In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology bas...In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.展开更多
The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str...The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.展开更多
The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explore...The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explores the synergistic application of AI and HTE, highlighting their role in accelerating catalyst discovery, optimizing reaction parameters, and understanding structure-performance relationships. HTE facilitates the rapid preparation, characterization, and evaluation of diverse catalyst formulations, generating large datasets essential for AI model training. Machine learning algorithms, including regression models, neural networks, and active learning frameworks, analyze these datasets to uncover the underlying relationships between the data, predict performance, and optimize experimental workflows in real-time. Case studies across heterogeneous, homogeneous, and electrocatalysis demonstrate significant advancements, including improved reaction selectivity, enhanced material stability, and shorten discovery cycles. The integration of AI with HTE has significantly accelerated discovery cycles, enabling the optimization of catalyst formulations and reaction conditions. Despite these achievements, challenges remain, including reliance on researcher expertise, real-time adaptability, and the complexity of large-scale data analysis. Addressing these limitations through refined experimental protocols, standardized datasets, and interpretable AI models will unlock the full potential of AI-HTE integration.展开更多
This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using s...This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using simulated pyrolysis data as the training dataset.Various feature extraction methods are utilized,and five neural network architectures are developed to predict the co-cracking product distribution based on molecular structures.High-throughput screening of 12946 molecules outside the training dataset identifies the top 10 initiators for each target product—ethylene,propylene,and butadiene.The relative error between predicted and simulated values is less than 7%.Additionally,reaction pathway analysis elucidates the mechanisms by which initiators influence the distribution of cracking products.The proposed framework provides a practical and efficient approach for the rapid identification and evaluation of high-performance cracking initiators.展开更多
In recent years,intensive human activities have increased the intensity of desertification,driving continual desertification process of peripheral meadows.To investigate the effects of restoration on soil microbial co...In recent years,intensive human activities have increased the intensity of desertification,driving continual desertification process of peripheral meadows.To investigate the effects of restoration on soil microbial communities,we analyzed vegetation-soil relationships in the Hulun Buir Sandy Land,northern China.Through the use of high-throughput sequencing,we examined the structure and diversity in the bacterial and fungal communities within the 0-20 cm soil layer after 9-15 a of restoration.Different slope positions were analyzed and spatial heterogeneity was assessed.The results showed progressive improvements in soil properties and vegetation with the increase of restoration duration,and the following order was as follows:bottom slope>middle slope>crest slope.During the restoration in the Hulun Buir Sandy Land,the bacterial communities were dominated by Proteobacteria,Actinobacteria,and Acidobacteria,whereas the fungal communities were dominated by Ascomycota and Basidiomycota.Eutrophic bacterial abundance increased with the restoration duration,whereas oligotrophic bacterial and fungal abundance levels decreased.The soil bacterial abundance significantly increased with the increasing restoration duration,whereas the fungal diversity decreased after 11 a of restoration,except that at the crest slope.Redundancy analysis showed that pH,soil moisture content,total nitrogen,and vegetation-related factors affected the bacterial community structure(45.43%of the total variance explained).Canonical correspondence analysis indicated that pH,total phosphorus,and vegetation-related factors shaped the bacterial community structure(31.82%of the total variance explained).Structural equation modeling highlighted greater bacterial responses(R^(2)=0.49-0.79)to changes in environmental factors than those of fungi(R^(2)=0.20-0.48).The soil bacterial community was driven mainly by pH,soil moisture content,electrical conductivity,plant coverage,and litter dry weight.The abundance and diversity of the soil fungal community were mainly driven by plant coverage,litter dry weight,and herbaceous aboveground biomass,while there was no significant correlation between the soil fungal community structure and environmental factors.These findings highlighted divergent microbial succession patterns and environmental sensitivities during sandy grassland restoration.展开更多
The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectrosc...The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectroscopy and first-principles calculations are time-and power-consuming,not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space.In the present work,an artificial intelligence ensemble comprising two classifiers(with F1 scores of 0.9125 and 0.925)and a regressor(with mean squared error of 0.0014 eV)is constructed to achieve high-precision prediction of the bandgap.The bandgap perovskite dataset is established through highthroughput prediction of bandgaps by the ensemble.Based on the self-built dataset,partial dependence analysis(PDA)is developed to interpret the bandgap influential mechanism.Meanwhile,an interpretable mathematical model with an R^(2)of 0.8417 is generated using the genetic programming symbolic regression(GPSR)technique.The constructed PDA maps agree well with the Shapley Additive exPlanations,the GPSR model,and experiment verification.Through PDA,we reveal the boundary effect,the bowing effect,and their evolution trends with key descriptors.展开更多
The labels of VU1 and VU2 in Fig.1(b)of the paper[Chin.Phys.B 34046801(2025)]were not correctly placed.The correct figure is provided.This modification does not affect the result presented in the paper.
