We present a compilation of published data(field,petrography,ages and geochemistry)from 73 ophiolitic complexes of the Central Asian Orogenic Belt.The ophiolitic complexes,ranging in age from Neoproterozoic to Triassi...We present a compilation of published data(field,petrography,ages and geochemistry)from 73 ophiolitic complexes of the Central Asian Orogenic Belt.The ophiolitic complexes,ranging in age from Neoproterozoic to Triassic.have been geochemically classified as subduction-related and subductionunrelated categories applying recent,well-established discrimination diagrams.The subductionunrelated category is further subdivided into Mid-Ocean Ridge type(MOR),a common rift-drift stage and Plume type,and the subduction-related category is subdivided into Backarc(BA),Forearc(FA).Backarc to Forearc(BA-FA)and Volcanic Arc(VA)types.The four subduction-related types define highly different geochemical features,with the BA and FA types defining end members showing subduction influence of 10%-100%and 90%-100%subduction influence,respectively,and the two other types(BAFA and VA)define values between the two end members.The subduction-related category comprises79%of the examined ophiolites,of which the BA type ophiolites is by far the dominant group,followed by the BA-FA type,and with FA and VA types as subordinate groups.The Neoproterozoic and Ordovician complexes exhibit the highest,whereas those of Silurian age exhibit the lowest subduction-influence.Of the remaining 21%subduction-unrelated ophiolites,the MOR type dominates.Both the subductionrelated and subduction-unrelated types,in particular the latter,are commonly associated with alkaline basalts taken to represent ocean island magmatism.Harzburgite,dunite,gabbro and basalt are the common lithologies in all ophiolite types,whereas the BA-FA,FA and VA types generally contain intermediate to felsic rocks,and in the FA type boninites occur.The subduction-related ophiolites types generally show low metamorphic grade,whereas greenschist.amphibolite and blueschist grades occur in the subduction-unrelated and BA types.The highly different subduction contribution(from 0 to 100%in the MOR and FA,respectively),attest to variable dips of the subducting slab,as well as variable flux of s展开更多
Face attribute classification(FAC)is a high-profile problem in biometric verification and face retrieval.Although recent research has been devoted to extracting more delicate image attribute features and exploiting th...Face attribute classification(FAC)is a high-profile problem in biometric verification and face retrieval.Although recent research has been devoted to extracting more delicate image attribute features and exploiting the inter-attribute correlations,significant challenges still remain.Wavelet scattering transform(WST)is a promising non-learned feature extractor.It has been shown to yield more discriminative representations and outperforms the learned representations in certain tasks.Applied to the image classification task,WST can enhance subtle image texture information and create local deformation stability.This paper designs a scattering-based hybrid block,to incorporate frequency-domain(WST)and image-domain features in a channel attention manner(Squeezeand-Excitation,SE),termed WS-SE block.Compared with CNN,WS-SE achieves a more efficient FAC performance and compensates for the model sensitivity of the small-scale affine transform.In addition,to further exploit the relationships among the attribute labels,we propose a learning strategy from a causal view.The cause attributes defined using the causalityrelated information can be utilized to infer the effect attributes with a high confidence level.Ablative analysis experiments demonstrate the effectiveness of our model,and our hybrid model obtains state-of-the-art results in two public datasets.展开更多
In this paper, we used SVM method to detect P300 signal. Before training a classification parameter for the SVM, several preprocessing operations were applied to the data including filtering, downsampling, single tria...In this paper, we used SVM method to detect P300 signal. Before training a classification parameter for the SVM, several preprocessing operations were applied to the data including filtering, downsampling, single trial extraction, windsorizing, electrode selection et al. With the SVM algorithm, the classification accuracy could be up to above 80%. In some cases, the accuracy could reach 100%. It is suitable to use SVM for P300 EEG recognition in the P300-based brain-computer interface (BCI) system. Our further work will include the improvement to yield higher classification accuracy using fewer trials.展开更多
