Forward radiative transfer(RT)models are essential for atmospheric applications such as remote sensing and weather and climate models,where computational efficiency becomes equally as important as accuracy for high-re...Forward radiative transfer(RT)models are essential for atmospheric applications such as remote sensing and weather and climate models,where computational efficiency becomes equally as important as accuracy for high-resolution hyperspectral measurements that need rigorous RT simulations for thousands of channels.This study introduces a fast and accurate RT model for the hyperspectral infrared(HIR)sounder based on principal component analysis(PCA)or machine learning(i.e.,neural network,NN).The Geosynchronous Interferometric Infrared Sounder(GIIRS),the first HIR sounder onboard the geostationary Fengyun-4 satellites,is considered to be a candidate example for model development and validation.Our method uses either PCA or NN(PCA/NN)twice for the atmospheric transmittance and radiance,respectively,to reduce the number of independent but similar simulations to accelerate RT simulations;thereby,it is referred to as a multi-domain compression model.The first PCA/NN gives monochromatic gas transmittance in both spectral and atmospheric pressure domains for each gas independently.The second PCA/NN is performed in the traditional spectral radiance domain.Meanwhile,a new method is introduced to choose representative variables for the PCA/NN scheme developments.The model is three orders of magnitude faster than the standard line-by-line-based simulations with averaged brightness temperature difference(BTD)less than 0.1 K,and the compressions based on PCA or NN methods result in comparable efficiency and accuracy.Our fast model not only avoids an excessively complicated transmittance scheme by using PCA/NN but is also highly flexible for hyperspectral instruments with similar spectral ranges simply by updating the corresponding spectral response functions.展开更多
This paper examines the prediction of film ratings.Firstly,in the data feature engineering,feature construction is performed based on the original features of the film dataset.Secondly,the clustering algorithm is util...This paper examines the prediction of film ratings.Firstly,in the data feature engineering,feature construction is performed based on the original features of the film dataset.Secondly,the clustering algorithm is utilized to remove singular film samples,and feature selections are carried out.When solving the problem that film samples of the target domain are unlabelled,it is impossible to train a model and address the inconsistency in the feature dimension for film samples from the source domain.Therefore,the domain adaptive transfer learning model combined with dimensionality reduction algorithms is adopted in this paper.At the same time,in order to reduce the prediction error of models,the stacking ensemble learning model for regression is also used.Finally,through comparative experiments,the effectiveness of the proposed method is verified,which proves to be better predicting film ratings in the target domain.展开更多
Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network lev...Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.展开更多
Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce ...Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce historical data,spectrum prediction based on traditional learning methods does not work well.Thus,this paper proposes a cross-band spectrum prediction model based on transfer learning.Firstly,by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping,the similarity between spectrum bands has been verified.Next,the features,which mainly affect the performance of transfer learning in the crossband spectrum prediction,are explored by leveraging transfer component analysis.Then,the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated.Further,experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-theart models when the historical spectrum data is limited.展开更多
BraLTP1 is an important member of lipid transfer protein family in Brassica napus.The aim of current study was to detect, classify and follow variations in metabolite profiling of B. napus with overexpression of BraLT...BraLTP1 is an important member of lipid transfer protein family in Brassica napus.The aim of current study was to detect, classify and follow variations in metabolite profiling of B. napus with overexpression of BraLTP1. In this study, metabolic change in leaves of BraLTP1-overexpressing B. napus plants (BraLTP1-22) and negative control (BraLTP1-22N) was investigated using nuclear magnetic resonance (NMR). Statistical strategy of principle component analysis (PCA) was performed to identify related difference metabolites. PCA score plots indicated not only high reproducibility of various treatments, but also significant difference of metabolite levels between different treatments. PCA loading plots indicated main responsible metabolites and a total of 50 metabolites were quantitatively determined. A wide range of metabolites were detected due to BraLTP1 overexpression,including biosynthesis and metabolism of sugars, amino acids, ammoniums compounds and organic acids. Furthermore, concentrations of 17 amino acids were determined for other set of samples by amino acids component analysis assay. The link between metabolite variations and phenotype were also discussed in BraLTP1-overexpressing B. napus.This work will help to gain insight into BraLTP1 function in B. napus in metabolism.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.42122038)。
文摘Forward radiative transfer(RT)models are essential for atmospheric applications such as remote sensing and weather and climate models,where computational efficiency becomes equally as important as accuracy for high-resolution hyperspectral measurements that need rigorous RT simulations for thousands of channels.This study introduces a fast and accurate RT model for the hyperspectral infrared(HIR)sounder based on principal component analysis(PCA)or machine learning(i.e.,neural network,NN).The Geosynchronous Interferometric Infrared Sounder(GIIRS),the first HIR sounder onboard the geostationary Fengyun-4 satellites,is considered to be a candidate example for model development and validation.Our method uses either PCA or NN(PCA/NN)twice for the atmospheric transmittance and radiance,respectively,to reduce the number of independent but similar simulations to accelerate RT simulations;thereby,it is referred to as a multi-domain compression model.The first PCA/NN gives monochromatic gas transmittance in both spectral and atmospheric pressure domains for each gas independently.The second PCA/NN is performed in the traditional spectral radiance domain.Meanwhile,a new method is introduced to choose representative variables for the PCA/NN scheme developments.The model is three orders of magnitude faster than the standard line-by-line-based simulations with averaged brightness temperature difference(BTD)less than 0.1 K,and the compressions based on PCA or NN methods result in comparable efficiency and accuracy.Our fast model not only avoids an excessively complicated transmittance scheme by using PCA/NN but is also highly flexible for hyperspectral instruments with similar spectral ranges simply by updating the corresponding spectral response functions.