The real-time screening of biomolecules and single cells in biochips is extremely important for disease prediction and diagnosis,cellular analysis,and life science research.Barcode biochip technology,which is integrat...The real-time screening of biomolecules and single cells in biochips is extremely important for disease prediction and diagnosis,cellular analysis,and life science research.Barcode biochip technology,which is integrated with microfluidics,typically comprises barcode array,sample loading,and reaction unit array chips.Here,we present a review of microfluidics barcode biochip analytical approaches for the high-throughput screening of biomolecules and single cells,including protein biomarkers,microRNA(miRNA),circulating tumor DNA(ctDNA),single-cell secreted proteins,single-cell exosomes,and cell interactions.We begin with an overview of current high-throughput detection and analysis approaches.Following this,we outline recent improvements in microfluidic devices for biomolecule and single-cell detection,highlighting the benefits and limitations of these devices.This paper focuses on the research and development of microfluidic barcode biochips,covering their self-assembly substrate materials and their specific applications with biomolecules and single cells.Looking forward,we explore the prospects and challenges of this technology,with the aim of contributing toward the use of microfluidic barcode detection biochips in medical diagnostics and therapies,and their large-scale commercialization.展开更多
Lithium-ion batteries(LiBs)with high energy density have gained significant popularity in smart grids and portable electronics.LiMn_(1-x)Fe_(x)PO_(4)(LMFP)is considered a leading candidate for the cathode,with the pot...Lithium-ion batteries(LiBs)with high energy density have gained significant popularity in smart grids and portable electronics.LiMn_(1-x)Fe_(x)PO_(4)(LMFP)is considered a leading candidate for the cathode,with the potential to combine the low cost of Li Fe PO_(4)(LFP)with the high theoretical energy density of LiMnPO_(4)(LMP).However,quantitative investigation of the intricate coupling between the Fe/Mn ratio and the resulting energy density is challenging due to the parametric complexity.It is crucial to develop a universal approach for the rapid construction of multi-parameter mapping.In this work,we propose an active learning-guided high-throughput workflow for quantitatively predicting the Fe/Mn ratio and the energy density mapping of LMFP.An optimal composition(LiMn_(0.66)Fe_(0.34)PO_(4))was effectively screened from 81 cathode materials via only 5 samples.Model-guided electrochemical analysis revealed a nonlinear relationship between the Fe/Mn ratio and electrochemical properties,including ion mobility and impedance,elucidating the quantitative chemical composition-energy density map of LMFP.The results demonstrated the efficacy of the method in high-throughput screening of LiBs cathode materials.展开更多
Sm–Co-based films play an irreplaceable role in special applications due to their high curie temperature and magnetocrystalline anisotropic energy,especially in heat-assisted magnetic recording(HAMR),but the complex ...Sm–Co-based films play an irreplaceable role in special applications due to their high curie temperature and magnetocrystalline anisotropic energy,especially in heat-assisted magnetic recording(HAMR),but the complex composition of Sm–Co phase and unclear synergistic coupling mechanisms of multi-elemental doping become the challenges to enhance the properties.In this work,a novel strategy combining magnetron sputtering and a high-throughput experiment method is applied to solve the above-mentioned problems.Fe/Cu co-doping highly increases the remanence while maintaining a coercivity larger than 26 kOe,leading to an enhancement of the magnetic energy product to 18.1 MGOe.X-ray diffraction(XRD)and high-resolution transmission electron microscope(HRTEM)reveals that SmCo_(5) phase occupies the major fraction,with Co atoms partially substituted by Fe and Cu atoms.In situ Lorentz transmission electron microscopy(LTEM)observations show that the Sm(Co,Cu)5 phase effectively prohibits domain wall motions,leading to an increase of coercivity(H_(c)).Fe doping increases the low saturation magnetization(M_(s))and low remanence(Mr)due to the Fe atom having a higher saturation magnetic moment.The magnetization reversal behaviors are further verified by micromagnetic simulations.Our results suggest that Sm–Co-based films prepared via Fe/Cu co-doping could be a promising candidate for high-performed HAMR in the future.展开更多
Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.H...Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.However,the vast complexity of multicomponent systems poses a major challenge for identifying promising candidates through conventional experimental or computational methods.A high-throughput CALPHAD framework is developed to identify compositions with potential TWIP/TRIP behaviors in the Cr-Co-Ni and Cr-Co-Ni-Fe systems through systematic screening of stacking fault energy(SFE),FCC phase stability,and FCC-to-HCP transition temperatures(T0).The approach combines TC-Python automation with parallel Gibbs energy calculations across hundreds of thousands of compositions,enabling efficient extraction of metastable FCC-dominant alloys.The high-throughput results find 214 compositions with desired properties from 160,000 candidates.Detailed analysis of the Gibbs energy distributions,phase fraction trends,and temperature-dependent SFE evolution reveals critical insights into the thermodynamic landscape governing plasticity mechanisms in HEAs.The results show that only a narrow region of the compositional space satisfies all screening criteria,emphasizing the necessity of an integrated approach.The screened compositions and trends provide a foundation for targeted experimental validation.Furthermore,this work demonstrates a scalable,composition-resolved strategy for predicting deformation mechanisms in multicomponent alloys and offers a blueprint for integrating thermodynamic screening with mechanistic understanding in HEA design.展开更多
Transpiration cooling is crucial for the performance of aerospace engine components,relying heavily on the processing quality and accuracy of microchannels.Laser powder bed fusion(LPBF)offers the potential for integra...Transpiration cooling is crucial for the performance of aerospace engine components,relying heavily on the processing quality and accuracy of microchannels.Laser powder bed fusion(LPBF)offers the potential for integrated manufacturing of complex parts and precise microchannel fabrication,essential for engine cooling applications.However,optimizing LPBF’s extensive process parameters to control processing quality and microchannel accuracy effectively remains a significant challenge,especially given the time-consuming and labor-intensive nature of handling numerous variables and the need for thorough data analysis and correlation discovery.This study introduced a combined methodology of high-throughput experiments and Gaussian process algorithms to optimize the processing quality and accuracy of nickel-based high-temperature alloy with microchannel structures.250 parameter combinations,including laser power,scanning speed,channel diameter,and spot compensation,were designed across ten high-throughput specimens.This setup allowed for rapid and efficient evaluation of processing quality and microchannel accuracy.Employing Bayesian optimization,the Gaussian process model accurately predicted processing outcomes over a broad parameter range.The correlation between various processing parameters,processing quality and accuracy was revealed,and various optimized process combinations were summarized.Verification through computed Tomography testing of the specimens confirmed the effectiveness and precision of this approach.The approach introduced in this research provides a way for quickly and efficiently optimizing the process parameters and establishing process-property relationships for LPBF,which has broad application value.展开更多
