminished its functions to further improved attenuation for tight junction injury on conventional Lrg1-knockout mice,as well as the inhibition effects on TGF-β1,and the phosphorylation of suppressor of mothers against decapentaplegic 2(Smad2)/Smad2 via western blot assay.Conclusion:Biochanin A could alleviate tight junction injury induced by cerebral ischemiareperfusion and blocked the Lrg1/TGF-β/Smad2 pathway to modulate leukocyte migration patterns.展开更多
The attenuation function of Dalbergia odorifera leaves on cerebral ischemia-reperfusion(I/R)is little known.The candidate targets for the Chinese herb were extracted from brain tissues through the high-affinity chroma...The attenuation function of Dalbergia odorifera leaves on cerebral ischemia-reperfusion(I/R)is little known.The candidate targets for the Chinese herb were extracted from brain tissues through the high-affinity chromatography.The molecular mechanism of D.odorifera leaves on cerebral I/R was investigated.Methods:Serial affinity chromatography based on D.odorifera leaves extract(DLE)affinity matrices were applied to find specific binding proteins in the brain tissues implemented on C57BL/6 mice by intraluminal middle cerebral artery occlusion for 1 h and reperfusion for 24 h.Specific binding proteins were subjected to mass-spectrometry to search for the differentially expressed proteins between control and DLE-affinity matrices.The hub genes were screened based on weighted gene co-expression network analysis(WGCNA).Then,predictive biology and potential experimental verification were performed for the candidate genes.The protective role of DLE in blood-brain barrier damage in cerebral I/R mice was evaluated by the leakage of Evans blue,western blotting,immunohistochemistry,and immunofluorescent staining.Results:952 differentially expressed proteins were classified into seven modules based on WGCNA under soft threshold 6.Based on WGCNA,AKT1,PIK3CA,NOS3,SMAD3,SMAD1,IL6,MAPK1,TGFBR2,TGFBR1,MAPK3,IGF1R,LRG1,mTOR,ROCK1,TGFB1,IL1B,SMAD2,and SMAD518 candidate hub proteins were involved in turquoise module.TGF-β,MAPK,focal adhesion,and adherens junction signaling pathway were associated with candidate hub proteins.Gene ontology analysis demonstrated that candidate hub proteins were related to the TGF-βreceptor signaling pathway,common-partner SMAD protein phosphorylation,etc.DLE could significantly reduce the leakage of Evans blue in mice with cerebral I/R,while attenuating the expression of occludin,claudin-5,and zonula occludens-1.Western blotting demonstrated that regulation of TGF-β/SMAD signaling pathway played an essential role in the protective effect of DLE.Conclusion:Thus,a number of candidate hub proteins were identified based on DLE affinity chromatography through WGCNA.DLE could attenuate the dysfunction of bloodbrain barrier in the TGF-β/SMAD signaling pathway induced by cerebral I/R.展开更多
Recent approaches to the internal quality inspection of apples with the application of hyperspectral imaging technology are highly cost-intensive because of labor involvement for the data collection on a fixed posture...Recent approaches to the internal quality inspection of apples with the application of hyperspectral imaging technology are highly cost-intensive because of labor involvement for the data collection on a fixed posture and manual selection of the region of interest(RoI).In addition,several studies have repeated the data acquisition for the same apple.Current methods cannot meet the automation requirements of the sorting line.Therefore,this study proposed a novel method for automatically selecting RoI in hyperspectral images of apples with random poses.Firstly,the preliminary RoI selection of apple hyperspectral image was carried out,followed by the performance of histogram statistics of each pixel with spectral intensity at 700 nm wavelength.The top 40%area of the spectral intensity was reserved to obtain the magnitude relationship of the spectral intensity of each pixel point and a morphological erosion operation.Original apple RoI was acquired and overexposed pixels were removed with spectral intensity greater than 3900(maximum 4095)in the reserved area at 700 nm.Secondly,the relationship between apple size and prediction accuracy was measured for the in-depth RoI analysis.A partial least square regression(PLSR)model was established between the average spectrum and apple sugar content of RoI with different sizes.Finally,the established model with the top 70%of the spectral intensity achieved the best prediction accuracy.Non-destructive estimation of apple sugar content was performed through hyperspectral imaging technology with reference to the proposed RoI selection method.A competitive adaptive reweighted sampling algorithm along the PLSR(CARS-PLSR)model was established after black-and-white correction and standard normal transformation(SNV)preprocessing and obtained the highest prediction accuracy.The determination coefficient of cross-validation(R_(cv))and root mean square error of cross-validation(RMSECV)were 0.9595 and 0.3203°Brix,respectively.The determination coefficient of prediction(R_(p))was 0.9308,and the root mean square error of prediction(RMSEP)was 0.4681°Brix.Results proved that the auto-selection of RoI is an efficient and accurate method,which can provide a foundation in practical application for online apple grading systems with hyperspectral imaging technology.展开更多
