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.

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.

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.

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.

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.

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.

Neoantigen cancer vaccines:a new star on the horizon

Immunotherapy represents a promising strategy for cancer treatment that utilizes immune cells or drugs to activate the patient's own immune system and eliminate cancer cells.One of the most exciting advances within this field is the targeting of neoantigens,which are peptides derived from non-synonymous somatic mutations that are found exclusively within cancer cells and absent in normal cells.Although neoantigen-based therapeutic vaccines have not received approval for standard cancer treatment,early clinical trials have yielded encouraging outcomes as standalone monotherapy or when combined with checkpoint inhibitors.Progress made in high-throughput sequencing and bioinformatics have greatly facilitated the precise and efficient identification of neoantigens.Consequently,personalized neoantigen-based vaccines tailored to each patient have been developed that are capable of eliciting a robust and long-lasting immune response which effectively eliminates tumors and prevents recurrences.This review provides a concise overview consolidating the latest clinical advances in neoantigen-based therapeutic vaccines,and also discusses challenges and future perspectives for this innovative approach,particularly emphasizing the potential of neoantigen-based therapeutic vaccines to enhance clinical efficacy against advanced solid tumors.

From Phenotypes to Molecules: Revolutionizing Gut Microbiota Identification Methods

The gut microbiota is a complex ecosystem composed of many bacteria and their metabolites.It plays an irreplaceable role in human digestion,nutrient absorption,energy supply,fat metabolism,immune regulation,and many other aspects.Exploring the structure and function of the gut microbiota,as well as their key genes and metabolites,will enable the early diagnosis and auxiliary diagnosis of diseases,new treatment methods,better effects of drug treatments,and better guidance in the use of antibiotics.The identification of gut microbiota plays an important role in clinical diagnosis and treatment,as well as in drug research and development.Therefore,it is necessary to conduct a comprehensive review of this rapidly evolving topic.Traditional identification methods cannot comprehensively capture the diversity of gut microbiota.Currently,with the rapid development of molecular biology,the classification and identification methods for gut microbiota have evolved from the initial phenotypic and chemical identification to identification at the molecular level.This review integrates the main methods of gut microbiota identification and evaluates their application.We pay special attention to the research progress on molecular biological methods and focus on the application of high-throughput sequencing technology in the identification of gut microbiota.This revolutionary method for intestinal flora identification heralds a new chapter in our understanding of the microbial world.

Research on the quality deterioration of UHT milk products during shelf life:core microorganisms and related characteristics

Commercial sterility does not guarantee the sustained stability of ultrahigh temperature(UHT)milk over 6 months shelf life.We explore the microbiota presented in normal(SZ)and quality deteriorated UHT milk(QY and WY)products from the same brand.Based on high-throughput sequencing research results,11 phyla and 54 genera were identified as dominant microbiota.Pseudomonas,Streptococcus,and Acinetobacter as core functional microbiota significantly influenced the UHT milk quality properties.Moreover,principal component analysis(PCA)and multivariate analyses were used to examine the quality characteristics,including 11 physicochemical parameters,10 fatty acids,and 2 enzyme activities,in normal and quality deteriorated UHT milk.We found that the abundance of Pseudomonas increased in quality deteriorated milk(WY)and showed a significant positive correlation with heat-resistant protease content.Acinetobacter in quality deteriorated milk(QY)also considerably contributed to the content of heat-resistant lipase,which resulted in spoilage deterioration of UHT milk.

RNA-sequencing expression profile and functional analysis of retinal pigment epithelium in atrophic age-related macular degeneration

The retinal pigment epithelium(RPE)is fundamental to sustaining retinal homeostasis.RPE abnormality leads to visual defects and blindness,including age-related macular degeneration(AMD).Although breakthroughs have been made in the treatment of neovascular AMD,effective intervention for atrophic AMD is largely absent.The adequate knowledge of RPE pathology is hindered by a lack of the patients'RPE datasets,especially at the single-cell resolution.In the current study,we delved into a large-scale single-cell resource of AMD donors,in which RPE cells were occupied in a substantial proportion.Bulk RNA-seq datasets of atrophic AMD were integrated to extract molecular characteristics of RPE in the pathogenesis of atrophic AMD.Both in vivo and in vitro models revealed that carboxypeptidase X,M14 family member 2(CPXM2),was specifically expressed in the RPE cells of atrophic AMD,which might be induced by oxidative stress and involved in the epithelial-mesenchymal transition of RPE cells.Additionally,silencing of CPXM2 inhibited the mesenchymal phenotype of RPE cells in an oxidative stress cell model.Thus,our results demonstrated that CPXM2 played a crucial role in regulating atrophic AMD and might serve as a potential therapeutic target for atrophic AMD.