High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys

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.

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

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.

From the perspective of experimental practice: High-throughput computational screening in photocatalysis

Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is now generating widespread interest in boosting the conversion effi-ciency of solar energy.In the past decade,computational technologies and theoretical simulations have led to a major leap in the development of high-throughput computational screening strategies for novel high-efficiency photocatalysts.In this viewpoint,we started with introducing the challenges of photocatalysis from the view of experimental practice,especially the inefficiency of the traditional“trial and error”method.Sub-sequently,a cross-sectional comparison between experimental and high-throughput computational screening for photocatalysis is presented and discussed in detail.On the basis of the current experimental progress in photocatalysis,we also exemplified the various challenges associated with high-throughput computational screening strategies.Finally,we offered a preferred high-throughput computational screening procedure for pho-tocatalysts from an experimental practice perspective(model construction and screening,standardized experiments,assessment and revision),with the aim of a better correlation of high-throughput simulations and experimental practices,motivating to search for better descriptors.

Development of a High-throughput Sequencing Platform for Detection of Viral Encephalitis Pathogens Based on Amplicon Sequencing

Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing.Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing.Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing.Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.

High-throughput microfluidic production of carbon capture microcapsules:fundamentals,applications,and perspectives

In the last three decades,carbon dioxide(CO_(2)) emissions have shown a significant increase from various sources.To address this pressing issue,the importance of reducing CO_(2) emissions has grown,leading to increased attention toward carbon capture,utilization,and storage strategies.Among these strategies,monodisperse microcapsules,produced by using droplet microfluidics,have emerged as promising tools for carbon capture,offering a potential solution to mitigate CO_(2) emissions.However,the limited yield of microcapsules due to the inherent low flow rate in droplet microfluidics remains a challenge.In this comprehensive review,the high-throughput production of carbon capture microcapsules using droplet microfluidics is focused on.Specifically,the detailed insights into microfluidic chip fabrication technologies,the microfluidic generation of emulsion droplets,along with the associated hydrodynamic considerations,and the generation of carbon capture microcapsules through droplet microfluidics are provided.This review highlights the substantial potential of droplet microfluidics as a promising technique for large-scale carbon capture microcapsule production,which could play a significant role in achieving carbon neutralization and emission reduction goals.

Interpretable Machine Learning-Assisted High-Throughput Screening for Understanding NRR Electrocatalyst Performance Modulation between Active Center and C-N Coordination

Understanding the correlation between the fundamental descriptors and catalytic performance is meaningful to guide the design of high-performance electrochemical catalysts.However,exploring key factors that affect catalytic performance in the vast catalyst space remains challenging for people.Herein,to accurately identify the factors that affect the performance of N2 reduction,we apply interpretable machine learning(ML)to analyze high-throughput screening results,which is also suited to other surface reactions in catalysis.To expound on the paradigm,33 promising catalysts are screened from 168 carbon-supported candidates,specifically single-atom catalysts(SACs)supported by a BC_(3)monolayer(TM@V_(B/C)-N_(n)=_(0-3)-BC_(3))via high-throughput screening.Subsequently,the hybrid sampling method and XGBoost model are selected to classify eligible and non-eligible catalysts.Through feature interpretation using Shapley Additive Explanations(SHAP)analysis,two crucial features,that is,the number of valence electrons(N_(v))and nitrogen substitution(N_(n)),are screened out.Combining SHAP analysis and electronic structure calculations,the synergistic effect between an active center with low valence electron numbers and reasonable C-N coordination(a medium fraction of nitrogen substitution)can exhibit high catalytic performance.Finally,six superior catalysts with a limiting potential lower than-0.4 V are predicted.Our workflow offers a rational approach to obtaining key information on catalytic performance from high-throughput screening results to design efficient catalysts that can be applied to other materials and reactions.