The coefficient of restitution(CoR)is an important parameter for designing vibration-harvesting machinery.There are three main types of fruit-to-fruit collisions during vibration harvesting:collision between fruits co...The coefficient of restitution(CoR)is an important parameter for designing vibration-harvesting machinery.There are three main types of fruit-to-fruit collisions during vibration harvesting:collision between fruits collected using a collection device and falling fruits,collision between fruits on branches before being removed,and collision of fruits in the air.The CoR for the first two types of collision was investigated separately using drop and pendulum methods.However,there have been few studies on CoR for the collision of fruits in the air.In this study,a platform was designed to simulate the collision of fruits in the air during vibration harvesting for the‘Gala’apple,where influences of collision velocity on CoR were studied.Images from a high-speed camera were processed based on RGB to Lab conversion to extract the bruise surface and calculate the bruise volume.Total bruise volume,the sum of two apple bruise volumes,was calculated and analyzed in relation to the CoR.Results showed that the CoR decreased with collision velocity increasing from 1.0 m/s to 1.4 m/s,where the CoR reached 0.93 or higher when collision velocity was 1.0 m/s,making fruits not bruise,while fruits began to bruise when collision velocity increased from 1.2 m/s.The CoR did not continue to decrease when collision velocity exceeded 1.4 m/s due to rotation.There was little correlation between total bruise volume and the CoR due to the composite motion of fruits in the air,indicating that the CoR may not be an indicator to determine the degree of fruit bruise when the fruit made a composite motion during the collision.Therefore,this research is expected to guide the establishment of a more accurate fruit model to design optimal vibration harvesting machinery.展开更多
Separating pulp and core is critical for apricot processing,but faces labor shortages.To address this challenge,a fully automated pitting machine(FAPM)based on automatic apricot orientation device(AAOD)was proposed to...Separating pulp and core is critical for apricot processing,but faces labor shortages.To address this challenge,a fully automated pitting machine(FAPM)based on automatic apricot orientation device(AAOD)was proposed to achieve mechanized pitting by apricot automatic orientation.The designed and constructed AAOD adopt with dynamic visual detection and mechanical orientation for apricot posture adjustment.YOLOv8 series models were applied for apricot and stem detection,and then estimating their three-dimensional posture.Compared with other YOLOv8 series models,YOLOv8n was selected as the preferred detection model with a detection speed of 10.3 ms and a size of 6.1 MB to meet the need of real-time detection and lightweight deployment.YOLOv8n achieved precision(P),recall(R),and mean average precision(mAP)values of 82.0%,90.9%,and 90.1%,respectively.Moreover,new indicators,namely positional offsets in the image coordinate system(Offset_(img)),positional offsets(Offset_(3D)),angular offsets in the 3D coordinate system(Offset_(ang)),and the ratio of intersection to manual bounding box areas(Ratio_(im)),were proposed to validate the performance of AAOD for position estimation in three varieties of apricot.The best performance was obtained in Saimaiti apricot and achieved Offset_(img)of 2.9 pixels,Offset_(3D)of 1.2 mm,and Offset_(ang)of 0.9°,with Ratio_(im) for apricot and stem were 99.3%and 97.3%.Experimental show that the optimal operating parameters for AAOD are 20 rps for alignment wheel rotation speed and the distance of 22.5 mm from apricot base to alignment wheel axis,which presented the best successful orientation rate of 91.5%with an Offset_(3D)of 1.8 mm.Result demonstrated that the dynamic detection-based orientation approach proposed in this study has great potential for automatic apricot pitting.展开更多
Accurate watermelon yield estimation is crucial to the agricultural value chain,as it guides the allocation of agricultural resources as well as facilitates inventory and logistics planning.The conventional method of ...Accurate watermelon yield estimation is crucial to the agricultural value chain,as it guides the allocation of agricultural resources as well as facilitates inventory and logistics planning.The conventional method of watermelon yield estimation relies heavily onmanual labor,which is both time-consuming and labor-intensive.To address this,this work proposes an algorithmic pipeline that utilizes unmanned aerial vehicle(UAV)videos for detection and counting of watermelons.This pipeline uses You Only Look Once version 8 s(YOLOv8s)with panorama stitching and overlap partitioning,which facilitates the overall number estimation ofwatermelons in field.The watermelon detection model,based on YOLOv8s and obtained using transfer learning,achieved a detection accuracy of 99.20%,demonstrating its potential for application in yield estimation.The panorama stitching and overlap partitioning based detection and counting method uses panoramic images as input and effectively mitigates the duplications comparedwith the video tracking based detection and countingmethod.The counting accuracy reached over 96.61%,proving a promising application for yield estimation.The high accuracy demonstrates the feasibility of applying this method for overall yield estimation in large watermelon fields.展开更多
The harvesting of fresh kiwifruit is a labor-intensive operation that accounts for more than 25%of annual production costs.Mechanized harvesting technologies are thus being developed to reduce labor requirements for h...The harvesting of fresh kiwifruit is a labor-intensive operation that accounts for more than 25%of annual production costs.Mechanized harvesting technologies are thus being developed to reduce labor requirements for harvesting kiwifruit.To improve the efficiency of a harvesting robot for picking kiwifruit,we designed an end-effector,which we report herein along with the results of tests to verify its operation.By using the established method of automated picking discussed in the literature and which is based on the characteristics of kiwifruit,we propose an automated method to pick kiwifruit that consists of separating the fruit from its stem on the tree.This method is experimentally verified by using it to pick clustered kiwifruit in a scaffolding canopy cultivation.In the experiment,the end-effector approaches a fruit from below and then envelops and grabs it with two bionic fingers.The fingers are then bent to separate the fruit from its stem.The grabbing,picking,and unloading processes are integrated,with automated picking and