Allele-specific Expression Reveals Multiple Paths to Highland Adaptation in Maize.

Maize is a staple food of smallholder farmers living in highland regions up to 4,000 m above sea level worldwide. Mexican and South American highlands are two major highland maize growing regions, and population genetic data suggest the maize's adaptation to these regions occurred largely independently, providing a case study for convergent evolution. To better understand the mechanistic basis of highland adaptation, we crossed maize landraces from 108 highland and lowland sites of Mexico and South America with the inbred line B73 to produce F1 hybrids and grew them in both highland and lowland sites in Mexico. We identified thousands of genes with divergent expression between highland and lowland populations. Hundreds of these genes show patterns of convergent evolution between Mexico and South America. To dissect the genetic architecture of the divergent gene expression, we developed a novel allele-specific expression analysis pipeline to detect genes with divergent functional cis-regulatory variation between highland and lowland populations. We identified hundreds of genes with divergent cis-regulation between highland and lowland landrace alleles, with 20 in common between regions, further suggesting convergence in the genes underlying highland adaptation. Further analyses suggest multiple mechanisms contribute to this convergence in gene regulation. Although the vast majority of evolutionary changes associated with highland adaptation were region specific, our findings highlight an important role for convergence at the gene expression and gene regulation levels as well.

Multi-Trait Genomic Prediction Improves Accuracy of Selection among Doubled Haploid Lines in Maize.

Recent advances in maize doubled haploid (DH) technology have enabled the development of large numbers of DH lines quickly and efficiently. However, testing all possible hybrid crosses among DH lines is a challenge. Phenotyping haploid progenitors created during the DH process could accelerate the selection of DH lines. Based on phenotypic and genotypic data of a DH population and its corresponding haploids, we compared phenotypes and estimated genetic correlations between the two populations, compared genomic prediction accuracy of multi-trait models against conventional univariate models within the DH population, and evaluated whether incorporating phenotypic data from haploid lines into a multi-trait model could better predict performance of DH lines. We found significant phenotypic differences between DH and haploid lines for nearly all traits; however, their genetic correlations between populations were moderate to strong. Furthermore, a multi-trait model taking into account genetic correlations between traits in the single-environment trial or genetic covariances in multi-environment trials can significantly increase genomic prediction accuracy. However, integrating information of haploid lines did not further improve our prediction. Our findings highlight the superiority of multi-trait models in predicting performance of DH lines in maize breeding, but do not support the routine phenotyping and selection on haploid progenitors of DH lines.

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations.

KEY MESSAGE: Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Heritable temporal gene expression patterns correlate with metabolomic seed content in developing hexaploid oat seed.

Oat ranks sixth in world cereal production and has a higher content of health-promoting compounds compared with other cereals. However, there is neither a robust oat reference genome nor transcriptome. Using deeply sequenced full-length mRNA libraries of oat cultivar Ogle-C, a de novo high-quality and comprehensive oat seed transcriptome was assembled. With this reference transcriptome and QuantSeq 3' mRNA sequencing, gene expression was quantified during seed development from 22 diverse lines across six time points. Transcript expression showed higher correlations between adjacent time points. Based on differentially expressed genes, we identified 22 major temporal co-expression (TCoE) patterns of gene expression and revealed enriched gene ontology biological processes. Within each TCoE set, highly correlated transcripts, putatively commonly affected by genetic background, were clustered and termed genetic co-expression (GCoE) sets. Seventeen of the 22 TCoE sets had GCoE sets with median heritabilities higher than 0.50, and these heritability estimates were much higher than that estimated from permutation analysis, with no divergence observed in cluster sizes between permutation and non-permutation analyses. Linear regression between 634 metabolites from mature seeds and the PC1 score of each of the GCoE sets showed significantly lower p-values than permutation analysis. Temporal expression patterns of oat avenanthramides and lipid biosynthetic genes were concordant with previous studies of avenanthramide biosynthetic enzyme activity and lipid accumulation. This study expands our understanding of physiological processes that occur during oat seed maturation and provides plant breeders the means to change oat seed composition through targeted manipulation of key pathways.

