Objective The International Federation of Gynecology and Obstetrics(FIGO)2000 scoring system classifies gestational trophoblastic neoplasia(GTN)patients into low-and high-risk groups,so that single-or multi-agent chem...Objective The International Federation of Gynecology and Obstetrics(FIGO)2000 scoring system classifies gestational trophoblastic neoplasia(GTN)patients into low-and high-risk groups,so that single-or multi-agent chemotherapy can be administered accordingly.However,a number of FIGO-defined low-risk patients still exhibit resistance to single-agent regimens,and the risk factors currently adopted in the FIGO scoring system possess inequable values for predicting single-agent chemoresistance.The purpose of this study is therefore to evaluate the efficacy of risk factors in predicting single-agent chemoresistance and explore the feasibility of simplifying the FIGO 2000 scoring system for GTN.Methods The clinical data of 578 GTN patients who received chemotherapy between January 2000 and December 2018 were retrospectively reviewed.Univariate and multivariate logistic regression analyses were carried out to identify risk factors associated with single-agent chemoresistance in low-risk GTN patients.Then,simplified models were built and compared with the original FIGO 2000 scoring system.Results Among the eight FIGO risk factors,the univariate and multivariate analyses identified that pretreatment serum human chorionic gonadotropin(hCG)level and interval from antecedent pregnancy were consistently independent predictors for both first-line and subsequent single-agent chemoresistance.The simplified model with two independent factors showed a better performance in predicting single-agent chemoresistance than the model with the other four non-independent factors.However,the addition of other co-factors did improve the efficiency.Overall,simplified models can achieve favorable performance,but the original FIGO 2000 prognostic system still features the highest discrimination.Conclusions Pretreatment serum hCG level and interval from antecedent pregnancy were independent predictors for both first-line and subsequent single-agent chemoresistance,and they had greater weight than other non-independent factors in predicting single-agent chemoresistance.The simplified model composed of certain selected factors is a promising alternative to the original FIGO 2000 prognostic system,and it shows comparable performance.展开更多
Objective and Impact Statement.Real-time monitoring of the temperatures of regional tissue microenvironments can serve as the diagnostic basis for treating various health conditions and diseases.Introduction.Tradition...Objective and Impact Statement.Real-time monitoring of the temperatures of regional tissue microenvironments can serve as the diagnostic basis for treating various health conditions and diseases.Introduction.Traditional thermal sensors allow measurements at surfaces or at near-surface regions of the skin or of certain body cavities.Evaluations at depth require implanted devices connected to external readout electronics via physical interfaces that lead to risks for infection and movement constraints for the patient.Also,surgical extraction procedures after a period of need can introduce additional risks and costs.Methods.Here,we report a wireless,bioresorbable class of temperature sensor that exploits multilayer photonic cavities,for continuous optical measurements of regional,deep-tissue microenvironments over a timeframe of interest followed by complete clearance via natural body processes.Results.The designs decouple the influence of detection angle from temperature on the reflection spectra,to enable high accuracy in sensing,as supported by in vitro experiments and optical simulations.Studies with devices implanted into subcutaneous tissues of both awake,freely moving and asleep animal models illustrate the applicability of this technology for in vivo measurements.Conclusion.The results demonstrate the use of bioresorbable materials in advanced photonic structures with unique capabilities in tracking of thermal signatures of tissue microenvironments,with potential relevance to human healthcare.展开更多
The main content of a news web page is a source of data for Natural Language Processing(NLP)and Information Retrieval(IR),which contains large quantities of valuable information.This paper proposes a method that formu...The main content of a news web page is a source of data for Natural Language Processing(NLP)and Information Retrieval(IR),which contains large quantities of valuable information.This paper proposes a method that formulates the main content extraction problem as a DOM tree node classification problem.In terms of feature extraction,we use the DOM tree node to represent HTML document and then develop multiple features by using the DOM tree node properties,such as text length,tag path,tag properties and so on.In consideration that the essence of the problem is the classification model,we use Xgboost to help select nodes.Experimental results show that the proposed approach is effective and efficient in extracting main content of new web pages,and achieves about 98%accuracy over 1083 news pages from 10 different new sites,and the average processing time per page is within 10 ms.展开更多
