Targeted multimodal sentiment classification(TMSC)aims to identify the sentiment polarity of a target mentioned in a multimodal post.The majority of current studies on this task focus on mapping the image and the text...Targeted multimodal sentiment classification(TMSC)aims to identify the sentiment polarity of a target mentioned in a multimodal post.The majority of current studies on this task focus on mapping the image and the text to a high-dimensional space in order to obtain and fuse implicit representations,ignoring the rich semantic information contained in the images and not taking into account the contribution of the visual modality in the multimodal fusion representation,which can potentially influence the results of TMSC tasks.This paper proposes a general model for Improving Targeted Multimodal Sentiment Classification with Semantic Description of Images(ITMSC)as a way to tackle these issues and improve the accu-racy of multimodal sentiment analysis.Specifically,the ITMSC model can automatically adjust the contribution of images in the fusion representation through the exploitation of semantic descriptions of images and text similarity relations.Further,we propose a target-based attention module to capture the target-text relevance,an image-based attention module to capture the image-text relevance,and a target-image matching module based on the former two modules to properly align the target with the image so that fine-grained semantic information can be extracted.Our experimental results demonstrate that our model achieves comparable performance with several state-of-the-art approaches on two multimodal sentiment datasets.Our findings indicate that incorporating semantic descriptions of images can enhance our understanding of multimodal content and lead to improved sentiment analysis performance.展开更多
Cancer,like other diseases accompanied by metabolic changes,shows characteristic DNA/RNA modifications and activities of modifying enzymes,resulting in fluctuations in nucleoside levels.In this study,we undertook targ...Cancer,like other diseases accompanied by metabolic changes,shows characteristic DNA/RNA modifications and activities of modifying enzymes,resulting in fluctuations in nucleoside levels.In this study,we undertook targeted metabolomic analyses of nucleotides in different cancer cell culture models using a sensitive and reproducible ion-pair HPLC method.The experimental data were analyzed by principal component analysis(PCA)to identify potential biomarkers in cancer cells,and statistical significance was determined by one-way analysis of variance.As a result,a clear differentiation of normal and tumor cells into two clusters was shown,indicating abnormal metabolism of nucleotides in tumor cells.Six variables(AMP,UDP,CTP levels with a significance of Po0.05;ATP,UTP and GMP levels with a significance of Po0.01)were considered as potential biomarkers;the content of AMP,UTP,GMP and ATP was significantly higher in cancer cells.The receiver operating characteristic(ROC)curve analysis allowed us to discriminate normal cells from tumor cells based on area under the curve(AUC).The sequence of their AUC values were:ATP(0.979)4UTP(0.938)4CTP¼GMP(0.896)4AMP(0.812)4UDP(0.792),so we conclude that ATP and UTP are the best potential biomarkers in tumor cells.This study may provide a valuable tool for studying minute alterations of intracellular nucleotide pools induced by anticancer/antiviral drugs,diseases or environmental factors.展开更多
The problem of estimation of underwater target motion parameters via bearings only is the most of ten encountered and most difficult to solve in the underwater target motion analysis.As the bearings-only target motion...The problem of estimation of underwater target motion parameters via bearings only is the most of ten encountered and most difficult to solve in the underwater target motion analysis.As the bearings-only target motion analysis is a nonlinear and multiextremal global optimization problem, so most classical estimation methods often lead the solution to convergence to one of the local extremes other than the global extreme, especially, when the noise of target bearing observation is added. In this paper we propose to use the Generalized Least Square method on the rough estimation of target motion parameters, and then use the Sequential Uniform Design method to gain a more precise estimation on the bases of rough estimation.The latter ensures that the result convergences to the global extreme. The algorithm based on the above two methods is profitable for the bearings-only target motion analysis even under conditions of large bearing observation error.展开更多
The method for Bearings-Only Target Motion Analysis (BO-TMA) based on bearing measurements fusion of two arrays is studied. The algorithms of pseudolinear processing, extended Kalman filter and maximum likelihood esti...The method for Bearings-Only Target Motion Analysis (BO-TMA) based on bearing measurements fusion of two arrays is studied. The algorithms of pseudolinear processing, extended Kalman filter and maximum likelihood estimation are presented. The results of simulation experiments show that the BO-TMA method based on association of multiple arrays not only makes contributions towards eliminating maneuvers needed by bearings-only TMA based on single array,but also improves the stabilization and global convergence for varied estimation algorithms.展开更多
Nucleotide pools in mammalian cells change due to the influence of antitumor drugs,which may help in evaluating the drug effect and understanding the mechanism of drug action.In this study,an ion-pair RP-HPLC method w...Nucleotide pools in mammalian cells change due to the influence of antitumor drugs,which may help in evaluating the drug effect and understanding the mechanism of drug action.In this study,an ion-pair RP-HPLC method was used for a simple,sensitive and simultaneous determination of the levels of 12 nucleotides in mammalian cells treated with antibiotic antitumor drugs(daunorubicin,epirubicin and dactinomycin D).Through the use of this targeted metabolomics approach to find potential biomarkers,UTP and ATP were verified to be the most appropriate biomarkers.Moreover,a holistic statistical approach was put forward to develop a model which could distinguish 4 categories of drugs with different mechanisms of action.This model can be further validated by evaluating drugs with different mechanismsof action.This targeted metabolomics study may provide a novel approach to predict the mechanism of action of antitumor drugs.展开更多
Embryonic stem (ES) cells are under precise control of both intrinsic self-renewal gene regulatory network and extrinsic growth factor-triggered signaling cascades.
