The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in ...The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.展开更多
BACKGROUND Trastuzumab-targeted therapy is currently the standard of care for advanced human epidermal growth factor receptor 2(HER2)-positive gastric cancer.However,the emergence of resistance to trastuzumab poses si...BACKGROUND Trastuzumab-targeted therapy is currently the standard of care for advanced human epidermal growth factor receptor 2(HER2)-positive gastric cancer.However,the emergence of resistance to trastuzumab poses significant challenges.AIM To identify the key genes associated with trastuzumab resistance.These results provide a basis for the development of interventions to address drug resistance and improve patient outcomes.METHODS High-throughput sequencing and bioinformatics were used to identify the differentially expressed pivotal gene BIRC3 and delineate its potential function and pathway regulation.Tumor samples were collected from patients with HER2-positive gastric cancer to evaluate the correlation between BIRC3 expression and trastuzumab resistance.We established gastric cancer cell lines with both highly expressed and suppressed levels of BIRC3,followed by comprehensive in vitro and in vivo experiments to confirm the involvement of BIRC3 in trastuzumab resistance and to elucidate its underlying mechanisms.RESULTS In patients with HER2-positive gastric cancer,there is a significant correlation between elevated BIRC3 expression in tumor tissues and higher T stage,tumor node metastasis stage,as well as poor overall survival and progressionfree survival.BIRC3 is highly expressed in trastuzumab-resistant gastric cancer cell lines,where it inhibits tumor cell apoptosis and enhances trastuzumab resistance by promoting the phosphorylation and activation of the phosphoinositide 3-kinase-Akt(PI3K-AKT)pathway in HER2-positive gastric cancer cells,both in vivo and in vitro.CONCLUSION This study revealed a robust association between high BIRC3 expression and an unfavorable prognosis in patients with HER2-positive gastric cancer.Thus,the high expression of BIRC3 stimulated PI3K-AKT phosphorylation and activation,stimulating the proliferation of HER2-positive tumor cells and suppressing apoptosis,ultimately leading to trastuzumab resistance.展开更多
Background Intelligent garments,a burgeoning class of wearable devices,have extensive applications in domains such as sports training and medical rehabilitation.Nonetheless,existing research in the smart wearables dom...Background Intelligent garments,a burgeoning class of wearable devices,have extensive applications in domains such as sports training and medical rehabilitation.Nonetheless,existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity,often skipping crucial aspects related to user experience and interaction.Methods To address this gap,this study introduces a novel real-time 3D interactive system based on intelligent garments.The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements,thereby achieving real-time interaction between users and sensors.Additionally,the system incorporates 3D human visualization functionality,which visualizes sensor data and recognizes human actions as 3D models in real time,providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion.This system has significant potential for applications in motion detection,medical monitoring,virtual reality,and other fields.The accurate classification of human actions contributes to the development of personalized training plans and injury prevention strategies.Conclusions This study has substantial implications in the domains of intelligent garments,human motion monitoring,and digital twin visualization.The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.展开更多
3D human pose estimation is a major focus area in the field of computer vision,which plays an important role in practical applications.This article summarizes the framework and research progress related to the estimat...3D human pose estimation is a major focus area in the field of computer vision,which plays an important role in practical applications.This article summarizes the framework and research progress related to the estimation of monocular RGB images and videos.An overall perspective ofmethods integrated with deep learning is introduced.Novel image-based and video-based inputs are proposed as the analysis framework.From this viewpoint,common problems are discussed.The diversity of human postures usually leads to problems such as occlusion and ambiguity,and the lack of training datasets often results in poor generalization ability of the model.Regression methods are crucial for solving such problems.Considering image-based input,the multi-view method is commonly used to solve occlusion problems.Here,the multi-view method is analyzed comprehensively.By referring to video-based input,the human prior knowledge of restricted motion is used to predict human postures.In addition,structural constraints are widely used as prior knowledge.Furthermore,weakly supervised learningmethods are studied and discussed for these two types of inputs to improve the model generalization ability.The problem of insufficient training datasets must also be considered,especially because 3D datasets are usually biased and limited.Finally,emerging and popular datasets and evaluation indicators are discussed.The characteristics of the datasets and the relationships of the indicators are explained and highlighted.Thus,this article can be useful and instructive for researchers who are lacking in experience and find this field confusing.In addition,by providing an overview of 3D human pose estimation,this article sorts and refines recent studies on 3D human pose estimation.It describes kernel problems and common useful methods,and discusses the scope for further research.展开更多