This paper studies the sensor selection problem for random field estimation in wireless sensor networks. The authors first prove that selecting a set of I sensors that minimize the estimation error under the D-optimal...This paper studies the sensor selection problem for random field estimation in wireless sensor networks. The authors first prove that selecting a set of I sensors that minimize the estimation error under the D-optimal criterion is NP-complete. The authors propose an iterative algorithm to pursue a suboptimal solution. Furthermore, in order to improve the bandwidth and energy efficiency of the wireless sensor networks, the authors propose a best linear unbiased estimator for a Gaussian random field with quantized measurements and study the corresponding sensor selection problem. In the case of unknown covariance matrix, the authors propose an estimator for the covariance matrix using measurements and also analyze the sensitivity of this estimator. Simulation results show the good performance of the proposed algorithms.展开更多
Predicting protein-coding genes still remains a significant challenge. Although a variety of computational programs that use commonly machine learning methods have emerged, the accuracy of predictions remains a low le...Predicting protein-coding genes still remains a significant challenge. Although a variety of computational programs that use commonly machine learning methods have emerged, the accuracy of predictions remains a low level when implementing in large genomic sequences. Moreover, computational gene finding in newly se- quenced genomes is especially a difficult task due to the absence of a training set of abundant validated genes. Here we present a new gene-finding program, SCGPred, to improve the accuracy of prediction by combining multiple sources of evidence. SCGPred can perform both supervised method in previously well-studied genomes and unsupervised one in novel genomes. By testing with datasets composed of large DNA sequences from human and a novel genome of Ustilago maydi, SCGPred gains a significant improvement in comparison to the popular ab initio gene predictors. We also demonstrate that SCGPred can significantly improve prediction in novel genomes by combining several foreign gene finders with similarity alignments, which is superior to other unsupervised methods. Therefore, SCGPred can serve as an alternative gene-finding tool for newly sequenced eukaryotic genomes. The program is freely available at http：//bio.scu.edu.cn/SCGPred/.展开更多
The growing integration of distributed energy resources(DERs)in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors.With large-scale DER penetration in distribution gr...The growing integration of distributed energy resources(DERs)in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors.With large-scale DER penetration in distribution grids,traditional outage detection methods,which rely on customers report and smart meters'“last gasp”signals,will have poor performance,because renewable generators and storage and the mesh structure in urban distribution grids can continue supplying power after line outages.To address these challenges,we propose a datadriven outage monitoring approach based on the stochastic time series analysis with a theoretical guarantee.Specifically,we prove via power flow analysis that dependency of time-series voltage measurements exhibits significant statistical changes after line outages.This makes the theory on optimal change-point detection suitable to identify line outages.However,existing change point detection methods require post-outage voltage distribution,which are unknown in distribution systems.Therefore,we design a maximum likelihood estimator to directly learn distribution parameters from voltage data.We prove the estimated parameters-based detection also achieves optimal performance,making it extremely useful for fast distribution grid outage identifications.Furthermore,since smart meters have been widely installed in distribution grids and advanced infrastructure(e.g,PMU)has not widely been available,our approach only requires voltage magnitude for quick outage identification.Simulation results show highly accurate outage identification in eight distribution grids with 17 configurations with and without DERs using smart meter data.展开更多
Background:Image-based automatic diagnosis of field diseases can help increase crop yields and is of great importance.However,crop lesion regions tend to be scattered and of varying sizes,this along with substantial i...Background:Image-based automatic diagnosis of field diseases can help increase crop yields and is of great importance.However,crop lesion regions tend to be scattered and of varying sizes,this along with substantial intraclass variation and small inter-class variation makes segmentation difficult.Methods:We propose a novel end-to-end system that only requires weak supervision of image-level labels for lesion region segmentation.First,a two-branch network is designed for joint disease classification and seed region generation.The generated seed regions are then used as input to the next segmentation stage where we design to use an encoder-decoder network.Different from previous works that use an encoder in the segmentation network,the encoder-decoder network is critical for our system to successfully segment images with small and scattered regions,which is the major challenge in image-based diagnosis of field diseases.We further propose a novel weakly supervised training strategy for the encoder-decoder semantic segmentation network,making use of the extracted seed regions.Results:Experimental results show that our system achieves better lesion region segmentation results than state of the arts.In addition to crop images,our method is also applicable to general scattered object segmentation.We demonstrate this by extending our framework to work on the PASCAL VOC dataset,which achieves comparable performance with the state-of-the-art DSRG(deep seeded region growing)method.Conclusion:Our method not only outperforms state-of-the-art semantic segmentation methods by a large margin for the lesion segmentation task,but also shows its capability to perform well on more general tasks.展开更多
