在分析相位噪声对16QAM通信性能影响的基础上,提出了一种基于误差判决的多模均衡算法(Multiple Modulus Algorithm Based on Error Judgment,MMA-EJ),该算法不仅利用信号的相位信息消除了卫星通信中载波频偏引入的相位失真,还考虑了多...在分析相位噪声对16QAM通信性能影响的基础上,提出了一种基于误差判决的多模均衡算法(Multiple Modulus Algorithm Based on Error Judgment,MMA-EJ),该算法不仅利用信号的相位信息消除了卫星通信中载波频偏引入的相位失真,还考虑了多电平幅度调制中不同半径星座点引入的幅度误差影响,因此相对于恒模算法(Constant Modulus Algorithm,CMA)来说,提高了均衡算法的精度。仿真结果表明,与传统的恒模均衡算法相比,提出的基于误差判决的MMA算法不仅可以消除高速信号传输时的码间串扰(Inter Symbol Interference,ISI),还可以消除传输系统中的相位噪声,提高16QAM通信系统的传输性能。展开更多
目的建立同步检测畲药树参中紫丁香苷、绿原酸、芥子醛葡萄糖苷、松柏醇、芦丁、山柰酚-3-O-芸香糖苷、3,4-O-二咖啡酰基奎宁酸、3,5-O-二咖啡酰基奎宁酸和4,5-O-二咖啡酰基奎宁酸含量的高效液相色谱一测多评(HPLC-QAMS)方法,并采用多...目的建立同步检测畲药树参中紫丁香苷、绿原酸、芥子醛葡萄糖苷、松柏醇、芦丁、山柰酚-3-O-芸香糖苷、3,4-O-二咖啡酰基奎宁酸、3,5-O-二咖啡酰基奎宁酸和4,5-O-二咖啡酰基奎宁酸含量的高效液相色谱一测多评(HPLC-QAMS)方法,并采用多元统计分析及加权优劣解距离(technique for order preference by similarity to ideal solution method,TOPSIS)法对其品质进行综合评价。方法以Waters Xbridge C 18色谱柱;乙腈-0.05%甲酸溶液为流动相,梯度洗脱;检测波长260 nm。以山柰酚-3-O-芸香糖苷为参照物,建立内参物与其他8个待测成分的相对校正因子(relative correction factor,RCF),进行RCF耐用性考察及色谱峰定位,同时与外标法实测结果进行对比,验证HPLC-QAMS法准确性和可靠性。运用主成分分析(principal component analysis,PCA)、正交偏最小二乘法-判别分析(orthogonal partial least squares-discriminant analysis,OPLS-DA)等多元统计分析以及W-TOPSIS法对9个成分HPLC-QAMS法含量结果的相关性进行分析,挖掘影响畲药树参产品质量的主要潜在标志物,建立畲药树参综合质量优劣评价方法。结果9种成分分别在3.27~81.75μg/mL、9.85~246.25μg/mL、0.43~0.75μg/mL、0.31~7.75μg/mL、1.58~39.50μg/mL、0.59~14.75μg/mL、1.26~31.50μg/mL、4.55~113.75μg/mL和1.98~49.50μg/mL范围内线性关系良好,平均加样回收率96.82%~100.07%(RSD<2.0%);HPLC-QAMS和外标法(ESM)含量测定结果差异无统计学意义(P>0.05),HPLC-QAMS法可用于畲药树参多组分定量控制;多元统计分析结果显示,前2个主成分累计方差贡献率89.589%,绿原酸、紫丁香苷、3,5-O-二咖啡酰基奎宁酸和4,5-O-二咖啡酰基奎宁酸是影响畲药树参产品质量的主要潜在标志物;加权TOPSIS法结果显示浙江地区所得畲药树参质量最优,其次为江西、安徽、湖南和湖北产树参,云南和贵州产树参位于排名后4位。结论所建立的HPLC-QAMS多组分定量控制方法,操作便捷、结果准确;多元统计分析联合加权TOPSIS法全面客观,可用于畲药树参品质的综合评价。展开更多
Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional ...Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.展开更多
Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior know...Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.展开更多
