The second-tier extended metropolises in the Changjiang (Yangtze) River Delta, including Suzhou, Wuxi and Changzhou near Shanghai, are becoming the most active and new innovative industrial agglomerating areas. Manufa...The second-tier extended metropolises in the Changjiang (Yangtze) River Delta, including Suzhou, Wuxi and Changzhou near Shanghai, are becoming the most active and new innovative industrial agglomerating areas. Manufacturing industries in these second-tier cities have been in rapid growth due to increasing foreign investment. Nevertheless, the economic prospect of these cities is still constrained by a lack of local R&D capacity and production services, which are mainly dependent upon Shanghai. This paper analyses the impact of globalization on the new economic sectors in these cities, the change of industrial structure, the limitation of urban development and the problem of sustainability. Then, the paper analyses the conditions for the high-tech industry and production services in these areas. Also, it applies the industrial organization theory to these cities and examines how these cities can cooperate with each other in terms of horizontal linkages. Finally, the paper gives the future growth prospects in high-tech industry and production services.展开更多
Introduction Methylmalonic acidemia(MMA)is a disorder of autosomal recessive inheritance,with an estimated prevalence of 1:50,000.First-tier clinical diagnostic tests often return many false positives[fve false positi...Introduction Methylmalonic acidemia(MMA)is a disorder of autosomal recessive inheritance,with an estimated prevalence of 1:50,000.First-tier clinical diagnostic tests often return many false positives[fve false positive(FP):one true positive(TP)].In this work,our goal was to refne a classifcation model that can minimize the number of false positives,currently an unmet need in the upstream diagnostics of MMA.Methods We developed machine learning multivariable screening models for MMA with utility as a secondary-tier tool for false positives reduction.We utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients,followed by additional ratio feature construction.Feature selection strategies(selection by flter,recursive feature elimination,and learned vector quantization)were used to determine the input set for evaluating the performance of 14 classifcation models to identify a candidate model set for an ensemble model development.Results Our work identifed computational models that explore metabolic analytes to reduce the number of false positives without compromising sensitivity.The best results[area under the receiver operating characteristic curve(AUROC)of 97%,sensitivity of 92%,and specifcity of 95%]were obtained utilizing an ensemble of the algorithms random forest,C5.0,sparse linear discriminant analysis,and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the supervisor.The model achieved a good performance trade-of for a screening application with 6%false-positive rate(FPR)at 95%sensitivity,35%FPR at 99%sensitivity,and 39%FPR at 100%sensitivity.Conclusions The classifcation results and approach of this research can be utilized by clinicians globally,to improve the overall discovery of MMA in pediatric patients.The improved method,when adjusted to 100%precision,can be used to further inform the diagnostic process journey of MMA and help reduce the burden for patients and their families.展开更多
Eating dry cornflakes with chopsticks.That’s my strongest memory of Shenyang men.That was a view from la Home Inn hotel breakfast room on a wintry December morning in the capital of
文摘The second-tier extended metropolises in the Changjiang (Yangtze) River Delta, including Suzhou, Wuxi and Changzhou near Shanghai, are becoming the most active and new innovative industrial agglomerating areas. Manufacturing industries in these second-tier cities have been in rapid growth due to increasing foreign investment. Nevertheless, the economic prospect of these cities is still constrained by a lack of local R&D capacity and production services, which are mainly dependent upon Shanghai. This paper analyses the impact of globalization on the new economic sectors in these cities, the change of industrial structure, the limitation of urban development and the problem of sustainability. Then, the paper analyses the conditions for the high-tech industry and production services in these areas. Also, it applies the industrial organization theory to these cities and examines how these cities can cooperate with each other in terms of horizontal linkages. Finally, the paper gives the future growth prospects in high-tech industry and production services.
基金supported by the National Key R&D Program of China grand No.2022YFC2703103the Clinical Research Plan of SHDC(SHDC2020CR6028,SHDC2020CR1047B)+1 种基金the Science and Technology Commission of Shanghai Municipality grant 22Y11906900the Second Century Fund(C2F),Chulalongkorn University,Bangkok,Thailand.
文摘Introduction Methylmalonic acidemia(MMA)is a disorder of autosomal recessive inheritance,with an estimated prevalence of 1:50,000.First-tier clinical diagnostic tests often return many false positives[fve false positive(FP):one true positive(TP)].In this work,our goal was to refne a classifcation model that can minimize the number of false positives,currently an unmet need in the upstream diagnostics of MMA.Methods We developed machine learning multivariable screening models for MMA with utility as a secondary-tier tool for false positives reduction.We utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients,followed by additional ratio feature construction.Feature selection strategies(selection by flter,recursive feature elimination,and learned vector quantization)were used to determine the input set for evaluating the performance of 14 classifcation models to identify a candidate model set for an ensemble model development.Results Our work identifed computational models that explore metabolic analytes to reduce the number of false positives without compromising sensitivity.The best results[area under the receiver operating characteristic curve(AUROC)of 97%,sensitivity of 92%,and specifcity of 95%]were obtained utilizing an ensemble of the algorithms random forest,C5.0,sparse linear discriminant analysis,and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the supervisor.The model achieved a good performance trade-of for a screening application with 6%false-positive rate(FPR)at 95%sensitivity,35%FPR at 99%sensitivity,and 39%FPR at 100%sensitivity.Conclusions The classifcation results and approach of this research can be utilized by clinicians globally,to improve the overall discovery of MMA in pediatric patients.The improved method,when adjusted to 100%precision,can be used to further inform the diagnostic process journey of MMA and help reduce the burden for patients and their families.
文摘Eating dry cornflakes with chopsticks.That’s my strongest memory of Shenyang men.That was a view from la Home Inn hotel breakfast room on a wintry December morning in the capital of
