Proton nuclear(^(1)H)is the observed nucleus on which most magnetic resonance imaging(MRI)applications depend.Most traditional^(1)H MRI can provide structural and functional information about organisms,while various n...Proton nuclear(^(1)H)is the observed nucleus on which most magnetic resonance imaging(MRI)applications depend.Most traditional^(1)H MRI can provide structural and functional information about organisms,while various non-proton nuclei(X-nuclei)MRI can provide more metabolic information.However,due to the relatively poor signal-to-noise ratio(SNR)of X-nuclei MRI,their applications are quite rare compared to^(1)H.Benefit from the rapid developments of MRI hardware and software technologies,X-nuclei MRI has recently attracted increasing interests in biomedical research.This review firstly introduces some current methods to improve the SNR of X-nuclei MRI.Secondly,this review describes biomedical applications of X-nuclei MRI,especially focusing on the current use of X-nuclei(^(13)C,^(17)O,^(19)F,^(23)Na and^(31)P)MRI to study related diseases in different organs,including the brain,liver,kidney,heart and bone.Finally,perspectives studies on X-nuclei imaging and its potential applications are described in biomedical research.展开更多
2P DC 1500 V框架隔离开关适用于光伏、储能系统。这类产品相比老产品而言更具成本上的优势。产品主要的设计改进点为:修改机构连杆加大开距;主弧触头、窄缝灭弧技术通过临界电流试验;电场磁场的仿真设计专用灭弧以及绝缘技术保证灭弧...2P DC 1500 V框架隔离开关适用于光伏、储能系统。这类产品相比老产品而言更具成本上的优势。产品主要的设计改进点为:修改机构连杆加大开距;主弧触头、窄缝灭弧技术通过临界电流试验;电场磁场的仿真设计专用灭弧以及绝缘技术保证灭弧可靠通过电寿命试验。经试验验证,2P DC 1500 V框架隔离开关符合标准要求。展开更多
Currently,nuclear imaging such as positron emission tomography(PET)and single photon emission computed tomography(SPECT)is increasingly used in the management of liver malignancy.18F-fluorodeoxyglucose(FDG)-PET is the...Currently,nuclear imaging such as positron emission tomography(PET)and single photon emission computed tomography(SPECT)is increasingly used in the management of liver malignancy.18F-fluorodeoxyglucose(FDG)-PET is the most widely used nuclear imaging in liver malignancy as in other cancers,and has been reported to be effective in diagnosis,response monitoring,recurrence evaluation,and prognosis prediction.Other PET imaging such as 11C-acetate PET is also used complementarily to FDG-PET in diagnosis of liver malignancy.Additionally,image-based evaluation of regional hepatic function can be performed using nuclear imaging.Those imaging modalities are also effective for candidate selection,treatment planning,and perioperative evaluation in liver surgery and transplantation.Recently,nuclear imaging has been actively adopted in the transarterial radioembolization therapy of liver malignancy,according to the concept of theragnosis.With the development of new hybrid imaging technologies such as PET/magnetic resonance imaging and SPECT/CT,nuclear imaging is expected to be more useful in the management of liver malignancy,particularly regarding liver surgery and transplantation.In this review,the efficacy and roles of nuclear imaging methods in diagnosis,transplantation and theragnosis are discussed.展开更多
目的探讨脑室-腹腔(V-P)分流术不同穿刺位置联合同期颅骨修补术在颅脑损伤脑积水患者中的效果及对凝血功能、粒系集落刺激因子(G-CSF)、肿瘤坏死因子-α(TNF-α)的影响。方法回顾性分析2020-06—2022-06晋城市人民医院121例颅脑损伤脑...目的探讨脑室-腹腔(V-P)分流术不同穿刺位置联合同期颅骨修补术在颅脑损伤脑积水患者中的效果及对凝血功能、粒系集落刺激因子(G-CSF)、肿瘤坏死因子-α(TNF-α)的影响。方法回顾性分析2020-06—2022-06晋城市人民医院121例颅脑损伤脑积水患者临床资料,根据手术方案分为A组(n=61)和B组(n=60),分别行V-P分流术(副三角区穿刺)+颅骨修补术、V-P分流术(侧脑室三角区穿刺)+颅骨修补术。统计2组脑积水改善情况、并发症、预后,对比术前、术后凝血功能、G-CSF、TNF-α、脑灌注情况、神经功能评分(NIHSS)。结果A组脑积水改善总有效率81.97%与B组80.00%比较,差异无统计学意义(P>0.05)。术后3 d、7 d A组血清APTT、PT水平低于B组,Fbg水平高于B组(P<0.05)。术后1个月A组血清G-CSF水平高于B组,TNF-α水平低于B组(P<0.05)。术前2组NIHSS评分、ICP、CCP比较,差异无统计学意义(P>0.05),术后1个月、3个月2组NIHSS评分、ICP低于术前,CCP高于术前(P<0.05)。A组并发症发生率(6.56%)低于B组(20.00%,P<0.05)。术后3个月,A组预后优良率(66.67%)与B组(67.24%)比较,差异无统计学意义(P>0.05)。结论V-P分流术不同穿刺位置联合同期颅骨修补术治疗颅脑损伤脑积水疗效相当,特别是副三角区穿刺,可减少并发症,降低血清G-CSF、TNF-α水平,且对凝血功能影响较小。展开更多