The capture of CO_(2)from CO_(2)/H_(2)gas mixtures in syngas is a crucial issue for hydrogen production from steam methane reforming in industry,as the presence of CO_(2)directly affects the purity of H_(2).A combinat...The capture of CO_(2)from CO_(2)/H_(2)gas mixtures in syngas is a crucial issue for hydrogen production from steam methane reforming in industry,as the presence of CO_(2)directly affects the purity of H_(2).A combination of a high-throughput screening method and grand canonical Monte Carlo simulation was utilized to evaluate and screen 1725 metal–organic frameworks(MOFs)in detail as a means of determining their adsorption performance for CO_(2)/H_(2)gas mixtures.The adsorption and separation performance of double-linker MOFs was comprehensively evaluated using eight evaluation indicators,namely,the largest cavity diameter,accessible surface area,pore occupied accessible volume,porosity,adsorption selectivity,working capacity,adsorbent performance score and percent regeneration.Six optimal performance frameworks were screened to further study their single-component adsorption and binary competitive adsorption of CO_(2)/H_(2)respectively.The CO_(2)adsorption selectivity at different CO_(2)/H_(2)feed ratios was also evaluated,which indicated their excellent adsorption and separation performance.The microscopic adsorption mechanisms for CO_(2)and H_(2)at the molecular level were investigated by analyzing the radial distribution function and density distribution.This study may provide directional guidance and reference for subsequent experiments on the adsorption and separation of CO_(2)/H_(2).展开更多
Kagome materials are known for hosting exotic quantum states,including quantum spin liquids,charge density waves,and unconventional superconductivity.The search for kagome monolayers is driven by their ability to exhi...Kagome materials are known for hosting exotic quantum states,including quantum spin liquids,charge density waves,and unconventional superconductivity.The search for kagome monolayers is driven by their ability to exhibit neat and well-defined kagome bands near the Fermi level,which are more easily realized in the absence of interlayer interactions.However,this absence also destabilizes the monolayer forms of many bulk kagome materials,posing significant challenges to their discovery.In this work,we propose a strategy to address this challenge by utilizing oxygen vacancies in transition metal oxides within a“1+3”design framework.Through high-throughput computational screening of 349 candidate materials,we identified 12 thermodynamically stable kagome monolayers with diverse electronic and magnetic properties.These materials were classified into three categories based on their lattice geometry,symmetry,band gaps,and magnetic configurations.Detailed analysis of three representative monolayers revealed kagome band features near their Fermi levels,with orbital contributions varying between oxygen 2p and transition metal d states.This study demonstrates the feasibility of the“1+3”strategy,offering a promising approach to uncovering low-dimensional kagome materials and advancing the exploration of their quantum phenomena.展开更多
For the advancement of fast-charging sodium-ion batteries(SIBs),the synthesis of cutting-edge cathode materials with superior structural stability and enhanced Na+diffusion kinetics is imperative.Multiphase layered tr...For the advancement of fast-charging sodium-ion batteries(SIBs),the synthesis of cutting-edge cathode materials with superior structural stability and enhanced Na+diffusion kinetics is imperative.Multiphase layered transition metal oxides(LTMOs),which leverage the synergistic properties of two distinct monophasic LTMOs,have garnered significant attention;however,their efficacy under fast-charging conditions remains underexplored.In this study,we developed a high-throughput computational screening framework to identify optimal dopants that maximize the electrochemical performance of LTMOs.Specifically,we evaluated the efficacy of 32 dopants based on P2/O3-type Mn/Fe-based Na_(x)Mn_(0.5)Fe_(0.5)O_(2)(NMFO)cathode material.Multiphase LTMOs satisfying criteria for thermodynamic and structural stability,minimized phase transitions,and enhanced Na^(+)diffusion were systematically screened for their suitability in fast-charging applications.The analysis identified two dopants,Ti and Zr,which met all predefined screening criteria.Furthermore,we ranked and scored dopants based on their alignment with these criteria,establishing a comprehensive dopant performance database.These findings provide a robust foundation for experimental exploration and offer detailed guidelines for tailoring dopants to optimize fast-charging SIBs.展开更多
Surface-confined metal-organic frameworks have emerged as versatile structures with a broad spectrum of applications such as nanoelectronics,catalysis,sensing,and molecular storage,owing to their unique structural and...Surface-confined metal-organic frameworks have emerged as versatile structures with a broad spectrum of applications such as nanoelectronics,catalysis,sensing,and molecular storage,owing to their unique structural and electronic properties.However,the exploration and optimization of molecular networks typically involve resource-intensive trial-and-error experiments.The complexity comes from factors like metal nodes,organic ligands,substrates,and the preparation conditions.To address this challenge,highthroughput methodologies have been used in materials exploration.In this work,we explored a highthroughput method for preparing sub-monolayer metals with continuous coverage spread on metal surfaces.By employing a physical mask during metal deposition under ultra-high vacuum conditions,we achieved sample libraries with copper(Cu)and silver(Ag)adatoms on the metal substrates,and constructed surface-supported metal-organic frameworks with varying metal-to-molecule stoichiometric ratios.This approach facilitates the exploration of surface-confined metal-organic frameworks,particularly in terms of varying metal-to-ligand stoichiometric ratios,offering an efficient pathway to unlock the potential of these intricate two-dimensional networks.展开更多
As an essential component of the Materials Genome Initiative aiming to shorten the period of materials research and development, combinatorial synthesis and rapid characterization technologies have been playing a more...As an essential component of the Materials Genome Initiative aiming to shorten the period of materials research and development, combinatorial synthesis and rapid characterization technologies have been playing a more and more important role in exploring new materials and comprehensively understanding materials properties. In this review, we discuss the advantages of high-throughput experimental techniques in researches on superconductors. The evolution of combinatorial thin-film technology and several high-speed screening devices are briefly introduced. We emphasize the necessity to develop new high-throughput research modes such as a combination of high-throughput techniques and conventional methods.展开更多