Intrinsic Properties of Macroscopically Tuned Gallium Nitride Single-Crystalline Facets for Electrocatalytic Hydrogen Evolution.

The anisotropy of crystalline materials results in different physical and chemical properties on different facets, which warrants an in-depth investigation. Macroscopically facet-tuned, high-purity gallium nitride (GaN) single crystals were synthesised and machined, and the electrocatalytic hydrogen evolution reaction (HER) was used as the model reaction to show the differences among the facets. DFT calculations revealed that the Ga and N sites of GaN (100) had a considerably smaller ΔGH* value than those of the metal Ga site of GaN (001) or N site of GaN (00-1), thereby indicating that GaN (100) should be more catalytically active for the HER on account of its nonpolar facet. Subsequent experiments testified that the electrocatalytic performance of GaN (100) was considerably more efficient than that of other facets for both acidic and alkaline HERs. Moreover, the GaN crystal with a preferentially (100) active facet had an excellently durable alkaline electrocatalytic HER for more than 10 days. This work provides fundamental insights into the exploration of the intrinsic properties of materials and designing advanced materials for physicochemical applications.

Ploidy effect and genetic architecture exploration of stalk traits using DH and its corresponding haploid populations in maize.

BACKGROUND: Doubled haploid (DH) lines produced via in vivo haploid induction have become indispensable in maize research and practical breeding, so it is important to understand traits characteristics in DH and its corresponding haploids which derived from each DH lines. In this study, a DH population derived from Zheng58 × Chang7-2 and a haploid population, were developed, genotyped and evaluated to investigate genetic architecture of eight stalk traits, especially rind penetrometer resistance (RPR) and in vitro dry matter digestion (IVDMD), which affecting maize stalk lodging-resistance and feeding values, respectively. RESULTS: Phenotypic correlation coefficients ranged from 0.38 to 0.69 between the two populations for eight stalk traits. Heritability values of all stalk traits ranged from 0.49 to 0.81 in the DH population, and 0.58 to 0.89 in the haploid population. Quantitative trait loci (QTL) mapping study showed that a total of 47 QTL for all traits accounting for genetic variations ranging from 1.6 to 36.5% were detected in two populations. One or more QTL sharing common region for each trait were detected between two different ploidy populations. Potential candidate genes predicated from the four QTL support intervals for RPR and IVDMD were indirectly or directly involved with cellulose and lignin biosynthesis, which participated in cell wall formation. The increased expression levels of lignin and cellulose synthesis key genes in the haploid situation illustrated that dosage compensation may account for genome dosage effect in our study. CONCLUSIONS: The current investigation extended understanding about the genetic basis of stalk traits and correlations between DH and its haploid populations, which showed consistence and difference between them in phenotype, QTL characters, and gene expression. The higher heritabilities and partly higher QTL detection power were presented in haploid population than in DH population. All of which described above could lay a preliminary foundation for genetic architecture study with haploid population and may benefit selection in haploid-stage to reduce cost in DH breeding.

Markov Task Network: A Framework for Service Composition under Uncertainty in Cyber-Physical Systems.

In novel collaborative systems, cooperative entities collaborate services to achieve local and global objectives. With the growing pervasiveness of cyber-physical systems, however, such collaboration is hampered by differences in the operations of the cyber and physical objects, and the need for the dynamic formation of collaborative functionality given high-level system goals has become practical. In this paper, we propose a cross-layer automation and management model for cyber-physical systems. This models the dynamic formation of collaborative services pursuing laid-down system goals as an ontology-oriented hierarchical task network. Ontological intelligence provides the semantic technology of this model, and through semantic reasoning, primitive tasks can be dynamically composed from high-level system goals. In dealing with uncertainty, we further propose a novel bridge between hierarchical task networks and Markov logic networks, called the Markov task network. This leverages the efficient inference algorithms of Markov logic networks to reduce both computational and inferential loads in task decomposition. From the results of our experiments, high-precision service composition under uncertainty can be achieved using this approach.