Vision-based target motion estimation based Kalman filtering or least-squares estimators is an important problem in many tasks such as vision-based swarming or vision-based target pursuit.In this paper,we focus on a p...Vision-based target motion estimation based Kalman filtering or least-squares estimators is an important problem in many tasks such as vision-based swarming or vision-based target pursuit.In this paper,we focus on a problem that is very specific yet we believe important.That is,from the vision measurements,we can formulate various measurements.Which and how the measurements should be used?These problems are very fundamental,but we notice that practitioners usually do not pay special attention to them and often make mistakes.Motivated by this,we formulate three pseudo-linear measurements based on the bearing and angle measurements,which are standard vision measurements that can be obtained.Different estimators based on Kalman filtering and least-squares estimation are established and compared based on numerical experiments.It is revealed that correctly analyzing the covariance noises is critical for the Kalman filtering-based estimators.When the variance of the original measurement noise is unknown,the pseudo-linear least-squares estimator that has the smallest magnitude of the transformed noise can be a good choice.展开更多
After decades of development,protein and peptide drugs have now grown into a major drug class in the marketplace.Target identification and validation are crucial for the discovery of protein and peptide drugs,and bioi...After decades of development,protein and peptide drugs have now grown into a major drug class in the marketplace.Target identification and validation are crucial for the discovery of protein and peptide drugs,and bioinformatics prediction of targets based on the characteristics of known target proteins will help improve the efficiency and success rate of target selection.However,owing to the developmental history in the pharmaceutical industry,previous systematic exploration of the target spaces has mainly focused on traditional small-molecule drugs,while studies related to protein and peptide drugs are lacking.Here,we systematically explore the target spaces in the human genome specifically for protein and peptide drugs.Compared with other proteins,both successful protein and peptide drug targets have many special characteristics,and are also significantly different from those of small-molecule drugs in many aspects.Based on these features,we develop separate effective genome-wide target prediction models for protein and peptide drugs.Finally,a user-friendly web server,Predictor Of Protein and Pept Ide drugs’therapeutic Targets(POPPIT)(http://poppit.ncpsb.org.cn/),is established,which provides not only target prediction specifically for protein and peptide drugs but also abundant annotations for predicted targets.展开更多
There exist a large class of acoustic sources which have an underlying periodic phenomenon. Unlike the well-studied Bearings-Only Tracking(BOT) of an aperiodic acoustic source,this paper considers the problem of track...There exist a large class of acoustic sources which have an underlying periodic phenomenon. Unlike the well-studied Bearings-Only Tracking(BOT) of an aperiodic acoustic source,this paper considers the problem of tracking a periodic acoustic source. For periodic acoustic tracking, the signal emission time is known. However, the true measurement reception time is unknown because it is corrupted by noise due to propagation delay. We augment the sensor’s signal reception time onto bearing measurements, and the information of the delay constraint is included in the original bearing measurements to compensate for the propagation delay. A Cubature Kalman Filter(CKF) is used for periodic acoustic source tracking, in which measurement prediction cannot be obtained directly because the sensor’s position at the true measurement reception time is unknown.We solve this problem by using the implicit Gauss-Helmert Sensor Model(GHSM) for estimating the sensor’s position, which consists of the sensor’s motion equation and the known measured sensor’s signal reception time equation related to the state. Then a CKF based on the GHSM(CF-GHSM) is developed for periodic acoustic tracking. Illustrative examples demonstrate that the CF-GHSM algorithm is better than other algorithms for periodic acoustic source tracking.展开更多
A combined approach of target,suspected target and non-target screening using liquid chromatography-high-resolution mass spectrometry(LC-HRMS)was used to develop a new concept for water monitoring.With the current LC-...A combined approach of target,suspected target and non-target screening using liquid chromatography-high-resolution mass spectrometry(LC-HRMS)was used to develop a new concept for water monitoring.With the current LC-MS/MS target approach for water monitoring,all targets can be quantified,but no additional information about the sample is collected.With the new concept,it is possible to detect 97%of the target compounds with a simplified quantification method without losing accuracy.Furthermore,a suspect target screening can be performed to get broader qualitative information about the water samples.In addition,the non-target screening offers the possibility to identify unknown micropollutants.All three evaluation steps depend on the same analytical measurement so that a lot of measurement and quality assurance effort can be saved.This concept could change water monitoring and assessment,and make it much more efficiently without losing information.There is a chance to measure less but learn more about the water bodies.展开更多