Hepatocellular carcinoma(HCC) is one of the most common cancers in Eastern Asia, and its incidence is increasing globally. Numerous experimental models have been developed to better our understanding of the pathogenic...Hepatocellular carcinoma(HCC) is one of the most common cancers in Eastern Asia, and its incidence is increasing globally. Numerous experimental models have been developed to better our understanding of the pathogenic mechanism of HCC and to evaluate novel therapeutic approaches. Molecular imaging is a convenient and up-to-date biomedical tool that enables the visualization, characterization and quantification of biologic processes in a living subject. Molecular imaging based on reporter gene expression, in particular, can elucidate tumor-specific events or processes by acquiring images of a reporter gene's expression driven by tumor-specific enhancers/promoters. In this review, we discuss the advantages and disadvantages of various experimental HCC mouse models and we present in vivo images of tumorspecific reporter gene expression driven by an alphafetoprotein(AFP) enhancer/promoter system in a mouse model of HCC. The current mouse models of HCC development are established by xenograft, carcinogen induction and genetic engineering, representing the spectrum of tumor-inducing factors and tumor locations. The imaging analysis approach of reporter genes driven by AFP enhancer/promoter is presented for these different HCC mouse models. Such molecular imaging can provide longitudinal information about carcinogenesis and tumor progression. We expect that clinical application of AFP-targeted reporter gene expression imaging systems will be useful for the detection of AFP-expressing HCC tumors and screening of increased/decreased AFP levels due to disease or drug treatment.展开更多
Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of re...Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research.To perform the investigation on Plant and pest classification,Transfer Learning(TL)approach is used on EfficientNet-V2.TL requires limited labelled data and shorter training time.However,the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters.EfficientNet-V2 is a Convolutional Neural Network(CNN)model with significant high speed learning rates across variable sized datasets.The model employs a form of progressive learning mechanism which expands the network topology gradually over the course of training process improving the model’s learning capacity.This provides a better interpretability of the model’s understanding on the test domains.With these insights,our work investigates the effectiveness of EfficienetV2 model trained on a class imbalanced dataset for plant disease classification and pest recognition by means of combining TL and progressive learning approach.This Progressive Learning for TL(PL-TL)is used in our work consisting of 38 classes of PlantVillage dataset of crops and fruit species,5 classes of cassava leaf diseases and another dataset with around 102 classes of crop pest images downloaded from popular dataset platforms,though it is not a benchmark dataset.To test the predictability rate of the model in classifying leaf diseases with similar visual symptoms,Mix-up data augmentation technique is used at the ratio of 1:4 on corn and tomato classes which has high probability of misinterpretation of disease classes.Also,the paper compares the TL approach performed on the above mentioned three types of data set using well established CNN based Inceptionv3,and Vision Transformer a non-CNN model.It clearly depicts that EfficientNetV2 has an outstanding performance of 99.5%,97.5%,80.1%on Cassava,PlantVillage and IP102 datasets respectively at a faster rate irrespective of the data size and class distribution as compared to Inception-V3 and ViT models.The performance metrics in terms of accuracy,precision,f1-score is also studied.展开更多