unloading performed using a connecting rod linkage following a trajectory model.The trajectory was analyzed and validated by using a simulation implemented in the software Automatic Dynamic Analysis of Mechanical Systems(ADAMS).In addition,a prototype of an end-effector was constructed,and its bionic fingers were equipped with fiber sensors to detect the best position for grabbing the kiwifruit and pressure sensors to ensure that the damage threshold was respected while picking.Tolerances for size and shape were incorporated by following a trajectory groove from grabbing and picking to unloading.The end-effector separates clustered kiwifruit and automatically grabs individual fruits.It takes on average 4–5 s to pick a single fruit,with a successful picking rate of 94.2%in an orchard test featuring 240 samples.This study shows the grabbing–picking–unloading robotic end-effector has significant potential to facilitate the harvesting of kiwifruit.展开更多
Apple picking robot is now being developed as an alternative to hand picking due to a great demand for labor during apple harvest season.Accurate detection and localization of target fruit is necessary for robotic app...Apple picking robot is now being developed as an alternative to hand picking due to a great demand for labor during apple harvest season.Accurate detection and localization of target fruit is necessary for robotic apple picking.Detection accuracy has a great influence on localization results.Although current researches on detection and localization of apples using traditional image algorithms can obtain good results under laboratory conditions,it is difficult to accurately detect and locate objects in natural field with complex environments.With the rapid development of artificial intelligence,accuracy of apple detection based on deep learning has been significantly improved.Therefore,a deep learningbased method was developed to accurately detect and locate the position of fruit.For different localization methods,binocular localization is a widely used localization method for its bionic principle and lower equipment cost.Hence,this paper proposed an improved binocular localization method for apple based on fruit detection using deep learning.First,apples of binocular images were detected by Faster R-CNN.After that,a segmentation based on chromatic aberration and chromatic aberration ratio was applied to segment apple and background pixels in bounding box of detected fruit.Furthermore,template matching with parallel polar line constraint was used to match apples in left and right images.Finally,two feature points on apples were selected to directly calculate three dimensional coordinates of feature points with the binocular localization principle.In this study,Faster R-CNN achieved an AP of 88.12%with an average detection speed of 0.32 s for an image.Meanwhile,standard deviation and localization precision of depth of two feature points on each apple were calculated to evaluate localization.Results showed that the average standard deviation and the average localization precision of 76 groups of datasets were 0.51 cm and 99.64%,respectively.Results indicated that the proposed improved binocular localization method is promising for fruit localization。展开更多
The success of organic and green agricultural fruit production depends on quality and cost.As the kiwifruit industry becomes ever more commercialized,it is in the interests of the industry to mechanize production,whic...The success of organic and green agricultural fruit production depends on quality and cost.As the kiwifruit industry becomes ever more commercialized,it is in the interests of the industry to mechanize production,which can promote industrialization and improve industrial value and market prospects.Currently,New Zealand,Italy,Chile,and China carry out research into the mechanism of kiwifruit production.This review describes in detail the current state of the art of pollination,harvesting and grading equipment,including detection and identification,non-destructive end effector,harvesting robots and grading devices.Process technologies that include artificial pollination,harvest mechanization,grading and standardization of production problems are analysed and compared.These problems directly affect the quality of kiwifruit products.Finally,to solve the various problems that the kiwifruit industry experiences,it is necessary to accelerate the development of mechanized kiwifruit production,realize the mechanization of information acquisition and standardization in order to advance precision agriculture and agricultural wisdom for the future.Mechanization of the kiwifruit industry must adapt to adjustments in how China’s economic structure develops.展开更多
To design an automatic harvesting machine for hydroponic lettuce(Lactuca sativa L.),physical and mechanical properties of hydroponic lettuce were investigated and analyzed.Moisture content of stem,root and leaf,geomet...To design an automatic harvesting machine for hydroponic lettuce(Lactuca sativa L.),physical and mechanical properties of hydroponic lettuce were investigated and analyzed.Moisture content of stem,root and leaf,geometric characteristics,pulling force,and root cutting force were studied for harvesting hydroponic lettuce.The pulling force was examined by a tensile experiment,while the root cutting force was investigated by a shear experiment on the electronic universal testing machine.The moisture content of hydroponic lettuce was obtained by direct drying.Experiment data were processed using regression analysis and mathematical statistics method.A regression equation and the law of numerical distribution were obtained.The results showed that the geometric size of different hydroponic lettuce had little difference,and the distribution of physical parameters was concentrated.Moisture content was found statistically similar in stem and root(around 91%),while the highest moisture content was found in the leaf of 95.73%.The root cutting force decrease with the increase of cutting speed and decrease with the cutting position move downward.The minimum average root cutting force in the experiment was 1.41 N.The average pulling force was 13 N.This study provides adequate theoretical support for the design of the automatic harvesting machine of hydroponic lettuce.展开更多