目的建立高效液相一测多评法(high performance liquid chromatography-quantitative analysis of multi-components by single marker,HPLC-QAMS)定量测定甘露消渴胶囊中毛蕊花糖苷、焦地黄苯乙醇苷B_(1)、东莨菪素、东莨菪苷、莫诺苷...目的建立高效液相一测多评法(high performance liquid chromatography-quantitative analysis of multi-components by single marker,HPLC-QAMS)定量测定甘露消渴胶囊中毛蕊花糖苷、焦地黄苯乙醇苷B_(1)、东莨菪素、东莨菪苷、莫诺苷、马钱苷、山茱萸新苷、泽泻醇F、泽泻醇A、24-乙酰泽泻醇A、23-乙酰泽泻醇B的含量,并结合化学计量学对3个厂家的产品质量进行评价。方法采用高效液相色谱仪,流动相选择乙腈-0.2%磷酸,色谱柱为Ultimate XB-C_(18)(250 mm×4.6 mm,5μm)进行梯度洗脱。检测波长分别为330 nm(检测毛蕊花糖苷、焦地黄苯乙醇苷B_(1)、东莨菪素、东莨菪苷)、240 nm(检测莫诺苷、马钱苷、山茱萸新苷)和208 nm(检测泽泻醇F、泽泻醇A、24-乙酰泽泻醇A、23-乙酰泽泻醇B)。以马钱苷为内参物,建立其他10种成分的相对校正因子,并计算各成分含量。采用SPSS 26.0统计软件对甘露消渴胶囊中11种成分含量测定结果进行聚类分析和主成分分析。结果毛蕊花糖苷、焦地黄苯乙醇苷B_(1)、东莨菪素、东莨菪苷、莫诺苷、马钱苷、山茱萸新苷、泽泻醇F、泽泻醇A、24-乙酰泽泻醇A、23-乙酰泽泻醇B分别在7.51~375.50、3.37~168.50、0.46~23.00、1.08~54.00、9.60~480.00、4.77~238.50、1.64~82.00、0.76~38.00、1.95~97.50、0.93~46.50和6.69~334.50μg·mL^(-1)范围内线性关系良好,相关系数(r=0.9991~0.9995),加样回收率及相应的RSD依次为99.11%(1.24%)、98.66%(1.45%)、97.71%(1.60%)、96.98%(0.93%)、100.20%(0.65%)、98.54%(1.18%)、97.90%(1.34%)、96.95%(1.07%)、98.47%(0.94%)、99.10%(0.89%)和100.08%(0.59%)。15批甘露消渴胶囊聚为3类,经主成分分析得4个主成分的累积贡献率达到85.338%。结论所建立的HPLC-QAMS法结合化学计量学可综合评价甘露消渴胶囊的质量。展开更多
目的建立解毒通淋丸中重楼皂苷Ⅶ、重楼皂苷Ⅵ、重楼皂苷Ⅰ、4′-去甲基鬼臼毒素、鬼臼毒素、泽泻醇F、泽泻醇A、24-乙酰泽泻醇A、23-乙酰泽泻醇B和11-去氧泽泻醇B高效液相色谱-一标多测(high performance liquid chromatography-quanti...目的建立解毒通淋丸中重楼皂苷Ⅶ、重楼皂苷Ⅵ、重楼皂苷Ⅰ、4′-去甲基鬼臼毒素、鬼臼毒素、泽泻醇F、泽泻醇A、24-乙酰泽泻醇A、23-乙酰泽泻醇B和11-去氧泽泻醇B高效液相色谱-一标多测(high performance liquid chromatography-quantitative analysis of multicomponents by single marker,HPLC-QAMS)含量测定方法。方法采用高效液相色谱法,以鬼臼毒素为内参物,运用外标法与一标多测法检测解毒通淋丸中10种成分的含量,并对比两种方法检测结果的差异性,验证一标多测法在解毒通淋丸质量控制研究中的可行性。结果重楼皂苷Ⅶ、重楼皂苷Ⅵ、重楼皂苷Ⅰ、4′-去甲基鬼臼毒素、鬼臼毒素、泽泻醇F、泽泻醇A、24-乙酰泽泻醇A、23-乙酰泽泻醇B和11-去氧泽泻醇B在各自范围内线性关系良好(r>0.999);平均加样回收率96.9%~100.1%(RSD小于2.0%);两种方法所测结果无差异(P>0.05)。结论采用HPLC-QAMS法对解毒通淋丸中10种成分进行定量评价分析的方法是可行的。展开更多
As a traditional herbal medicine,the major alkaloids in Uncaria rhynchophylla have been proven to have blood pressure-lowering and sedative effects.It is essential to develop an effective method for the determination ...As a traditional herbal medicine,the major alkaloids in Uncaria rhynchophylla have been proven to have blood pressure-lowering and sedative effects.It is essential to develop an effective method for the determination of the major alkaloids in U.rhynchophylla.In this research,a rapid quantitative analysis involving multi-components analysis by a single marker