In positive-ion fast atom bombardment (FAB) mass spectrometry, when mono- and di- saccharides are mixed with an appropriate amount of NH4Cl, a highly abundan peak [M+NH4]+appers in FAB mass spectra . From the adduct ...In positive-ion fast atom bombardment (FAB) mass spectrometry, when mono- and di- saccharides are mixed with an appropriate amount of NH4Cl, a highly abundan peak [M+NH4]+appers in FAB mass spectra . From the adduct ion [M+NH4]+, the molecular weights of mono- and di- saccharides can be determined definitively展开更多
Ultrasound plays an important role not only in preoperative diagnosis but also in intraoperative guidance for liver surgery.Intraoperative ultrasound(IOUS)has become an indispensable tool for modern liver surgeons,esp...Ultrasound plays an important role not only in preoperative diagnosis but also in intraoperative guidance for liver surgery.Intraoperative ultrasound(IOUS)has become an indispensable tool for modern liver surgeons,especially for minimally invasive surgeries,partially substituting for the surgeon’s hands.In fundamental mode,Doppler mode,contrast enhancement,elastography,and real-time virtual sonography,IOUS can provide additional real-time information regarding the intrahepatic anatomy,tumor site and characteristics,macrovascular invasion,resection margin,transection plane,perfusion and outflow of the remnant liver,and local ablation efficacy for both open and minimally invasive liver resections.Identification and localization of intrahepatic lesions and surrounding structures are crucial for performing liver resection,preserving the adjacent vital vascular and bile ducts,and sparing the functional liver parenchyma.Intraoperative ultrasound can provide critical information for intraoperative decision-making and navigation.Therefore,all liver surgeons must master IOUS techniques,and IOUS should be included in the training of modern liver surgeons.Further investigation of the potential benefits and advances in these techniques will increase the use of IOUS in modern liver surgeries worldwide.This study comprehensively reviews the current use of IOUS in modern liver surgeries.展开更多
为了快速精准地识别复杂果园环境下的葡萄目标,该研究基于YOLOv5s提出一种改进的葡萄检测模型(MRWYOLOv5s)。首先,为了减少模型参数量,采用轻量型网络MobileNetv3作为特征提取网络,并在MobileNetv3的bneck结构中嵌入坐标注意力模块(coor...为了快速精准地识别复杂果园环境下的葡萄目标,该研究基于YOLOv5s提出一种改进的葡萄检测模型(MRWYOLOv5s)。首先,为了减少模型参数量,采用轻量型网络MobileNetv3作为特征提取网络,并在MobileNetv3的bneck结构中嵌入坐标注意力模块(coordinate attention,CA)以加强网络的特征提取能力;其次,在颈部网络中引入RepVGG Block,融合多分支特征提升模型的检测精度,并利用RepVGG Block的结构重参数化进一步加快模型的推理速度;最后,采用基于动态非单调聚焦机制的损失(wise intersection over union loss,WIoU Loss)作为边界框回归损失函数,加速网络收敛并提高模型的检测准确率。结果表明,改进的MRW-YOLOv5s模型参数量仅为7.56 M,在测试集上的平均精度均值(mean average precision,mAP)达到97.74%,相较于原YOLOv5s模型提升了2.32个百分点,平均每幅图片的检测时间为10.03 ms,比原YOLOv5s模型减少了6.13 ms。与主流的目标检测模型SSD、RetinaNet、YOLOv4、YOLOv7和YOLOX相比,MRW-YOLOv5s模型的mAP分别高出9.89、7.53、2.12、0.91、2.42个百分点,并且在模型参数量大小和检测速度方面有着很大的优势,该研究可为果园智能化、采摘机械化提供技术支持。展开更多
边界框回归分支是深度目标跟踪器的关键模块,其性能直接影响跟踪器的精度.评价精度的指标之一是交并比(Intersection over union,IoU).基于IoU的损失函数取代了l_(n)-norm损失成为目前主流的边界框回归损失函数,然而IoU损失函数存在2个...边界框回归分支是深度目标跟踪器的关键模块,其性能直接影响跟踪器的精度.评价精度的指标之一是交并比(Intersection over union,IoU).基于IoU的损失函数取代了l_(n)-norm损失成为目前主流的边界框回归损失函数,然而IoU损失函数存在2个固有缺陷:1)当预测框与真值框不相交时IoU为常量0,无法梯度下降更新边界框的参数;2)在IoU取得最优值时其梯度不存在,边界框很难收敛到IoU最优处.揭示了在回归过程中IoU最优的边界框各参数之间蕴含的定量关系,指出在边界框中心处于特定位置时存在多种尺寸不同的边界框使IoU损失最优的情况,这增加了边界框尺寸回归的不确定性.从优化两个统计分布之间散度的视角看待边界框回归问题,提出了光滑IoU(Smooth-IoU,SIoU)损失,即构造了在全局上光滑(即连续可微)且极值唯一的损失函数,该损失函数自然蕴含边界框各参数之间特定的最优关系,其唯一取极值的边界框可使IoU达到最优.光滑性确保了在全局上梯度存在使得边界框更容易回归到极值处,而极值唯一确保了在全局上可梯度下降更新参数,从而避开了IoU损失的固有缺陷.提出的光滑损失可以很容易取代IoU损失集成到现有的深度目标跟踪器上训练边界框回归,在LaSOT、GOT-10k、TrackingNet、OTB2015和VOT2018测试基准上所取得的结果,验证了光滑IoU损失的易用性和有效性.展开更多