To investigate the optimal parameters combination of reciprocating cutter for harvesting hydroponic lettuce automatically,a shear fixture was designed for cutting lettuce stems on a universal materials tester.Effects ...To investigate the optimal parameters combination of reciprocating cutter for harvesting hydroponic lettuce automatically,a shear fixture was designed for cutting lettuce stems on a universal materials tester.Effects of blade distance,sliding cutting angle,skew cutting angle,and shearing angle on shearing stress were investigated in this study.The orders of the significance of a single factor and double factors were analyzed using the response surface methodology(RSM).A scanning electron microscope was used to observe the microstructure of the lettuce stem to analyze the shearing characteristics at the microscopic level.The RSM results showed that the order of significance for single factors was(i)sliding cutting angle,(ii)shearing angle,(iii)skew cutting angle,and(iv)blade distance.The sliding cutting angle had a highly significant influence on the shearing stress.The order of significance for double factors was(i)blade distance and shearing angle,(ii)sliding cutting angle and skew cutting angle,and(iii)the sliding cutting angle and shearing angle.A quadratic model of the factors and shearing stress was built according to the response-surface results.The optimized combination of factors that gives the minimum shearing stress was observed that it reduced 69.9%of the maximum shearing stress value.This research can provide a reference for designing lettuce-cutting devices.展开更多
Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting.Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce.This study aims to demonstra...Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting.Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce.This study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models,i.e.Multiple Linear Regression(MLR),K-Nearest Neighbor(KNN),and Support Vector Machine(SVM).One-way analysis of variance was applied to reduce RGB,HSV,and L*a*b*features number of hydroponic lettuce images.Image binarization,image mask,and image filling methods were employed to segment hydroponic lettuce from an image for models testing.Results showed that G,H,and a*were selected from RGB,HSV,and L*a*b*for training models.It took about 20.25 s to detect an image with 30244032 pixels by KNN,which was much longer than MLR(0.61 s)and SVM(1.98 s).MLR got detection accuracies of 89.48%and 99.29%for yellow and rotten leaves,respectively,while SVM reached 98.33%and 97.91%,respectively.SVM was more robust than MLR in detecting yellow and rotten leaves of hydroponic.Thus,it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.展开更多
基金supported by the Foundation Project:National Natural Science Foundation of China(Nos.:82460249,82100417,81760094)The Foundation of Jiangxi Provincial Department of Science and Technology Outstanding Youth Fund Project(20242BAB23080).
文摘ABSTRACT:Background:After ischemic stroke,neutrophils hyperactivate,increasing in number and worsening inflammation,causing neural damage.Prior scRNA-seq showed Lrg1 modulates cells subsentence to cerebral ischemiareperfusion injury,but its mechanism in regulating neutrophil accumulation/differentiation post-injury is unclear.Methods:Lrg1 knockout impact on neutrophil accumulation was assessed via immunofluorescence and western blot.Three-dimensional reconstruction of immunofluorescent staining analyzed cell-cell interactions among neutrophils and microglia.scRNA-seq of WT and Lrg1^(-/-)mice from GSE245386 and GSE279462 was conducted.Each group conducted oxidative phosphorylation scoring via Gene Set Enrichment Analysis(GSEA),while Metascape was employed to perform GO and KEGG enrichment analyses for elucidating functional mechanisms.CellChat exhibited cell-cell communication.Furthermore,alterations in microglial phagocytic activity were evaluated by immunostaining for CD68,a well-established marker of phagolysosomal activity in phagocytic cells.Brain energy metabolism was evaluated via glutamate dehydrogenase activity and ATP levels with ELISA,and enzyme expression was analyzed by immunofluorescence and western blot.Results:Lrg1 knockout decreased neutrophil accumulation and NET formation in mice.3D immunofluorescence reconstruction confirmed neutrophil co-localization with endothelial cells/microglia.scRNA-seq revealed that the oxidative phosphorylation score was significantly higher in the MCAO/R+WT group compared to both the Sham-operated+WT and Lrg1^(-/-)groups.Notably,the oxidative phosphorylation score was further elevated in the MCAO/R+Lrg1^(-/-)group.Immunostaining showed that Lrg1 knockout elevated CD68+lysosome expression post-MCAO/R,with TMEM119 colocalizing with these lysosomes.MCAO/R raised CD68 expression in ischemic brains,an effect further intensified by Lrg1 knockout.KEGG analysis linked differential genes to oxidative phosphorylation pathways.Validation in MCAO/R vs.sham groups revealed increased ROS production and reduced expression of complex enzymes I-V(NDUFB8,SDHB,UQCRC1,MTCO2,ATP5A1).Lrg1 intervention increased enzyme expression.Immunofluorescence and western blot in brain tissue showed similar patterns in microglia and enzymes I-V.Conclusions:Lrg1 knockout significantly enhances microglial phagocytic activity towards neutrophils subsequent to cerebral ischemia-reperfusion injury,through its regulatory effect on the oxidative phosphorylation pathway.This finding accentuates Lrg1 as a highly potential therapeutic target for intervening in and modulating post-ischemic inflammatory responses.
基金supported by a Foundation Project:National Natural Science Foundation of China(Nos.82100417,81760094),ChinaThe Foundation of Jiangxi Provincial Department of Science and Technology Project(Nos.20202ACBL206001,20212BAB206022,20181BAB205026).