Semantic Agent-Based Service Middleware and Simulation for Smart Cities.

With the development of Machine-to-Machine (M2M) technology, a variety of embedded and mobile devices is integrated to interact via the platform of the Internet of Things, especially in the domain of smart cities. One of the primary challenges is that selecting the appropriate services or service combination for upper layer applications is hard, which is due to the absence of a unified semantical service description pattern, as well as the service selection mechanism. In this paper, we define a semantic service representation model from four key properties: Capability (C), Deployment (D), Resource (R) and IOData (IO). Based on this model, an agent-based middleware is built to support semantic service enablement. In this middleware, we present an efficient semantic service discovery and matching approach for a service combination process, which calculates the semantic similarity between services, and a heuristic algorithm to search the service candidates for a specific service request. Based on this design, we propose a simulation of virtual urban fire fighting, and the experimental results manifest the feasibility and efficiency of our design.

A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks.

In large-scale distributed sensor networks, sensed data is required to be relayed around the network so that one or few sensors can gather adequate relative data to produce high quality information for decision-making. In regards to very high energy-constraint sensor nodes, data transmission should be extremely economical. However, traditional data delivery protocols are potentially inefficient relaying unpredictable sensor readings for data fusion in large distributed networks for either overwhelming query transmissions or unnecessary data coverage. By building sensors' local model from their previously transmitted data in three matrixes, we have developed a novel energy-saving data relay algorithm, which allows sensors to proactively make broadcast decisions by using a neat matrix computation to provide balance between transmission and energy-saving. In addition, we designed a heuristic maintenance algorithm to efficiently update these three matrices. This can easily be deployed to large-scale mobile networks in which decisions of sensors are based on their local matrix models no matter how large the network is, and the local models of these sensors are updated constantly. Compared with some traditional approaches based on our simulations, the efficiency of this approach is manifested in uncertain environment. The results show that our approach is scalable and can effectively balance aggregating data with minimizing energy consumption.

Fine mapping of qhir8 affecting in vivo haploid induction in maize.

KEY MESSAGE: The QTL qhir8 affecting in vivo haploid induction in maize was mapped to a 789 kb region, embryo abortion rate and segregation ratios were analyzed, linkage markers for MAS were developed. The doubled-haploid (DH) technology has become an important tool for line development in modern maize breeding. However, the genetic basis of haploid induction remains elusive. In previous QTL mapping research, qhir8 besides qhir1 significantly affected haploid induction rate (HIR). Our objective was to fine map qhir8 and assess its effect on HIR, segregation distortion (SD) and embryo abortion (EmA). A total of 3989 F2 plants from the cross of inducers CAUHOI and UH400 were screened for recombinants in the qhir8 region. F2 plants and F3 plants from selfing progenies of 34 recombinant F2 plants were evaluated for HIR, SD and EmA. In parallel, we developed 31 new markers providing good coverage of the qhir8 region. We confirmed that qhir8 has an increasing effect on HIR and EmA, but not on SD. Moreover, we successfully narrowed down the qhir8 locus to a 789 kb region flanked by markers 4292232 and umc1867.

MegaLMM improves genomic predictions in new environments using environmental covariates.

Multi-environment trials (METs) are crucial for identifying varieties that perform well across a target population of environments (TPE). However, METs are typically too small to sufficiently represent all relevant environment-types, and face challenges from changing environment-types due to climate change. Statistical methods that enable prediction of variety performance for new environments beyond the METs are needed. We recently developed MegaLMM, a statistical model that can leverage hundreds of trials to significantly improve genetic value prediction accuracy within METs. Here, we extend MegaLMM to enable genomic prediction in new environments by learning regressions of latent factor loadings on Environmental Covariates (ECs) across trials. We evaluated the extended MegaLMM using the maize Genome-To-Fields dataset, consisting of 4402 varieties cultivated in 195 trials with 87.1\% of phenotypic values missing, and demonstrated its high accuracy in genomic prediction under various breeding scenarios. Furthermore, we showcased MegaLMM's superiority over univariate GBLUP in predicting trait performance of experimental genotypes in new environments. Finally, we explored the use of higher-dimensional quantitative ECs and discussed when and how detailed environmental data can be leveraged for genomic prediction from METs. We propose that MegaLMM can be applied to plant breeding of diverse crops and different fields of genetics where large-scale linear mixed models are utilized.