文摘In positive-ion fast atom bombardment (FAB) mass spectrometry, when mono- and di- saccharides are mixed with an appropriate amount of NH4Cl, a highly abundan peak [M+NH4]+appers in FAB mass spectra . From the adduct ion [M+NH4]+, the molecular weights of mono- and di- saccharides can be determined definitively
基金Supported by a grant from Japan China Sasakawa Medical Fellowship。
文摘Ultrasound plays an important role not only in preoperative diagnosis but also in intraoperative guidance for liver surgery.Intraoperative ultrasound(IOUS)has become an indispensable tool for modern liver surgeons,especially for minimally invasive surgeries,partially substituting for the surgeon’s hands.In fundamental mode,Doppler mode,contrast enhancement,elastography,and real-time virtual sonography,IOUS can provide additional real-time information regarding the intrahepatic anatomy,tumor site and characteristics,macrovascular invasion,resection margin,transection plane,perfusion and outflow of the remnant liver,and local ablation efficacy for both open and minimally invasive liver resections.Identification and localization of intrahepatic lesions and surrounding structures are crucial for performing liver resection,preserving the adjacent vital vascular and bile ducts,and sparing the functional liver parenchyma.Intraoperative ultrasound can provide critical information for intraoperative decision-making and navigation.Therefore,all liver surgeons must master IOUS techniques,and IOUS should be included in the training of modern liver surgeons.Further investigation of the potential benefits and advances in these techniques will increase the use of IOUS in modern liver surgeries worldwide.This study comprehensively reviews the current use of IOUS in modern liver surgeries.
文摘为了快速精准地识别复杂果园环境下的葡萄目标,该研究基于YOLOv5s提出一种改进的葡萄检测模型(MRWYOLOv5s)。首先,为了减少模型参数量,采用轻量型网络MobileNetv3作为特征提取网络,并在MobileNetv3的bneck结构中嵌入坐标注意力模块(coordinate attention,CA)以加强网络的特征提取能力;其次,在颈部网络中引入RepVGG Block,融合多分支特征提升模型的检测精度,并利用RepVGG Block的结构重参数化进一步加快模型的推理速度;最后,采用基于动态非单调聚焦机制的损失(wise intersection over union loss,WIoU Loss)作为边界框回归损失函数,加速网络收敛并提高模型的检测准确率。结果表明,改进的MRW-YOLOv5s模型参数量仅为7.56 M,在测试集上的平均精度均值(mean average precision,mAP)达到97.74%,相较于原YOLOv5s模型提升了2.32个百分点,平均每幅图片的检测时间为10.03 ms,比原YOLOv5s模型减少了6.13 ms。与主流的目标检测模型SSD、RetinaNet、YOLOv4、YOLOv7和YOLOX相比,MRW-YOLOv5s模型的mAP分别高出9.89、7.53、2.12、0.91、2.42个百分点,并且在模型参数量大小和检测速度方面有着很大的优势,该研究可为果园智能化、采摘机械化提供技术支持。
文摘边界框回归分支是深度目标跟踪器的关键模块,其性能直接影响跟踪器的精度.评价精度的指标之一是交并比(Intersection over union,IoU).基于IoU的损失函数取代了l_(n)-norm损失成为目前主流的边界框回归损失函数,然而IoU损失函数存在2个固有缺陷:1)当预测框与真值框不相交时IoU为常量0,无法梯度下降更新边界框的参数;2)在IoU取得最优值时其梯度不存在,边界框很难收敛到IoU最优处.揭示了在回归过程中IoU最优的边界框各参数之间蕴含的定量关系,指出在边界框中心处于特定位置时存在多种尺寸不同的边界框使IoU损失最优的情况,这增加了边界框尺寸回归的不确定性.从优化两个统计分布之间散度的视角看待边界框回归问题,提出了光滑IoU(Smooth-IoU,SIoU)损失,即构造了在全局上光滑(即连续可微)且极值唯一的损失函数,该损失函数自然蕴含边界框各参数之间特定的最优关系,其唯一取极值的边界框可使IoU达到最优.光滑性确保了在全局上梯度存在使得边界框更容易回归到极值处,而极值唯一确保了在全局上可梯度下降更新参数,从而避开了IoU损失的固有缺陷.提出的光滑损失可以很容易取代IoU损失集成到现有的深度目标跟踪器上训练边界框回归,在LaSOT、GOT-10k、TrackingNet、OTB2015和VOT2018测试基准上所取得的结果,验证了光滑IoU损失的易用性和有效性.
