As surgical procedures transition from conventional resection to advanced tissue-regeneration technologies,human disease therapy has witnessed a great leap forward.In particular,three-dimensional(3D)bioprinting stands...As surgical procedures transition from conventional resection to advanced tissue-regeneration technologies,human disease therapy has witnessed a great leap forward.In particular,three-dimensional(3D)bioprinting stands as a landmark in this setting,by promising the precise integration of biomaterials,cells,and bioactive molecules,thus opening up a novel avenue for tissue/organ regeneration.Curated by the editorial board of Bio-Design and Manufacturing,this review brings together a cohort of leading young scientists in China to dissect the core functionalities and evolutionary trajectory of 3D bioprinting,by elucidating the intricate challenges encountered in the manufacturing of transplantable organs.We further delve into the translational pathway from scientific research to clinical application,emphasizing the imperativeness of establishing a regulatory framework and rigorously enforcing quality-control measures.Finally,this review outlines the strategic landscape and innovative achievements of China in this field and provides a comprehensive roadmap for researchers worldwide to propel this field collectively to even greater heights.展开更多
Soybean(Glycine max)is a globally important crop that serves as a primary source of edible oil and protein for both humans and animals.Cultivated soybean varieties exhibit considerable genetic diversity depending on t...Soybean(Glycine max)is a globally important crop that serves as a primary source of edible oil and protein for both humans and animals.Cultivated soybean varieties exhibit considerable genetic diversity depending on their geographical origin.Heinong 531(HN531)is an elite cultivar that was released in China in June 2021 with 22.34%seed oil,high resistance to soybean cyst nematode(SCN)race 3,and enhanced yield.However,the genetic basis for these desirable agronomic traits is unclear.In this study,we generated a high-quality genome assembly for HN531 and used it to systematically analyze genes related to agronomic traits such as resistance to SCN.The assembled genome spans 981.20 Mb,featuring a contig N50 of 19.47 Mb,and contains 58,151 predicted gene models.Pan-genomic comparison with 27 previously reported soybean genomes revealed 95,071 structural variants(SVs)of>50 bp,of which 602 were HN531-specific.Furthermore,we identified a copy number variation at rhg1 that underlies resistance to SCN,and we found elite alleles of functional genes underlying important agronomic traits such as seed oil content,adaptability,and yield.This high-quality HN531 genome can be used to explore the genetic basis for the excellent agronomic traits of this cultivar,and is a valuable resource for breeders aiming to improve HN531 and related cultivars.展开更多
Soybean(Glycine max)is an important and valuable crop,providing oil and proteins for both humans and animals.Seed weight is a key trait that determines soybean yields;however,the genes and mechanisms controlling seed ...Soybean(Glycine max)is an important and valuable crop,providing oil and proteins for both humans and animals.Seed weight is a key trait that determines soybean yields;however,the genes and mechanisms controlling seed weight remain poorly understood.Here,we used genome-wide association study(GWAS)and joint linkage mapping to identify a ubiquitin-specific protease,GmSW17.1,which regulates 100-seed weight in soybean.Two natural allelic variants of GmSW17.1 resulted in significantly different 100-seed weight,with GmSW17.1T conferring heavier seeds.We used CRISPR/Cas9 technology to knock out GmSW17.1,resulting in lighter and smaller seeds;however,these mutants produced more seeds than the wild type,resulting in similar overall yields.Owing to the increased number of seeds,we determined that GmSW17.1 is highly transcribed in developing seeds,and its encoded protein physically interacts in the nucleus with GmSGF11,which plays a crucial role in the deubiquitinating pathway.Analysis of genomic sequences from more than 1714 soybean accessions suggested that the natural allele GmSW17.1T was selected during the domestication and genetic improvement,resulting in its rapid expansion in cultivated soybean.These findings provide important insights into the role of GmSW17.1 in 100-seed weight and offer valuable clues for the molecular breeding of soybean.展开更多
