The passive radar is a hot research topic. A multi-channel wideband passive radar experimental system is designed and the digital television terrestrial broadcasting (DTTB) signal is chosen to carry out the target det...The passive radar is a hot research topic. A multi-channel wideband passive radar experimental system is designed and the digital television terrestrial broadcasting (DTTB) signal is chosen to carry out the target detection experiment of civil aviation aircraft. The polarization and spatial filtering methods are used to solve the strong direct path interference suppression problems brought by the receiving system location;combined with the characteristics of DTTB signal, the block length selection interval in the block batch processing method for range-Doppler images calculation is given;the clutter suppression performance is compared through the experimental data receiving from different bistatic polarization channels, the conclusion is different from the monostatic radar and it can guide the passive radar experiment.展开更多
Background： The increase in global population, climate change and stagnancy in crop yield on unit land area basis in recent decades urgently call for a new approach to support contemporary crop improvements, ePlant i...Background： The increase in global population, climate change and stagnancy in crop yield on unit land area basis in recent decades urgently call for a new approach to support contemporary crop improvements, ePlant is a mathematical model of plant growth and development with a high level of mechanistic details to meet this challenge. Results： ePlant integrates modules developed for processes occurring at drastically different temporal （10-8-106 seconds） and spatial （10-10-10 meters） scales, incorporating diverse physical, biophysical and biochemical processes including gene regulation, metabolic reaction, substrate transport and diffusion, energy absorption, transfer and conversion, organ morphogenesis, plant environment interaction, etc. Individual modules are developed using a divide-and-conquer approach; modules at different temporal and spatial scales are integrated through transfer variables. We further propose a supervised learning procedure based on information geometry to combine model and data for both knowledge discovery and model extension or advances. We finally discuss the recent formation of a global consortium, which includes experts in plant biology, computer science, statistics, agronomy, phenomics, etc. aiming to expedite the development and application of ePlant or its equivalents by promoting a new model development paradigm where models are developed as a community effort instead of driven mainly by individual labs＇ effort. Conclusions： ePlant, as a major research tool to support quantitative and predictive plant science research, will play a crucial role in the future model guided crop engineering, breeding and agronomy.展开更多
Background:Single-cell RNA sequencing(scRNA-seq)technology is now becoming a widely applied method of transcriptome exploration that helps to reveal cell-type composition as well as cell-state heterogeneity for specif...Background:Single-cell RNA sequencing(scRNA-seq)technology is now becoming a widely applied method of transcriptome exploration that helps to reveal cell-type composition as well as cell-state heterogeneity for specific biological processes.Distinct sequencing platforms and processing pipelines may contribute to various results even for the same sequencing samples.Therefore,benchmarking sequencing platforms and processing pipelines was considered as a necessary step to interpret scRNA-seq data.However,recent comparing efforts were constrained in sequencing platforms or analyzing pipelines.There is still a lack of knowledge of analyzing pipelines matched with specific sequencing platforms in aspects of sensitivity,precision,and so on.Methods:We downloaded public scRNA-seq data that was generated by two distinct sequencers,NovaSeq 6000 and MGISEQ 2000.Then data was processed through the Drop-seq-tools,UMI-tools and Cell Ranger pipeline respectively.We calculated multiple measurements based on the expression profiles of the six platform-pipeline combinations.Results:We found that all three pipelines had comparable performance,the Cell Ranger pipeline achieved the best performance in precision while UMI-tools prevailed in terms of sensitivity and marker calling.Conclusions:Our work provided an insight into the selection of scRNA-seq data processing tools for two sequencing platforms as well as a framework to evaluate platform-pipeline combinations.展开更多
The comprehensive analyses of the SARS-CoV-2 genomes could provide a global picture of how the virus was transmitted among different populations,which may help predict the oncoming trends of the pandemic.The main appr...The comprehensive analyses of the SARS-CoV-2 genomes could provide a global picture of how the virus was transmitted among different populations,which may help predict the oncoming trends of the pandemic.The main approach for the molecular tracing of viral transmission is to thoroughly compare the genomes of different viral strains.展开更多
文摘The passive radar is a hot research topic. A multi-channel wideband passive radar experimental system is designed and the digital television terrestrial broadcasting (DTTB) signal is chosen to carry out the target detection experiment of civil aviation aircraft. The polarization and spatial filtering methods are used to solve the strong direct path interference suppression problems brought by the receiving system location;combined with the characteristics of DTTB signal, the block length selection interval in the block batch processing method for range-Doppler images calculation is given;the clutter suppression performance is compared through the experimental data receiving from different bistatic polarization channels, the conclusion is different from the monostatic radar and it can guide the passive radar experiment.