This study was conducted to investigate the phylogenetic diversity of archaea in the rumen of adult and elderly yaks. Six domesticated female yaks, 3 adult yaks （（5.3±0.6） years old）, and 3 elderly yaks （（1...This study was conducted to investigate the phylogenetic diversity of archaea in the rumen of adult and elderly yaks. Six domesticated female yaks, 3 adult yaks （（5.3±0.6） years old）, and 3 elderly yaks （（10.7±0.6） years old）, were used for the rumen contents collection. Illumina MiSeq high-throughput sequencing technology was applied to examine the archaeal composition of rumen contents. A total of 92 901 high-quality archaeal sequences were analyzed, and these were assigned to 2 033 operational taxonomic units （OTUs）. Among these, 974 OTUs were unique to adult yaks while 846 OTUs were unique to elderly yaks; 213 OTUs were shared by both groups. At the phylum level, more than 99% of the obtained OTUs belonged to the Euryarchaeota phylum. At the genus level, the archaea could be divided into 7 archaeal genera. The 7 genera （i.e., Methanobrevibacter, Methanobacterium, Methanosphaera, Thermogymnomonas, Methanomicrobiu, Meth- animicrococcus and the unclassified genus） were shared by all yaks, and their total abundance accounted for 99% of the rumen archaea. The most abundant archaea in elderly and adult yaks were Methanobrevibacterand Thermogymnomonas, respectively. The abundance of Methanobacteria （class）, Methanobacteriales （order）, Methanobacteriaceae （family）, and Methanobrevibacter （genus） in elderly yaks was significantly higher than in adult yaks. In contrast, the abundance of Ther-mogymnomonas in elderly yaks was 34% lower than in adult yaks, though the difference was not statistically significant. The difference in abundance of other archaea was not significant between the two groups. These results suggested that the structure of archaea in the rumen of yaks changed with age. This is the first study to compare the phytogenetic differences of rumen archaeal structure and composition using the yak model.展开更多
Chromosome segment substitution lines（CSSLs） are useful for the precise mapping of quantitative trait loci（QTLs） and dissection of the genetic basis of complex traits.In this study,two whole-genome sequenced rice ...Chromosome segment substitution lines（CSSLs） are useful for the precise mapping of quantitative trait loci（QTLs） and dissection of the genetic basis of complex traits.In this study,two whole-genome sequenced rice cultivars,the japonica Nipponbare and indica 9311 were used as recipient and donor,respectively.A population with 57 CSSLs was developed after crossing and back-crossing assisted by molecular markers, and genotypes were identified using a high-throughput resequencing strategy.Detailed graphical genotypes of 38 lines were constructed based on resequencing data.These CSSLs had a total of 95 substituted segments derived from indica 9311,with an average of about 2.5 segments per CSSL and eight segments per chromosome,and covered about 87.4%of the rice whole genome.A multiple linear regression QTL analysis mapped four QTLs for 1000-grain weight.The largest-effect QTL was located in a region on chromosome 5 that contained a cloned major QTL GW5/qSW5 for grain size in rice.These CSSLs with a background of Nipponbare may provide powerful tools for future whole-genome展开更多
Background: RNA editing is a co/posttranscriptional modification mechanism that increases the diversity of transcripts, with potential functional consequences. The advent of next-generation sequencing technologies has...Background: RNA editing is a co/posttranscriptional modification mechanism that increases the diversity of transcripts, with potential functional consequences. The advent of next-generation sequencing technologies has enabled the identification of RNA edits at unprecedented throughput and resolution. However, our knowledge of RNA editing in swine is still limited.Results: Here, we utilized RES-Scanner to identify RNA editing sites in the brain, subcutaneous fat, heart, liver,muscle, lung and ovary in three 180-day-old Large White gilts based on matched strand-specific RNA sequencing and whole-genome resequencing datasets. In total, we identified 74863 editing sites, and 92.1% of these sites caused adenosine-to-guanosine(A-to-G) conversion. Most A-to-G sites were located in noncoding regions and generally had low editing levels. In total, 151 A-to-G sites were detected in coding regions(CDS), including 94 sites that could lead to nonsynonymous amino acid changes. We provide further evidence supporting a previous observation that pig transcriptomes are highly editable at PRE-1 elements. The number of A-to-G editing sites ranged from 4155(muscle) to 25001(brain) across the seven tissues. The expression levels of the ADAR enzymes could explain some but not all of this variation across tissues. The functional analysis of the genes with tissuespecific editing sites in each tissue revealed that RNA editing might play important roles in tissue function.Specifically, more pathways showed significant enrichment in the fat and liver than in other tissues, while no pathway was enriched in the muscle.Conclusions: This study identified a total of 74863 nonredundant RNA editing sites in seven tissues and revealed the potential importance of RNA editing in tissue function. Our findings largely extend the porcine editome and enhance our understanding of RNA editing in swine.展开更多
基金financially supported by National Key R&D Program(2021YFF0701905)。
文摘In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.
基金financial support from the National Key Research and Development Program of China(2021YFB 3501501)the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011)+1 种基金Beijing Natural Science Foundation(No.Z230023)Beijing Science and Technology Commission(No.Z211100004321001).
文摘The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.
基金supported by the Special Project of National Natural Science Foundation(42341204)the the National Natural Science Foundation of China(W2411009).
文摘The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explores the synergistic application of AI and HTE, highlighting their role in accelerating catalyst discovery, optimizing reaction parameters, and understanding structure-performance relationships. HTE facilitates the rapid preparation, characterization, and evaluation of diverse catalyst formulations, generating large datasets essential for AI model training. Machine learning algorithms, including regression models, neural networks, and active learning frameworks, analyze these datasets to uncover the underlying relationships between the data, predict performance, and optimize experimental workflows in real-time. Case studies across heterogeneous, homogeneous, and electrocatalysis demonstrate significant advancements, including improved reaction selectivity, enhanced material stability, and shorten discovery cycles. The integration of AI with HTE has significantly accelerated discovery cycles, enabling the optimization of catalyst formulations and reaction conditions. Despite these achievements, challenges remain, including reliance on researcher expertise, real-time adaptability, and the complexity of large-scale data analysis. Addressing these limitations through refined experimental protocols, standardized datasets, and interpretable AI models will unlock the full potential of AI-HTE integration.