Few studies have examined the effects of different stimuli at a single acupoint using functional magnetic resonance imaging.The present study applied acupuncture at the Neiguan（PC 6）,Waiguan（SJ 5）,Zhigou（SJ 6） a...Few studies have examined the effects of different stimuli at a single acupoint using functional magnetic resonance imaging.The present study applied acupuncture at the Neiguan（PC 6）,Waiguan（SJ 5）,Zhigou（SJ 6） and Yanglingquan（GB 34） acupoints in healthy volunteers.fMRI was used to examine the activation of brain areas in response to different types of acupuncture（cutaneous or routine acupuncture） at each acupoint.There were no significant differences in the distribution of activation in the regions of interest between cutaneous and routine acupuncture at the Neiguan,Waiguan,and Zhigou acupoints,but some differences were observed between the two methods of acupuncture at the Yanglingquan acupoint.There were no significant differences in the intensity of induced activation between cutaneous and routine acupuncture at the Neiguan,Zhigou and Yanglingquan acupoints,but the activation intensity in the right cerebellum induced by routine acupuncture at the Waiguan acupoint was greater than that induced by cutaneous acupuncture.Results confirmed that cutaneous and routine acupuncture at the Neiguan,Waiguan,Zhigou and Yanglingquan acupoints activated different functional brain areas,and caused activation of different intensities in some areas.展开更多
In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact.Traditional plant taxonomic identification requires high expertise,which is time-con...In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact.Traditional plant taxonomic identification requires high expertise,which is time-consuming.Most nature reserves have problems such as incomplete species surveys,inaccurate taxonomic identification,and untimely updating of status data.Simple and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network model.Taking 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects,this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang,such as species investigation and monitoring,by using deep learning.Since desert plant species were not included in the public dataset,the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China(PPBC).After the sorting process and statistical analysis,a total of 2331 plant images were finally collected(2071 images from field collection and 260 images from the PPBC),including 24 plant species belonging to 14 families and 22 genera.A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance,from different perspectives,to find the optimal network model that is most suitable for the image recognition of desert plant species in Xinjiang.The results revealed 24 models with a recognition Accuracy,of greater than 70.000%.Among which,Residual Network X_8GF(RegNetX_8GF)performs the best,with Accuracy,Precision,Recall,and F1(which refers to the harmonic mean of the Precision and Recall values)values of 78.33%,77.65%,69.55%,and 71.26%,respectively.Considering the demand factors of hardware equipment and inference time,Mobile NetworkV2 achieves the best balance among the Accuracy,the number of parameters and the number of floating-point operations.The number of parameters for Mobile Network V2(MobileNetV2)is 1/16 of RegNetX_8GF,and the number of floating-point operations is 1/24.Our findings can facilitate efficient decision-making for the management of species survey,cataloging,inspection,and monitoring in the nature reserves in Xinjiang,providing a scientific basis for the protection and utilization of natural plant resources.展开更多
基金supported by Chinese Academy of Sciences MRI Technology Alliance under Grant 2020GZ1003.
文摘Proton nuclear(^(1)H)is the observed nucleus on which most magnetic resonance imaging(MRI)applications depend.Most traditional^(1)H MRI can provide structural and functional information about organisms,while various non-proton nuclei(X-nuclei)MRI can provide more metabolic information.However,due to the relatively poor signal-to-noise ratio(SNR)of X-nuclei MRI,their applications are quite rare compared to^(1)H.Benefit from the rapid developments of MRI hardware and software technologies,X-nuclei MRI has recently attracted increasing interests in biomedical research.This review firstly introduces some current methods to improve the SNR of X-nuclei MRI.Secondly,this review describes biomedical applications of X-nuclei MRI,especially focusing on the current use of X-nuclei(^(13)C,^(17)O,^(19)F,^(23)Na and^(31)P)MRI to study related diseases in different organs,including the brain,liver,kidney,heart and bone.Finally,perspectives studies on X-nuclei imaging and its potential applications are described in biomedical research.