文摘Background:Biochanin A is an excellent dietary isoflavone that has the concomitant function of both medicine and foodstuff.The attenuation function of biochanin A on blood-brain barrier(BBB)damage induced by cerebral ischemia-reperfusion remains unclear.Methods:C57BL/6 mice were subjected to 1 h middle cerebral artery occlusion(MCAO)followed by 24 h reperfusion.The infarct volume of the brain was stained by TTC,while leakage of the brain was quantitatively stained by Evans blue,and the neurologic deficit score was measured.Microglial-induced morphologic changes were observed via immunofluorescence staining,and rolling and adhering leukocytes in venules were observed via two-photon imaging,while the inner fluorescein isothiocyanate-albumin of venules were compared with those of surrounding interstitial area through venular albumin leakage.Results:The attenuation effect of biochanin A on tight junction injury was compared in ischemia-reperfusion mice or conventional knockdown of leucine-richα2-glycoprotein 1(Lrg1)mice.Biochanin A could ameliorate BBB injury in mice with cerebral ischemiareperfusion in a dose-dependent manner by strengthening the immunostaining volume of occludin,claudin-5,and zonula occludens-1.The amoeba morphologic changes of microglial combined with the elevated expression of Lrg1 could be relieved under the treatment of biochanin A.Biochanin A played a countervailing role on the rolling leukocytes in the vessel,while the leakage of blood vessels was reduced.Biochanin A diminished its functions to further improved attenuation for tight junction injury on conventional Lrg1-knockout mice,as well as the inhibition effects on TGF-β1,and the phosphorylation of suppressor of mothers against decapentaplegic 2(Smad2)/Smad2 via western blot assay.Conclusion:Biochanin A could alleviate tight junction injury induced by cerebral ischemiareperfusion and blocked the Lrg1/TGF-β/Smad2 pathway to modulate leukocyte migration patterns.
基金supported by National Natural Science Foundation of China(Nos.82100417,81760094,81760724)The Foundation of Jiangxi Provincial Department of Science and Technology Project(Nos.20202ACBL206001,20212BAB206022,20181BAB205026)+1 种基金Youth Project of Jiangxi Education Department(No.GJJ200217)Open Project of Key Laboratory of Modern of TCM,Ministry of Education Jiangxi University of Traditional Chinese Medicine(TCM-2019010).
文摘The attenuation function of Dalbergia odorifera leaves on cerebral ischemia-reperfusion(I/R)is little known.The candidate targets for the Chinese herb were extracted from brain tissues through the high-affinity chromatography.The molecular mechanism of D.odorifera leaves on cerebral I/R was investigated.Methods:Serial affinity chromatography based on D.odorifera leaves extract(DLE)affinity matrices were applied to find specific binding proteins in the brain tissues implemented on C57BL/6 mice by intraluminal middle cerebral artery occlusion for 1 h and reperfusion for 24 h.Specific binding proteins were subjected to mass-spectrometry to search for the differentially expressed proteins between control and DLE-affinity matrices.The hub genes were screened based on weighted gene co-expression network analysis(WGCNA).Then,predictive biology and potential experimental verification were performed for the candidate genes.The protective role of DLE in blood-brain barrier damage in cerebral I/R mice was evaluated by the leakage of Evans blue,western blotting,immunohistochemistry,and immunofluorescent staining.Results:952 differentially expressed proteins were classified into seven modules based on WGCNA under soft threshold 6.Based on WGCNA,AKT1,PIK3CA,NOS3,SMAD3,SMAD1,IL6,MAPK1,TGFBR2,TGFBR1,MAPK3,IGF1R,LRG1,mTOR,ROCK1,TGFB1,IL1B,SMAD2,and SMAD518 candidate hub proteins were involved in turquoise module.TGF-β,MAPK,focal adhesion,and adherens junction signaling pathway were associated with candidate hub proteins.Gene ontology analysis demonstrated that candidate hub proteins were related to the TGF-βreceptor signaling pathway,common-partner SMAD protein phosphorylation,etc.DLE could significantly reduce the leakage of Evans blue in mice with cerebral I/R,while attenuating the expression of occludin,claudin-5,and zonula occludens-1.Western blotting demonstrated that regulation of TGF-β/SMAD signaling pathway played an essential role in the protective effect of DLE.Conclusion:Thus,a number of candidate hub proteins were identified based on DLE affinity chromatography through WGCNA.DLE could attenuate the dysfunction of bloodbrain barrier in the TGF-β/SMAD signaling pathway induced by cerebral I/R.
基金financially supported by Shandong Provincial Natural Science Foundation,China(Grant No.ZR2022MC067)the National Key R&D Program of China(Grant No.2021YFB3901303)+2 种基金the Key R&D Program of Shandong Province,China(Grant No.2022 CXGC010610)the Agricultural Scientific and Technological Innovation Project of Shandong Academy of Agricultural Sciences,China(Grant No.CXGC2023D02)the Special International Cooperation Program of Shandong Academy of Agricultural Sciences,China(Grant No.CXGC2023G24).