基金The financial support provided by the Project of the National Natural Science Foundation of China (22308314,U22A20415)the Natural Science Foundation of Zhejiang Province (LQ24B060001)+1 种基金the "Pioneer" and "Leading Goose" Research & Development Program of Zhejiang (2022C01SA442617)the SINOPEC Technology Development Project (224244)
文摘This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using simulated pyrolysis data as the training dataset.Various feature extraction methods are utilized,and five neural network architectures are developed to predict the co-cracking product distribution based on molecular structures.High-throughput screening of 12946 molecules outside the training dataset identifies the top 10 initiators for each target product—ethylene,propylene,and butadiene.The relative error between predicted and simulated values is less than 7%.Additionally,reaction pathway analysis elucidates the mechanisms by which initiators influence the distribution of cracking products.The proposed framework provides a practical and efficient approach for the rapid identification and evaluation of high-performance cracking initiators.
基金supported by the National Ecological Environment Survey and Assessment(2024-vertical-0107)the Fundamental Research Funds for the Central Public-interest Scientific Institution(2023YSKY-26)the Hulun Buir Grassland Ecological Restoration Comprehensive Survey Project(DD20230474).
文摘In recent years,intensive human activities have increased the intensity of desertification,driving continual desertification process of peripheral meadows.To investigate the effects of restoration on soil microbial communities,we analyzed vegetation-soil relationships in the Hulun Buir Sandy Land,northern China.Through the use of high-throughput sequencing,we examined the structure and diversity in the bacterial and fungal communities within the 0-20 cm soil layer after 9-15 a of restoration.Different slope positions were analyzed and spatial heterogeneity was assessed.The results showed progressive improvements in soil properties and vegetation with the increase of restoration duration,and the following order was as follows:bottom slope>middle slope>crest slope.During the restoration in the Hulun Buir Sandy Land,the bacterial communities were dominated by Proteobacteria,Actinobacteria,and Acidobacteria,whereas the fungal communities were dominated by Ascomycota and Basidiomycota.Eutrophic bacterial abundance increased with the restoration duration,whereas oligotrophic bacterial and fungal abundance levels decreased.The soil bacterial abundance significantly increased with the increasing restoration duration,whereas the fungal diversity decreased after 11 a of restoration,except that at the crest slope.Redundancy analysis showed that pH,soil moisture content,total nitrogen,and vegetation-related factors affected the bacterial community structure(45.43%of the total variance explained).Canonical correspondence analysis indicated that pH,total phosphorus,and vegetation-related factors shaped the bacterial community structure(31.82%of the total variance explained).Structural equation modeling highlighted greater bacterial responses(R^(2)=0.49-0.79)to changes in environmental factors than those of fungi(R^(2)=0.20-0.48).The soil bacterial community was driven mainly by pH,soil moisture content,electrical conductivity,plant coverage,and litter dry weight.The abundance and diversity of the soil fungal community were mainly driven by plant coverage,litter dry weight,and herbaceous aboveground biomass,while there was no significant correlation between the soil fungal community structure and environmental factors.These findings highlighted divergent microbial succession patterns and environmental sensitivities during sandy grassland restoration.
基金supported by the National Research Foundation of Korea(NRF)funded by the Korean government(MSIT)(Grant number:RS-2025-02316700,and RS-2025-00522430)the China Scholarship Council Program。
文摘The bandgap is a key parameter for understanding and designing hybrid perovskite material properties,as well as developing photovoltaic devices.Traditional bandgap calculation methods like ultravioletvisible spectroscopy and first-principles calculations are time-and power-consuming,not to mention capturing bandgap change mechanisms for hybrid perovskite materials across a wide range of unknown space.In the present work,an artificial intelligence ensemble comprising two classifiers(with F1 scores of 0.9125 and 0.925)and a regressor(with mean squared error of 0.0014 eV)is constructed to achieve high-precision prediction of the bandgap.The bandgap perovskite dataset is established through highthroughput prediction of bandgaps by the ensemble.Based on the self-built dataset,partial dependence analysis(PDA)is developed to interpret the bandgap influential mechanism.Meanwhile,an interpretable mathematical model with an R^(2)of 0.8417 is generated using the genetic programming symbolic regression(GPSR)technique.The constructed PDA maps agree well with the Shapley Additive exPlanations,the GPSR model,and experiment verification.Through PDA,we reveal the boundary effect,the bowing effect,and their evolution trends with key descriptors.
文摘The labels of VU1 and VU2 in Fig.1(b)of the paper[Chin.Phys.B 34046801(2025)]were not correctly placed.The correct figure is provided.This modification does not affect the result presented in the paper.
基金supported by the National Key Research and Development Plan of China(2023YFB3210400)the Natural Science Innovation Group Foundation of China(T2321004)+3 种基金the National Natural Science Foundation of China(62174101)Shandong University Integrated Research and Cultivation Project(2022JC001)Key Research and Development Plan of Shandong Province(Major Science and Technology Innovation Project2022CXGC020501).
文摘The real-time screening of biomolecules and single cells in biochips is extremely important for disease prediction and diagnosis,cellular analysis,and life science research.Barcode biochip technology,which is integrated with microfluidics,typically comprises barcode array,sample loading,and reaction unit array chips.Here,we present a review of microfluidics barcode biochip analytical approaches for the high-throughput screening of biomolecules and single cells,including protein biomarkers,microRNA(miRNA),circulating tumor DNA(ctDNA),single-cell secreted proteins,single-cell exosomes,and cell interactions.We begin with an overview of current high-throughput detection and analysis approaches.Following this,we outline recent improvements in microfluidic devices for biomolecule and single-cell detection,highlighting the benefits and limitations of these devices.This paper focuses on the research and development of microfluidic barcode biochips,covering their self-assembly substrate materials and their specific applications with biomolecules and single cells.Looking forward,we explore the prospects and challenges of this technology,with the aim of contributing toward the use of microfluidic barcode detection biochips in medical diagnostics and therapies,and their large-scale commercialization.