High-throughput calculation-based rational design of Fe-doped MoS_(2) nanosheets for electrocatalytic p H-universal overall water splitting

Electrocatalytic water splitting is crucial for H2generation via hydrogen evolution reaction(HER)but subject to the sluggish dynamics of oxygen evolution reaction(OER).In this work,single Fe atomdoped MoS_(2)nanosheets(SFe-DMNs)were prepared based on the high-throughput density functional theory(DFT)calculation screening.Due to the synergistic effect between Fe atom and MoS_(2)and optimized intermediate binding energy,the SFe-DMNs could deliver outstanding activity for both HER and OER.When assembled into a two-electrode electrolytic cell,the SFe-DMNs could achieve the current density of 50 mA cm^(-2)at a low cell voltage of 1.55 V under neutral condition.These results not only confirmed the effectiveness of high-throughput screening,but also revealed the excellent activity and thus the potential applications in fuel cells of SFe-DMNs.

High-throughput computational screening and in vitro evaluation identifies 5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione(C3),as a novel EGFR—HER2 dual inhibitor in gastric tumors

Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation,adhesion,angiogenesis,and metastasis.Conventional therapies are ineffective due to the intra-tumoral heterogeneity and concomitant genetic mutations.Hence,dual inhibition strategies are recommended to increase potency and reduce cytotoxicity.In this study,we have conducted computational high-throughput screening of the ChemBridge library followed by in vitro assays and identified novel selective inhibitors that have a dual impediment of EGFR/HER2 kinase activities.Diversity-based High-throughput Virtual Screening(D-HTVS)was used to screen the whole ChemBridge small molecular library against EGFR and HER2.The atomistic molecular dynamic simulation was conducted to understand the dynamics and stability of the protein-ligand complexes.EGFR/HER2 kinase enzymes,KATOIII,and Snu-5 cells were used for in vitro validations.The atomistic Molecular Dynamics simulations followed by solvent-based Gibbs binding free energy calculation of top molecules,identified compound C3(5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione)to have a good affinity for both EGFR and HER2.The predicted compound,C3,was promising with better binding energy,good binding pose,and optimum interactions with the EGFR and HER2 residues.C3 inhibited EGFR and HER2 kinases with IC50 values of 37.24 and 45.83 nM,respectively.The GI50 values of C3 to inhibit KATOIII and Snu-5 cells were 84.76 and 48.26 nM,respectively.Based on these findings,we conclude that the identified compound C3 showed a conceivable dual inhibitory activity on EGFR/HER2 kinase,and therefore can be considered as a plausible lead-like molecule for treating gastric cancers with minimal side effects,though testing in higher models with pharmacokinetic approach is required.

Variation of microbiological and small molecule metabolite profiles of Nuodeng ham during ripening by high-throughput sequencing and GC-TOF-MS

The internal microbial diversity and small molecular metabolites of Nuodeng ham in different processing years(the first,second and third year sample)were analyzed by high-throughput sequencing technology and gas chromatography-time of flight mass spectrography(GC-TOF-MS)to study the effects of microorganisms and small molecular metabolites on the quality of ham in different processing years.The results showed that the dominant bacteria phyla of Nuodeng ham in different processing years were Proteobacteria and Firmicutes,the dominant fungi phyla were Ascomycota and Basidiomycota,while Staphylococcus and Aspergillus were the dominant bacteria and fungi of Nuodeng ham,respectively.Totally,252 kinds of small molecular metabolites were identified from Nuodeng ham in different processing years,and 12 different metabolites were screened through multivariate statistical analysis.Further metabolic pathway analysis showed that 23 metabolic pathways were related to ham fermentation,of which 8 metabolic pathways had significant effects on ham fermentation(Impact>0.01,P<0.05).The content of L-proline,phenyllactic acid,L-lysine,carnosine,taurine,D-proline,betaine and creatine were significantly positively correlated with the relative abundance of Staphylococcus and Serratia,but negatively correlated with the relative abundance of Halomonas,Aspergillus and Yamadazyma.