The conductive polymer poly-3,4-ethylenedioxythiophene(PEDOT),recognized for its superior electrical conductivity and biocompatibility,has become an attractive material for developing wearable technologies and bioelec...The conductive polymer poly-3,4-ethylenedioxythiophene(PEDOT),recognized for its superior electrical conductivity and biocompatibility,has become an attractive material for developing wearable technologies and bioelectronics.Nevertheless,the complexities associated with PEDOT's patterning synthesis on diverse substrates persist despite recent technological progress.In this study,we introduce a novel deep eutectic solvent(DES)-induced vapor phase polymerization technique,facilitating nonrestrictive patterning polymerization of PEDOT across diverse substrates.By controlling the quantity of DES adsorbed per unit area on the substrates,PEDOT can be effectively patternized on cellulose,wood,plastic,glass,and even hydrogels.The resultant patterned PEDOT exhibits numerous benefits,such as an impressive electronic conductivity of 282 S·m-1,a high specific surface area of 5.29 m^(2)·g-1,and an extensive electrochemical stability range from-1.4 to 2.4 V in a phosphate-buffered saline.To underscore the practicality and diverse applications of this DES-induced approach,we present multiple examples emphasizing its integration into self-supporting flexible electrodes,neuroelectrode interfaces,and precision circuit repair methodologies.展开更多
Climate change and the increasing frequency of floods have undermined China’s food security.Creating detailed maps of flooded croplands is essential to improve prevention and adopt effective adaptation initiatives.Pr...Climate change and the increasing frequency of floods have undermined China’s food security.Creating detailed maps of flooded croplands is essential to improve prevention and adopt effective adaptation initiatives.Previous large-scale flood mapping efforts were hampered by limited meteorological and hydrological data,and the susceptibility of optical satellite images to cloud cover,leading to high uncertainty when downscaled to the cropland-scale.Here,using 4968 near-real-time(NRT)Sentinel-1 SAR(S1)images(spatial resolution:10 m),we generated China’s first set of high-resolution flooded cropland maps covering the period from 2017 to 2021.Our results demonstrate that croplands accounted for 43.8%to49.8%of China’s total flooded areas(ranging from 82,175 km^(2) to 122,037 km^(2)).We also created highresolution flood maps specifically for rice and maize crops.The inundated rice areas ranged from 8428 km^(2) to 22,123 km^(2),accounting for 22.34%to 41.91%of the annual flooded croplands,or 2.82%to7.45%of the annual rice cropland.In comparison,the inundated maize cropland fluctuated from 2619 km^(2) to 5397 km^(2),representing 5.38%to 13.56%of the annual flooded croplands.Our findings revealed extensive floods in rural areas,highlighting the urgent need to prioritize flood prevention and mitigation efforts in such regions.In light of China’s allocation of an additional 1-trillion-RMB treasury bonds for water infrastructure projects,the high-resolution flood maps can be used to select sites for flood control projects,and evaluate the impact of flooding on crop yields and food security,thus targeting poverty alleviation in rural areas of China.展开更多
Sparse Knowledge Graph(KG)scenarios pose a challenge for previous Knowledge Graph Completion(KGC)methods,that is,the completion performance decreases rapidly with the increase of graph sparsity.This problem is also ex...Sparse Knowledge Graph(KG)scenarios pose a challenge for previous Knowledge Graph Completion(KGC)methods,that is,the completion performance decreases rapidly with the increase of graph sparsity.This problem is also exacerbated because of the widespread existence of sparse KGs in practical applications.To alleviate this challenge,we present a novel framework,LR-GCN,that is able to automatically capture valuable long-range dependency among entities to supplement insufficient structure features and distill logical reasoning knowledge for sparse KGC.The proposed approach comprises two main components:a GNN-based predictor and a reasoning path distiller.The reasoning path distiller explores high-order graph structures such as reasoning paths and encodes them as rich-semantic edges,explicitly compositing long-range dependencies into the predictor.This step also plays an essential role in densifying KGs,effectively alleviating the sparse issue.Furthermore,the path distiller further distills logical reasoning knowledge from these mined reasoning paths into the predictor.These two components are jointly optimized using a well-designed variational EM algorithm.Extensive experiments and analyses on four sparse benchmarks demonstrate the effectiveness of our proposed method.展开更多
