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Abstract Background Cancer recurrence following surgical resection is a major cause of treatment failure. Finding effective methods to prevent postoperative recurrence and wound infection is an important component of successful surgery. With the development of new nanotechnology, more treatment options have been provided for postoperative adjuvant therapy. This study presents an innovative hydrogel system that stimulates tumoricidal immunity after surgical resection of non-small cell lung cancer (NSCLC) and prevents cancer relapse. Results The hydrogel system is based on the excellent photothermal conversion performance of single-atom platinum (CN-Pt) along with the delivery and release of the chemotherapy drug, gemcitabine (GEM). The system is coated onto the wound surface after tumor removal with subsequent near-infrared (NIR) photothermal therapy, which efficiently induces necroptosis of residual cancer cells, amplifies the levels of damage-associated molecular patterns (DAMPs), and increases the number of M1 macrophages. The significantly higher levels of phagocytic macrophages enhance tumor immunogenicity and sensitize cancer cells to CD8 + T-cell immunity to control postoperative recurrence, which has been verified using an animal model of postoperative lung cancer recurrence. The CN-Pt-GEM-hydrogel with NIR can also inhibit postoperative wound infection. Conclusions These findings introduce an alternative strategy for supplementing antitumor immunity in patients undergoing resection of NSCLC tumors. The CN-Pt-GEM-hydrogel with the NIR system also exhibits good biosafety and may be adaptable for clinical application in relation to tumor resection surgery, wound tissue filling, infection prevention, and recurrence prevention.

Abstract Background Alfalfa (Medicago sativa L.) is the most cultivated forage legume around the world. Under a variety of growing conditions, forage yield in alfalfa is stymied by biotic and abiotic stresses including heat, salt, drought, and disease. Given the sessile nature of plants, they use strategies including, but not limited to, differential gene expression to respond to environmental cues. Transcription factors control the expression of genes that contribute to or enable tolerance and survival during periods of stress. Basic-leucine zipper (bZIP) transcription factors have been demonstrated to play a critical role in regulating plant growth and development as well as mediate the responses to abiotic stress in several species, including Arabidopsis thaliana, Oryza sativa, Lotus japonicus and Medicago truncatula. However, there is little information about bZIP transcription factors in cultivated alfalfa. Result In the present study, 237 bZIP genes were identified in alfalfa from publicly available sequencing data. Multiple sequence alignments showed the presence of intact bZIP motifs in the identified sequences. Based on previous phylogenetic analyses in A. thaliana, alfalfa bZIPs were similarly divided and fell into 10 groups. The physico-chemical properties, motif analysis and phylogenetic study of the alfalfa bZIPs revealed high specificity within groups. The differential expression of alfalfa bZIPs in a suite of tissues indicates that bZIP genes are specifically expressed at different developmental stages in alfalfa. Similarly, expression analysis in response to ABA, cold, drought and salt stresses, indicates that a subset of bZIP genes are also differentially expressed and likely play a role in abiotic stress signaling and/or tolerance. RT-qPCR analysis on selected genes further verified these differential expression patterns. Conclusions Taken together, this work provides a framework for the future study of bZIPs in alfalfa and presents candidate bZIPs involved in stress-response signaling.

Abstract Modern agriculture is a complex system that demands real‐time and large‐scale quantification of trait values for evidence‐based decisions. However, high‐profile traits determining market values often lack high‐throughput phenotyping technologies to achieve this objective; therefore, risks of undermining crop values through arbitrary decisions are high. Because environmental conditions are major contributors to performance fluctuation, with the contemporary informatics infrastructures, we proposed enviromic prediction as a potential strategy to assess traits for informed decisions. We demonstrated this concept with wheat falling number (FN), a critical end‐use quality trait that significantly impacts wheat market values but is measured using a low‐throughput technology. Using 8 years of FN records from elite variety testing trials, we developed a predictive model capturing the general trend of FN based on biologically meaningful environmental conditions. An explicit environmental index that was highly correlated (r = 0.646) with the FN trend observed from variety testing trials was identified. An independent validation experiment verified the biological relevance of this index. An enviromic prediction model based on this index achieved accurate and on‐target predictions for the FN trend in new growing seasons. Two applications designed for production fields illustrated how such enviromic prediction models could assist informed decision along the food supply chain. We envision that enviromic prediction would have a vital role in sustaining food security amidst rapidly changing climate. As conducting variety testing trials is a standard component in modern agricultural industry, the strategy of leveraging historical trial data is widely applicable for other high‐profile traits in various crops.

