Several therocephalian species,mainly represented by cranial material from the late Permian,have been reported from China in recent years.Here we describe a tiny new baurioid therocephalian,Jiucaiyuangnathus confusus ...Several therocephalian species,mainly represented by cranial material from the late Permian,have been reported from China in recent years.Here we describe a tiny new baurioid therocephalian,Jiucaiyuangnathus confusus gen.et sp.nov.,from the Jiucaiyuan Formation,Xinjiang,China.The new taxon is represented by a partial snout with occluded partial lower jaw and two postcranial skeletons.Although juvenile in stage,the new species is diagnosed by the following features:round pit in middle of lateral surface of maxilla;lacrimal contact nasal;fossa for dentary tooth on the posterior end of the premaxilla,lateral to the anterior choana;two small vertical triangular ridges extending dorsally and ventrally on the vomerine anterior portion,and bordering a thin vomerine foramen laterally;anterior projection of the lateral part of the frontal on the nasal;symphyseal region of the dentary projected anteriorly;5 upper premaxillary teeth,upper and lower canines absent,diastema between the last premaxillary upper incisor and first maxillary tooth present,no diastema separating anterior from posterior dentition in the mandible,10 maxillary teeth and 12 dentary teeth,posterior postcanine expands mesiodistally,having a main large cusps and tiny anterior and posterior accessory cusps in line;neural arches of the atlas fused by the neural spine,neural spine of the axis projected posteriorly,procoracoid foramen lies between procoracoid and scapula.Features of the dentition resembles those of the small baurioid Ericiolacerta parva from South Africa and Silphedosuchus orenburgensis from Russia.The specimens provide the rare opportunity to know in detail the postcranial skeleton of baurioids.展开更多
Background:Single-cell RNA-sequencing(scRNA-seq)is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution.Despite the explosive growth in the number of cells th...Background:Single-cell RNA-sequencing(scRNA-seq)is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution.Despite the explosive growth in the number of cells that can be assayed by a single experiment,scRNA-seq still has several limitations,including high rates of dropouts,which result in a large number of genes having zero read count in the scRNA-seq data,and complicate downstream analyses.Methods:To overcome this problem,we treat zeros as missing values and develop nonparametric deep learning methods for imputation.Specifically,our LATE(Learning with AuToEncoder)method trains an autoencoder with random initial values of the parameters,whereas our TRANSLATE(TRANSfer learning with LATE)method further allows for the use of a reference gene expression data set to provide LATE with an initial set of parameter estimates.Results:On both simulated and real data,LATE and TRANSLATE outperform existing scRNA-seq imputation methods,achieving lower mean squared error in most cases,recovering nonlinear gene-gene relationships,and better separating cell types.They are also highly scalable and can efficiently process over 1 million cells in just a few hours on a GPU.Conclusions:We demonstrate that our nonparametric approach to imputation based on autoencoders is powerful and highly efficient.展开更多
Africa is host to coal deposits stretching from the far north to the far south and ranging in age from the Carboniferous through to the Miocene.Coal production in the north of the continent is however currently of a v...Africa is host to coal deposits stretching from the far north to the far south and ranging in age from the Carboniferous through to the Miocene.Coal production in the north of the continent is however currently of a very limited nature compared to that in the south,where due mainly to its low cost and relative abundance,the commodity has long been the primary source of energy.Coal is also used extensively in the metallurgical(titanium,ferrochrome,ferromanganese and steel)industries.All of the main exploited coal deposits in South-Central Africa are hosted in sedimentary rocks of the Late Carboniferous to Middle Jurassic aged,Karoo Supergroup and their temporal equivalents.展开更多
文摘Several therocephalian species,mainly represented by cranial material from the late Permian,have been reported from China in recent years.Here we describe a tiny new baurioid therocephalian,Jiucaiyuangnathus confusus gen.et sp.nov.,from the Jiucaiyuan Formation,Xinjiang,China.The new taxon is represented by a partial snout with occluded partial lower jaw and two postcranial skeletons.Although juvenile in stage,the new species is diagnosed by the following features:round pit in middle of lateral surface of maxilla;lacrimal contact nasal;fossa for dentary tooth on the posterior end of the premaxilla,lateral to the anterior choana;two small vertical triangular ridges extending dorsally and ventrally on the vomerine anterior portion,and bordering a thin vomerine foramen laterally;anterior projection of the lateral part of the frontal on the nasal;symphyseal region of the dentary projected anteriorly;5 upper premaxillary teeth,upper and lower canines absent,diastema between the last premaxillary upper incisor and first maxillary tooth present,no diastema separating anterior from posterior dentition in the mandible,10 maxillary teeth and 12 dentary teeth,posterior postcanine expands mesiodistally,having a main large cusps and tiny anterior and posterior accessory cusps in line;neural arches of the atlas fused by the neural spine,neural spine of the axis projected posteriorly,procoracoid foramen lies between procoracoid and scapula.Features of the dentition resembles those of the small baurioid Ericiolacerta parva from South Africa and Silphedosuchus orenburgensis from Russia.The specimens provide the rare opportunity to know in detail the postcranial skeleton of baurioids.
文摘Background:Single-cell RNA-sequencing(scRNA-seq)is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution.Despite the explosive growth in the number of cells that can be assayed by a single experiment,scRNA-seq still has several limitations,including high rates of dropouts,which result in a large number of genes having zero read count in the scRNA-seq data,and complicate downstream analyses.Methods:To overcome this problem,we treat zeros as missing values and develop nonparametric deep learning methods for imputation.Specifically,our LATE(Learning with AuToEncoder)method trains an autoencoder with random initial values of the parameters,whereas our TRANSLATE(TRANSfer learning with LATE)method further allows for the use of a reference gene expression data set to provide LATE with an initial set of parameter estimates.Results:On both simulated and real data,LATE and TRANSLATE outperform existing scRNA-seq imputation methods,achieving lower mean squared error in most cases,recovering nonlinear gene-gene relationships,and better separating cell types.They are also highly scalable and can efficiently process over 1 million cells in just a few hours on a GPU.Conclusions:We demonstrate that our nonparametric approach to imputation based on autoencoders is powerful and highly efficient.
文摘Africa is host to coal deposits stretching from the far north to the far south and ranging in age from the Carboniferous through to the Miocene.Coal production in the north of the continent is however currently of a very limited nature compared to that in the south,where due mainly to its low cost and relative abundance,the commodity has long been the primary source of energy.Coal is also used extensively in the metallurgical(titanium,ferrochrome,ferromanganese and steel)industries.All of the main exploited coal deposits in South-Central Africa are hosted in sedimentary rocks of the Late Carboniferous to Middle Jurassic aged,Karoo Supergroup and their temporal equivalents.