High-throughput screening system of citrus bacterial cankerassociated transcription factors and its application to the regulation of citrus canker resistance

One of the main diseases that adversely impacts the global citrus industry is citrus bacterial canker(CBC),caused by the bacteria Xanthomonas citri subsp.citri(Xcc).Response to CBC is a complex process,with both proteinDNA as well as protein–protein interactions for the regulatory network.To detect such interactions in CBC resistant regulation,a citrus high-throughput screening system with 203 CBC-inducible transcription factors(TFs),were developed.Screening the upstream regulators of target by yeast-one hybrid(Y1H)methods was also performed.A regulatory module of CBC resistance was identified based on this system.One TF(CsDOF5.8)was explored due to its interactions with the 1-kb promoter fragment of CsPrx25,a resistant gene of CBC involved in reactive oxygen species(ROS)homeostasis regulation.Electrophoretic mobility shift assay(EMSA),dual-LUC assays,as well as transient overexpression of CsDOF5.8,further validated the interactions and transcriptional regulation.The CsDOF5.8–CsPrx25 promoter interaction revealed a complex pathway that governs the regulation of CBC resistance via H2O2homeostasis.The high-throughput Y1H/Y2H screening system could be an efficient tool for studying regulatory pathways or network of CBC resistance regulation.In addition,it could highlight the potential of these candidate genes as targets for efforts to breed CBC-resistant citrus varieties.

Transfer learning aided high-throughput computational design of oxygen evolution reaction catalysts in acid conditions

Sluggish oxygen evolution reaction(OER)in acid conditions is one of the bottlenecks that prevent the wide adoption of proton exchange membrane water electrolyzer for green hydrogen production.Despite recent advancements in developing high-performance catalysts for acid OER,the current electrocatalysts still rely on iridium-and ruthenium-based materials,urging continuous efforts to discover better performance catalysts as well as reduce the usage of noble metals.Pyrochlore structured oxide is a family of potential high-performance acid OER catalysts with a flexible compositional space to tune the electrochemical capabilities.However,exploring the large composition space of pyrochlore compounds demands an imperative approach to enable efficient screening.Here we present a highthroughput screening pipeline that integrates density functional theory calculations and a transfer learning approach to predict the critical properties of pyrochlore compounds.The high-throughput screening recommends three sets of candidates for potential acid OER applications,totaling 61 candidates from 6912 pyrochlore compounds.In addition to 3d-transition metals,p-block metals are identified as promising dopants to improve the catalytic activity of pyrochlore oxides.This work demonstrates not only an efficient approach for finding suitable pyrochlores towards acid OER but also suggests the great compositional flexibility of pyrochlore compounds to be considered as a new materials platform for a variety of applications.

High-throughput sorting of two-color fluorescent-labeled zebrafish embryos

The zebrafish embryos were widely employed in genetics,development and drug discovery studies as miniatured animal models.Sorting of two-color fluorescent embryos is often required in large-scale experiments but it is challenging to manually sort with high efficiency.Here,we reported a high-throughput sorting system for two-color fluorescent zebraflsh embryos.The embryos can be automatically loaded from a sample pool and sorted based on the average fluorescent intensity.The two-color fluorescent signals were split into two lines and detected by an area array camera.The system achieves the sorting of 100 embryos in less than 10 min with an accuracy of greater than 95%.

Development and Characterization of Microsatellite Markers for Harpadon nehereus Based on High-Throughput Sequencing and Cross-Species Amplification in Three Myctophiformes Fishes