The thermal deformation behavior of spray formed Al–Zn–Mg–Cu alloy was investigated by the isothermal hot compression tests at the strain rates of 0.001–10 s−1 with the temperature range of 350–450 °C. An Arrhenius constitutive equation was established by the corrected strain-stress curves, and then the processing map was constructed based on the 3D instability map and 3D power dissipation map. The influence of strain rate and temperature on both flow stress and dynamic softening has been unveiled. The results show that dynamic recovery (DRV) is the primary softening mechanism and dynamic recrystallization (DRX) is substantively retarded due to the fine dispersed particles uniformly distributed in the spray formed alloy. Continuous dynamic recrystallization (CDRX) is the primary DRX mechanism at a low temperature or high strain rate. DRX grain was observed at the grain boundaries, especially at the triple junctions. The appropriate thermal deformation condition is recommended at the temperature range from 430 °C to 450 °C and strain rates from 0.003 s−1 to 0.1 s−1.

The introduction of quinoa into new growing regions and environments is of interest to farmers, consumers, and stakeholders around the world. Many plant breeding programs have already started to adapt quinoa to the environmental and agronomic conditions of their local fields. Formal quinoa breeding efforts in Washington State started in 2010, led by Professor Kevin Murphy out of Washington State University. Preharvest sprouting appeared as the primary obstacle to increased production in the coastal regions of the Pacific Northwest. Preharvest sprouting (PHS) is the undesirable sprouting of seeds that occurs before harvest, is triggered by rain or humid conditions, and is responsible for yield losses and lower nutrition in cereal grains. PHS has been extensively studied in wheat, barley, and rice, but there are limited reports for quinoa, partly because it has only recently emerged as a problem. This study aimed to better understand PHS in quinoa by adapting a PHS screening method commonly used in cereals. This involved carrying out panicle-wetting tests and developing a scoring scale specific for panicles to quantify sprouting. Assessment of the trait was performed in a diversity panel (N = 336), and the resulting phenotypes were used to create PHS tolerance rankings and undertake a GWAS analysis (n = 279). Our findings indicate that PHS occurred at varying degrees across a subset of the quinoa germplasm tested and that it is possible to access PHS tolerance from natural sources. Ultimately, these genotypes can be used as parental lines in future breeding programs aiming to incorporate tolerance to PHS.

Abstract Genetic improvement of grain quality is more challenging in hybrid rice than in inbred rice due to additional nonadditive effects such as dominance. Here, we describe a pipeline developed for joint analysis of phenotypes, effects, and generations (JPEG). As a demonstration, we analyze 12 grain quality traits of 113 inbred lines (male parents), five tester lines (female parents), and 565 (113×5) of their hybrids. We sequence the parents for single nucleotide polymorphisms calling and infer the genotypes of the hybrids. Genome-wide association studies with JPEG identify 128 loci associated with at least one of the 12 traits, including 44, 97, and 13 loci with additive effects, dominant effects, and both additive and dominant effects, respectively. These loci together explain more than 30% of the genetic variation in hybrid performance for each of the traits. The JEPG statistical pipeline can help to identify superior crosses for breeding rice hybrids with improved grain quality.

Background: Root system architecture (RSA) is of growing interest in implementing plant improvements with belowground root traits. Modern computing technology applied to images offers new pathways forward to plant trait improvements and selection through RSA analysis (using images to discern/classify root types and traits). However, a major stumbling block to image-based RSA phenotyping is image label noise, which reduces the accuracies of models that take images as direct inputs. To address the label noise problem, this study utilized an artificial intelligence model capable of classifying the RSA of alfalfa (Medicago sativa L.) directly from images and coupled it with downstream label improvement methods. Images were compared with different model outputs with manual root classifications, and confident machine learning (CL) and reactive machine learning (RL) methods were tested to minimize the effects of subjective labeling to improve labeling and prediction accuracies. Results: The CL algorithm modestly improved the Random Forest model’s overall prediction accuracy of the Minnesota dataset (1%) while larger gains in accuracy were observed with the ResNet-18 model results. The ResNet-18 cross-population prediction accuracy was improved (~8% to 13%) with CL compared to the original/preprocessed datasets. Training and testing data combinations with the highest accuracies (86%) resulted from the CL- and/or RL-corrected datasets for predicting taproot RSAs. Similarly, the highest accuracies achieved for the intermediate RSA class resulted from corrected data combinations. The highest overall accuracy (~75%) using the ResNet-18 model involved CL on a pooled dataset containing images from both sample locations. Conclusions: ResNet-18 DNN prediction accuracies of alfalfa RSA image labels are increased when CL and RL are employed. By increasing the dataset to reduce overfitting while concurrently finding and correcting image label errors, it is demonstrated here that accuracy increases by as much as ~11% to 13% can be achieved with semi-automated, computer-assisted preprocessing and data cleaning (CL/RL).

