Objective:To develop a patient-reported traditional Chinese medicine kidney deficiency pattern(TCMKDP)scale for colorectal cancer(CRC)patients and evaluate its reliability and validity.Methods:We administered the TCM-...Objective:To develop a patient-reported traditional Chinese medicine kidney deficiency pattern(TCMKDP)scale for colorectal cancer(CRC)patients and evaluate its reliability and validity.Methods:We administered the TCM-KDP questionnaire to postoperative patients with stage II and III CRC as part of a multicenter randomized controlled trial(RCT)conducted in China from December 2018 to September 2021.The TCM-KDP scale consists of eight items on patient-reported symptoms and is scored on a five-point Likert scale.The scale’s reliability was assessed using Cronbach’s a and test-retest reliability,while content validity was evaluated with the content validity index.We compared the differences in serum cytokine levels and other clinical factors between patients with higher and lower KDP scores.Results:Of the 378 patients analyzed in the original RCT,352(93.2%)completed the TCM-KDP questionnaire.The Cronbach’s a of the eight-item TCM-KDP scale was 0.734,and the test-retest reliability was 0.745.Our exploratory factor analysis yielded eight factors that explained the variance of 50.34%.The mean TCM-KDP score was 2.80±0.92.Compared with patients with stage II CRC,those with stage III CRC had significantly higher TCM-KDP scores(2.25 vs.2.50,P=.026).We categorized all patients into highor low-KDP score groups(the cut-off score was 2.8).Patients with lower TCM-KDP scores had significantly higher serum interleukin-1b expression levels(P=.04).Conclusion:The patient-reported TCM-KDP scale demonstrated relatively good feasibility,internal consistency,and test-retest reliability among patients with CRC.Future studies could apply this scale to other cancer types and diseases.展开更多
Pattern classification is an important field in machine learning; least squares support vector machine (LSSVM) is a powerful tool for pattern classification. A new version of LSSVM, SVD-LSSVM, to save time of selectin...Pattern classification is an important field in machine learning; least squares support vector machine (LSSVM) is a powerful tool for pattern classification. A new version of LSSVM, SVD-LSSVM, to save time of selecting hyper parameters for LSSVM is proposed. SVD-LSSVM is trained through singular value decomposition (SVD) of kernel matrix. Cross validation time of selecting hyper parameters can be saved because a new hyper parameter, singular value contribution rate (SVCR), replaces the penalty factor of LSSVM. Several UCI benchmarking data and the Olive classification problem were used to test SVD-LSSVM. The result showed that SVD-LSSVM has good performance in classification and saves time for cross validation.展开更多
A methodology integrating correlation,regression(MLR),machine learning(ML),and pattern analysis of long-term weekly net ecosystem exchange(NEE)datasets are applied to four deciduous broadleaf forest(DBF)sites forming ...A methodology integrating correlation,regression(MLR),machine learning(ML),and pattern analysis of long-term weekly net ecosystem exchange(NEE)datasets are applied to four deciduous broadleaf forest(DBF)sites forming part of the AmeriFlux(FLUXNET2015)database.Such analysis effectively characterizes and distinguishes those DBF sites for which long-term NEE patterns can be accurately predicted using the recorded environmental variables,from those sites cannot be so delineated.Comparisons of twelve NEE prediction models(5 MLR;7 ML),using multi-fold cross-validation analysis,reveal that support vector regression generates the most accurate and reliable predictions for each site considered,based on fits involving between 16 and 24 available environmental variables.SVR can accurately predict NEE for datasets for DBF sites US-MMS and US-MOz,but fail to reliably do so for sites CA-Cbo and MX-Tes.For the latter two sites the predicted versus recorded NEE weekly data follow a Y≠X pattern and are characterized by rapid fluctuations between low and high NEE values across leaf-on seasonal periods.Variable influences on NEE,determined by their importance to MLR and