High-throughput phenotyping enables the efficient collection of plant trait data at scale.One example involves using imaging systems over key phases of a crop growing season.Although the resulting images provide rich ...High-throughput phenotyping enables the efficient collection of plant trait data at scale.One example involves using imaging systems over key phases of a crop growing season.Although the resulting images provide rich data for statistical analyses of plant phenotypes,image processing for trait extraction is required as a prerequisite.Current methods for trait extraction are mainly based on supervised learning with human labeled data or semisupervised learning with a mixture of human labeled data and unsupervised data.Unfortunately,preparing a sufficiently large training data is both time and labor-intensive.We describe a self-supervised pipeline(KAT4IA)that uses K-means clustering on greenhouse images to construct training data for extracting and analyzing plant traits from an image-based field phenotyping system.The KAT4IA pipeline includes these main steps:self-supervised training set construction,plant segmentation from images of field-grown plants,automatic separation of target plants,calculation of plant traits,and functional curve fitting of the extracted traits.To deal with the challenge of separating target plants from noisy backgrounds in field images,we describe a novel approach using row-cuts and column-cuts on images segmented by transform domain neural network learning,which utilizes plant pixels identified from greenhouse images to train a segmentation model for field images.This approach is efficient and does not require human intervention.Our results show that KAT4IA is able to accurately extract plant pixels and estimate plant heights.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52325504,52235007,and T2121004).
文摘As surgical procedures transition from conventional resection to advanced tissue-regeneration technologies,human disease therapy has witnessed a great leap forward.In particular,three-dimensional(3D)bioprinting stands as a landmark in this setting,by promising the precise integration of biomaterials,cells,and bioactive molecules,thus opening up a novel avenue for tissue/organ regeneration.Curated by the editorial board of Bio-Design and Manufacturing,this review brings together a cohort of leading young scientists in China to dissect the core functionalities and evolutionary trajectory of 3D bioprinting,by elucidating the intricate challenges encountered in the manufacturing of transplantable organs.We further delve into the translational pathway from scientific research to clinical application,emphasizing the imperativeness of establishing a regulatory framework and rigorously enforcing quality-control measures.Finally,this review outlines the strategic landscape and innovative achievements of China in this field and provides a comprehensive roadmap for researchers worldwide to propel this field collectively to even greater heights.
基金supported by National Natural Science Foundation of China(32201759,32172002)Inner Mongolia Innovation Center of Biological Breeding Technology,National Key Research and Development Program of China(2021YFD1201600)+1 种基金Earmarked Fund for CARS(CARS-04-PS01)Agricultural Science and Technology Innovation Program(ASTIP).
文摘Soybean(Glycine max)is a globally important crop that serves as a primary source of edible oil and protein for both humans and animals.Cultivated soybean varieties exhibit considerable genetic diversity depending on their geographical origin.Heinong 531(HN531)is an elite cultivar that was released in China in June 2021 with 22.34%seed oil,high resistance to soybean cyst nematode(SCN)race 3,and enhanced yield.However,the genetic basis for these desirable agronomic traits is unclear.In this study,we generated a high-quality genome assembly for HN531 and used it to systematically analyze genes related to agronomic traits such as resistance to SCN.The assembled genome spans 981.20 Mb,featuring a contig N50 of 19.47 Mb,and contains 58,151 predicted gene models.Pan-genomic comparison with 27 previously reported soybean genomes revealed 95,071 structural variants(SVs)of>50 bp,of which 602 were HN531-specific.Furthermore,we identified a copy number variation at rhg1 that underlies resistance to SCN,and we found elite alleles of functional genes underlying important agronomic traits such as seed oil content,adaptability,and yield.This high-quality HN531 genome can be used to explore the genetic basis for the excellent agronomic traits of this cultivar,and is a valuable resource for breeders aiming to improve HN531 and related cultivars.