文摘2P DC 1500 V框架隔离开关适用于光伏、储能系统。这类产品相比老产品而言更具成本上的优势。产品主要的设计改进点为:修改机构连杆加大开距;主弧触头、窄缝灭弧技术通过临界电流试验;电场磁场的仿真设计专用灭弧以及绝缘技术保证灭弧可靠通过电寿命试验。经试验验证,2P DC 1500 V框架隔离开关符合标准要求。
文摘Currently,nuclear imaging such as positron emission tomography(PET)and single photon emission computed tomography(SPECT)is increasingly used in the management of liver malignancy.18F-fluorodeoxyglucose(FDG)-PET is the most widely used nuclear imaging in liver malignancy as in other cancers,and has been reported to be effective in diagnosis,response monitoring,recurrence evaluation,and prognosis prediction.Other PET imaging such as 11C-acetate PET is also used complementarily to FDG-PET in diagnosis of liver malignancy.Additionally,image-based evaluation of regional hepatic function can be performed using nuclear imaging.Those imaging modalities are also effective for candidate selection,treatment planning,and perioperative evaluation in liver surgery and transplantation.Recently,nuclear imaging has been actively adopted in the transarterial radioembolization therapy of liver malignancy,according to the concept of theragnosis.With the development of new hybrid imaging technologies such as PET/magnetic resonance imaging and SPECT/CT,nuclear imaging is expected to be more useful in the management of liver malignancy,particularly regarding liver surgery and transplantation.In this review,the efficacy and roles of nuclear imaging methods in diagnosis,transplantation and theragnosis are discussed.
文摘目的探讨脑室-腹腔(V-P)分流术不同穿刺位置联合同期颅骨修补术在颅脑损伤脑积水患者中的效果及对凝血功能、粒系集落刺激因子(G-CSF)、肿瘤坏死因子-α(TNF-α)的影响。方法回顾性分析2020-06—2022-06晋城市人民医院121例颅脑损伤脑积水患者临床资料,根据手术方案分为A组(n=61)和B组(n=60),分别行V-P分流术(副三角区穿刺)+颅骨修补术、V-P分流术(侧脑室三角区穿刺)+颅骨修补术。统计2组脑积水改善情况、并发症、预后,对比术前、术后凝血功能、G-CSF、TNF-α、脑灌注情况、神经功能评分(NIHSS)。结果A组脑积水改善总有效率81.97%与B组80.00%比较,差异无统计学意义(P>0.05)。术后3 d、7 d A组血清APTT、PT水平低于B组,Fbg水平高于B组(P<0.05)。术后1个月A组血清G-CSF水平高于B组,TNF-α水平低于B组(P<0.05)。术前2组NIHSS评分、ICP、CCP比较,差异无统计学意义(P>0.05),术后1个月、3个月2组NIHSS评分、ICP低于术前,CCP高于术前(P<0.05)。A组并发症发生率(6.56%)低于B组(20.00%,P<0.05)。术后3个月,A组预后优良率(66.67%)与B组(67.24%)比较,差异无统计学意义(P>0.05)。结论V-P分流术不同穿刺位置联合同期颅骨修补术治疗颅脑损伤脑积水疗效相当,特别是副三角区穿刺,可减少并发症,降低血清G-CSF、TNF-α水平,且对凝血功能影响较小。
基金Supported by Korea Science and Engineering Foundation,No.2012M2A2A7013480 and No.2013M2C2A1074238
文摘Hepatocellular carcinoma(HCC) is one of the most common cancers in Eastern Asia, and its incidence is increasing globally. Numerous experimental models have been developed to better our understanding of the pathogenic mechanism of HCC and to evaluate novel therapeutic approaches. Molecular imaging is a convenient and up-to-date biomedical tool that enables the visualization, characterization and quantification of biologic processes in a living subject. Molecular imaging based on reporter gene expression, in particular, can elucidate tumor-specific events or processes by acquiring images of a reporter gene's expression driven by tumor-specific enhancers/promoters. In this review, we discuss the advantages and disadvantages of various experimental HCC mouse models and we present in vivo images of tumorspecific reporter gene expression driven by an alphafetoprotein(AFP) enhancer/promoter system in a mouse model of HCC. The current mouse models of HCC development are established by xenograft, carcinogen induction and genetic engineering, representing the spectrum of tumor-inducing factors and tumor locations. The imaging analysis approach of reporter genes driven by AFP enhancer/promoter is presented for these different HCC mouse models. Such molecular imaging can provide longitudinal information about carcinogenesis and tumor progression. We expect that clinical application of AFP-targeted reporter gene expression imaging systems will be useful for the detection of AFP-expressing HCC tumors and screening of increased/decreased AFP levels due to disease or drug treatment.
