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
Magnesium(Mg)alloys have shown great prospects as both structural and biomedical materials,while poor corrosion resistance limits their further application.In this work,to avoid the time-consuming and laborious experi...Magnesium(Mg)alloys have shown great prospects as both structural and biomedical materials,while poor corrosion resistance limits their further application.In this work,to avoid the time-consuming and laborious experiment trial,a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics,from both the thermodynamic and kinetic perspectives.The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified.Then,the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated,and the corrosion exchange current density is further calculated by a hydrogen evolution reaction(HER)kinetic model.Several intermetallics,e.g.Y_(3)Mg,Y_(2)Mg and La_(5)Mg,are identified to be promising intermetallics which might effectively hinder the cathodic HER.Furthermore,machine learning(ML)models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function(W_(f))and weighted first ionization energy(WFIE).The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error(RMSE)of 0.11 eV.This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion,but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy,which can be extended to ternary Mg alloys or other alloy systems.展开更多
OBJECTIVE:To explore the mechanism of Xianglian Huazhuo formula(香连化浊方,XLHZ)blocking the development of chronic atrophic gastritis(CAG)to gastric cancer(GC)through bioinformatics analysis and in vitro.METHODS:Path...OBJECTIVE:To explore the mechanism of Xianglian Huazhuo formula(香连化浊方,XLHZ)blocking the development of chronic atrophic gastritis(CAG)to gastric cancer(GC)through bioinformatics analysis and in vitro.METHODS:Pathological morphology of gastric mucosa of rats were observed.High-throughput sequencing was used to analyze the miRNA expression profile of gastric mucosa.The miRanda,miRDB and miRWalk databases were used to predict the differential target genes.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were performed for differential target genes.Real-time quantitative reverse transcription polymerase chain reaction(qRTPCR)was used to verify the differentially expressed miRNAs and target genes.Western blot,EdU,wound healing and flow cytometry were used to observe the effect of XLHZ on epithelial-mesenchymal transition(EMT)markers,proliferation,migration,apoptosis and cell cycle of CAG cells in vitro.RESULTS:A total of five differentially expressed miRNAs and four differential target genes were screened in this study.GO analysis showed that the target genes were enriched in regulation of neuron development,regulation of transcription factor activity and regulation of RNA polymerase.KEGG pathways database differences in gene enrichment of target genes in the Wnt signaling pathway,Phospholipase D signaling pathway and mitogen-activated protein kinase signaling pathway.qRTPCR confirmed that miRNAs and its target genes were consistent with the screening results.In vitro,our study revealed that XLHZ could increase the expression of Ecadherin,decrease the expression of transforming growth factorβ1,vimentin andβ-catenin,inhibite the proliferation and migration of CAG cells,cause cell cycle arrest at G0/G1 and G2/M phase,induce the apoptosis of CAG cells,and prevent the progression of CAG to GC.CONCLUSION:This study provided a new idea for the mechanism of blocking the progression of CAG to GC by XLHZ,which may be related to the expression of miR-20a-3p,miR-320-3p,miR-34b-5p,miR-483-3p and miR-883-3p and their target genes transferrin receptor,nuclear receptor subfamily 4 member 2,delta like canonical Notch ligand 1 and a kinase anchor protein 12 in CAG.In the future,we will continue to investigate the linkage between the active ingredients of XLHZ and the relevant miRNAs and their target genes,so as to provide more sufficient experimental basis for clinically effective prevention of CAG to GC.展开更多