文摘Recent approaches to the internal quality inspection of apples with the application of hyperspectral imaging technology are highly cost-intensive because of labor involvement for the data collection on a fixed posture and manual selection of the region of interest(RoI).In addition,several studies have repeated the data acquisition for the same apple.Current methods cannot meet the automation requirements of the sorting line.Therefore,this study proposed a novel method for automatically selecting RoI in hyperspectral images of apples with random poses.Firstly,the preliminary RoI selection of apple hyperspectral image was carried out,followed by the performance of histogram statistics of each pixel with spectral intensity at 700 nm wavelength.The top 40%area of the spectral intensity was reserved to obtain the magnitude relationship of the spectral intensity of each pixel point and a morphological erosion operation.Original apple RoI was acquired and overexposed pixels were removed with spectral intensity greater than 3900(maximum 4095)in the reserved area at 700 nm.Secondly,the relationship between apple size and prediction accuracy was measured for the in-depth RoI analysis.A partial least square regression(PLSR)model was established between the average spectrum and apple sugar content of RoI with different sizes.Finally,the established model with the top 70%of the spectral intensity achieved the best prediction accuracy.Non-destructive estimation of apple sugar content was performed through hyperspectral imaging technology with reference to the proposed RoI selection method.A competitive adaptive reweighted sampling algorithm along the PLSR(CARS-PLSR)model was established after black-and-white correction and standard normal transformation(SNV)preprocessing and obtained the highest prediction accuracy.The determination coefficient of cross-validation(R_(cv))and root mean square error of cross-validation(RMSECV)were 0.9595 and 0.3203°Brix,respectively.The determination coefficient of prediction(R_(p))was 0.9308,and the root mean square error of prediction(RMSEP)was 0.4681°Brix.Results proved that the auto-selection of RoI is an efficient and accurate method,which can provide a foundation in practical application for online apple grading systems with hyperspectral imaging technology.
基金supported by the National Natural Science Foundation of China(Grant No.32171897)National Foreign Expert Project,Ministry of Human Resources and Social Security,China(Grant No.H20240238,No.Y20240046)Science and Technology Program of Yulin City,China(Grant No.2023-CXY-183).
文摘The coefficient of restitution(CoR)is an important parameter for designing vibration-harvesting machinery.There are three main types of fruit-to-fruit collisions during vibration harvesting:collision between fruits collected using a collection device and falling fruits,collision between fruits on branches before being removed,and collision of fruits in the air.The CoR for the first two types of collision was investigated separately using drop and pendulum methods.However,there have been few studies on CoR for the collision of fruits in the air.In this study,a platform was designed to simulate the collision of fruits in the air during vibration harvesting for the‘Gala’apple,where influences of collision velocity on CoR were studied.Images from a high-speed camera were processed based on RGB to Lab conversion to extract the bruise surface and calculate the bruise volume.Total bruise volume,the sum of two apple bruise volumes,was calculated and analyzed in relation to the CoR.Results showed that the CoR decreased with collision velocity increasing from 1.0 m/s to 1.4 m/s,where the CoR reached 0.93 or higher when collision velocity was 1.0 m/s,making fruits not bruise,while fruits began to bruise when collision velocity increased from 1.2 m/s.The CoR did not continue to decrease when collision velocity exceeded 1.4 m/s due to rotation.There was little correlation between total bruise volume and the CoR due to the composite motion of fruits in the air,indicating that the CoR may not be an indicator to determine the degree of fruit bruise when the fruit made a composite motion during the collision.Therefore,this research is expected to guide the establishment of a more accurate fruit model to design optimal vibration harvesting machinery.
基金supported by the Tianshan Top Youth Science and Technology Talent of Xinjiang,China(2022TSYCCX0066)Xinjiang Minority Scientific and Technological Talents Special Training Project(2022D03007)+1 种基金National Natural Science Foundation of China(32171897)National Foreign Expert Project,Ministry of Science and Technology,China(DL2022172003L,QN2022172006L).
文摘Separating pulp and core is critical for apricot processing,but faces labor shortages.To address this challenge,a fully automated pitting machine(FAPM)based on automatic apricot orientation device(AAOD)was proposed to achieve mechanized pitting by apricot automatic orientation.The designed and constructed AAOD adopt with dynamic visual detection and mechanical orientation for apricot posture adjustment.YOLOv8 series models were applied for apricot and stem detection,and then estimating their three-dimensional posture.Compared with other YOLOv8 series models,YOLOv8n was selected as the preferred detection model with a detection speed of 10.3 ms and a size of 6.1 MB to meet the need of real-time detection and lightweight deployment.YOLOv8n achieved precision(P),recall(R),and mean average precision(mAP)values of 82.0%,90.9%,and 90.1%,respectively.Moreover,new indicators,namely positional offsets in the image coordinate system(Offset_(img)),positional offsets(Offset_(3D)),angular offsets in the 3D coordinate system(Offset_(ang)),and the ratio of intersection to manual bounding box areas(Ratio_(im)),were proposed to validate the performance of AAOD for position estimation in three varieties of apricot.The best performance was obtained in Saimaiti apricot and achieved Offset_(img)of 2.9 pixels,Offset_(3D)of 1.2 mm,and Offset_(ang)of 0.9°,with Ratio_(im) for apricot and stem were 99.3%and 97.3%.Experimental show that the optimal operating parameters for AAOD are 20 rps for alignment wheel rotation speed and the distance of 22.5 mm from apricot base to alignment wheel axis,which presented the best successful orientation rate of 91.5%with an Offset_(3D)of 1.8 mm.Result demonstrated that the dynamic detection-based orientation approach proposed in this study has great potential for automatic apricot pitting.
