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.

Comparison of rumen archaeal diversity in adult and elderly yaks(Bos grunniens)using 16S rRNA gene high-throughput sequencing

This study was conducted to investigate the phylogenetic diversity of archaea in the rumen of adult and elderly yaks. Six domesticated female yaks, 3 adult yaks （（5.3±0.6） years old）, and 3 elderly yaks （（10.7±0.6） years old）, were used for the rumen contents collection. Illumina MiSeq high-throughput sequencing technology was applied to examine the archaeal composition of rumen contents. A total of 92 901 high-quality archaeal sequences were analyzed, and these were assigned to 2 033 operational taxonomic units （OTUs）. Among these, 974 OTUs were unique to adult yaks while 846 OTUs were unique to elderly yaks; 213 OTUs were shared by both groups. At the phylum level, more than 99% of the obtained OTUs belonged to the Euryarchaeota phylum. At the genus level, the archaea could be divided into 7 archaeal genera. The 7 genera （i.e., Methanobrevibacter, Methanobacterium, Methanosphaera, Thermogymnomonas, Methanomicrobiu, Meth- animicrococcus and the unclassified genus） were shared by all yaks, and their total abundance accounted for 99% of the rumen archaea. The most abundant archaea in elderly and adult yaks were Methanobrevibacterand Thermogymnomonas, respectively. The abundance of Methanobacteria （class）, Methanobacteriales （order）, Methanobacteriaceae （family）, and Methanobrevibacter （genus） in elderly yaks was significantly higher than in adult yaks. In contrast, the abundance of Ther-mogymnomonas in elderly yaks was 34% lower than in adult yaks, though the difference was not statistically significant. The difference in abundance of other archaea was not significant between the two groups. These results suggested that the structure of archaea in the rumen of yaks changed with age. This is the first study to compare the phytogenetic differences of rumen archaeal structure and composition using the yak model.

Genome-wide identification of RNA editing in seven porcine tissues by matched DNA and RNA high-throughput sequencing

Background: RNA editing is a co/posttranscriptional modification mechanism that increases the diversity of transcripts, with potential functional consequences. The advent of next-generation sequencing technologies has enabled the identification of RNA edits at unprecedented throughput and resolution. However, our knowledge of RNA editing in swine is still limited.Results: Here, we utilized RES-Scanner to identify RNA editing sites in the brain, subcutaneous fat, heart, liver,muscle, lung and ovary in three 180-day-old Large White gilts based on matched strand-specific RNA sequencing and whole-genome resequencing datasets. In total, we identified 74863 editing sites, and 92.1% of these sites caused adenosine-to-guanosine(A-to-G) conversion. Most A-to-G sites were located in noncoding regions and generally had low editing levels. In total, 151 A-to-G sites were detected in coding regions(CDS), including 94 sites that could lead to nonsynonymous amino acid changes. We provide further evidence supporting a previous observation that pig transcriptomes are highly editable at PRE-1 elements. The number of A-to-G editing sites ranged from 4155(muscle) to 25001(brain) across the seven tissues. The expression levels of the ADAR enzymes could explain some but not all of this variation across tissues. The functional analysis of the genes with tissuespecific editing sites in each tissue revealed that RNA editing might play important roles in tissue function.Specifically, more pathways showed significant enrichment in the fat and liver than in other tissues, while no pathway was enriched in the muscle.Conclusions: This study identified a total of 74863 nonredundant RNA editing sites in seven tissues and revealed the potential importance of RNA editing in tissue function. Our findings largely extend the porcine editome and enhance our understanding of RNA editing in swine.

High-throughput phenotyping: Breaking through the bottleneck in future crop breeding

With the rapid development of genetic analysis techniques and crop population size,phenotyping has become the bottleneck restricting crop breeding.Breaking through this bottleneck will require phenomics,defined as the accurate,high-throughput acquisition and analysis of multi-dimensional phenotypes during crop growth at organism-wide levels,ranging from cells to organs,individual plants,plots,and fields.Here we offer an overview of crop phenomics research from technological and platform viewpoints at various scales,including microscopic,ground-based,and aerial phenotyping and phenotypic data analysis.We describe recent applications of high-throughput phenotyping platforms for abiotic/biotic stress and yield assessment.Finally,we discuss current challenges and offer perspectives on future phenomics research.