文摘Objective The International Federation of Gynecology and Obstetrics(FIGO)2000 scoring system classifies gestational trophoblastic neoplasia(GTN)patients into low-and high-risk groups,so that single-or multi-agent chemotherapy can be administered accordingly.However,a number of FIGO-defined low-risk patients still exhibit resistance to single-agent regimens,and the risk factors currently adopted in the FIGO scoring system possess inequable values for predicting single-agent chemoresistance.The purpose of this study is therefore to evaluate the efficacy of risk factors in predicting single-agent chemoresistance and explore the feasibility of simplifying the FIGO 2000 scoring system for GTN.Methods The clinical data of 578 GTN patients who received chemotherapy between January 2000 and December 2018 were retrospectively reviewed.Univariate and multivariate logistic regression analyses were carried out to identify risk factors associated with single-agent chemoresistance in low-risk GTN patients.Then,simplified models were built and compared with the original FIGO 2000 scoring system.Results Among the eight FIGO risk factors,the univariate and multivariate analyses identified that pretreatment serum human chorionic gonadotropin(hCG)level and interval from antecedent pregnancy were consistently independent predictors for both first-line and subsequent single-agent chemoresistance.The simplified model with two independent factors showed a better performance in predicting single-agent chemoresistance than the model with the other four non-independent factors.However,the addition of other co-factors did improve the efficiency.Overall,simplified models can achieve favorable performance,but the original FIGO 2000 prognostic system still features the highest discrimination.Conclusions Pretreatment serum hCG level and interval from antecedent pregnancy were independent predictors for both first-line and subsequent single-agent chemoresistance,and they had greater weight than other non-independent factors in predicting single-agent chemoresistance.The simplified model composed of certain selected factors is a promising alternative to the original FIGO 2000 prognostic system,and it shows comparable performance.
基金This work utilized Northwestern University Micro/Nano Fabrication Facility(NUFAB)which is partially supported by Soft and Hybrid Nanotechnology Experimental(SHyNE)Resource(NSF ECCS-1542205)+3 种基金the Materials Research Science and Engineering Center(DMR-1720139)the State of Illinois,and Northwestern University.Y.H.acknowledges the support from the National Science Foundation,USA(grant no.CMMI1635443)supported by Querrey Simpson Institute for Bioelectronicssupported by Cancer Center Support Grant P30 CA060553 from the National Cancer Institute awarded to the Robert H.Lurie Comprehensive Cancer Center.
文摘Objective and Impact Statement.Real-time monitoring of the temperatures of regional tissue microenvironments can serve as the diagnostic basis for treating various health conditions and diseases.Introduction.Traditional thermal sensors allow measurements at surfaces or at near-surface regions of the skin or of certain body cavities.Evaluations at depth require implanted devices connected to external readout electronics via physical interfaces that lead to risks for infection and movement constraints for the patient.Also,surgical extraction procedures after a period of need can introduce additional risks and costs.Methods.Here,we report a wireless,bioresorbable class of temperature sensor that exploits multilayer photonic cavities,for continuous optical measurements of regional,deep-tissue microenvironments over a timeframe of interest followed by complete clearance via natural body processes.Results.The designs decouple the influence of detection angle from temperature on the reflection spectra,to enable high accuracy in sensing,as supported by in vitro experiments and optical simulations.Studies with devices implanted into subcutaneous tissues of both awake,freely moving and asleep animal models illustrate the applicability of this technology for in vivo measurements.Conclusion.The results demonstrate the use of bioresorbable materials in advanced photonic structures with unique capabilities in tracking of thermal signatures of tissue microenvironments,with potential relevance to human healthcare.
基金supported by National Key R&D Program of China(Grant No.2018YFC0830300)Science and Technology Program of Fujian,China(Grant No.2018H0035)+1 种基金Science and Technology Program of Xiamen,China(3502Z20183011)Fund of XMU-ZhangShu FinTech Joint Lab
文摘The main content of a news web page is a source of data for Natural Language Processing(NLP)and Information Retrieval(IR),which contains large quantities of valuable information.This paper proposes a method that formulates the main content extraction problem as a DOM tree node classification problem.In terms of feature extraction,we use the DOM tree node to represent HTML document and then develop multiple features by using the DOM tree node properties,such as text length,tag path,tag properties and so on.In consideration that the essence of the problem is the classification model,we use Xgboost to help select nodes.Experimental results show that the proposed approach is effective and efficient in extracting main content of new web pages,and achieves about 98%accuracy over 1083 news pages from 10 different new sites,and the average processing time per page is within 10 ms.