With the advancement of image sensing technology, estimating 3Dhuman pose frommonocular video has becomea hot research topic in computer vision. 3D human pose estimation is an essential prerequisite for subsequentacti...With the advancement of image sensing technology, estimating 3Dhuman pose frommonocular video has becomea hot research topic in computer vision. 3D human pose estimation is an essential prerequisite for subsequentaction analysis and understanding. It empowers a wide spectrum of potential applications in various areas, suchas intelligent transportation, human-computer interaction, and medical rehabilitation. Currently, some methodsfor 3D human pose estimation in monocular video employ temporal convolutional network (TCN) to extractinter-frame feature relationships, but the majority of them suffer from insufficient inter-frame feature relationshipextractions. In this paper, we decompose the 3D joint location regression into the bone direction and length, wepropose the TCG, a temporal convolutional network incorporating Gaussian error linear units (GELU), to solvebone direction. It enablesmore inter-frame features to be captured andmakes the utmost of the feature relationshipsbetween data. Furthermore, we adopt kinematic structural information to solve bone length enhancing the use ofintra-frame joint features. Finally, we design a loss function for joint training of the bone direction estimationnetwork with the bone length estimation network. The proposed method has extensively experimented on thepublic benchmark dataset Human3.6M. Both quantitative and qualitative experimental results showed that theproposed method can achieve more accurate 3D human pose estimations.展开更多
3-dimension(3-D)printing technology is growing strongly with many applications,one of which is the garment industry.The application of human body models to the garment industry is necessary to respond to the increasin...3-dimension(3-D)printing technology is growing strongly with many applications,one of which is the garment industry.The application of human body models to the garment industry is necessary to respond to the increasing personalization demand and still guarantee aesthetics.This paper proposes amethod to construct 3-D human models by applying deep learning.We calculate the location of the main slices of the human body,including the neck,chest,belly,buttocks,and the rings of the extremities,using pre-existing information.Then,on the positioning frame,we find the key points(fixed and unaltered)of these key slices and update these points tomatch the current parameters.To add points to a star slice,we use a deep learning model tomimic the form of the human body at that slice position.We use interpolation to produce sub-slices of different body sections based on the main slices to create complete body parts morphologically.We combine all slices to construct a full 3-D representation of the human body.展开更多
Purpose: Patient-specific quality assurance (PSQA) requires manual operation of different workstations, which is time-consuming and error-prone. Therefore, developing automated solutions to improve efficiency and accu...Purpose: Patient-specific quality assurance (PSQA) requires manual operation of different workstations, which is time-consuming and error-prone. Therefore, developing automated solutions to improve efficiency and accuracy is a priority. The purpose of this study was to develop a general software interface with scripting on a human interactive device (HID) for improving the efficiency and accuracy of manual quality assurance (QA) procedures. Methods: As an initial application, we aimed to automate our PSQA workflow that involves Varian Eclipse treatment planning system, Elekta MOSAIQ oncology information system and PTW Verisoft application. A general platform, the AutoFrame interface with two imbedded subsystems—the AutoFlow and the PyFlow, was developed with a scripting language for automating human operations of aforementioned systems. The interface included three functional modules: GUI module, UDF script interpreter and TCP/IP communication module. All workstations in the PSQA process were connected, and most manual operations were automated by AutoFrame sequentially or in parallel. Results: More than 20 PSQA tasks were performed both manually and using the developed AutoFrame interface. On average, 175 (±12) manual operations of the PSQA procedure were eliminated and performed by the automated process. The time to complete a PSQA task was 8.23 (±0.78) minutes for the automated workflow, in comparison to 13.91 (±3.01) minutes needed for manual operations. Conclusion: We have developed the AutoFrame interface framework that successfully automated our PSQA procedure, and significantly reduced the time, human (control/clicking/typing) errors, and operators’ stress. Future work will focus on improving the system’s flexibility and stability and extending its operations to other QA procedures.展开更多