基金Supported by the Scientific Research Foundation of Liaoning Provincial Department of Education(No.LJKZ0139).
文摘This paper examines the prediction of film ratings.Firstly,in the data feature engineering,feature construction is performed based on the original features of the film dataset.Secondly,the clustering algorithm is utilized to remove singular film samples,and feature selections are carried out.When solving the problem that film samples of the target domain are unlabelled,it is impossible to train a model and address the inconsistency in the feature dimension for film samples from the source domain.Therefore,the domain adaptive transfer learning model combined with dimensionality reduction algorithms is adopted in this paper.At the same time,in order to reduce the prediction error of models,the stacking ensemble learning model for regression is also used.Finally,through comparative experiments,the effectiveness of the proposed method is verified,which proves to be better predicting film ratings in the target domain.
基金supported by the National Natural Science Foundation of China,Nos.81871836(to MZ),82172554(to XH),and 81802249(to XH),81902301(to JW)the National Key R&D Program of China,Nos.2018YFC2001600(to JX)and 2018YFC2001604(to JX)+3 种基金Shanghai Rising Star Program,No.19QA1409000(to MZ)Shanghai Municipal Commission of Health and Family Planning,No.2018YQ02(to MZ)Shanghai Youth Top Talent Development PlanShanghai“Rising Stars of Medical Talent”Youth Development Program,No.RY411.19.01.10(to XH)。
文摘Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.
基金supported by the National Key R&D Program of China under Grant 2018AAA0102303 and Grant 2018YFB1801103the National Natural Science Foundation of China (No. 61871398 and No. 61931011)+1 种基金the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province (No. BK20190030)the Equipment Advanced Research Field Foundation (No. 61403120304)
文摘Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce historical data,spectrum prediction based on traditional learning methods does not work well.Thus,this paper proposes a cross-band spectrum prediction model based on transfer learning.Firstly,by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping,the similarity between spectrum bands has been verified.Next,the features,which mainly affect the performance of transfer learning in the crossband spectrum prediction,are explored by leveraging transfer component analysis.Then,the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated.Further,experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-theart models when the historical spectrum data is limited.
基金supported by Central Public-interest Scientific Institution Basal Research FundMajor Research Project of CAAS Science and the Technology Innovation Program+1 种基金National Natural Science Foundation of China (31400243)Natural Science Foundation of Hubei Province (ZRMS2016000076)
文摘BraLTP1 is an important member of lipid transfer protein family in Brassica napus.The aim of current study was to detect, classify and follow variations in metabolite profiling of B. napus with overexpression of BraLTP1. In this study, metabolic change in leaves of BraLTP1-overexpressing B. napus plants (BraLTP1-22) and negative control (BraLTP1-22N) was investigated using nuclear magnetic resonance (NMR). Statistical strategy of principle component analysis (PCA) was performed to identify related difference metabolites. PCA score plots indicated not only high reproducibility of various treatments, but also significant difference of metabolite levels between different treatments. PCA loading plots indicated main responsible metabolites and a total of 50 metabolites were quantitatively determined. A wide range of metabolites were detected due to BraLTP1 overexpression,including biosynthesis and metabolism of sugars, amino acids, ammoniums compounds and organic acids. Furthermore, concentrations of 17 amino acids were determined for other set of samples by amino acids component analysis assay. The link between metabolite variations and phenotype were also discussed in BraLTP1-overexpressing B. napus.This work will help to gain insight into BraLTP1 function in B. napus in metabolism.