Enhancing Electrocatalytic Kinetics via Synergy of Co Nanoparticles and Co/Ni-N4-C- and N-Doped Porous Carbon.

Transition-metal species embedded in carbon have sparked intense interest in the fields of oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). However, improvement of the electrocatalytic kinetics remains a challenge caused by the synergistic assembly. Here, we propose a biochemical strategy to fabricate the Co nanoparticles (NPs) and Co/Ni-N4-C co-embedded N-doped porous carbon (CoNPs&Co/Ni-N4-C@NC) catalysts via constructing the zeolitic imidazolate framework (ZIF)@yeast precursor. The rich amino groups provide the possibility for the anchorage of Co2+/Ni2+ ions as well as the construction of Co/Ni-ZIF@yeast through the yeast cell biomineralization effect. The functional design induces the formation of CoNPs and Co/Ni-N4-C sites in N-doped carbon as well as regulates the porosity for exposing such sites. Synergy of CoNPs, Co/Ni-N4-C, and porous N-doped carbon delivered excellent electrocatalytic kinetics (the ORR Tafel slope of 76.3 mV dec-1 and the OER Tafel slope of 80.4 mV dec-1) and a high voltage of 1.15 V at 10 mA cm-2 for the discharge process in zinc air batteries. It provides an effective strategy to fabricate high-performance catalysts.

Inversions encounter relaxed genetic constraints and balance birth and death of TPS genes in Curcuma.

Evolutionary dynamics of inversion and its impact on biochemical traits are a puzzling question. Here, we show abundance of inversions in three Curcuma species (turmeric, hidden ginger and Siam tulip). Genes within inversions display higher long terminal repeat content and lower expression level compared with genomic background, suggesting inversions in Curcuma experience relaxed genetic constraints. It is corroborated by depletion of selected SNPs and enrichment of deleterious mutations in inversions detected among 56 Siam tulip cultivars. Functional verification of tandem duplicated terpene synthase (TPS) genes reveals that genes within inversions become pseudogenes, while genes outside retain catalytic function. Our findings suggest that inversions act as a counteracting force against tandem duplication in balancing birth and death of TPS genes and modulating terpenoid contents in Curcuma. This study provides an empirical example that inversions are likely not adaptive but affect biochemical traits.

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams' methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

QTL mapping of stalk bending strength in a recombinant inbred line maize population.

Stalk bending strength (SBS) is a reliable indicator for evaluating stalk lodging resistance of maize plants. Based on biomechanical considerations, the maximum load exerted to breaking (F max), the breaking moment (M max) and critical stress (σ max) are three important parameters to characterize SBS. We investigated the genetic architecture of SBS by phenotyping F max, M max and σ max of the fourth internode of maize plants in a population of 216 recombinant inbred lines derived from the cross B73 × Ce03005 evaluated in four environments. Heritability of F max, M max and σ max was 0.81, 0.79 and 0.75, respectively. F max and σ max were positively correlated with several other stalk characters. By using a linkage map with 129 SSR markers, we detected two, three and two quantitative trait loci (QTL) explaining 22.4, 26.1 and 17.2 % of the genotypic variance for F max, M max and σ max, respectively. The QTL for F max, M max and σ max located in adjacent bins 5.02 and 5.03 as well as in bin 10.04 for F max were detected with high frequencies in cross-validation. As our QTL mapping results suggested a complex polygenic inheritance for SBS-related traits, we also evaluated the prediction accuracy of two genomic prediction methods (GBLUP and BayesB). In general, we found that both explained considerably higher proportions of the genetic variance than the values obtained in QTL mapping with cross-validation. Nevertheless, the identified QTL regions could be used as a starting point for fine mapping and gene cloning.