基金supported by Research and Application of Technological Innovation in Inner Mongolia Soybean Industry (2023DXZD0002)the National Natural Science Foundation of China (32201756)+4 种基金the National Key Research and Development Program of China (2021YFD1201600)the Agricultural Science and Technology Innovation Program (ASTIP)of Chinese Academy of Agricultural Sciences (CAAS-ZDRW202109,01-ICS-05)the earmarked fund for CARS (CARS-04-PS01)Scientific Innovation 2030 Project (2022ZD0401703)the National Science Foundation for Post-doctoral Scientists of China (2021 M703554).
文摘Soybean(Glycine max)is an important and valuable crop,providing oil and proteins for both humans and animals.Seed weight is a key trait that determines soybean yields;however,the genes and mechanisms controlling seed weight remain poorly understood.Here,we used genome-wide association study(GWAS)and joint linkage mapping to identify a ubiquitin-specific protease,GmSW17.1,which regulates 100-seed weight in soybean.Two natural allelic variants of GmSW17.1 resulted in significantly different 100-seed weight,with GmSW17.1T conferring heavier seeds.We used CRISPR/Cas9 technology to knock out GmSW17.1,resulting in lighter and smaller seeds;however,these mutants produced more seeds than the wild type,resulting in similar overall yields.Owing to the increased number of seeds,we determined that GmSW17.1 is highly transcribed in developing seeds,and its encoded protein physically interacts in the nucleus with GmSGF11,which plays a crucial role in the deubiquitinating pathway.Analysis of genomic sequences from more than 1714 soybean accessions suggested that the natural allele GmSW17.1T was selected during the domestication and genetic improvement,resulting in its rapid expansion in cultivated soybean.These findings provide important insights into the role of GmSW17.1 in 100-seed weight and offer valuable clues for the molecular breeding of soybean.
基金supported by the National Science Fund for Distinguished Young Scholars(no.31925028)the National Natural Science Foundation of China(nos.32171720 and 32371823).
文摘The conductive polymer poly-3,4-ethylenedioxythiophene(PEDOT),recognized for its superior electrical conductivity and biocompatibility,has become an attractive material for developing wearable technologies and bioelectronics.Nevertheless,the complexities associated with PEDOT's patterning synthesis on diverse substrates persist despite recent technological progress.In this study,we introduce a novel deep eutectic solvent(DES)-induced vapor phase polymerization technique,facilitating nonrestrictive patterning polymerization of PEDOT across diverse substrates.By controlling the quantity of DES adsorbed per unit area on the substrates,PEDOT can be effectively patternized on cellulose,wood,plastic,glass,and even hydrogels.The resultant patterned PEDOT exhibits numerous benefits,such as an impressive electronic conductivity of 282 S·m-1,a high specific surface area of 5.29 m^(2)·g-1,and an extensive electrochemical stability range from-1.4 to 2.4 V in a phosphate-buffered saline.To underscore the practicality and diverse applications of this DES-induced approach,we present multiple examples emphasizing its integration into self-supporting flexible electrodes,neuroelectrode interfaces,and precision circuit repair methodologies.