Harpadon nehereus is a widespread economical fish found in the coastal seas of China and has important ecological value in the marine ecosystem.H_(o)wever,its germplasm resources have been seriously degraded due to natural factors and anthropogenic activities.In this study,high-throughput sequencing was applied to search for microsatellite loci in H.nehereus transcriptome to provide references for its resource conservation and utilization.Polymorphic loci were developed by non-denaturing polyacrylamide gel electrophoresis,and their cross-species amplified ability was detected in three related species.A total of 5652 microsatellites were identified from 16974320 unigenes.Among the primer pairs designed for 100 SSRs for PCR amplification,80%were successfully amplified,and 26 loci were polymorphic with a high number of alleles from 3 to 11 each.The expected(H_(e))and observed(H_(o))heterozygosities were 0.355–0.885 and 0.375–0.958,respectively.Most of the loci were highly polymorphic(polymorphism information content:0.316–0.852;mean:0.713),and these markers can be applied in the population genetic diversity research of H.nehereus.H_(o)wever,the transferability of these primers was low,probably because of the close relation of the collected species.In follow-up work,simple sequence repeats will be excavated with genome-based technologies,and related species will be gathered to address the present inadequacies.

An integrated microfluidics platform with high-throughput single-cell cloning array and concentration gradient generator for efficient cancer drug effect screening

Background:Tumor cell heterogeneity mediated drug resistance has been recognized as the stumbling block of cancer treatment.Elucidating the cytotoxicity of anticancer drugs at single-cell level in a high-throughput way is thus of great value for developing precision therapy.However,current techniques suffer from limitations in dynamically characterizing the responses of thousands of single cells or cell clones presented to multiple drug conditions.Methods:We developed a new microfluidics-based“SMART”platform that is Simple to operate,able to generate a Massive single-cell array and Multiplex drug concentrations,capable of keeping cells Alive,Retainable and Trackable in the microchambers.These features are achieved by integrating a Microfluidic chamber Array(4320 units)and a sixConcentration gradient generator(MAC),which enables highly efficient analysis of leukemia drug effects on single cells and cell clones in a high-throughput way.Results:A simple procedure produces 6 on-chip drug gradients to treat more than 3000 single cells or single-cell derived clones and thus allows an efficient and precise analysis of cell heterogeneity.The statistic results reveal that Imatinib(Ima)and Resveratrol(Res)combination treatment on single cells or clones is much more efficient than Ima or Res single drug treatment,indicated by the markedly reduced half maximal inhibitory concentration(IC50).Additionally,single-cell derived clones demonstrate a higher IC_(50) in each drug treatment compared to single cells.Moreover,primary cells isolated from two leukemia patients are also found with apparent heterogeneity upon drug treatment on MAC.Conclusions:This microfluidics-based“SMART”platform allows high-throughput single-cell capture and culture,dynamic drug-gradient treatment and cell response monitoring,which represents a new approach to efficiently investigate anticancer drug effects and should benefit drug discovery for leukemia and other cancers.

High-throughput mechanistic study of highly selective hydrogen-bonded organic frameworks for electrochemical nitrate reduction to ammonia

Hydrogen-bonded organic frameworks(HOFs),an emerging porous macrocyclic materials linked by hydrogen-bond,hold potential for gas separation and storage,sensors,optical,and electrocatalysts.Here,HOF-based electrocatalysts are rationally developed for nitrates reduction to ammonia,allowing not only to regulate wastewater pollution but also to accomplish carbon-neutral ammonia(NH_(3))synthesis.We preform high-throughput computational screening of thirty-six HOFs with various metals as active sites,denoted as HOF-M1,for nitrate reduction reaction(NO_(3)RR)toward NH_(3).We have implemented a hierarchical four-step screening strategy,and ultimately,HOF-Ti1 was selected based on its exceptional catalytic activity and selectivity in the NO_(3)RR process.Through additional analysis,we discovered that the d band center of the active metal sites serves as an effective parameter for designing and predicting the performance of HOFs in NO_(3)RR.This research not only showcases the immense potential of electrocatalysis in transforming NO_(3)RR into NH_(3)but also provides researchers with a compelling incentive to undertake further experimental investigations.