strategy coupled with core-shell column HPLC was adopted to analyse four alkaloids(corynoxeine,isocorynoxeine,isorhynchophylline,rhynchophylline)in U.rhynchophylla.Isorhynchophylline was selected as the internal reference substance,the content of which was determined by the traditional external standard method.Relative correction factors(RCF)between isorhynchophylline and the other three alkaloids were calculated respectively.The results showed that the QAMS method had good robustness under different HPLC instruments.Nineteen batches of U.rhynchophylla were tested.No significant difference was observed between the results by QAMS and EMS(Correlation coefficient>0.99,p>0.05).The QAMS method could be employed as a rapid,effective technique for the quality control of U.rhynchophylla.展开更多
文摘在分析相位噪声对16QAM通信性能影响的基础上,提出了一种基于误差判决的多模均衡算法(Multiple Modulus Algorithm Based on Error Judgment,MMA-EJ),该算法不仅利用信号的相位信息消除了卫星通信中载波频偏引入的相位失真,还考虑了多电平幅度调制中不同半径星座点引入的幅度误差影响,因此相对于恒模算法(Constant Modulus Algorithm,CMA)来说,提高了均衡算法的精度。仿真结果表明,与传统的恒模均衡算法相比,提出的基于误差判决的MMA算法不仅可以消除高速信号传输时的码间串扰(Inter Symbol Interference,ISI),还可以消除传输系统中的相位噪声,提高16QAM通信系统的传输性能。
文摘目的建立同步检测畲药树参中紫丁香苷、绿原酸、芥子醛葡萄糖苷、松柏醇、芦丁、山柰酚-3-O-芸香糖苷、3,4-O-二咖啡酰基奎宁酸、3,5-O-二咖啡酰基奎宁酸和4,5-O-二咖啡酰基奎宁酸含量的高效液相色谱一测多评(HPLC-QAMS)方法,并采用多元统计分析及加权优劣解距离(technique for order preference by similarity to ideal solution method,TOPSIS)法对其品质进行综合评价。方法以Waters Xbridge C 18色谱柱;乙腈-0.05%甲酸溶液为流动相,梯度洗脱;检测波长260 nm。以山柰酚-3-O-芸香糖苷为参照物,建立内参物与其他8个待测成分的相对校正因子(relative correction factor,RCF),进行RCF耐用性考察及色谱峰定位,同时与外标法实测结果进行对比,验证HPLC-QAMS法准确性和可靠性。运用主成分分析(principal component analysis,PCA)、正交偏最小二乘法-判别分析(orthogonal partial least squares-discriminant analysis,OPLS-DA)等多元统计分析以及W-TOPSIS法对9个成分HPLC-QAMS法含量结果的相关性进行分析,挖掘影响畲药树参产品质量的主要潜在标志物,建立畲药树参综合质量优劣评价方法。结果9种成分分别在3.27~81.75μg/mL、9.85~246.25μg/mL、0.43~0.75μg/mL、0.31~7.75μg/mL、1.58~39.50μg/mL、0.59~14.75μg/mL、1.26~31.50μg/mL、4.55~113.75μg/mL和1.98~49.50μg/mL范围内线性关系良好,平均加样回收率96.82%~100.07%(RSD<2.0%);HPLC-QAMS和外标法(ESM)含量测定结果差异无统计学意义(P>0.05),HPLC-QAMS法可用于畲药树参多组分定量控制;多元统计分析结果显示,前2个主成分累计方差贡献率89.589%,绿原酸、紫丁香苷、3,5-O-二咖啡酰基奎宁酸和4,5-O-二咖啡酰基奎宁酸是影响畲药树参产品质量的主要潜在标志物;加权TOPSIS法结果显示浙江地区所得畲药树参质量最优,其次为江西、安徽、湖南和湖北产树参,云南和贵州产树参位于排名后4位。结论所建立的HPLC-QAMS多组分定量控制方法,操作便捷、结果准确;多元统计分析联合加权TOPSIS法全面客观,可用于畲药树参品质的综合评价。