Objective:To calculate the health poverty vulnerability index of elderly households in rural areas of central and western China,and then to classify these samples,lastly to decompose their influencing factors.Methods:...Objective:To calculate the health poverty vulnerability index of elderly households in rural areas of central and western China,and then to classify these samples,lastly to decompose their influencing factors.Methods:First,based on survey data in 2018,the three-stage feasible generalized least squares was used to calculate the health poverty vulnerability index of elderly households,and then combined with whether the household income was below the poverty line and whether the family was healthy poverty vulnerability,the sample households were divided into four categories,and then used multiple unordered logistic regression to analyze various types of influencing factors,and finally used the Shapley index to decompose the contribution of each influencing factor.Results:The average vulnerability of health poverty was 0.5979±0.25199,with 1169 households greater than or equal to 0.5,accounting for 63.26%;the number of households stuck in poverty,temporary poverty,potential poverty,and escaped from poverty were 489,300,680,and 379 households,accounting for 26.46%,16.23%,36.80%,and 20.51%of the total sample;compared with escaped from poverty families,the three variables of marital status,the number of chronically ill patients,and the number of annual hospitalizations were the common influencing factors of other three types families;The Shapley decomposition showed that the interviewees’education level and family members engaged in non-agricultural work have contributed significantly to the three types,however two indicators:time required to visit a medical institution and self-assessment of health status of the main interviewees showed great differences in different types of families.Conclusion:Rural elderly households have a high level of vulnerability to health poverty;potential poverty households and persistent poverty households account for a large proportion,and continuous intervention should be carried out;it is necessary to unify the implementation of basic poverty alleviation work,but also to enhance ref展开更多
The Cheng index distinguishes indica andjaponica rice based on six taxonomic traits.This index has been widely used for classifi- cation of indica and japonica varieties in China.In this study,a double haploid(DH)popu...The Cheng index distinguishes indica andjaponica rice based on six taxonomic traits.This index has been widely used for classifi- cation of indica and japonica varieties in China.In this study,a double haploid(DH)popula-tion derived from anther culture of ZYQ8/JX17 F,a typical inter-subspecies hybrid,was used to investigate the six taxonomictraits,i.e.leaf hairiness(LH),color of hullwhen heading(CHH),hairiness of hull(HH),length of the first and second panicle internode(LPI),length/width of grain(L/W),andphenol reaction(PH).The morphological in- dex(MI)was also calculated.Based on themolecular linkage map constructed from this展开更多
Lung adenocarcinoma (LUAD) is the most common and deadliest subtype of lung cancer. To select moretargeted and effective treatments for individuals, further advances in classifying LUAD are urgently needed. Thenumber,...Lung adenocarcinoma (LUAD) is the most common and deadliest subtype of lung cancer. To select moretargeted and effective treatments for individuals, further advances in classifying LUAD are urgently needed. Thenumber, type, and function of T cells in the tumor microenvironment (TME) determine the progression andtreatment response of LUAD. Long noncoding RNAs (lncRNAs), may regulate T cell differentiation, development,and activation. Thus, our aim was to identify T cell-related lncRNAs (T cell-Lncs) in LUAD and to investigatewhether T cell-Lncs could serve as potential stratifiers and therapeutic targets. Seven T cell-Lncs were identified tofurther establish the T cell-related lncRNA risk score (TRS) in LUAD. Low TRS individuals were characterized byrobust immune status, fewer genomic alterations, and remarkably longer survival than high TRS individuals. Theexcellent accuracy of TRS in predicting overall survival (OS) was validated in the TCGA-LUAD training cohort andthe GEO-LUAD validation cohort. Our data demonstrated the favorable predictive power of the TRS-basednomogram, which had important clinical significance in estimating the survival probability for individuals. Inaddition, individuals with low TRS could respond better to chemotherapy and immunotherapy than those with highTRS. LINC00525 was identified as a valuable study target, and the ability of LUAD to proliferate or invade wassignificantly attenuated by downregulation of LINC00525. In conclusion, the TRS established by T cell-Lncs couldunambiguously classify LUAD patients, predict their prognosis and guide their management. Moreover, our identifiedT cell-Lncs could provide potential therapeutic targets for LUAD.展开更多
