The cosmopolitan family Solanaceae,which originated and first diversified in South America,is economically important.The tribe Hyoscyameae is one of the three clades in Solanaceae that occurs outside of the New World;...The cosmopolitan family Solanaceae,which originated and first diversified in South America,is economically important.The tribe Hyoscyameae is one of the three clades in Solanaceae that occurs outside of the New World;Hyoscyameae genera are distributed mainly in Europe and Asia,and have centers of species diversity in the Qinghai-Tibet Plateau and adjacent regions.Although many phylogenetic studies have focused on Solanaceae,the phylogenetic relationships within the tribe Hyoscyameae and its biogeographic history remain obscure.In this study,we reconstructed the phylogeny of Hyoscyameae based on whole chloroplast genome data,and estimated lineage divergence times according to the newly reported fruit fossil from the Eocene Patagonia,Physalis infinemundi,the earliest known fossil of Solanaceae.We reconstructed a robust phylogeny of Hyoscyameae that reveals the berry fruit-type Atropa is sister to the six capsule-bearing genera(Hyoscyameae sensu stricto),Atropanthe is sister to the clade(Scopolia,Physochlaina,Przewalskia),and together they are sister to the robustly supported AnisoduseHyoscyamus clade.The stem age of Hyoscyameae was inferred to be in the Eocene(47.11 Ma,95%HPD:36.75e57.86 Ma),and the crown ages of Hyoscyameae sensu stricto were estimated as the early Miocene(22.52 Ma,95%HPD:15.19e30.53 Ma),which shows a close correlation with the rapid uplift of the Qinghai-Tibet Plateau at the Paleogene/Neogene boundary.Our results provide insights into the phylogenetic relationships and the history of the biogeographic diversification of the tribe Hyoscyameae,as well as plant diversification on the Qinghai-Tibet Plateau.展开更多
Quantum machine learning(QML)is a rapidly rising research eld that incorporates ideas from quantum computing and machine learning to develop emerging tools for scientic research and improving data processing.How to ef...Quantum machine learning(QML)is a rapidly rising research eld that incorporates ideas from quantum computing and machine learning to develop emerging tools for scientic research and improving data processing.How to efciently control or manipulate the quantum system is a fundamental and vexing problem in quantum computing.It can be described as learning or approximating a unitary operator.Since the success of the hybrid-based quantum machine learning model proposed in recent years,we investigate to apply the techniques from QML to tackle this problem.Based on the Choi–Jamiołkowski isomorphism in quantum computing,we transfer the original problem of learning a unitary operator to a min–max optimization problem which can also be viewed as a quantum generative adversarial network.Besides,we select the spectral norm between the target and generated unitary operators as the regularization term in the loss function.Inspired by the hybrid quantum-classical framework widely used in quantum machine learning,we employ the variational quantum circuit and gradient descent based optimizers to solve the min-max optimization problem.In our numerical experiments,the results imply that our proposed method can successfully approximate the desired unitary operator and dramatically reduce the number of quantum gates of the traditional approach.The average delity between the states that are produced by applying target and generated unitary on random input states is around 0.997.展开更多
The ecological consequences of precipitation change and increased atmospheric nitrogen(N)deposition have profound impacts on ecosystem CO2 exchange in grassland ecosystems.Water and N can largely influence grassland p...The ecological consequences of precipitation change and increased atmospheric nitrogen(N)deposition have profound impacts on ecosystem CO2 exchange in grassland ecosystems.Water and N can largely influence grassland productivity,community composition and ecosystem functions.However,the influences of water and N addition on the ecosystem CO2 exchange of alpine grassland ecosystems remain unclear.A field manipulative experiment with water and N additions was conducted in an alpine meadow on the Tibetan Plateau over 4 years with contrasting precipitation patterns.There were four treatments:control(Ctrl),N addition(N),water addition(W)and N and water addition(NW),each replicated three times.N addition,but not water addition,increased gross ecosystem productivity(GEP),plant biomass,community cover and community-weighted mean height.The responses of ecosystem CO2 exchange to water and N addition varied between the wet and dry years.Water addition had a positive effect on net ecosystem carbon exchange(NEE)due to a larger increase in GEP than in ecosystem respiration(ER)only in the dry year.On the contrary,N addition significantly enhanced ecosystem CO2 exchange only in the wet year.The increased GEP in N addition was attributed to the larger increase in NEE than ER.Moreover,N addition stimulated NEE mainly through increasing the cover of dominant species.Our observations highlight the important roles of precipitation and dominant species in regulating ecosystem CO2 exchange response to global environmental change in alpine grasslands.展开更多