AIM:To determine whether the microRNA-27b-3p(miR-27b-3p)/NF-E2-related factor 2(Nrf2)pathway plays a role in human retinal pigment epithelial(hRPE)cell response to high glucose,how miR-27b-3p and Nrf2 expression are r...AIM:To determine whether the microRNA-27b-3p(miR-27b-3p)/NF-E2-related factor 2(Nrf2)pathway plays a role in human retinal pigment epithelial(hRPE)cell response to high glucose,how miR-27b-3p and Nrf2 expression are regulated,and whether this pathway could be specifically targeted.METHODS:hRPE cells were cultured in normal glucose or high glucose for 1,3,or 6d before measuring cellular proliferation rates using cell counting kit-8 and reactive oxygen species(ROS)levels using a dihydroethidium kit.miR-27b-3p,Nrf2,NAD(P)H quinone oxidoreductase 1(NQO1)and heme oxygenase-1(HO-1)mRNA and protein levels were analyzed using reverse transcription quantitative polymerase chain reaction(RT-qPCR)and immunocytofluorescence(ICF),respectively.Western blot analyses were performed to determine nuclear and total Nrf2 protein levels.Nrf2,NQO1,and HO-1 expression levels by RT-qPCR,ICF,or Western blot were further tested after miR-27b-3p overexpression or inhibitor lentiviral transfection.Finally,the expression level of those target genes was analyzed after treating hRPE cells with pyridoxamine.RESULTS:Persistent exposure to high glucose gradually suppressed hRPE Nrf2,NQO1,and HO-1 mRNA and protein levels and increased miR-27b-3p mRNA levels.High glucose also promoted ROS release and inhibited cellular proliferation.Nrf2,NQO1,and HO-1 mRNA levels decreased after miR-27b-3p overexpression and,conversely,both mRNA and protein levels increased after expressing a miR-27b-3p inhibitor.After treating hRPE cells exposed to high glucose with pyridoxamine,ROS levels tended to decreased,proliferation rate increased,Nrf2,NQO1,and HO-1 mRNA and protein levels were upregulated,and miR-27b-3p mRNA levels were suppressed.CONCLUSION:Nrf2 is a downstream target of miR-27b-3p.Furthermore,the miR-27b-3p inhibitor pyridoxamine can alleviate high glucose injury by regulating the miR-27b-3p/Nrf2 axis.展开更多
基金supported by the Medical Special Cultivation Project of Anhui University of Science and Technology(Grant No.YZ2023H2B013)the Anhui Provincial Key Research and Development Project(Grant No.2022i01020015)the Open Project of Key Laboratory of Conveyance Equipment(East China Jiaotong University),Ministry of Education(KLCE2022-01).
文摘The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.
文摘BACKGROUND Trastuzumab-targeted therapy is currently the standard of care for advanced human epidermal growth factor receptor 2(HER2)-positive gastric cancer.However,the emergence of resistance to trastuzumab poses significant challenges.AIM To identify the key genes associated with trastuzumab resistance.These results provide a basis for the development of interventions to address drug resistance and improve patient outcomes.METHODS High-throughput sequencing and bioinformatics were used to identify the differentially expressed pivotal gene BIRC3 and delineate its potential function and pathway regulation.Tumor samples were collected from patients with HER2-positive gastric cancer to evaluate the correlation between BIRC3 expression and trastuzumab resistance.We established gastric cancer cell lines with both highly expressed and suppressed levels of BIRC3,followed by comprehensive in vitro and in vivo experiments to confirm the involvement of BIRC3 in trastuzumab resistance and to elucidate its underlying mechanisms.RESULTS In patients with HER2-positive gastric cancer,there is a significant correlation between elevated BIRC3 expression in tumor tissues and higher T stage,tumor node metastasis stage,as well as poor overall survival and progressionfree survival.BIRC3 is highly expressed in trastuzumab-resistant gastric cancer cell lines,where it inhibits tumor cell apoptosis and enhances trastuzumab resistance by promoting the phosphorylation and activation of the phosphoinositide 3-kinase-Akt(PI3K-AKT)pathway in HER2-positive gastric cancer cells,both in vivo and in vitro.CONCLUSION This study revealed a robust association between high BIRC3 expression and an unfavorable prognosis in patients with HER2-positive gastric cancer.Thus,the high expression of BIRC3 stimulated PI3K-AKT phosphorylation and activation,stimulating the proliferation of HER2-positive tumor cells and suppressing apoptosis,ultimately leading to trastuzumab resistance.