种植抗异丙隆转基因水稻和施用异丙隆对转基因水稻根际微生物群落的影响

【目的】明确种植抗异丙隆转基因水稻和施用除草剂异丙隆对转基因水稻根际微生物群落的影响,为抗异丙隆水稻种植的生态风险评价提供科学依据。【方法】基于盆栽试验,采用高通量测序技术分析2个抗异丙隆转基因水稻株系及其亲本和施用异丙隆转基因在不同水稻生长阶段的根际微生物群落多样性与组成的差异。【结果】高通量测序结果显示,在水稻所有生长阶段,变形菌门(Proteobacteria)、酸杆菌门(Acidobacteria)、芽单胞菌门(Gemmatimonadota)、拟杆菌门(Bacteroidota)和粘球菌门(Myxococcota)是根际微生物中的优势细菌门;罗兹菌门(Rozellomycota)、子囊菌门(Ascomycota)、担子菌门(Basidiomycota)、球囊菌门(Glomeromycota)和被孢霉门(Mortierellomycota)是根际微生物中的优势真菌门。在水稻相同生长阶段,无论是种植抗异丙隆转基因水稻、非转基因水稻还是施用除草剂异丙隆,水稻根际微生物群落的丰富度指数、辛普森多样性指数和香农多样性指数均未受到显著影响(P>0.05,下同);抗异丙隆转基因水稻、施用异丙隆的转基因水稻与非转基因水稻根际微生物中多数优势微生物门类相对丰度无显著差异,个别优势微生物门在水稻特定的生长阶段发生变化。基于相对丰度前15位微生物属的聚类分析显示,在水稻某些生长阶段根际微生物群落的组成在抗异丙隆转基因水稻、施用异丙隆的转基因水稻与非转基因水稻间存在一定差异。根际微生物群落的主成分分析显示,抗异丙隆转基因水稻、施用异丙隆的转基因水稻和非转基因水稻根际微生物群落组成间无显著差异,但不同生长阶段的根际微生物群落组成出现空间分异。根际微生物群落变差分解分析显示,水稻生长阶段对根际土壤中的细菌群落结构变化的影响为45.62%,而品种和施用异丙隆对根际土壤中细菌群落结构变化的影响分别为1.15%和2.76%;水稻生长阶段对根际土壤中真菌群落结构变化的影响为48.36%,而品种和施用异丙隆对根际土壤中真菌群落结构变化的影响分别为1.36%和2.45%。【结论】种植抗异丙隆转基因水稻和施用异丙隆并未对转基因水稻根际微生物群落产生显著影响,但根际土壤中的细菌和真菌群落一定程度上受水稻生长阶段的影响。

Composition Engineering Opens an Avenue Toward Efficient and Sustainable Nitrogen Fixation

In this work,we open an avenue toward rational design of potential efficient catalysts for sustainable ammonia synthesis through composition engineering strategy by exploiting the synergistic effects among the active sites as exemplified by diatomic metals anchored graphdiyne via the combination of hierarchical high-throughput screening,first-principles calculations,and molecular dynamics simulations.Totally 43 highly efficient catalysts feature ultralow onset potentials(|U_(onset)|≤0.40 V)with Rh-Hf and Rh-Ta showing negligible onset potentials of 0 and-0.04 V,respectively.Extremely high catalytic activities of Rh-Hf and Rh-Ta can be ascribed to the synergistic effects.When forming heteronuclears,the combinations of relatively weak(such as Rh)and relatively strong(such as Hf or Ta)components usually lead to the optimal strengths of adsorption Gibbs free energies of reaction intermediates.The origin can be ascribed to the mediate d-band centers of Rh-Hf and Rh-Ta,which lead to the optimal adsorption strengths of intermediates,thereby bringing the high catalytic activities.Our work provides a new and general strategy toward the architecture of highly efficient catalysts not only for electrocatalytic nitrogen reduction reaction(eNRR)but also for other important reactions.We expect that our work will boost both experimental and theoretical efforts in this direction.