基金supported by the National Key Research and Development Program of China(No.2021YFB3702102)support from the“Initiation Program for New Teachers”(No.AF0500207)+1 种基金Shanghai Jiao Tong Universitysupport from the Changsha Science and Technology Plan International and Regional Cooperation Project(No.kh2304002)。
文摘Lithium-ion batteries(LiBs)with high energy density have gained significant popularity in smart grids and portable electronics.LiMn_(1-x)Fe_(x)PO_(4)(LMFP)is considered a leading candidate for the cathode,with the potential to combine the low cost of Li Fe PO_(4)(LFP)with the high theoretical energy density of LiMnPO_(4)(LMP).However,quantitative investigation of the intricate coupling between the Fe/Mn ratio and the resulting energy density is challenging due to the parametric complexity.It is crucial to develop a universal approach for the rapid construction of multi-parameter mapping.In this work,we propose an active learning-guided high-throughput workflow for quantitatively predicting the Fe/Mn ratio and the energy density mapping of LMFP.An optimal composition(LiMn_(0.66)Fe_(0.34)PO_(4))was effectively screened from 81 cathode materials via only 5 samples.Model-guided electrochemical analysis revealed a nonlinear relationship between the Fe/Mn ratio and electrochemical properties,including ion mobility and impedance,elucidating the quantitative chemical composition-energy density map of LMFP.The results demonstrated the efficacy of the method in high-throughput screening of LiBs cathode materials.
基金supported by the National Key R&D Program of China(No.2022YFB3505700)the National Natural Science Foundation of China(No.51901079)+4 种基金Guangdong Science and Technology Program(No.2023A0505050145)the Natural Science Foundation of Guangdong Province(Nos.2024A1515030178,2020A1515010736 and 2021A1515010451)Guangzhou Municipal Science and Technology Program(No.202007020008)the Fundamental Research Funds for the Central Universities,the Opening Project of National Engineering Research Center for Powder Metallurgy of Titanium&Rare Metals,the Fundamental Research Funds for the Central Universities and Zhongshan Municipal Science and Technology Program(No.191007102629094)Zhongshan Collaborative Innovation Fund(No.2018C1001).
文摘Sm–Co-based films play an irreplaceable role in special applications due to their high curie temperature and magnetocrystalline anisotropic energy,especially in heat-assisted magnetic recording(HAMR),but the complex composition of Sm–Co phase and unclear synergistic coupling mechanisms of multi-elemental doping become the challenges to enhance the properties.In this work,a novel strategy combining magnetron sputtering and a high-throughput experiment method is applied to solve the above-mentioned problems.Fe/Cu co-doping highly increases the remanence while maintaining a coercivity larger than 26 kOe,leading to an enhancement of the magnetic energy product to 18.1 MGOe.X-ray diffraction(XRD)and high-resolution transmission electron microscope(HRTEM)reveals that SmCo_(5) phase occupies the major fraction,with Co atoms partially substituted by Fe and Cu atoms.In situ Lorentz transmission electron microscopy(LTEM)observations show that the Sm(Co,Cu)5 phase effectively prohibits domain wall motions,leading to an increase of coercivity(H_(c)).Fe doping increases the low saturation magnetization(M_(s))and low remanence(Mr)due to the Fe atom having a higher saturation magnetic moment.The magnetization reversal behaviors are further verified by micromagnetic simulations.Our results suggest that Sm–Co-based films prepared via Fe/Cu co-doping could be a promising candidate for high-performed HAMR in the future.
基金supported by the U.S.Army Research Laboratory through their award#W911NF-22-2-0040the Ministry of Education,Youth and Sports of the Czech Republic through the e-INFRA CZ(ID:90254).
文摘Designing high-performance high-entropy alloys(HEAs)with transformation-induced plasticity(TRIP)or twinning-induced plasticity(TWIP)effects requires precise control over stacking fault energy(SFE)and phase stability.However,the vast complexity of multicomponent systems poses a major challenge for identifying promising candidates through conventional experimental or computational methods.A high-throughput CALPHAD framework is developed to identify compositions with potential TWIP/TRIP behaviors in the Cr-Co-Ni and Cr-Co-Ni-Fe systems through systematic screening of stacking fault energy(SFE),FCC phase stability,and FCC-to-HCP transition temperatures(T0).The approach combines TC-Python automation with parallel Gibbs energy calculations across hundreds of thousands of compositions,enabling efficient extraction of metastable FCC-dominant alloys.The high-throughput results find 214 compositions with desired properties from 160,000 candidates.Detailed analysis of the Gibbs energy distributions,phase fraction trends,and temperature-dependent SFE evolution reveals critical insights into the thermodynamic landscape governing plasticity mechanisms in HEAs.The results show that only a narrow region of the compositional space satisfies all screening criteria,emphasizing the necessity of an integrated approach.The screened compositions and trends provide a foundation for targeted experimental validation.Furthermore,this work demonstrates a scalable,composition-resolved strategy for predicting deformation mechanisms in multicomponent alloys and offers a blueprint for integrating thermodynamic screening with mechanistic understanding in HEA design.