基金The work in XGZ＇s lab is supported by CAS strategic leading project on designer breeding by molecular module （No. XDA08020301）, the National High Technology Development Plan of the Ministry of Science and Technology of China （2014AA101601）, the National Natural Science Foundation of China （No. C020401）, the National Key Basic Research Program of China （No. 2015CB150104）, Bill and Melinda Gates Foundation （No. OPP1060461）, CAS-CSIRO Cooperative Research Program （No. GJHZ1501）.
文摘Background： The increase in global population, climate change and stagnancy in crop yield on unit land area basis in recent decades urgently call for a new approach to support contemporary crop improvements, ePlant is a mathematical model of plant growth and development with a high level of mechanistic details to meet this challenge. Results： ePlant integrates modules developed for processes occurring at drastically different temporal （10-8-106 seconds） and spatial （10-10-10 meters） scales, incorporating diverse physical, biophysical and biochemical processes including gene regulation, metabolic reaction, substrate transport and diffusion, energy absorption, transfer and conversion, organ morphogenesis, plant environment interaction, etc. Individual modules are developed using a divide-and-conquer approach; modules at different temporal and spatial scales are integrated through transfer variables. We further propose a supervised learning procedure based on information geometry to combine model and data for both knowledge discovery and model extension or advances. We finally discuss the recent formation of a global consortium, which includes experts in plant biology, computer science, statistics, agronomy, phenomics, etc. aiming to expedite the development and application of ePlant or its equivalents by promoting a new model development paradigm where models are developed as a community effort instead of driven mainly by individual labs＇ effort. Conclusions： ePlant, as a major research tool to support quantitative and predictive plant science research, will play a crucial role in the future model guided crop engineering, breeding and agronomy.
基金This work was supported by Strategic Priority Research Program of Chinese Academy of Sciences(Nos.XDB38050200 and XDA26040304).
文摘Background:Single-cell RNA sequencing(scRNA-seq)technology is now becoming a widely applied method of transcriptome exploration that helps to reveal cell-type composition as well as cell-state heterogeneity for specific biological processes.Distinct sequencing platforms and processing pipelines may contribute to various results even for the same sequencing samples.Therefore,benchmarking sequencing platforms and processing pipelines was considered as a necessary step to interpret scRNA-seq data.However,recent comparing efforts were constrained in sequencing platforms or analyzing pipelines.There is still a lack of knowledge of analyzing pipelines matched with specific sequencing platforms in aspects of sensitivity,precision,and so on.Methods:We downloaded public scRNA-seq data that was generated by two distinct sequencers,NovaSeq 6000 and MGISEQ 2000.Then data was processed through the Drop-seq-tools,UMI-tools and Cell Ranger pipeline respectively.We calculated multiple measurements based on the expression profiles of the six platform-pipeline combinations.Results:We found that all three pipelines had comparable performance,the Cell Ranger pipeline achieved the best performance in precision while UMI-tools prevailed in terms of sensitivity and marker calling.Conclusions:Our work provided an insight into the selection of scRNA-seq data processing tools for two sequencing platforms as well as a framework to evaluate platform-pipeline combinations.
基金supported by the National Key Research and Development Program of China (2021YFC0863300, 2021YFF0703703, and 2020YFC0845900)CAS Strategic Priority Research Program (XDB38060100, XDB38030100, XDB38050000, XDB38040100, and XDC01040100)Science and Technology Service Network Initiative of Chinese Academy of Sciences (KFJSTS-QYZD-126)
文摘The comprehensive analyses of the SARS-CoV-2 genomes could provide a global picture of how the virus was transmitted among different populations,which may help predict the oncoming trends of the pandemic.The main approach for the molecular tracing of viral transmission is to thoroughly compare the genomes of different viral strains.