基金the Agritech National Research Center supported by European Union Next-Generation EU(PIANO NAZIONALE DI RIPRESA E RESILIENZA(PNRR)-MIS-SIONE 4 COMPONENTE 2,INVESTIMENTO 1.4-D.D.103217/06/2022,CN00000022)The China Scholarship Council。
文摘Climate change and the increasing frequency of floods have undermined China’s food security.Creating detailed maps of flooded croplands is essential to improve prevention and adopt effective adaptation initiatives.Previous large-scale flood mapping efforts were hampered by limited meteorological and hydrological data,and the susceptibility of optical satellite images to cloud cover,leading to high uncertainty when downscaled to the cropland-scale.Here,using 4968 near-real-time(NRT)Sentinel-1 SAR(S1)images(spatial resolution:10 m),we generated China’s first set of high-resolution flooded cropland maps covering the period from 2017 to 2021.Our results demonstrate that croplands accounted for 43.8%to49.8%of China’s total flooded areas(ranging from 82,175 km^(2) to 122,037 km^(2)).We also created highresolution flood maps specifically for rice and maize crops.The inundated rice areas ranged from 8428 km^(2) to 22,123 km^(2),accounting for 22.34%to 41.91%of the annual flooded croplands,or 2.82%to7.45%of the annual rice cropland.In comparison,the inundated maize cropland fluctuated from 2619 km^(2) to 5397 km^(2),representing 5.38%to 13.56%of the annual flooded croplands.Our findings revealed extensive floods in rural areas,highlighting the urgent need to prioritize flood prevention and mitigation efforts in such regions.In light of China’s allocation of an additional 1-trillion-RMB treasury bonds for water infrastructure projects,the high-resolution flood maps can be used to select sites for flood control projects,and evaluate the impact of flooding on crop yields and food security,thus targeting poverty alleviation in rural areas of China.
基金supported by the National Key R&D Program of China(2022YFF0903301)the National Natural Science Foundation of China(Grant Nos.U22B2059,61976073,62276083)+1 种基金the Shenzhen Foundational Research Funding(JCYJ20200109113441941)the Major Key Project of PCL(PCL2021A06).
文摘Sparse Knowledge Graph(KG)scenarios pose a challenge for previous Knowledge Graph Completion(KGC)methods,that is,the completion performance decreases rapidly with the increase of graph sparsity.This problem is also exacerbated because of the widespread existence of sparse KGs in practical applications.To alleviate this challenge,we present a novel framework,LR-GCN,that is able to automatically capture valuable long-range dependency among entities to supplement insufficient structure features and distill logical reasoning knowledge for sparse KGC.The proposed approach comprises two main components:a GNN-based predictor and a reasoning path distiller.The reasoning path distiller explores high-order graph structures such as reasoning paths and encodes them as rich-semantic edges,explicitly compositing long-range dependencies into the predictor.This step also plays an essential role in densifying KGs,effectively alleviating the sparse issue.Furthermore,the path distiller further distills logical reasoning knowledge from these mined reasoning paths into the predictor.These two components are jointly optimized using a well-designed variational EM algorithm.Extensive experiments and analyses on four sparse benchmarks demonstrate the effectiveness of our proposed method.
基金the US National Science Foundation under grant HDR:TRIPODS 19-34884the United States Department of Agriculture National Institute of Food and Agriculture Hatch project IOW03617,the Office of Science(BER),U.S.Department of Energy,Grant no.DE-SC0020355the Plant Sciences Institute,Iowa State University,Scholars Program.
文摘High-throughput phenotyping enables the efficient collection of plant trait data at scale.One example involves using imaging systems over key phases of a crop growing season.Although the resulting images provide rich data for statistical analyses of plant phenotypes,image processing for trait extraction is required as a prerequisite.Current methods for trait extraction are mainly based on supervised learning with human labeled data or semisupervised learning with a mixture of human labeled data and unsupervised data.Unfortunately,preparing a sufficiently large training data is both time and labor-intensive.We describe a self-supervised pipeline(KAT4IA)that uses K-means clustering on greenhouse images to construct training data for extracting and analyzing plant traits from an image-based field phenotyping system.The KAT4IA pipeline includes these main steps:self-supervised training set construction,plant segmentation from images of field-grown plants,automatic separation of target plants,calculation of plant traits,and functional curve fitting of the extracted traits.To deal with the challenge of separating target plants from noisy backgrounds in field images,we describe a novel approach using row-cuts and column-cuts on images segmented by transform domain neural network learning,which utilizes plant pixels identified from greenhouse images to train a segmentation model for field images.This approach is efficient and does not require human intervention.Our results show that KAT4IA is able to accurately extract plant pixels and estimate plant heights.