ML model solutions,identify distinctive sets of the most and least influential variables for each site studied.Such information is valuable for monitoring and modelling the likely impacts of changing climate on the ability of these sites to serve as long-term carbon sinks.The periodically oscillating NEE weekly patterns distinguished for sites CA-Cbo and MX-Tes are not readily explained in terms of the currently recorded environmental variables.More detailed analysis of the biological processes at work in the forest understory and soil at these sites are recommended to determine additional suitable variables to measure that might better explain such fluctuations.展开更多
Experimental validation of laser intensity is particularly important for the study of fundamental physics at extremely high intensities.However,reliable diagnosis of the focal spot and peak intensity faces huge challe...Experimental validation of laser intensity is particularly important for the study of fundamental physics at extremely high intensities.However,reliable diagnosis of the focal spot and peak intensity faces huge challenges.In this work,we demonstrate for the firs time that the coherent radiation farfiel patterns from laser–foil interactions can serve as an in situ,real-time,and easy-to-implement diagnostic for an ultraintense laser focus.The laser-driven electron sheets,curved by the spatially varying laser fiel and leaving the targets at nearly the speed of light,produce doughnut-shaped patterns depending on the shapes of the focal spot and the absolute laser intensities.Assisted by particle-in-cell simulations,we can achieve measurements of the intensity and the focal spot,and provide immediate feedback to optimize the focal spots for extremely high intensity.展开更多
运用数据挖掘中的聚类技术对电力系统日负荷曲线进行分析,提出一种基于特性指标降维的日负荷曲线聚类方法——特性指标聚类(pattern index clustering,PIC),通过负荷率、日峰谷差率等6个日负荷特性指标对日负荷曲线进行降维处理,利用基...运用数据挖掘中的聚类技术对电力系统日负荷曲线进行分析,提出一种基于特性指标降维的日负荷曲线聚类方法——特性指标聚类(pattern index clustering,PIC),通过负荷率、日峰谷差率等6个日负荷特性指标对日负荷曲线进行降维处理,利用基于聚类有效性修正的德尔菲方法配置各指标权重,以加权欧式距离作为相似性判据,对日负荷曲线进行聚类。算例结果表明所提方法运行时间短,鲁棒性好,提高了负荷曲线聚类质量,能直观反映典型负荷曲线的特点。展开更多
基金supported by Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences,Youth Program(C12021A01819)China National Key R&D Plan Special Program for Modernization of Traditional Chinese Medicine,Ministry of Science and Technology of the People’s Republic of China,2017YFC1700604+1 种基金Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences(CI2021B009)Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(ZYYCXTD-C-202205).
文摘Objective:To develop a patient-reported traditional Chinese medicine kidney deficiency pattern(TCMKDP)scale for colorectal cancer(CRC)patients and evaluate its reliability and validity.Methods:We administered the TCM-KDP questionnaire to postoperative patients with stage II and III CRC as part of a multicenter randomized controlled trial(RCT)conducted in China from December 2018 to September 2021.The TCM-KDP scale consists of eight items on patient-reported symptoms and is scored on a five-point Likert scale.The scale’s reliability was assessed using Cronbach’s a and test-retest reliability,while content validity was evaluated with the content validity index.We compared the differences in serum cytokine levels and other clinical factors between patients with higher and lower KDP scores.Results:Of the 378 patients analyzed in the original RCT,352(93.2%)completed the TCM-KDP questionnaire.The Cronbach’s a of the eight-item TCM-KDP scale was 0.734,and the test-retest reliability was 0.745.Our exploratory factor analysis yielded eight factors that explained the variance of 50.34%.The mean TCM-KDP score was 2.80±0.92.Compared with patients with stage II CRC,those with stage III CRC had significantly higher TCM-KDP scores(2.25 vs.2.50,P=.026).We categorized all patients into highor low-KDP score groups(the cut-off score was 2.8).Patients with lower TCM-KDP scores had significantly higher serum interleukin-1b expression levels(P=.04).Conclusion:The patient-reported TCM-KDP scale demonstrated relatively good feasibility,internal consistency,and test-retest reliability among patients with CRC.Future studies could apply this scale to other cancer types and diseases.