State-of-the-Art Review of High-Throughput Statistical Spatial-Mapping Characterization Technology and Its Applications

Macroscopic materials are heterogeneous,multi-elementary,and complex.No material is homogeneous or isotropic at a certain small scale.Parts of the material that differ from one another can be termed"natural chips."At different spots on the material,the composition,structure,and properties vary slightly,and the combination of these slight differences establishes the overall material performance.This article presents a state-of-the-art review of research and applications of high-throughput statistical spatialmapping characterization technology based on the intrinsic heterogeneity within materials.Highthroughput statistical spatial-mapping uses a series of rapid characterization techniques for analysis from the macroscopic to the microscopic scale.Datasets of composition,structure,and properties at each location are obtained rapidly for practical sample sizes.Accurate positional coordinate information and references to a point-to-point correspondence are used to set up a database that contains spatialmapping lattices.Based on material research and development design requirements,dataset spatialmapping within required target intervals is selected from the database.Statistical analysis can be used to select a suitable design that better meets the targeted requirements.After repeated verification,genetic units that reflect the material properties are determined.By optimizing process parameters,the assembly of these genetic unit(s)is verified at the mesoscale,and quantitative correlations are established between the microscale,mesoscale,macroscale,practical sample,across-the-scale span composition,structure,and properties.The high-throughput statistical spatial-mapping characterization technology has been applied to numerous material systems,such as steels,superalloys,galvanization,and ferrosilicon alloys.This approach has guided the composition and the process optimization of various materials.

High-throughput sequencing analysis of the regulation of intestinal flora in giant pandas with indigestion using a probiotic agent LyPB

This study aimed to investigate the eff ect of LyPB on the intestinal microfl ora of giant pandas with indigestion,using high-throughput sequencing(HTS)technology.The species distribution and microfl oral density and diversity before and after administration of the LyPB probiotic agent were analyzed.LyPB evidently has the ability to adjust the fl oral imbalance in the panda’s intestine.To test the eff ects of LyPB on the microfl ora of the panda gut,fecal samples were taken from a healthy giant panda(Anan)without administration of LyPB and from a dyspeptic giant panda Yangyang before and after LyPB administration.Compared with the sample obtained from healthy Anan(anan-c)and that obtained from dyspeptic Yangyang before LyPB administration(yangyang1),the sample taken from Yangyang(yangyang2)after LyPB administration displayed a signifi cant increase in the operational taxonomic unit index.An increase in the Chao index indicated an increase in the microfl oral richness,while an increase in the Shannon index indicated an increase in microfl oral diversity.At phylum and genus levels,a signifi cant increase was observed in the density of probiotic bacteria of phylum fi rmicutes,genus Streptococcus,while a drastic reduction in the density of Escherichia coli/Escherichia coli Shigella/bacteria of genus Shigella was observed.Data obtained in this study shows that LyPB preparations successfully improve the microbial structure within the panda’s intestinal canal by signifi cantly increasing the eff ective microbial community and decreasing the number of pathogenic microbes.

High-throughput Sequencing Technology and Its Application

Gene sequencing is a great way to interpret life, and high-throughput sequencing technology is a revolutionary technological innovation in gene sequencing researches. This technology is characterized by low cost and high-throughput data. Currently, high-throughput sequencing technology has been widely applied in multi-level researches on genomics, transcriptomics and epigenomics. And it has fundamentally changed the way we approach problems in basic and translational researches and created many new possibilities. This paper presented a general description of high-throughput sequencing technology and a comprehensive review of its application with plain, concisely and precisely. In order to help researchers finish their work faster and better, promote science amateurs and understand it easier and better.

Microbial diversity in Huguangyan Maar Lake of China revealed by high-throughput sequencing

Huguangyan Maar Lake is a typical maar lake in the southeast of China. It is well preserved and not disturbed by anthropogenic activities. In this study, microbial community structures in sediment and water samples from Huguangyan Maar Lake were investigated using a high-throughput sequencing method. We found significant differences between the microbial community compositions of the water and the sediment. The sediment samples contained more diverse Bacteria and Archaea than did the water samples. Actinobacteria, Betaproteobacteria, Cyanobacteria, and Deltaproteobacteria predominated in the water samples while Deltaproteobacteria, Anaerolineae, Nitrospira, and Dehalococcoidia were the major bacterial groups in the sediment. As for Archaea, Woesearchaeota (DHVEG-6), unclassified Archaea, and Deep Sea Euryarchaeotic Group were detected at higher abundances in the water, whereas the Miscellaneous Crenarchaeotic Group, Thermoplasmata, and Methanomicrobia were significantly more abundant in the sediment. Interactions between Bacteria and Archaea were common in both the water column and the sediment. The concentrations of major nutrients (NO^3-, PO4^3-, SiO3^2- and NH4^+) shaped the microbial population structures in the water. At the higher phylogenetic levels including phylum and class, many of the dominant groups were those that were also abundant in other lakes;however, novel microbial populations (unclassified) were often seen at the lower phylogenetic levels. Our study lays a foundation for examining microbial biogeochemical cycling in sequestered lakes or reservoirs.