文摘Targeted multimodal sentiment classification(TMSC)aims to identify the sentiment polarity of a target mentioned in a multimodal post.The majority of current studies on this task focus on mapping the image and the text to a high-dimensional space in order to obtain and fuse implicit representations,ignoring the rich semantic information contained in the images and not taking into account the contribution of the visual modality in the multimodal fusion representation,which can potentially influence the results of TMSC tasks.This paper proposes a general model for Improving Targeted Multimodal Sentiment Classification with Semantic Description of Images(ITMSC)as a way to tackle these issues and improve the accu-racy of multimodal sentiment analysis.Specifically,the ITMSC model can automatically adjust the contribution of images in the fusion representation through the exploitation of semantic descriptions of images and text similarity relations.Further,we propose a target-based attention module to capture the target-text relevance,an image-based attention module to capture the image-text relevance,and a target-image matching module based on the former two modules to properly align the target with the image so that fine-grained semantic information can be extracted.Our experimental results demonstrate that our model achieves comparable performance with several state-of-the-art approaches on two multimodal sentiment datasets.Our findings indicate that incorporating semantic descriptions of images can enhance our understanding of multimodal content and lead to improved sentiment analysis performance.
基金support of Natural Science Foundation of Liaoning Province(No.201102210)Program for Liaoning Innovative Research Team in University(No.LH2012018)National Undergraduate Training Programs for Innovation and Entrepreneurship(No.201210163007).
文摘Cancer,like other diseases accompanied by metabolic changes,shows characteristic DNA/RNA modifications and activities of modifying enzymes,resulting in fluctuations in nucleoside levels.In this study,we undertook targeted metabolomic analyses of nucleotides in different cancer cell culture models using a sensitive and reproducible ion-pair HPLC method.The experimental data were analyzed by principal component analysis(PCA)to identify potential biomarkers in cancer cells,and statistical significance was determined by one-way analysis of variance.As a result,a clear differentiation of normal and tumor cells into two clusters was shown,indicating abnormal metabolism of nucleotides in tumor cells.Six variables(AMP,UDP,CTP levels with a significance of Po0.05;ATP,UTP and GMP levels with a significance of Po0.01)were considered as potential biomarkers;the content of AMP,UTP,GMP and ATP was significantly higher in cancer cells.The receiver operating characteristic(ROC)curve analysis allowed us to discriminate normal cells from tumor cells based on area under the curve(AUC).The sequence of their AUC values were:ATP(0.979)4UTP(0.938)4CTP¼GMP(0.896)4AMP(0.812)4UDP(0.792),so we conclude that ATP and UTP are the best potential biomarkers in tumor cells.This study may provide a valuable tool for studying minute alterations of intracellular nucleotide pools induced by anticancer/antiviral drugs,diseases or environmental factors.
文摘The problem of estimation of underwater target motion parameters via bearings only is the most of ten encountered and most difficult to solve in the underwater target motion analysis.As the bearings-only target motion analysis is a nonlinear and multiextremal global optimization problem, so most classical estimation methods often lead the solution to convergence to one of the local extremes other than the global extreme, especially, when the noise of target bearing observation is added. In this paper we propose to use the Generalized Least Square method on the rough estimation of target motion parameters, and then use the Sequential Uniform Design method to gain a more precise estimation on the bases of rough estimation.The latter ensures that the result convergences to the global extreme. The algorithm based on the above two methods is profitable for the bearings-only target motion analysis even under conditions of large bearing observation error.
文摘The method for Bearings-Only Target Motion Analysis (BO-TMA) based on bearing measurements fusion of two arrays is studied. The algorithms of pseudolinear processing, extended Kalman filter and maximum likelihood estimation are presented. The results of simulation experiments show that the BO-TMA method based on association of multiple arrays not only makes contributions towards eliminating maneuvers needed by bearings-only TMA based on single array,but also improves the stabilization and global convergence for varied estimation algorithms.
基金supported financially by the Natural Science Foundation of Liaoning Province,China (No.201102210)the Program for Liaoning Innovative Research Team in University (No.LH2012018)
文摘Nucleotide pools in mammalian cells change due to the influence of antitumor drugs,which may help in evaluating the drug effect and understanding the mechanism of drug action.In this study,an ion-pair RP-HPLC method was used for a simple,sensitive and simultaneous determination of the levels of 12 nucleotides in mammalian cells treated with antibiotic antitumor drugs(daunorubicin,epirubicin and dactinomycin D).Through the use of this targeted metabolomics approach to find potential biomarkers,UTP and ATP were verified to be the most appropriate biomarkers.Moreover,a holistic statistical approach was put forward to develop a model which could distinguish 4 categories of drugs with different mechanisms of action.This model can be further validated by evaluating drugs with different mechanismsof action.This targeted metabolomics study may provide a novel approach to predict the mechanism of action of antitumor drugs.