Effect of Flame Treatment on Bonding Performance of GF/EP Pultrusion Sheets Used for VARI Process.

This paper presents an easy and low-cost flame treatment method to improve the bonding performance of GF/EP (Glass Fiber-Reinforced Epoxy) pultrusion plates, which are using widely for large size wind blades. In order to explore the effect of flame treatment on the bonding performance of the precast GF/EP pultruded sheet vs. the infusion plate, the GF/EP pultruded sheets were treated with different flame treatment cycles and were embedded in the fiber fabrics during the vacuum-assisted resin infusion process (VARI). The bonding shear strengths were measured by tensile shear tests. It is found that after 1, 3, 5, and 7 flame treatments, the tensile shear strength between the GF/EP pultrusion plate and infusion plate increased by 8.0%, 13.3%, 22.44%, and -2.1%, respectively. This indicates that the maximum tensile shear strength can be obtained after five times of flame treatment. In addition, DCB and ENF tests were also adopted to characterize the fracture toughness of the bonding interface with the optimal flame treatment. It is found that the optimal treatment gives increments of 21.84% and 78.36% for G I C and G II C, respectively. Finally, the surficial topography of the flame-treated GF/EP pultruded sheets were characterized by optical microscopy, SEM, contact angle test, FTIR, and XPS. The results show that flame treatment plays an impact on the interfacial performance through the combination of physical meshing locking and chemical bonding mechanism. Proper flame treatment would remove the weak boundary layer and mold release agent on the surface of the GF/EP pultruded sheet, etch the bonding surface and improve the oxygen-containing polar groups, such as C-O and O-C=O, to improve the surface roughness and surface tension coefficient of pultruded sheet to enhance the bonding performance. Excessive flame treatment destroys the integrity of epoxy matrix on bonding surface which results into the exposure of the glass fiber, and the carbonization of release agent and resin on the surface loosen the surficial structure, which reduces the bonding properties.

Investigation on the Influence of Fiber Bundle Undulating Architecture on Tensile Behavior of Filament Wound Composite Laminates.

In filament wound composites, fiber bundles cross each other and form an undulating architecture, which may significantly affect the mechanical behavior of composites. In this study, the tensile mechanical behavior of filament wound laminates was studied experimentally and numerically, and the influences of the bundle thickness and winding angle on the mechanical behavior of the filament wound plates were also explored. In the experiments, tensile tests were carried out on filament wound plates and laminated plates. It was found that, compared to laminated plates, filament wound plates had lower stiffness, greater failure displacement, similar failure loads, and more obvious strain concentration areas. In numerical analysis, mesoscale finite element models, which take into account the fiber bundles' undulating morphology, were created. The numerical predictions correlated well with the experimental ones. Further numerical studies have shown that the stiffness reduction coefficient of filament wound plates with a winding angle of ±55° decreased from 0.78 to 0.74 as the bundle thickness increased from 0.4 mm to 0.8 mm. The stiffness reduction coefficients of filament wound plates with wound angles of ±15°, ±25°, and ±45° were 0.86, 0.83, and 0.8, respectively.

Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa).