基金supported by the Department of Earth Science,University of Bergen,Norwaysupported by the Ministry of Education and Science of the Russian Federation,grant#14.Y26.31.0018+1 种基金Foundation for Basic Research(Grant#16-05-00313)Scientific Project of IGM SB RAS No.0330-2016-0003
文摘We present a compilation of published data(field,petrography,ages and geochemistry)from 73 ophiolitic complexes of the Central Asian Orogenic Belt.The ophiolitic complexes,ranging in age from Neoproterozoic to Triassic.have been geochemically classified as subduction-related and subductionunrelated categories applying recent,well-established discrimination diagrams.The subductionunrelated category is further subdivided into Mid-Ocean Ridge type(MOR),a common rift-drift stage and Plume type,and the subduction-related category is subdivided into Backarc(BA),Forearc(FA).Backarc to Forearc(BA-FA)and Volcanic Arc(VA)types.The four subduction-related types define highly different geochemical features,with the BA and FA types defining end members showing subduction influence of 10%-100%and 90%-100%subduction influence,respectively,and the two other types(BAFA and VA)define values between the two end members.The subduction-related category comprises79%of the examined ophiolites,of which the BA type ophiolites is by far the dominant group,followed by the BA-FA type,and with FA and VA types as subordinate groups.The Neoproterozoic and Ordovician complexes exhibit the highest,whereas those of Silurian age exhibit the lowest subduction-influence.Of the remaining 21%subduction-unrelated ophiolites,the MOR type dominates.Both the subductionrelated and subduction-unrelated types,in particular the latter,are commonly associated with alkaline basalts taken to represent ocean island magmatism.Harzburgite,dunite,gabbro and basalt are the common lithologies in all ophiolite types,whereas the BA-FA,FA and VA types generally contain intermediate to felsic rocks,and in the FA type boninites occur.The subduction-related ophiolites types generally show low metamorphic grade,whereas greenschist.amphibolite and blueschist grades occur in the subduction-unrelated and BA types.The highly different subduction contribution(from 0 to 100%in the MOR and FA,respectively),attest to variable dips of the subducting slab,as well as variable flux of s
基金supported by the National Key Research and Development Project of China(Grant No.2018AAA0100802)Opening Foundation of National Engineering Laboratory for Intelligent Video Analysis and Application,and Experimental Center of Artificial Intelligence of Beijing Normal University.
文摘Face attribute classification(FAC)is a high-profile problem in biometric verification and face retrieval.Although recent research has been devoted to extracting more delicate image attribute features and exploiting the inter-attribute correlations,significant challenges still remain.Wavelet scattering transform(WST)is a promising non-learned feature extractor.It has been shown to yield more discriminative representations and outperforms the learned representations in certain tasks.Applied to the image classification task,WST can enhance subtle image texture information and create local deformation stability.This paper designs a scattering-based hybrid block,to incorporate frequency-domain(WST)and image-domain features in a channel attention manner(Squeezeand-Excitation,SE),termed WS-SE block.Compared with CNN,WS-SE achieves a more efficient FAC performance and compensates for the model sensitivity of the small-scale affine transform.In addition,to further exploit the relationships among the attribute labels,we propose a learning strategy from a causal view.The cause attributes defined using the causalityrelated information can be utilized to infer the effect attributes with a high confidence level.Ablative analysis experiments demonstrate the effectiveness of our model,and our hybrid model obtains state-of-the-art results in two public datasets.
基金Natural Science Foundation of Shandong Provincegrant number:Y2007G31
文摘In this paper, we used SVM method to detect P300 signal. Before training a classification parameter for the SVM, several preprocessing operations were applied to the data including filtering, downsampling, single trial extraction, windsorizing, electrode selection et al. With the SVM algorithm, the classification accuracy could be up to above 80%. In some cases, the accuracy could reach 100%. It is suitable to use SVM for P300 EEG recognition in the P300-based brain-computer interface (BCI) system. Our further work will include the improvement to yield higher classification accuracy using fewer trials.
基金supported by the National Natural Science Foundation of China(No.72074086&No.71673093)Humanities and Social Sciences of Ministry of Education Planning Fund of China(No.16YJA840013).