基金supported by the National Natural Science Foundation of China(32371999)Science and Technology Program of Yulin City,China(2023-CXY-183)+1 种基金Open Project of Key Laboratory of Agricultural Equipment for Hilly and Mountainous Areas in Southeastern China(Co-construction by Ministry and Province),Ministry of Agriculture and Rural Affairs,China(QSKF2023002)National Foreign Expert Project,Ministry of Science and Technology,China(QN2022172006L,DL2022172003L).
文摘Accurate watermelon yield estimation is crucial to the agricultural value chain,as it guides the allocation of agricultural resources as well as facilitates inventory and logistics planning.The conventional method of watermelon yield estimation relies heavily onmanual labor,which is both time-consuming and labor-intensive.To address this,this work proposes an algorithmic pipeline that utilizes unmanned aerial vehicle(UAV)videos for detection and counting of watermelons.This pipeline uses You Only Look Once version 8 s(YOLOv8s)with panorama stitching and overlap partitioning,which facilitates the overall number estimation ofwatermelons in field.The watermelon detection model,based on YOLOv8s and obtained using transfer learning,achieved a detection accuracy of 99.20%,demonstrating its potential for application in yield estimation.The panorama stitching and overlap partitioning based detection and counting method uses panoramic images as input and effectively mitigates the duplications comparedwith the video tracking based detection and countingmethod.The counting accuracy reached over 96.61%,proving a promising application for yield estimation.The high accuracy demonstrates the feasibility of applying this method for overall yield estimation in large watermelon fields.
基金This research was conducted in the College of Mechanical and Electronic Engineering,Northwest A&F University,and was supported by research grants from the General Program of the National Natural Science Foundation of China(61175099).
文摘The harvesting of fresh kiwifruit is a labor-intensive operation that accounts for more than 25%of annual production costs.Mechanized harvesting technologies are thus being developed to reduce labor requirements for harvesting kiwifruit.To improve the efficiency of a harvesting robot for picking kiwifruit,we designed an end-effector,which we report herein along with the results of tests to verify its operation.By using the established method of automated picking discussed in the literature and which is based on the characteristics of kiwifruit,we propose an automated method to pick kiwifruit that consists of separating the fruit from its stem on the tree.This method is experimentally verified by using it to pick clustered kiwifruit in a scaffolding canopy cultivation.In the experiment,the end-effector approaches a fruit from below and then envelops and grabs it with two bionic fingers.The fingers are then bent to separate the fruit from its stem.The grabbing,picking,and unloading processes are integrated,with automated picking and unloading performed using a connecting rod linkage following a trajectory model.The trajectory was analyzed and validated by using a simulation implemented in the software Automatic Dynamic Analysis of Mechanical Systems(ADAMS).In addition,a prototype of an end-effector was constructed,and its bionic fingers were equipped with fiber sensors to detect the best position for grabbing the kiwifruit and pressure sensors to ensure that the damage threshold was respected while picking.Tolerances for size and shape were incorporated by following a trajectory groove from grabbing and picking to unloading.The end-effector separates clustered kiwifruit and automatically grabs individual fruits.It takes on average 4–5 s to pick a single fruit,with a successful picking rate of 94.2%in an orchard test featuring 240 samples.This study shows the grabbing–picking–unloading robotic end-effector has significant potential to facilitate the harvesting of kiwifruit.
基金the National Natural Science of China(32171897)Youth Science and Technology Nova Program in Shaanxi Province of China(2021KJXX-94)+1 种基金Science and Technology Promotion Program of Northwest A&F University(TGZX2021-29)Recruitment Program of High-End Foreign Experts of the State Administration of Foreign Experts Affairs,Ministry of Science and Technology,China(G20200027075).
文摘Apple picking robot is now being developed as an alternative to hand picking due to a great demand for labor during apple harvest season.Accurate detection and localization of target fruit is necessary for robotic apple picking.Detection accuracy has a great influence on localization results.Although current researches on detection and localization of apples using traditional image algorithms can obtain good results under laboratory conditions,it is difficult to accurately detect and locate objects in natural field with complex environments.With the rapid development of artificial intelligence,accuracy of apple detection based on deep learning has been significantly improved.Therefore,a deep learningbased method was developed to accurately detect and locate the position of fruit.For different localization methods,binocular localization is a widely used localization method for its bionic principle and lower equipment cost.Hence,this paper proposed an improved binocular localization method for apple based on fruit detection using deep learning.First,apples of binocular images were detected by Faster R-CNN.After that,a segmentation based on chromatic aberration and chromatic aberration ratio was applied to segment apple and background pixels in bounding box of detected fruit.Furthermore,template matching with parallel polar line constraint was used to match apples in left and right images.Finally,two feature points on apples were selected to directly calculate three dimensional coordinates of feature points with the binocular localization principle.In this study,Faster R-CNN achieved an AP of 88.12%with an average detection speed of 0.32 s for an image.Meanwhile,standard deviation and localization precision of depth of two feature points on each apple were calculated to evaluate localization.Results showed that the average standard deviation and the average localization precision of 76 groups of datasets were 0.51 cm and 99.64%,respectively.Results indicated that the proposed improved binocular localization method is promising for fruit localization。
基金supported by research grants from the General Program of the National Natural Science Foundation of China(61175099).