文摘Embryonic stem (ES) cells are under precise control of both intrinsic self-renewal gene regulatory network and extrinsic growth factor-triggered signaling cascades.
文摘Vision-based target motion estimation based Kalman filtering or least-squares estimators is an important problem in many tasks such as vision-based swarming or vision-based target pursuit.In this paper,we focus on a problem that is very specific yet we believe important.That is,from the vision measurements,we can formulate various measurements.Which and how the measurements should be used?These problems are very fundamental,but we notice that practitioners usually do not pay special attention to them and often make mistakes.Motivated by this,we formulate three pseudo-linear measurements based on the bearing and angle measurements,which are standard vision measurements that can be obtained.Different estimators based on Kalman filtering and least-squares estimation are established and compared based on numerical experiments.It is revealed that correctly analyzing the covariance noises is critical for the Kalman filtering-based estimators.When the variance of the original measurement noise is unknown,the pseudo-linear least-squares estimator that has the smallest magnitude of the transformed noise can be a good choice.
基金supported by the National Key R&D Program of China(Grant Nos.2020YFE0202200 and 2017YFC1700105)the National Natural Science Foundation of China(Grant Nos.31601064,31871341,and 32088101)+1 种基金the Beijing Nova Program of China(Grant No.Z171100001117117)the State Key Laboratory of Proteomics of China(Grant No.SKLPO202010)。
文摘After decades of development,protein and peptide drugs have now grown into a major drug class in the marketplace.Target identification and validation are crucial for the discovery of protein and peptide drugs,and bioinformatics prediction of targets based on the characteristics of known target proteins will help improve the efficiency and success rate of target selection.However,owing to the developmental history in the pharmaceutical industry,previous systematic exploration of the target spaces has mainly focused on traditional small-molecule drugs,while studies related to protein and peptide drugs are lacking.Here,we systematically explore the target spaces in the human genome specifically for protein and peptide drugs.Compared with other proteins,both successful protein and peptide drug targets have many special characteristics,and are also significantly different from those of small-molecule drugs in many aspects.Based on these features,we develop separate effective genome-wide target prediction models for protein and peptide drugs.Finally,a user-friendly web server,Predictor Of Protein and Pept Ide drugs’therapeutic Targets(POPPIT)(http://poppit.ncpsb.org.cn/),is established,which provides not only target prediction specifically for protein and peptide drugs but also abundant annotations for predicted targets.
基金supported in part by the National Key Research and Development Plan,China(No.2017YFB1301101)the National Natural Science Foundation of China(Nos.61673317 and 61673313)。
文摘There exist a large class of acoustic sources which have an underlying periodic phenomenon. Unlike the well-studied Bearings-Only Tracking(BOT) of an aperiodic acoustic source,this paper considers the problem of tracking a periodic acoustic source. For periodic acoustic tracking, the signal emission time is known. However, the true measurement reception time is unknown because it is corrupted by noise due to propagation delay. We augment the sensor’s signal reception time onto bearing measurements, and the information of the delay constraint is included in the original bearing measurements to compensate for the propagation delay. A Cubature Kalman Filter(CKF) is used for periodic acoustic source tracking, in which measurement prediction cannot be obtained directly because the sensor’s position at the true measurement reception time is unknown.We solve this problem by using the implicit Gauss-Helmert Sensor Model(GHSM) for estimating the sensor’s position, which consists of the sensor’s motion equation and the known measured sensor’s signal reception time equation related to the state. Then a CKF based on the GHSM(CF-GHSM) is developed for periodic acoustic tracking. Illustrative examples demonstrate that the CF-GHSM algorithm is better than other algorithms for periodic acoustic source tracking.
文摘A combined approach of target,suspected target and non-target screening using liquid chromatography-high-resolution mass spectrometry(LC-HRMS)was used to develop a new concept for water monitoring.With the current LC-MS/MS target approach for water monitoring,all targets can be quantified,but no additional information about the sample is collected.With the new concept,it is possible to detect 97%of the target compounds with a simplified quantification method without losing accuracy.Furthermore,a suspect target screening can be performed to get broader qualitative information about the water samples.In addition,the non-target screening offers the possibility to identify unknown micropollutants.All three evaluation steps depend on the same analytical measurement so that a lot of measurement and quality assurance effort can be saved.This concept could change water monitoring and assessment,and make it much more efficiently without losing information.There is a chance to measure less but learn more about the water bodies.