Selection for more nutritious crop plants is an important goal of plant breeding to improve food quality and contribute to human health outcomes. While there are efforts to integrate genomic prediction to accelerate breeding progress, an ongoing challenge is identifying strategies to improve accuracy when predicting within biparental populations in breeding programs. We tested multiple genomic prediction methods for 12 seed fatty acid content traits in oat (Avena sativa L.), as unsaturated fatty acids are a key nutritional trait in oat. Using two well-characterized oat germplasm panels and other biparental families as training populations, we predicted family mean and individual values within families. Genomic prediction of family mean exceeded a mean accuracy of 0.40 and 0.80 using an unrelated and related germplasm panel, respectively, where the related germplasm panel outperformed prediction based on phenotypic means (0.54). Within family prediction accuracy was more variable: training on the related germplasm had higher accuracy than the unrelated panel (0.14-0.16 and 0.05-0.07, respectively), but variability between families was not easily predicted by parent relatedness, segregation of a locus detected by a genome-wide association study in the panel, or other characteristics. When using other families as training populations, prediction accuracies were comparable to the related germplasm panel (0.11-0.23), and families that had half-sib families in the training set had higher prediction accuracy than those that did not. Overall, this work provides an example of genomic prediction of family means and within biparental families for an important nutritional trait and suggests that using related germplasm panels as training populations can be effective.

Identifying quantitative trait loci and determining closely related stalk traits for rind penetrometer resistance in a high-oil maize population.

Stalk lodging in maize causes annual yield losses between 5 and 20% worldwide. Many studies have indicated that maize stalk strength significantly negatively correlates with lodging observed in the field. Rind penetrometer resistance (RPR) measurements can be used to effectively evaluate maize stalk strength, but little is known about the genetic basis of this parameter. The objective of this study was to explore a genetic model and detect quantitative trait loci (QTL) of RPR and determine relationships between RPR and other stalk traits, especially cell wall chemical components. RPR is quantitative trait in nature, and both additive and non-additive effects may be important to consider for the improvement of RPR. Nine additive-effect QTLs covering nine chromosomes, except chromosome 5, and one pair of epistatic QTLs were detected for RPR. CeSA11 involved in cellulose synthesis and colorless2 involved in lignin synthesis were identified as possible candidate genes for RPR. Internode diameter (InD), fresh weight of internode (FreW), dry weight of internode (DryW), fresh weight and dry weight as well as cell wall components per unit volume significantly positively correlated with RPR. The internode water content (InW) significantly negatively correlated with RPR. Notably, these traits significantly correlated with RPR, and the QTLs of these traits co-localized with those of RPR. The corresponding results obtained from correlation analysis and QTL mapping suggested the presence of pleitropism or linkage between genes and indicated that these different approaches may be used for cross authentication of relationships between different traits.

Modeling Progressive Damage and Failure of Single-Lap Thin-Ply-Laminated Composite-Bolted Joint Using LaRC Failure Criterion.

Thin-ply composite failure modes also significantly differ from conventional ply composite failure modes, with the final failure mechanism switching from irregular progressive failure to direct fracture characterized by a uniform fracture with the reduction of the ply thickness. When open holes and bolt joints are involved, thin-ply-laminated composites exhibit more complex stress states, damage evolution, and failure modes. Compared to the experimental study of thin-ply-laminated composite-bolted joints, there are few reports about numerical analysis. In order to understand the damage evolution and failure mechanism of thin-ply-laminated composites jointed by single-lap bolt, a progressive damage model based on three-dimensional (3D) LaRC failure criterion combined with cohesive element is constructed. Through an energy-based damage evolution method, this model can capture some significant mechanical characteristics in thin-ply-laminated structures, such as the in situ effect, delamination inhibition, and fiber compressive kinking failure. The comparisons between the numerical predictions and experimental observations are made to verify the accuracy of the proposed model. It is found that the predicted stress-displacement curves, failure modes, damage morphologies, etc., are consistent with the experimental results, indicating that the presented progressive damage analysis method displays excellent accuracy. The predicted stress at the onset of delamination is 50% higher than that of the conventional thick materials, which is also consistent with experimental results. Moreover, the numerical model provides evidence that the microstructure of thin-ply-laminated composite performs better in uniformity, which is more conducive to inhibiting the intra-layer damage and the expansion of delamination damage between layers. This study on the damage inhibition mechanism of thin-ply provides a potential analytical tool for evaluating damage tolerance and bearing capabilities in thin-ply-laminated composite-bolted joints.