基金project supported by the National Natural Science Foundation of China(Grant Nos.52225503 and 52405380)National Key Research and Development Program(Grant Nos.2023YFB4603303 and 2023YFB4603304)+4 种基金Key Research and Development Program of Jiangsu Province(Grant Nos.BE2022069 and BE2022069-3)National Natural Science Foundation of China for Creative Research Groups(Grant No.51921003)The 15th Batch of“Six Talents Peaks”Innovative Talents Team Program of Jiangsu province(Grant Nos.TD-GDZB-001)Shanghai Aerospace Science and Technology Innovation Fund Project(Grant No.SAST2023-066)The Fundamental Research Funds for the Central Universities(Grant Nos.NS2023035 and NP2024128)。
文摘Transpiration cooling is crucial for the performance of aerospace engine components,relying heavily on the processing quality and accuracy of microchannels.Laser powder bed fusion(LPBF)offers the potential for integrated manufacturing of complex parts and precise microchannel fabrication,essential for engine cooling applications.However,optimizing LPBF’s extensive process parameters to control processing quality and microchannel accuracy effectively remains a significant challenge,especially given the time-consuming and labor-intensive nature of handling numerous variables and the need for thorough data analysis and correlation discovery.This study introduced a combined methodology of high-throughput experiments and Gaussian process algorithms to optimize the processing quality and accuracy of nickel-based high-temperature alloy with microchannel structures.250 parameter combinations,including laser power,scanning speed,channel diameter,and spot compensation,were designed across ten high-throughput specimens.This setup allowed for rapid and efficient evaluation of processing quality and microchannel accuracy.Employing Bayesian optimization,the Gaussian process model accurately predicted processing outcomes over a broad parameter range.The correlation between various processing parameters,processing quality and accuracy was revealed,and various optimized process combinations were summarized.Verification through computed Tomography testing of the specimens confirmed the effectiveness and precision of this approach.The approach introduced in this research provides a way for quickly and efficiently optimizing the process parameters and establishing process-property relationships for LPBF,which has broad application value.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11304079,11404094,and 11504088)Science and Technology Research Project of Henan Science and Technology Department(Grant No.182102410076)。
文摘The capture of CO_(2)from CO_(2)/H_(2)gas mixtures in syngas is a crucial issue for hydrogen production from steam methane reforming in industry,as the presence of CO_(2)directly affects the purity of H_(2).A combination of a high-throughput screening method and grand canonical Monte Carlo simulation was utilized to evaluate and screen 1725 metal–organic frameworks(MOFs)in detail as a means of determining their adsorption performance for CO_(2)/H_(2)gas mixtures.The adsorption and separation performance of double-linker MOFs was comprehensively evaluated using eight evaluation indicators,namely,the largest cavity diameter,accessible surface area,pore occupied accessible volume,porosity,adsorption selectivity,working capacity,adsorbent performance score and percent regeneration.Six optimal performance frameworks were screened to further study their single-component adsorption and binary competitive adsorption of CO_(2)/H_(2)respectively.The CO_(2)adsorption selectivity at different CO_(2)/H_(2)feed ratios was also evaluated,which indicated their excellent adsorption and separation performance.The microscopic adsorption mechanisms for CO_(2)and H_(2)at the molecular level were investigated by analyzing the radial distribution function and density distribution.This study may provide directional guidance and reference for subsequent experiments on the adsorption and separation of CO_(2)/H_(2).
基金financial support from the National Key Research&Development Program of China(Grant No.2023YFA1406500)the National Natural Science Foundation of China(Grant Nos.12104504,52461160327 and 92477205)the Fundamental Research Funds for the Central Universities,and the Research Funds of Renmin University of China[Grant Nos.22XNKJ30(W.J.)and 24XNKJ17(C.W.)]。
文摘Kagome materials are known for hosting exotic quantum states,including quantum spin liquids,charge density waves,and unconventional superconductivity.The search for kagome monolayers is driven by their ability to exhibit neat and well-defined kagome bands near the Fermi level,which are more easily realized in the absence of interlayer interactions.However,this absence also destabilizes the monolayer forms of many bulk kagome materials,posing significant challenges to their discovery.In this work,we propose a strategy to address this challenge by utilizing oxygen vacancies in transition metal oxides within a“1+3”design framework.Through high-throughput computational screening of 349 candidate materials,we identified 12 thermodynamically stable kagome monolayers with diverse electronic and magnetic properties.These materials were classified into three categories based on their lattice geometry,symmetry,band gaps,and magnetic configurations.Detailed analysis of three representative monolayers revealed kagome band features near their Fermi levels,with orbital contributions varying between oxygen 2p and transition metal d states.This study demonstrates the feasibility of the“1+3”strategy,offering a promising approach to uncovering low-dimensional kagome materials and advancing the exploration of their quantum phenomena.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2022R1F1A1074339)。
文摘For the advancement of fast-charging sodium-ion batteries(SIBs),the synthesis of cutting-edge cathode materials with superior structural stability and enhanced Na+diffusion kinetics is imperative.Multiphase layered transition metal oxides(LTMOs),which leverage the synergistic properties of two distinct monophasic LTMOs,have garnered significant attention;however,their efficacy under fast-charging conditions remains underexplored.In this study,we developed a high-throughput computational screening framework to identify optimal dopants that maximize the electrochemical performance of LTMOs.Specifically,we evaluated the efficacy of 32 dopants based on P2/O3-type Mn/Fe-based Na_(x)Mn_(0.5)Fe_(0.5)O_(2)(NMFO)cathode material.Multiphase LTMOs satisfying criteria for thermodynamic and structural stability,minimized phase transitions,and enhanced Na^(+)diffusion were systematically screened for their suitability in fast-charging applications.The analysis identified two dopants,Ti and Zr,which met all predefined screening criteria.Furthermore,we ranked and scored dopants based on their alignment with these criteria,establishing a comprehensive dopant performance database.These findings provide a robust foundation for experimental exploration and offer detailed guidelines for tailoring dopants to optimize fast-charging SIBs.
基金supported by National Natural Science Foundation of China(Nos.22072086,22302120).
文摘Surface-confined metal-organic frameworks have emerged as versatile structures with a broad spectrum of applications such as nanoelectronics,catalysis,sensing,and molecular storage,owing to their unique structural and electronic properties.However,the exploration and optimization of molecular networks typically involve resource-intensive trial-and-error experiments.The complexity comes from factors like metal nodes,organic ligands,substrates,and the preparation conditions.To address this challenge,highthroughput methodologies have been used in materials exploration.In this work,we explored a highthroughput method for preparing sub-monolayer metals with continuous coverage spread on metal surfaces.By employing a physical mask during metal deposition under ultra-high vacuum conditions,we achieved sample libraries with copper(Cu)and silver(Ag)adatoms on the metal substrates,and constructed surface-supported metal-organic frameworks with varying metal-to-molecule stoichiometric ratios.This approach facilitates the exploration of surface-confined metal-organic frameworks,particularly in terms of varying metal-to-ligand stoichiometric ratios,offering an efficient pathway to unlock the potential of these intricate two-dimensional networks.
