目的:建立桃红逐瘀合剂的质量标准。方法:采用薄层色谱法(TLC)定性鉴别桃红逐瘀合剂中当归、川芎、赤芍;采用高效液相色谱法(HPLC)测定该制剂中的芍药苷含量,色谱柱为ODS-BP柱(4.6 mm×250 mm, 5μm),流动相为甲醇∶0.15%磷酸溶液(2...目的:建立桃红逐瘀合剂的质量标准。方法:采用薄层色谱法(TLC)定性鉴别桃红逐瘀合剂中当归、川芎、赤芍;采用高效液相色谱法(HPLC)测定该制剂中的芍药苷含量,色谱柱为ODS-BP柱(4.6 mm×250 mm, 5μm),流动相为甲醇∶0.15%磷酸溶液(25∶75),柱温:35℃;流速:1.0 mL·min^(-1);检测波长:230 nm;进样量为10μL。结果:当归、川芎、赤芍的TLC图斑点清晰,分离度较好,阴性样品无干扰。芍药苷在0.160~1.596μg范围内,线性关系良好(r=0.9999);芍药苷的加样回收率为98.20%,RSD为0.73%(n=5)。结论:该方法简便可行,专属性、重复性好,可作为桃红逐瘀合剂的内控标准。展开更多
Background:Stretching has wide appeal,but there seems to exist some mismatch between its purported applications and what the evidence shows.There is compelling evidence for some stretching applications,but for others,...Background:Stretching has wide appeal,but there seems to exist some mismatch between its purported applications and what the evidence shows.There is compelling evidence for some stretching applications,but for others,the evidence seems heterogeneous or unsupportive.The discrepancies even affect some systematic reviews,possibly due to heterogeneous eligibility criteria and search strategies.This consensus paper seeks to unify the divergent findings on stretching and its implications for both athletic performance and clinical practices by delivering evidence-based recommendations.Methods:A panel of 20 experts with a blend of practical experience and scholarly knowledge was assembled.The panel meticulously reviewed existing systematic reviews,defined key terminologies(e.g.,consensus definitions for different stretching modes),and crafted guidelines using a Delphi consensus approach(minimum required agreement:80%).The analysis focused on 8 topics,including stretching's acute and chronic(long-term)effects on range of motion,strength performance,muscle hypertrophy,stiffness,injury prevention,muscle recovery,posture correction,and cardiovascular health.Results:There was consensus that chronic and acute stretching(a)improves range of motion(although alternatives exist)and(b)reduces muscle stiffness(which may not always be desirable);the panel also agreed that chronic stretching(c)may promote vascular health,but more research is warranted.In contrast,consensus was found that stretch training does not(a)contribute substantively to muscle growth,(b)serve as an allencompassing injury prevention strategy,(c)improve posture,or(d)acutely enhance post-exercise recovery.Conclusion:These recommendations provide guidance for athletes and practitioners,highlighting research gaps that should be addressed to more comprehensively understand the full scope of stretching effects.展开更多
The automated interpretation of rock structure can improve the efficiency,accuracy,and consistency of the geological risk assessment of tunnel face.Because of the high uncertainties in the geological images as a resul...The automated interpretation of rock structure can improve the efficiency,accuracy,and consistency of the geological risk assessment of tunnel face.Because of the high uncertainties in the geological images as a result of different regional rock types,as well as in-situ conditions(e.g.,temperature,humidity,and construction procedure),previous automated methods have limited performance in classification of rock structure of tunnel face during construction.This paper presents a framework for classifying multiple rock structures based on the geological images of tunnel face using convolutional neural networks(CNN),namely Inception-ResNet-V2(IRV2).A prototype recognition system is implemented to classify 5 types of rock structures including mosaic,granular,layered,block,and fragmentation structures.The proposed IRV2 network is trained by over 35,000 out of 42,400 images extracted from over 150 sections of tunnel faces and tested by the remaining 7400 images.Furthermore,different hyperparameters of the CNN model are introduced to optimize the most efficient algorithm parameter.Among all the discussed models,i.e.,ResNet-50,ResNet-101,and Inception-v4,Inception-ResNet-V2 exhibits the best performance in terms of various indicators,such as precision,recall,F-score,and testing time per image.Meanwhile,the model trained by a large database can obtain the object features more comprehensively,leading to higher accuracy.Compared with the original image classification method,the sub-image method is closer to the reality considering both the accuracy and the perspective of error divergence.The experimental results reveal that the proposed method is optimal and efficient for automated classification of rock structure using the geological images of the tunnel face.展开更多