Machine Learning-Assisted High-Throughput Virtual Screening for On-Demand Customization of Advanced Energetic Materials

Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error.Herein,a methodology combining domain knowledge,a machine learning algorithm,and experiments is presented for accelerating the discovery of novel energetic materials.A high-throughput virtual screening(HTVS)system integrating on-demand molecular generation and machine learning models covering the prediction of molecular properties and crystal packing mode scoring is established.With the proposed HTVS system,candidate molecules with promising properties and a desirable crystal packing mode are rapidly targeted from the generated molecular space containing 25112 molecules.Furthermore,a study of the crystal structure and properties shows that the good comprehensive performances of the target molecule are in agreement with the predicted results,thus verifying the effectiveness of the proposed methodology.This work demonstrates a new research paradigm for discovering novel energetic materials and can be extended to other organic materials without manifest obstacles.

Characterization of Bacterial Communities Associating with Larval Development of Yesso Scallop(Patinopecten yessoensisis Jay, 1857) by High-Throughput Sequencing

Bacterial community presumably plays an essential role in inhibiting pathogen colonization and maintaining the health of scallop larvae, but limiting data are available for Yesso scallop （Patinopecten yessoensisis Jay, 1857） larval development stages. The aim of this study was to characterize and compare the bacterial communities associating with Yesso scallop larval development at fertilized egg S l, trochophora S2, D-shaped larvae S3, umbo larvae S4, and juvenile scallop S5 stages by Illumina high-throughput sequencing. Genomic DNA was extracted from the larvae and their associating baetera, and a gene segment covering V3-V4 region of 16S rRNA gene was amplified and sequenced using an Illumina Miseq sequencer. Overall, 106760 qualified sequences with an average length of 449 bp were obtained. Sequences were compared with those retrieved from 16S rRNA gene databases, and 4 phyla, 7 classes, 15 orders, 21 families, 31 genera were identified. Proteobacteria was predominant phylum, accounting for more than 99%, at all 5 larval development stages. At genus level, Pseudomonas was dominant at stages S1 （80.60%）, S2 （87.77%） and S5 （68.71%）, followed by Photobacterium （17.06%） and Aeromonas （1.64%） at stage S1, Serratia （6.94%）, Stenotrophomonas （3.08%） and Acinetobacter （1.2%） at stage S2, Shewanella （25.95%） and Pseudoalteromonas （4.57%） at stage S5. Moreover, genus Pseudoal- teromonas became dominant at stages S3 （44.85%） and S4 （56.02%）, followed by Photobacterium （29.82%）, Pseudomonas （11.86%）, Aliivibrio （8.60%） and Shewanella （3.39%） at stage S3, Pseudomonas （18.16%）, Aliivibrio （14.29%）, Shewanella （4.11%）, Psychro- monas （4.04%） and Psychrobacter （1.81%） at stage S4. From the results, we concluded that the bacterial community changed sig- nificantly at different development stages of Yesso Scallop larvae.

High-throughput volumetric reconstruction for 3D wheat plant architecture studies

For many tller crops,the plant archit ecture(PA),including the plant fresh weight,plant height,number of tllrs,tller angle and stem diameter,sigificantly afects the grain yield.In this study,we propose a method based on volumetric reconstruction for high-throughput three-dimensional(3D)wheat PA studies.The proposed methodology involves plant volumetric reconst ruction from multiple images,plant model processing and phenotypic parameter estimation and analysis.This study was performed on 80 Triticum aestium plants,and the results were analyzed.Comparing the automated measurements with manual measurements,the mean absolute per-centage error(MAPE)in the plant height and the plant fresh weight was 2.71%(1.08cm with an average plant height of 40.07cm)and 10.06%(1.41g with an average plant fresh weight of 14.06 g),respectively.The root mean square error(RMSE)was 137 cm and 1.79g for the plant height and plant fresh weight,respectively.The correlation cofficients were 0.95 and 0.96 for the plant height and plant fresh weight,respectively.Additionally,the proposed methodology,in-cluding plant reconstruction,model processing and trait ext raction,required only approximately 20s on average per plant using parallel computing on a graphics processing unit(GPU),dem-onstrating that the methodology would be valuable for a high-throughput phenotyping platform.