基金Project supported by the National Key Basic Research Program of China(Grant Nos.2015CB921000,2016YFA0300301,2017YFA0303003,and 2017YFA0302902)the National Natural Science Foundation of China(Grant Nos.11674374,11804378,and 11574372)+3 种基金the Beijing Municipal Science and Technology Project(Grant No.Z161100002116011)the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant Nos.QYZDB-SSW-SLH008 and QYZDY-SSW-SLH001)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB07020100)the Opening Project of Wuhan National High Magnetic Field Center(Grant No.PHMFF2015008)
文摘As an essential component of the Materials Genome Initiative aiming to shorten the period of materials research and development, combinatorial synthesis and rapid characterization technologies have been playing a more and more important role in exploring new materials and comprehensively understanding materials properties. In this review, we discuss the advantages of high-throughput experimental techniques in researches on superconductors. The evolution of combinatorial thin-film technology and several high-speed screening devices are briefly introduced. We emphasize the necessity to develop new high-throughput research modes such as a combination of high-throughput techniques and conventional methods.
基金the International Cooperation Project of the Ministry of Sciences and Technology of China(2014DFA32860)the National Natural Science Foundation of China(31402104)for their financial support
文摘This study was conducted to investigate the phylogenetic diversity of archaea in the rumen of adult and elderly yaks. Six domesticated female yaks, 3 adult yaks （（5.3±0.6） years old）, and 3 elderly yaks （（10.7±0.6） years old）, were used for the rumen contents collection. Illumina MiSeq high-throughput sequencing technology was applied to examine the archaeal composition of rumen contents. A total of 92 901 high-quality archaeal sequences were analyzed, and these were assigned to 2 033 operational taxonomic units （OTUs）. Among these, 974 OTUs were unique to adult yaks while 846 OTUs were unique to elderly yaks; 213 OTUs were shared by both groups. At the phylum level, more than 99% of the obtained OTUs belonged to the Euryarchaeota phylum. At the genus level, the archaea could be divided into 7 archaeal genera. The 7 genera （i.e., Methanobrevibacter, Methanobacterium, Methanosphaera, Thermogymnomonas, Methanomicrobiu, Meth- animicrococcus and the unclassified genus） were shared by all yaks, and their total abundance accounted for 99% of the rumen archaea. The most abundant archaea in elderly and adult yaks were Methanobrevibacterand Thermogymnomonas, respectively. The abundance of Methanobacteria （class）, Methanobacteriales （order）, Methanobacteriaceae （family）, and Methanobrevibacter （genus） in elderly yaks was significantly higher than in adult yaks. In contrast, the abundance of Ther-mogymnomonas in elderly yaks was 34% lower than in adult yaks, though the difference was not statistically significant. The difference in abundance of other archaea was not significant between the two groups. These results suggested that the structure of archaea in the rumen of yaks changed with age. This is the first study to compare the phytogenetic differences of rumen archaeal structure and composition using the yak model.
基金supported in part by the National Basic Research Program(Nos.2012CB944803 and 2011CB 100202)the National Natural Science Foundation(No.31071383)+1 种基金the National Special Program for Transgenic Research(No. 2009ZX08009-008B)the Priority Academic Program Development from Jiangsu Government of China
文摘Chromosome segment substitution lines（CSSLs） are useful for the precise mapping of quantitative trait loci（QTLs） and dissection of the genetic basis of complex traits.In this study,two whole-genome sequenced rice cultivars,the japonica Nipponbare and indica 9311 were used as recipient and donor,respectively.A population with 57 CSSLs was developed after crossing and back-crossing assisted by molecular markers, and genotypes were identified using a high-throughput resequencing strategy.Detailed graphical genotypes of 38 lines were constructed based on resequencing data.These CSSLs had a total of 95 substituted segments derived from indica 9311,with an average of about 2.5 segments per CSSL and eight segments per chromosome,and covered about 87.4%of the rice whole genome.A multiple linear regression QTL analysis mapped four QTLs for 1000-grain weight.The largest-effect QTL was located in a region on chromosome 5 that contained a cloned major QTL GW5/qSW5 for grain size in rice.These CSSLs with a background of Nipponbare may provide powerful tools for future whole-genome
基金supported by the National Key Technology R&D Program of China(2015BAD03B02–2)Beijing Natural Science Foundation(6174047)+1 种基金earmarked fund for Modern Agro-industry Technology Research System(CARS-35)Agricultural Science and Technology Innovation Program(ASTIP-IAS02)
文摘Background: RNA editing is a co/posttranscriptional modification mechanism that increases the diversity of transcripts, with potential functional consequences. The advent of next-generation sequencing technologies has enabled the identification of RNA edits at unprecedented throughput and resolution. However, our knowledge of RNA editing in swine is still limited.Results: Here, we utilized RES-Scanner to identify RNA editing sites in the brain, subcutaneous fat, heart, liver,muscle, lung and ovary in three 180-day-old Large White gilts based on matched strand-specific RNA sequencing and whole-genome resequencing datasets. In total, we identified 74863 editing sites, and 92.1% of these sites caused adenosine-to-guanosine(A-to-G) conversion. Most A-to-G sites were located in noncoding regions and generally had low editing levels. In total, 151 A-to-G sites were detected in coding regions(CDS), including 94 sites that could lead to nonsynonymous amino acid changes. We provide further evidence supporting a previous observation that pig transcriptomes are highly editable at PRE-1 elements. The number of A-to-G editing sites ranged from 4155(muscle) to 25001(brain) across the seven tissues. The expression levels of the ADAR enzymes could explain some but not all of this variation across tissues. The functional analysis of the genes with tissuespecific editing sites in each tissue revealed that RNA editing might play important roles in tissue function.Specifically, more pathways showed significant enrichment in the fat and liver than in other tissues, while no pathway was enriched in the muscle.Conclusions: This study identified a total of 74863 nonredundant RNA editing sites in seven tissues and revealed the potential importance of RNA editing in tissue function. Our findings largely extend the porcine editome and enhance our understanding of RNA editing in swine.