文摘The success of organic and green agricultural fruit production depends on quality and cost.As the kiwifruit industry becomes ever more commercialized,it is in the interests of the industry to mechanize production,which can promote industrialization and improve industrial value and market prospects.Currently,New Zealand,Italy,Chile,and China carry out research into the mechanism of kiwifruit production.This review describes in detail the current state of the art of pollination,harvesting and grading equipment,including detection and identification,non-destructive end effector,harvesting robots and grading devices.Process technologies that include artificial pollination,harvest mechanization,grading and standardization of production problems are analysed and compared.These problems directly affect the quality of kiwifruit products.Finally,to solve the various problems that the kiwifruit industry experiences,it is necessary to accelerate the development of mechanized kiwifruit production,realize the mechanization of information acquisition and standardization in order to advance precision agriculture and agricultural wisdom for the future.Mechanization of the kiwifruit industry must adapt to adjustments in how China’s economic structure develops.
基金supported by the Key Research and Development Program in Shaanxi Province of China[grant number 2018TSCXL-NY-05-04,2019ZDLNY02-04].
文摘To design an automatic harvesting machine for hydroponic lettuce(Lactuca sativa L.),physical and mechanical properties of hydroponic lettuce were investigated and analyzed.Moisture content of stem,root and leaf,geometric characteristics,pulling force,and root cutting force were studied for harvesting hydroponic lettuce.The pulling force was examined by a tensile experiment,while the root cutting force was investigated by a shear experiment on the electronic universal testing machine.The moisture content of hydroponic lettuce was obtained by direct drying.Experiment data were processed using regression analysis and mathematical statistics method.A regression equation and the law of numerical distribution were obtained.The results showed that the geometric size of different hydroponic lettuce had little difference,and the distribution of physical parameters was concentrated.Moisture content was found statistically similar in stem and root(around 91%),while the highest moisture content was found in the leaf of 95.73%.The root cutting force decrease with the increase of cutting speed and decrease with the cutting position move downward.The minimum average root cutting force in the experiment was 1.41 N.The average pulling force was 13 N.This study provides adequate theoretical support for the design of the automatic harvesting machine of hydroponic lettuce.
基金This research was supported by the Key Research and Development Program in Shaanxi Province of China(Grant No.2018TSCXL-NY-05-04,2019ZDLNY02-04)Science and Technology Program in Yulin City of China(Grant No.CXY-2020-076).
文摘To investigate the optimal parameters combination of reciprocating cutter for harvesting hydroponic lettuce automatically,a shear fixture was designed for cutting lettuce stems on a universal materials tester.Effects of blade distance,sliding cutting angle,skew cutting angle,and shearing angle on shearing stress were investigated in this study.The orders of the significance of a single factor and double factors were analyzed using the response surface methodology(RSM).A scanning electron microscope was used to observe the microstructure of the lettuce stem to analyze the shearing characteristics at the microscopic level.The RSM results showed that the order of significance for single factors was(i)sliding cutting angle,(ii)shearing angle,(iii)skew cutting angle,and(iv)blade distance.The sliding cutting angle had a highly significant influence on the shearing stress.The order of significance for double factors was(i)blade distance and shearing angle,(ii)sliding cutting angle and skew cutting angle,and(iii)the sliding cutting angle and shearing angle.A quadratic model of the factors and shearing stress was built according to the response-surface results.The optimized combination of factors that gives the minimum shearing stress was observed that it reduced 69.9%of the maximum shearing stress value.This research can provide a reference for designing lettuce-cutting devices.
基金the Science and Technology Program in Yulin City of China(CXY-2020-076,CXY-2019-129)Youth Science and Technology Nova Program in Shaanxi Province of China(2021KJXX-94)+1 种基金Key Research and Development Program of Shaanxi(2021NY-135)Recruitment Program of High-End Foreign Experts of the State Administration of Foreign Experts Affairs,Ministry of Science and Technology,China(G20200027075)。
文摘Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting.Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce.This study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models,i.e.Multiple Linear Regression(MLR),K-Nearest Neighbor(KNN),and Support Vector Machine(SVM).One-way analysis of variance was applied to reduce RGB,HSV,and L*a*b*features number of hydroponic lettuce images.Image binarization,image mask,and image filling methods were employed to segment hydroponic lettuce from an image for models testing.Results showed that G,H,and a*were selected from RGB,HSV,and L*a*b*for training models.It took about 20.25 s to detect an image with 30244032 pixels by KNN,which was much longer than MLR(0.61 s)and SVM(1.98 s).MLR got detection accuracies of 89.48%and 99.29%for yellow and rotten leaves,respectively,while SVM reached 98.33%and 97.91%,respectively.SVM was more robust than MLR in detecting yellow and rotten leaves of hydroponic.Thus,it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.