Based on experimental data,machine learning(ML) models for Young's modulus,hardness,and hot-working ability of Ti-based alloys were constructed.In the models,the interdiffusion and mechanical property data were hi...Based on experimental data,machine learning(ML) models for Young's modulus,hardness,and hot-working ability of Ti-based alloys were constructed.In the models,the interdiffusion and mechanical property data were high-throughput re-evaluated from composition variations and nanoindentation data of diffusion couples.Then,the Ti-(22±0.5)at.%Nb-(30±0.5)at.%Zr-(4±0.5)at.%Cr(TNZC) alloy with a single body-centered cubic(BCC) phase was screened in an interactive loop.The experimental results exhibited a relatively low Young's modulus of(58±4) GPa,high nanohardness of(3.4±0.2) GPa,high microhardness of HV(520±5),high compressive yield strength of(1220±18) MPa,large plastic strain greater than 30%,and superior dry-and wet-wear resistance.This work demonstrates that ML combined with high-throughput analytic approaches can offer a powerful tool to accelerate the design of multicomponent Ti alloys with desired properties.Moreover,it is indicated that TNZC alloy is an attractive candidate for biomedical applications.展开更多
Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have rev...Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.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financially supported by the National Key Research and Development Program of China(No.2016YFB0701202,No.2017YFB0701500 and No.2020YFB1505901)National Natural Science Foundation of China(General Program No.51474149,52072240)+3 种基金Shanghai Science and Technology Committee(No.18511109300)Science and Technology Commission of the CMC(2019JCJQZD27300)financial support from the University of Michigan and Shanghai Jiao Tong University joint funding,China(AE604401)Science and Technology Commission of Shanghai Municipality(No.18511109302).
文摘Magnesium(Mg)alloys have shown great prospects as both structural and biomedical materials,while poor corrosion resistance limits their further application.In this work,to avoid the time-consuming and laborious experiment trial,a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics,from both the thermodynamic and kinetic perspectives.The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified.Then,the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated,and the corrosion exchange current density is further calculated by a hydrogen evolution reaction(HER)kinetic model.Several intermetallics,e.g.Y_(3)Mg,Y_(2)Mg and La_(5)Mg,are identified to be promising intermetallics which might effectively hinder the cathodic HER.Furthermore,machine learning(ML)models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function(W_(f))and weighted first ionization energy(WFIE).The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error(RMSE)of 0.11 eV.This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion,but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy,which can be extended to ternary Mg alloys or other alloy systems.
基金Construction Project of National Clinical Research Base of Traditional Chinese Medicine(Science Letter[2018]No.131,State Office of Traditional Chinese Medicine)Natural Science Foundation of Hebei Province:Study on the Mechanism of Action of Traditional Chinese Medicine on Disease and Syndrome(No.H2023423001)+6 种基金Key Research Project of the Ministry of Science and Technology(No.2018YFC1704100)Key Research Project of the Ministry of Science and Technology:Li Diangui Famous Old Chinese Medicine of Traditional Chinese Medicine Academic View Characteristic,Diagnosis and Treatment Methods and Experience of Prevention and Control of Major Diseases(No.2018YFC1704102)Provincial Science and Technology Program of Hebei Province:Prevention and Treatment of Gastric Cancer by Blocking the"Inflammation-Cancer Transformation"Based on the Theory of Turbidimetric Toxicity(No.21377724D)Provincial Science and Technology Program of Hebei Province:to Study the Clinical Efficacy and Mechanism of Huazhuo Jiedu Formula in the Treatment of Chronic Atrophic Gastritis based on Epidermal Growth Factor Receptor/Mitogen Activated Protein Kinase/Extracellular Signal-Regulated Kinase Signaling Pathway(No.21377740D)Scientific Research Project of Hebei Administration of Traditional Chinese Medicine:Clinical Study of Huazhuo Jiedu Formula Blocking the Pathological Evolution of Chronic Atrophic Gastritis(No.2022026)Scientific Research Project of Hebei Administration of Traditional Chinese Medicine:Study on the Medication Rules of Spleen and Stomach Diseases of Famous Yanzhao Medical Doctors Based on Data Mining(No.2022032)Scientific Research Project of Hebei Administration of Traditional Chinese Medicine:to Explore the Mechanism of Xianglian Huazhuo Formula in the Treatment of Chronic Atrophic Gastritis based on Transcriptomics(No.2023022)。