基金We thank Dr.Xiao-Feng Chi and Dr.Ofelia Vargas-Ponce for contributing the whole chloroplast genome sequences of Anisodus tanguticus(Maxim.)Pascher and Physalis philadelphica Lam.before the release in NCBI.This work was supported by the Beijing Natural Science Foundation(Grant No.5192012)National Natural Science Foundation of China(grant number 32070235)the China Scholarship Council(Grant No.201906515009)。
文摘The cosmopolitan family Solanaceae,which originated and first diversified in South America,is economically important.The tribe Hyoscyameae is one of the three clades in Solanaceae that occurs outside of the New World;Hyoscyameae genera are distributed mainly in Europe and Asia,and have centers of species diversity in the Qinghai-Tibet Plateau and adjacent regions.Although many phylogenetic studies have focused on Solanaceae,the phylogenetic relationships within the tribe Hyoscyameae and its biogeographic history remain obscure.In this study,we reconstructed the phylogeny of Hyoscyameae based on whole chloroplast genome data,and estimated lineage divergence times according to the newly reported fruit fossil from the Eocene Patagonia,Physalis infinemundi,the earliest known fossil of Solanaceae.We reconstructed a robust phylogeny of Hyoscyameae that reveals the berry fruit-type Atropa is sister to the six capsule-bearing genera(Hyoscyameae sensu stricto),Atropanthe is sister to the clade(Scopolia,Physochlaina,Przewalskia),and together they are sister to the robustly supported AnisoduseHyoscyamus clade.The stem age of Hyoscyameae was inferred to be in the Eocene(47.11 Ma,95%HPD:36.75e57.86 Ma),and the crown ages of Hyoscyameae sensu stricto were estimated as the early Miocene(22.52 Ma,95%HPD:15.19e30.53 Ma),which shows a close correlation with the rapid uplift of the Qinghai-Tibet Plateau at the Paleogene/Neogene boundary.Our results provide insights into the phylogenetic relationships and the history of the biogeographic diversification of the tribe Hyoscyameae,as well as plant diversification on the Qinghai-Tibet Plateau.
基金support from the National Key Research and Devel-opment Plan of China under Grant No.2018YFA0306703.
文摘Quantum machine learning(QML)is a rapidly rising research eld that incorporates ideas from quantum computing and machine learning to develop emerging tools for scientic research and improving data processing.How to efciently control or manipulate the quantum system is a fundamental and vexing problem in quantum computing.It can be described as learning or approximating a unitary operator.Since the success of the hybrid-based quantum machine learning model proposed in recent years,we investigate to apply the techniques from QML to tackle this problem.Based on the Choi–Jamiołkowski isomorphism in quantum computing,we transfer the original problem of learning a unitary operator to a min–max optimization problem which can also be viewed as a quantum generative adversarial network.Besides,we select the spectral norm between the target and generated unitary operators as the regularization term in the loss function.Inspired by the hybrid quantum-classical framework widely used in quantum machine learning,we employ the variational quantum circuit and gradient descent based optimizers to solve the min-max optimization problem.In our numerical experiments,the results imply that our proposed method can successfully approximate the desired unitary operator and dramatically reduce the number of quantum gates of the traditional approach.The average delity between the states that are produced by applying target and generated unitary on random input states is around 0.997.
基金supported by the Outstanding Youth Scientist Program of NSFC(41725003)National Key Research&Development Program of China(2017YFA0604802)+2 种基金the National Natural Science Foundation of China(41991234,42077422)the National Key Research&Development Program of China(2016YFC0501802)the Strategic Priority Research Program of Chinese Academy of Sciences(XDA20050102).
文摘The ecological consequences of precipitation change and increased atmospheric nitrogen(N)deposition have profound impacts on ecosystem CO2 exchange in grassland ecosystems.Water and N can largely influence grassland productivity,community composition and ecosystem functions.However,the influences of water and N addition on the ecosystem CO2 exchange of alpine grassland ecosystems remain unclear.A field manipulative experiment with water and N additions was conducted in an alpine meadow on the Tibetan Plateau over 4 years with contrasting precipitation patterns.There were four treatments:control(Ctrl),N addition(N),water addition(W)and N and water addition(NW),each replicated three times.N addition,but not water addition,increased gross ecosystem productivity(GEP),plant biomass,community cover and community-weighted mean height.The responses of ecosystem CO2 exchange to water and N addition varied between the wet and dry years.Water addition had a positive effect on net ecosystem carbon exchange(NEE)due to a larger increase in GEP than in ecosystem respiration(ER)only in the dry year.On the contrary,N addition significantly enhanced ecosystem CO2 exchange only in the wet year.The increased GEP in N addition was attributed to the larger increase in NEE than ER.Moreover,N addition stimulated NEE mainly through increasing the cover of dominant species.Our observations highlight the important roles of precipitation and dominant species in regulating ecosystem CO2 exchange response to global environmental change in alpine grasslands.