基金Project (No. 20276063) supported by the National Natural Science Foundation of China
文摘Pattern classification is an important field in machine learning; least squares support vector machine (LSSVM) is a powerful tool for pattern classification. A new version of LSSVM, SVD-LSSVM, to save time of selecting hyper parameters for LSSVM is proposed. SVD-LSSVM is trained through singular value decomposition (SVD) of kernel matrix. Cross validation time of selecting hyper parameters can be saved because a new hyper parameter, singular value contribution rate (SVCR), replaces the penalty factor of LSSVM. Several UCI benchmarking data and the Olive classification problem were used to test SVD-LSSVM. The result showed that SVD-LSSVM has good performance in classification and saves time for cross validation.
文摘A methodology integrating correlation,regression(MLR),machine learning(ML),and pattern analysis of long-term weekly net ecosystem exchange(NEE)datasets are applied to four deciduous broadleaf forest(DBF)sites forming part of the AmeriFlux(FLUXNET2015)database.Such analysis effectively characterizes and distinguishes those DBF sites for which long-term NEE patterns can be accurately predicted using the recorded environmental variables,from those sites cannot be so delineated.Comparisons of twelve NEE prediction models(5 MLR;7 ML),using multi-fold cross-validation analysis,reveal that support vector regression generates the most accurate and reliable predictions for each site considered,based on fits involving between 16 and 24 available environmental variables.SVR can accurately predict NEE for datasets for DBF sites US-MMS and US-MOz,but fail to reliably do so for sites CA-Cbo and MX-Tes.For the latter two sites the predicted versus recorded NEE weekly data follow a Y≠X pattern and are characterized by rapid fluctuations between low and high NEE values across leaf-on seasonal periods.Variable influences on NEE,determined by their importance to MLR and ML model solutions,identify distinctive sets of the most and least influential variables for each site studied.Such information is valuable for monitoring and modelling the likely impacts of changing climate on the ability of these sites to serve as long-term carbon sinks.The periodically oscillating NEE weekly patterns distinguished for sites CA-Cbo and MX-Tes are not readily explained in terms of the currently recorded environmental variables.More detailed analysis of the biological processes at work in the forest understory and soil at these sites are recommended to determine additional suitable variables to measure that might better explain such fluctuations.
基金supported by the Guangdong High Level Innovation Research Institute(Grant No.2021B0909050006)the National Grand Instrument Project(Grant No.2019YFF01014402)+1 种基金the National Natural Science Foundation of China(Grant No.12205008)support from the National Science Fund for Distinguished Young Scholars(Grant No.12225501)。
文摘Experimental validation of laser intensity is particularly important for the study of fundamental physics at extremely high intensities.However,reliable diagnosis of the focal spot and peak intensity faces huge challenges.In this work,we demonstrate for the firs time that the coherent radiation farfiel patterns from laser–foil interactions can serve as an in situ,real-time,and easy-to-implement diagnostic for an ultraintense laser focus.The laser-driven electron sheets,curved by the spatially varying laser fiel and leaving the targets at nearly the speed of light,produce doughnut-shaped patterns depending on the shapes of the focal spot and the absolute laser intensities.Assisted by particle-in-cell simulations,we can achieve measurements of the intensity and the focal spot,and provide immediate feedback to optimize the focal spots for extremely high intensity.
文摘运用数据挖掘中的聚类技术对电力系统日负荷曲线进行分析,提出一种基于特性指标降维的日负荷曲线聚类方法——特性指标聚类(pattern index clustering,PIC),通过负荷率、日峰谷差率等6个日负荷特性指标对日负荷曲线进行降维处理,利用基于聚类有效性修正的德尔菲方法配置各指标权重,以加权欧式距离作为相似性判据,对日负荷曲线进行聚类。算例结果表明所提方法运行时间短,鲁棒性好,提高了负荷曲线聚类质量,能直观反映典型负荷曲线的特点。