Rational design of MXene-based vacancy-confined single-atom catalyst for efficient oxygen evolution reaction

Two-dimensional transition metal carbides(MXenes) have been demonstrated to be promising supports for single-atom catalysts(SACs) to enable efficient oxygen evolution reaction(OER).However,the rational design of MXene-based SACs depends on an experimental trial-and-error approach.A theoretical guidance principle is highly expected for the efficient evaluation of MXene-based SACs.Herein,highthroughput screening was performed through first-principles calculations and machine learning techniques.Ti_(3)C_(2)(OH)_(x),V_(3)C_(2)(OH)_(x),Zr_(3)C_(2)(OH)_(x),Nb_(3)C_(2)(OH)_(x),Hf_(3)C_(2)(OH)_(x),Ta_(3)C_(2)(OH)_(x),and W_(3)C_(2)(OH)_(x) were screened out based on their excellent stability.Zn,Pd,Ag,Cd,Au,and Hg were proposed to be promising single atoms anchored in MXenes based on cohesive energy analysis.Hf_(3)C_(2)(OH)_(x) with a Pd single atom delivers a theoretical overpotential of 81 mV.Both moderate electron-deficient state and high covalency of metal-carbon bonds were critical features for the high OER reactivity.This principle is expected to be a promising approach to the rational design of OER catalysts for metal-air batteries,fuel cells,and other OER-based energy storage devices.

Variations of phyllosphere microorganisms in asymptomatic and tobacco brown spot leaves before and after spraying 12% difenoconazole + fluxapyroxad SC

12%difenoconazole+fluxapyroxad SC(commercial name:Jiangong)was first released by BASF in China in 2016.It has been registered to control many diseases,including pear scab,apple Alternaria leaf spot,tomato early blight,cucumber powdery mildew,etc.This study evaluated the bioactivity of Jiangong against Alternaria alternata and explored variations of phyllosphere microorganisms in both asymptomatic and tobacco brown spot leaves at different persistence periods(0,5,10,and 15 days post-fungicide application)using high-throughput sequencing technology.The results indicated that Jiangong effectively inhibited mycelial growth(average EC_(50) value of 0.51μg/mL),conidia germination(average EC_(50) value of 3.47μg/mL),and the carbon metabolism of A.alternata.Both asymptomatic and symptomatic leaves presented complex microbial communities.Higher fungal diversity was noted in asymptomatic leaves,while higher bacterial diversity was found in symptomatic leaves.After application,the diversity and abundance of microbial community structures in both types of leaves changed over time.Fungal microbiome communities showed greater sensitivity than bacterial groups,with the microbiome communities of asymptomatic leaves being more affected than those of symptomatic leaves.Fungal community diversity decreased for both symptomatic and asymptomatic leaves after 5 days of application,while the diversity of fungal community in symptomatic leaves showed an upward trend after 10 days of application.Meanwhile,bacterial community diversity increased in both symptomatic and asymptomatic leaves after 5 days of application but then declined in asymptomatic leaves after 15 days.The abundance of the dominant function group of phyllosphere bacteria(metabolism,genetic information processing,environmental information processing)was not affected by the application of Jiangong.However,the abundance of the dominant function group of phyllosphere fungi(animal pathogen-endophyte-wood saprotroph,endophyte-plant pathogen,plant pathogen-undefined saprotroph)was significantly affected by the application of Jiangong,and high variation was found in symptomatic leaves than that of asymptomatic leaves.The application of Jiangong-induced alterations in the community structure of the tobacco phyllosphere microbiome provides a basis for future tobacco brown spot control strategies based on phyllospheric microecology.

Toward Next-Generation Heterogeneous Catalysts:Empowering Surface Reactivity Prediction with Machine Learning

Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes,and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility.Computational high-throughput screening presents a viable solution to this challenge,as machine learning(ML)has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information.This review focuses on recent progress in applying ML in adsorption energy prediction,which predominantly quantifies the catalytic potential of a solid catalyst.ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed.At the end of the review,an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied.We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and,ultimately,reshape the chemical industry and energy landscape.