文摘Objective:To calculate the health poverty vulnerability index of elderly households in rural areas of central and western China,and then to classify these samples,lastly to decompose their influencing factors.Methods:First,based on survey data in 2018,the three-stage feasible generalized least squares was used to calculate the health poverty vulnerability index of elderly households,and then combined with whether the household income was below the poverty line and whether the family was healthy poverty vulnerability,the sample households were divided into four categories,and then used multiple unordered logistic regression to analyze various types of influencing factors,and finally used the Shapley index to decompose the contribution of each influencing factor.Results:The average vulnerability of health poverty was 0.5979±0.25199,with 1169 households greater than or equal to 0.5,accounting for 63.26%;the number of households stuck in poverty,temporary poverty,potential poverty,and escaped from poverty were 489,300,680,and 379 households,accounting for 26.46%,16.23%,36.80%,and 20.51%of the total sample;compared with escaped from poverty families,the three variables of marital status,the number of chronically ill patients,and the number of annual hospitalizations were the common influencing factors of other three types families;The Shapley decomposition showed that the interviewees’education level and family members engaged in non-agricultural work have contributed significantly to the three types,however two indicators:time required to visit a medical institution and self-assessment of health status of the main interviewees showed great differences in different types of families.Conclusion:Rural elderly households have a high level of vulnerability to health poverty;potential poverty households and persistent poverty households account for a large proportion,and continuous intervention should be carried out;it is necessary to unify the implementation of basic poverty alleviation work,but also to enhance ref
文摘The Cheng index distinguishes indica andjaponica rice based on six taxonomic traits.This index has been widely used for classifi- cation of indica and japonica varieties in China.In this study,a double haploid(DH)popula-tion derived from anther culture of ZYQ8/JX17 F,a typical inter-subspecies hybrid,was used to investigate the six taxonomictraits,i.e.leaf hairiness(LH),color of hullwhen heading(CHH),hairiness of hull(HH),length of the first and second panicle internode(LPI),length/width of grain(L/W),andphenol reaction(PH).The morphological in- dex(MI)was also calculated.Based on themolecular linkage map constructed from this
基金supported by the following funds:the Key Research and Development Project of the Science and Technology Department of Sichuan Province(Grant Nos.2021YFS0202 and 2021YFS0229)the Natural Science Foundation of Sichuan Province(Grant No.2022NSFSC1326)+1 种基金Postdoctoral Research Fund of West China Hospital(Grant Nos.2019HXBH056 and 2020HXBH066)China Postdoctoral Science Foundation(Grant No.2022T150454).
文摘Lung adenocarcinoma (LUAD) is the most common and deadliest subtype of lung cancer. To select moretargeted and effective treatments for individuals, further advances in classifying LUAD are urgently needed. Thenumber, type, and function of T cells in the tumor microenvironment (TME) determine the progression andtreatment response of LUAD. Long noncoding RNAs (lncRNAs), may regulate T cell differentiation, development,and activation. Thus, our aim was to identify T cell-related lncRNAs (T cell-Lncs) in LUAD and to investigatewhether T cell-Lncs could serve as potential stratifiers and therapeutic targets. Seven T cell-Lncs were identified tofurther establish the T cell-related lncRNA risk score (TRS) in LUAD. Low TRS individuals were characterized byrobust immune status, fewer genomic alterations, and remarkably longer survival than high TRS individuals. Theexcellent accuracy of TRS in predicting overall survival (OS) was validated in the TCGA-LUAD training cohort andthe GEO-LUAD validation cohort. Our data demonstrated the favorable predictive power of the TRS-basednomogram, which had important clinical significance in estimating the survival probability for individuals. Inaddition, individuals with low TRS could respond better to chemotherapy and immunotherapy than those with highTRS. LINC00525 was identified as a valuable study target, and the ability of LUAD to proliferate or invade wassignificantly attenuated by downregulation of LINC00525. In conclusion, the TRS established by T cell-Lncs couldunambiguously classify LUAD patients, predict their prognosis and guide their management. Moreover, our identifiedT cell-Lncs could provide potential therapeutic targets for LUAD.