基金Supported by the National Natural Science Foundation of China (62202346)Hubei Key Research and Development Program (2021BAA042)+3 种基金Open project of Engineering Research Center of Hubei Province for Clothing Information (2022HBCI01)Wuhan Applied Basic Frontier Research Project (2022013988065212)MIIT′s AI Industry Innovation Task Unveils Flagship Projects (Key Technologies,Equipment,and Systems for Flexible Customized and Intelligent Manufacturing in the Clothing Industry)Hubei Science and Technology Project of Safe Production Special Fund (Scene Control Platform Based on Proprioception Information Computing of Artificial Intelligence)。
文摘Background Intelligent garments,a burgeoning class of wearable devices,have extensive applications in domains such as sports training and medical rehabilitation.Nonetheless,existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity,often skipping crucial aspects related to user experience and interaction.Methods To address this gap,this study introduces a novel real-time 3D interactive system based on intelligent garments.The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements,thereby achieving real-time interaction between users and sensors.Additionally,the system incorporates 3D human visualization functionality,which visualizes sensor data and recognizes human actions as 3D models in real time,providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion.This system has significant potential for applications in motion detection,medical monitoring,virtual reality,and other fields.The accurate classification of human actions contributes to the development of personalized training plans and injury prevention strategies.Conclusions This study has substantial implications in the domains of intelligent garments,human motion monitoring,and digital twin visualization.The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.
基金supported by the Program of Entrepreneurship and Innovation Ph.D.in Jiangsu Province(JSSCBS20211175)the School Ph.D.Talent Funding(Z301B2055)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(21KJB520002).
文摘3D human pose estimation is a major focus area in the field of computer vision,which plays an important role in practical applications.This article summarizes the framework and research progress related to the estimation of monocular RGB images and videos.An overall perspective ofmethods integrated with deep learning is introduced.Novel image-based and video-based inputs are proposed as the analysis framework.From this viewpoint,common problems are discussed.The diversity of human postures usually leads to problems such as occlusion and ambiguity,and the lack of training datasets often results in poor generalization ability of the model.Regression methods are crucial for solving such problems.Considering image-based input,the multi-view method is commonly used to solve occlusion problems.Here,the multi-view method is analyzed comprehensively.By referring to video-based input,the human prior knowledge of restricted motion is used to predict human postures.In addition,structural constraints are widely used as prior knowledge.Furthermore,weakly supervised learningmethods are studied and discussed for these two types of inputs to improve the model generalization ability.The problem of insufficient training datasets must also be considered,especially because 3D datasets are usually biased and limited.Finally,emerging and popular datasets and evaluation indicators are discussed.The characteristics of the datasets and the relationships of the indicators are explained and highlighted.Thus,this article can be useful and instructive for researchers who are lacking in experience and find this field confusing.In addition,by providing an overview of 3D human pose estimation,this article sorts and refines recent studies on 3D human pose estimation.It describes kernel problems and common useful methods,and discusses the scope for further research.
基金supported by the Key Project of NSFC(Grant No.U1908214)Special Project of Central Government Guiding Local Science and Technology Development(Grant No.2021JH6/10500140)+5 种基金the Program for Innovative Research Team in University of Liaoning Province(LT2020015)the Support Plan for Key Field Innovation Team of Dalian(2021RT06)the Support Plan for Leading Innovation Team of Dalian University(XLJ202010)the Science and Technology Innovation Fund of Dalian(Grant No.2020JJ25CY001)in part by the National Natural Science Foundation of China under Grant 61906032the FundamentalResearch Funds for the Central Universities under Grant DUT21TD107.
文摘With the advancement of image sensing technology, estimating 3Dhuman pose frommonocular video has becomea hot research topic in computer vision. 3D human pose estimation is an essential prerequisite for subsequentaction analysis and understanding. It empowers a wide spectrum of potential applications in various areas, suchas intelligent transportation, human-computer interaction, and medical rehabilitation. Currently, some methodsfor 3D human pose estimation in monocular video employ temporal convolutional network (TCN) to extractinter-frame feature relationships, but the majority of them suffer from insufficient inter-frame feature relationshipextractions. In this paper, we decompose the 3D joint location regression into the bone direction and length, wepropose the TCG, a temporal convolutional network incorporating Gaussian error linear units (GELU), to solvebone direction. It enablesmore inter-frame features to be captured andmakes the utmost of the feature relationshipsbetween data. Furthermore, we adopt kinematic structural information to solve bone length enhancing the use ofintra-frame joint features. Finally, we design a loss function for joint training of the bone direction estimationnetwork with the bone length estimation network. The proposed method has extensively experimented on thepublic benchmark dataset Human3.6M. Both quantitative and qualitative experimental results showed that theproposed method can achieve more accurate 3D human pose estimations.
基金Funding for this study from Sai Gon University(Grant No.CSA2021–08).