基金supported by the National Natural Science Foundation of China-Key Program under Grant No. 61032001the National Natural Science Foundation of China under Grant No.60828006
文摘This paper studies the sensor selection problem for random field estimation in wireless sensor networks. The authors first prove that selecting a set of I sensors that minimize the estimation error under the D-optimal criterion is NP-complete. The authors propose an iterative algorithm to pursue a suboptimal solution. Furthermore, in order to improve the bandwidth and energy efficiency of the wireless sensor networks, the authors propose a best linear unbiased estimator for a Gaussian random field with quantized measurements and study the corresponding sensor selection problem. In the case of unknown covariance matrix, the authors propose an estimator for the covariance matrix using measurements and also analyze the sensitivity of this estimator. Simulation results show the good performance of the proposed algorithms.
基金This work was partially supported by the National Natural Science Foundation of China (No.30470984)
文摘Predicting protein-coding genes still remains a significant challenge. Although a variety of computational programs that use commonly machine learning methods have emerged, the accuracy of predictions remains a low level when implementing in large genomic sequences. Moreover, computational gene finding in newly se- quenced genomes is especially a difficult task due to the absence of a training set of abundant validated genes. Here we present a new gene-finding program, SCGPred, to improve the accuracy of prediction by combining multiple sources of evidence. SCGPred can perform both supervised method in previously well-studied genomes and unsupervised one in novel genomes. By testing with datasets composed of large DNA sequences from human and a novel genome of Ustilago maydi, SCGPred gains a significant improvement in comparison to the popular ab initio gene predictors. We also demonstrate that SCGPred can significantly improve prediction in novel genomes by combining several foreign gene finders with similarity alignments, which is superior to other unsupervised methods. Therefore, SCGPred can serve as an alternative gene-finding tool for newly sequenced eukaryotic genomes. The program is freely available at http：//bio.scu.edu.cn/SCGPred/.
文摘The growing integration of distributed energy resources(DERs)in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors.With large-scale DER penetration in distribution grids,traditional outage detection methods,which rely on customers report and smart meters'“last gasp”signals,will have poor performance,because renewable generators and storage and the mesh structure in urban distribution grids can continue supplying power after line outages.To address these challenges,we propose a datadriven outage monitoring approach based on the stochastic time series analysis with a theoretical guarantee.Specifically,we prove via power flow analysis that dependency of time-series voltage measurements exhibits significant statistical changes after line outages.This makes the theory on optimal change-point detection suitable to identify line outages.However,existing change point detection methods require post-outage voltage distribution,which are unknown in distribution systems.Therefore,we design a maximum likelihood estimator to directly learn distribution parameters from voltage data.We prove the estimated parameters-based detection also achieves optimal performance,making it extremely useful for fast distribution grid outage identifications.Furthermore,since smart meters have been widely installed in distribution grids and advanced infrastructure(e.g,PMU)has not widely been available,our approach only requires voltage magnitude for quick outage identification.Simulation results show highly accurate outage identification in eight distribution grids with 17 configurations with and without DERs using smart meter data.
基金This work was partially supported by the National Natural Science Foundation of China(Nos.61725204 and 62002258)a Grant from Science and Technology Department of Jiangsu Province,China.
文摘Background:Image-based automatic diagnosis of field diseases can help increase crop yields and is of great importance.However,crop lesion regions tend to be scattered and of varying sizes,this along with substantial intraclass variation and small inter-class variation makes segmentation difficult.Methods:We propose a novel end-to-end system that only requires weak supervision of image-level labels for lesion region segmentation.First,a two-branch network is designed for joint disease classification and seed region generation.The generated seed regions are then used as input to the next segmentation stage where we design to use an encoder-decoder network.Different from previous works that use an encoder in the segmentation network,the encoder-decoder network is critical for our system to successfully segment images with small and scattered regions,which is the major challenge in image-based diagnosis of field diseases.We further propose a novel weakly supervised training strategy for the encoder-decoder semantic segmentation network,making use of the extracted seed regions.Results:Experimental results show that our system achieves better lesion region segmentation results than state of the arts.In addition to crop images,our method is also applicable to general scattered object segmentation.We demonstrate this by extending our framework to work on the PASCAL VOC dataset,which achieves comparable performance with the state-of-the-art DSRG(deep seeded region growing)method.Conclusion:Our method not only outperforms state-of-the-art semantic segmentation methods by a large margin for the lesion segmentation task,but also shows its capability to perform well on more general tasks.