基金supported in part by the Research on the Application of Multimodal Artificial Intelligence in Diagnosis and Treatment of Type 2 Diabetes under Grant No.2020SK50910in part by the Hunan Provincial Natural Science Foundation of China under Grant 2023JJ60020.
文摘Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.
基金supported by the National Natural Science Foundation of China(Grant Nos.62005307 and 61975228).
文摘Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.
文摘目的建立解毒通淋丸中重楼皂苷Ⅶ、重楼皂苷Ⅵ、重楼皂苷Ⅰ、4′-去甲基鬼臼毒素、鬼臼毒素、泽泻醇F、泽泻醇A、24-乙酰泽泻醇A、23-乙酰泽泻醇B和11-去氧泽泻醇B高效液相色谱-一标多测(high performance liquid chromatography-quantitative analysis of multicomponents by single marker,HPLC-QAMS)含量测定方法。方法采用高效液相色谱法,以鬼臼毒素为内参物,运用外标法与一标多测法检测解毒通淋丸中10种成分的含量,并对比两种方法检测结果的差异性,验证一标多测法在解毒通淋丸质量控制研究中的可行性。结果重楼皂苷Ⅶ、重楼皂苷Ⅵ、重楼皂苷Ⅰ、4′-去甲基鬼臼毒素、鬼臼毒素、泽泻醇F、泽泻醇A、24-乙酰泽泻醇A、23-乙酰泽泻醇B和11-去氧泽泻醇B在各自范围内线性关系良好(r>0.999);平均加样回收率96.9%~100.1%(RSD小于2.0%);两种方法所测结果无差异(P>0.05)。结论采用HPLC-QAMS法对解毒通淋丸中10种成分进行定量评价分析的方法是可行的。
文摘As a traditional herbal medicine,the major alkaloids in Uncaria rhynchophylla have been proven to have blood pressure-lowering and sedative effects.It is essential to develop an effective method for the determination of the major alkaloids in U.rhynchophylla.In this research,a rapid quantitative analysis involving multi-components analysis by a single marker strategy coupled with core-shell column HPLC was adopted to analyse four alkaloids(corynoxeine,isocorynoxeine,isorhynchophylline,rhynchophylline)in U.rhynchophylla.Isorhynchophylline was selected as the internal reference substance,the content of which was determined by the traditional external standard method.Relative correction factors(RCF)between isorhynchophylline and the other three alkaloids were calculated respectively.The results showed that the QAMS method had good robustness under different HPLC instruments.Nineteen batches of U.rhynchophylla were tested.No significant difference was observed between the results by QAMS and EMS(Correlation coefficient>0.99,p>0.05).The QAMS method could be employed as a rapid,effective technique for the quality control of U.rhynchophylla.