文摘3-dimension(3-D)printing technology is growing strongly with many applications,one of which is the garment industry.The application of human body models to the garment industry is necessary to respond to the increasing personalization demand and still guarantee aesthetics.This paper proposes amethod to construct 3-D human models by applying deep learning.We calculate the location of the main slices of the human body,including the neck,chest,belly,buttocks,and the rings of the extremities,using pre-existing information.Then,on the positioning frame,we find the key points(fixed and unaltered)of these key slices and update these points tomatch the current parameters.To add points to a star slice,we use a deep learning model tomimic the form of the human body at that slice position.We use interpolation to produce sub-slices of different body sections based on the main slices to create complete body parts morphologically.We combine all slices to construct a full 3-D representation of the human body.
文摘Purpose: Patient-specific quality assurance (PSQA) requires manual operation of different workstations, which is time-consuming and error-prone. Therefore, developing automated solutions to improve efficiency and accuracy is a priority. The purpose of this study was to develop a general software interface with scripting on a human interactive device (HID) for improving the efficiency and accuracy of manual quality assurance (QA) procedures. Methods: As an initial application, we aimed to automate our PSQA workflow that involves Varian Eclipse treatment planning system, Elekta MOSAIQ oncology information system and PTW Verisoft application. A general platform, the AutoFrame interface with two imbedded subsystems—the AutoFlow and the PyFlow, was developed with a scripting language for automating human operations of aforementioned systems. The interface included three functional modules: GUI module, UDF script interpreter and TCP/IP communication module. All workstations in the PSQA process were connected, and most manual operations were automated by AutoFrame sequentially or in parallel. Results: More than 20 PSQA tasks were performed both manually and using the developed AutoFrame interface. On average, 175 (±12) manual operations of the PSQA procedure were eliminated and performed by the automated process. The time to complete a PSQA task was 8.23 (±0.78) minutes for the automated workflow, in comparison to 13.91 (±3.01) minutes needed for manual operations. Conclusion: We have developed the AutoFrame interface framework that successfully automated our PSQA procedure, and significantly reduced the time, human (control/clicking/typing) errors, and operators’ stress. Future work will focus on improving the system’s flexibility and stability and extending its operations to other QA procedures.
基金Supported by National Natural Science Foundation of China(No.2020J01652)the Training Project for Young and Middleaged Core Talents in Health System of Fujian Province(No.2016-ZQN-62).
文摘AIM:To determine whether the microRNA-27b-3p(miR-27b-3p)/NF-E2-related factor 2(Nrf2)pathway plays a role in human retinal pigment epithelial(hRPE)cell response to high glucose,how miR-27b-3p and Nrf2 expression are regulated,and whether this pathway could be specifically targeted.METHODS:hRPE cells were cultured in normal glucose or high glucose for 1,3,or 6d before measuring cellular proliferation rates using cell counting kit-8 and reactive oxygen species(ROS)levels using a dihydroethidium kit.miR-27b-3p,Nrf2,NAD(P)H quinone oxidoreductase 1(NQO1)and heme oxygenase-1(HO-1)mRNA and protein levels were analyzed using reverse transcription quantitative polymerase chain reaction(RT-qPCR)and immunocytofluorescence(ICF),respectively.Western blot analyses were performed to determine nuclear and total Nrf2 protein levels.Nrf2,NQO1,and HO-1 expression levels by RT-qPCR,ICF,or Western blot were further tested after miR-27b-3p overexpression or inhibitor lentiviral transfection.Finally,the expression level of those target genes was analyzed after treating hRPE cells with pyridoxamine.RESULTS:Persistent exposure to high glucose gradually suppressed hRPE Nrf2,NQO1,and HO-1 mRNA and protein levels and increased miR-27b-3p mRNA levels.High glucose also promoted ROS release and inhibited cellular proliferation.Nrf2,NQO1,and HO-1 mRNA levels decreased after miR-27b-3p overexpression and,conversely,both mRNA and protein levels increased after expressing a miR-27b-3p inhibitor.After treating hRPE cells exposed to high glucose with pyridoxamine,ROS levels tended to decreased,proliferation rate increased,Nrf2,NQO1,and HO-1 mRNA and protein levels were upregulated,and miR-27b-3p mRNA levels were suppressed.CONCLUSION:Nrf2 is a downstream target of miR-27b-3p.Furthermore,the miR-27b-3p inhibitor pyridoxamine can alleviate high glucose injury by regulating the miR-27b-3p/Nrf2 axis.