文摘Background:Stretching has wide appeal,but there seems to exist some mismatch between its purported applications and what the evidence shows.There is compelling evidence for some stretching applications,but for others,the evidence seems heterogeneous or unsupportive.The discrepancies even affect some systematic reviews,possibly due to heterogeneous eligibility criteria and search strategies.This consensus paper seeks to unify the divergent findings on stretching and its implications for both athletic performance and clinical practices by delivering evidence-based recommendations.Methods:A panel of 20 experts with a blend of practical experience and scholarly knowledge was assembled.The panel meticulously reviewed existing systematic reviews,defined key terminologies(e.g.,consensus definitions for different stretching modes),and crafted guidelines using a Delphi consensus approach(minimum required agreement:80%).The analysis focused on 8 topics,including stretching's acute and chronic(long-term)effects on range of motion,strength performance,muscle hypertrophy,stiffness,injury prevention,muscle recovery,posture correction,and cardiovascular health.Results:There was consensus that chronic and acute stretching(a)improves range of motion(although alternatives exist)and(b)reduces muscle stiffness(which may not always be desirable);the panel also agreed that chronic stretching(c)may promote vascular health,but more research is warranted.In contrast,consensus was found that stretch training does not(a)contribute substantively to muscle growth,(b)serve as an allencompassing injury prevention strategy,(c)improve posture,or(d)acutely enhance post-exercise recovery.Conclusion:These recommendations provide guidance for athletes and practitioners,highlighting research gaps that should be addressed to more comprehensively understand the full scope of stretching effects.
基金supported by the Natural Science Foundation Committee Program of China(Grant Nos.1538009 and 51778474)Science and Technology Project of Yunnan Provincial Transportation Department(Grant No.25 of 2018)+1 种基金the Fundamental Research Funds for the Central Universities in China(Grant No.0200219129)Key innovation team program of innovation talents promotion plan by MOST of China(Grant No.2016RA4059)。
文摘The automated interpretation of rock structure can improve the efficiency,accuracy,and consistency of the geological risk assessment of tunnel face.Because of the high uncertainties in the geological images as a result of different regional rock types,as well as in-situ conditions(e.g.,temperature,humidity,and construction procedure),previous automated methods have limited performance in classification of rock structure of tunnel face during construction.This paper presents a framework for classifying multiple rock structures based on the geological images of tunnel face using convolutional neural networks(CNN),namely Inception-ResNet-V2(IRV2).A prototype recognition system is implemented to classify 5 types of rock structures including mosaic,granular,layered,block,and fragmentation structures.The proposed IRV2 network is trained by over 35,000 out of 42,400 images extracted from over 150 sections of tunnel faces and tested by the remaining 7400 images.Furthermore,different hyperparameters of the CNN model are introduced to optimize the most efficient algorithm parameter.Among all the discussed models,i.e.,ResNet-50,ResNet-101,and Inception-v4,Inception-ResNet-V2 exhibits the best performance in terms of various indicators,such as precision,recall,F-score,and testing time per image.Meanwhile,the model trained by a large database can obtain the object features more comprehensively,leading to higher accuracy.Compared with the original image classification method,the sub-image method is closer to the reality considering both the accuracy and the perspective of error divergence.The experimental results reveal that the proposed method is optimal and efficient for automated classification of rock structure using the geological images of the tunnel face.