文摘OBJECTIVE:To explore the mechanism of Xianglian Huazhuo formula(香连化浊方,XLHZ)blocking the development of chronic atrophic gastritis(CAG)to gastric cancer(GC)through bioinformatics analysis and in vitro.METHODS:Pathological morphology of gastric mucosa of rats were observed.High-throughput sequencing was used to analyze the miRNA expression profile of gastric mucosa.The miRanda,miRDB and miRWalk databases were used to predict the differential target genes.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were performed for differential target genes.Real-time quantitative reverse transcription polymerase chain reaction(qRTPCR)was used to verify the differentially expressed miRNAs and target genes.Western blot,EdU,wound healing and flow cytometry were used to observe the effect of XLHZ on epithelial-mesenchymal transition(EMT)markers,proliferation,migration,apoptosis and cell cycle of CAG cells in vitro.RESULTS:A total of five differentially expressed miRNAs and four differential target genes were screened in this study.GO analysis showed that the target genes were enriched in regulation of neuron development,regulation of transcription factor activity and regulation of RNA polymerase.KEGG pathways database differences in gene enrichment of target genes in the Wnt signaling pathway,Phospholipase D signaling pathway and mitogen-activated protein kinase signaling pathway.qRTPCR confirmed that miRNAs and its target genes were consistent with the screening results.In vitro,our study revealed that XLHZ could increase the expression of Ecadherin,decrease the expression of transforming growth factorβ1,vimentin andβ-catenin,inhibite the proliferation and migration of CAG cells,cause cell cycle arrest at G0/G1 and G2/M phase,induce the apoptosis of CAG cells,and prevent the progression of CAG to GC.CONCLUSION:This study provided a new idea for the mechanism of blocking the progression of CAG to GC by XLHZ,which may be related to the expression of miR-20a-3p,miR-320-3p,miR-34b-5p,miR-483-3p and miR-883-3p and their target genes transferrin receptor,nuclear receptor subfamily 4 member 2,delta like canonical Notch ligand 1 and a kinase anchor protein 12 in CAG.In the future,we will continue to investigate the linkage between the active ingredients of XLHZ and the relevant miRNAs and their target genes,so as to provide more sufficient experimental basis for clinically effective prevention of CAG to GC.
基金the financial supports from the National Key Research and Development Program of China (No. 2022YFB3707501)the National Natural Science Foundation of China (No. 51701083)+1 种基金the GDAS Project of Science and Technology Development, China (No. 2022GDASZH2022010107)the Guangzhou Basic and Applied Basic Research Foundation, China (No. 202201010686)。
文摘Based on experimental data,machine learning(ML) models for Young's modulus,hardness,and hot-working ability of Ti-based alloys were constructed.In the models,the interdiffusion and mechanical property data were high-throughput re-evaluated from composition variations and nanoindentation data of diffusion couples.Then,the Ti-(22±0.5)at.%Nb-(30±0.5)at.%Zr-(4±0.5)at.%Cr(TNZC) alloy with a single body-centered cubic(BCC) phase was screened in an interactive loop.The experimental results exhibited a relatively low Young's modulus of(58±4) GPa,high nanohardness of(3.4±0.2) GPa,high microhardness of HV(520±5),high compressive yield strength of(1220±18) MPa,large plastic strain greater than 30%,and superior dry-and wet-wear resistance.This work demonstrates that ML combined with high-throughput analytic approaches can offer a powerful tool to accelerate the design of multicomponent Ti alloys with desired properties.Moreover,it is indicated that TNZC alloy is an attractive candidate for biomedical applications.
基金supported by a grant from the Standardization and Integration of Resources Information for Seed-cluster in Hub-Spoke Material Bank Program,Rural Development Administration,Republic of Korea(PJ01587004).
文摘Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.