文摘Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research.To perform the investigation on Plant and pest classification,Transfer Learning(TL)approach is used on EfficientNet-V2.TL requires limited labelled data and shorter training time.However,the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters.EfficientNet-V2 is a Convolutional Neural Network(CNN)model with significant high speed learning rates across variable sized datasets.The model employs a form of progressive learning mechanism which expands the network topology gradually over the course of training process improving the model’s learning capacity.This provides a better interpretability of the model’s understanding on the test domains.With these insights,our work investigates the effectiveness of EfficienetV2 model trained on a class imbalanced dataset for plant disease classification and pest recognition by means of combining TL and progressive learning approach.This Progressive Learning for TL(PL-TL)is used in our work consisting of 38 classes of PlantVillage dataset of crops and fruit species,5 classes of cassava leaf diseases and another dataset with around 102 classes of crop pest images downloaded from popular dataset platforms,though it is not a benchmark dataset.To test the predictability rate of the model in classifying leaf diseases with similar visual symptoms,Mix-up data augmentation technique is used at the ratio of 1:4 on corn and tomato classes which has high probability of misinterpretation of disease classes.Also,the paper compares the TL approach performed on the above mentioned three types of data set using well established CNN based Inceptionv3,and Vision Transformer a non-CNN model.It clearly depicts that EfficientNetV2 has an outstanding performance of 99.5%,97.5%,80.1%on Cassava,PlantVillage and IP102 datasets respectively at a faster rate irrespective of the data size and class distribution as compared to Inception-V3 and ViT models.The performance metrics in terms of accuracy,precision,f1-score is also studied.
基金the National Basic Research Program of China (973 Program), No. 2006CB504505the National Natural Science Foundation of China, No. 90709027
文摘Few studies have examined the effects of different stimuli at a single acupoint using functional magnetic resonance imaging.The present study applied acupuncture at the Neiguan（PC 6）,Waiguan（SJ 5）,Zhigou（SJ 6） and Yanglingquan（GB 34） acupoints in healthy volunteers.fMRI was used to examine the activation of brain areas in response to different types of acupuncture（cutaneous or routine acupuncture） at each acupoint.There were no significant differences in the distribution of activation in the regions of interest between cutaneous and routine acupuncture at the Neiguan,Waiguan,and Zhigou acupoints,but some differences were observed between the two methods of acupuncture at the Yanglingquan acupoint.There were no significant differences in the intensity of induced activation between cutaneous and routine acupuncture at the Neiguan,Zhigou and Yanglingquan acupoints,but the activation intensity in the right cerebellum induced by routine acupuncture at the Waiguan acupoint was greater than that induced by cutaneous acupuncture.Results confirmed that cutaneous and routine acupuncture at the Neiguan,Waiguan,Zhigou and Yanglingquan acupoints activated different functional brain areas,and caused activation of different intensities in some areas.
基金supported by the West Light Foundation of the Chinese Academy of Sciences(2019-XBQNXZ-A-007)the National Natural Science Foundation of China(12071458,71731009).
文摘In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact.Traditional plant taxonomic identification requires high expertise,which is time-consuming.Most nature reserves have problems such as incomplete species surveys,inaccurate taxonomic identification,and untimely updating of status data.Simple and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network model.Taking 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects,this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang,such as species investigation and monitoring,by using deep learning.Since desert plant species were not included in the public dataset,the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China(PPBC).After the sorting process and statistical analysis,a total of 2331 plant images were finally collected(2071 images from field collection and 260 images from the PPBC),including 24 plant species belonging to 14 families and 22 genera.A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance,from different perspectives,to find the optimal network model that is most suitable for the image recognition of desert plant species in Xinjiang.The results revealed 24 models with a recognition Accuracy,of greater than 70.000%.Among which,Residual Network X_8GF(RegNetX_8GF)performs the best,with Accuracy,Precision,Recall,and F1(which refers to the harmonic mean of the Precision and Recall values)values of 78.33%,77.65%,69.55%,and 71.26%,respectively.Considering the demand factors of hardware equipment and inference time,Mobile NetworkV2 achieves the best balance among the Accuracy,the number of parameters and the number of floating-point operations.The number of parameters for Mobile Network V2(MobileNetV2)is 1/16 of RegNetX_8GF,and the number of floating-point operations is 1/24.Our findings can facilitate efficient decision-making for the management of species survey,cataloging,inspection,and monitoring in the nature reserves in Xinjiang,providing a scientific basis for the protection and utilization of natural plant resources.