Roots are essential for acquiring water and nutrients to sustain and support plant growth and anchorage. However, they have been studied less than the aboveground traits in phenotyping and plant breeding until recent decades. In modern times, root properties such as morphology and root system architecture (RSA) have been recognized as increasingly important traits for creating more and higher quality food in the “Second Green Revolution”. To address the paucity in RSA and other root research, new technologies are being investigated to fill the increasing demand to improve plants via root traits and overcome currently stagnated genetic progress in stable yields. Artificial intelligence (AI) is now a cutting-edge technology proving to be highly successful in many applications, such as crop science and genetic research to improve crop traits. A burgeoning field in crop science is the application of AI to high-resolution imagery in analyses that aim to answer questions related to crops and to better and more speedily breed desired plant traits such as RSA into new cultivars. This review is a synopsis concerning the origins, applications, challenges, and future directions of RSA research regarding image analyses using AI.

Abstract The Hagberg–Perten falling number (HFN) method is the international standard used to evaluate the damage to wheat (Triticum aestivum) grain quality due to preharvest sprouting (PHS) and late maturity alpha‐amylase (LMA). However, the HFN test requires specialized laboratory facilities and is time consuming. Spectrometers were known as a potential tool for quick HFN assessment, but none of the studies have validated the assessment results across different datasets. In this study, an independent validation was conducted using independent samples and spectral instruments. The calibration set had 462 grain samples of 92 varieties grown at 24 locations in 2019 and examined using a near‐infrared spectrometer. In the validation set, 19 varieties collected from 10 locations in 2 years that experienced either PHS or LMA were scanned with a hyperspectral camera. The association between spectra and HFN was modeled by partial least square regression. As a result, the independent validation correlation accuracy was r = 0.72 and a mean absolute error of 56 s. Furthermore, this study showed a cost‐effective alternative using only 10 spectral bands to predict HFN, and it achieved better performance than the full spectrum of the hyperspectral system. In conclusion, this is the first study that showed the potential that wheat HFN could be predicted on an independent dataset measured by a different instrument. The result suggested that spectrometers can potentially serve as a faster alternative for plant breeders to develop varieties resistant to PHS and LMA, and for growers to screen damaged grains in transportation processes.

Abstract Although immunotherapy has improved the clinical treatment of lung adenocarcinoma (LUAD), many tumors have poor responses to immunotherapy. In this study, we confirmed that high expression of Cyclin‐Dependent Kinase 7 (CDK7) promoted an immunosuppressive macrophage phenotype and macrophage infiltration in LUAD. Thus, we have developed an internalizing‐RGD (iRGD)‐conjugated gold nanoparticle (AuNP) system which carries siCDK7 to activate the antitumor immune response. The iRGD‐conjugated AuNP/siCDK7 system exhibited good tumor targeting performance and photothermal effects. The AuNP/siCDK7 system with excellent biosafety exerted a significant photothermal antitumor effect by inducing tumor cell necroptosis. Furthermore, the AuNP/siCDK7 system ameliorated the immunosuppressive microenvironment and enhanced the efficacy of anti‐PD‐1 treatment by increasing CD8+ T cell infiltration and decreasing M2 macrophage infiltration. Hence, this iRGD‐conjugated AuNP/siCDK7 system is a potential treatment strategy for lung adenocarcinoma, which exerts its effects by triggering tumor cell necroptosis and immunotherapeutic responses.

Alfalfa (Medicago sativa L.) is the most important legume forage crop worldwide with high nutritional value and yield. For a long time, the breeding of alfalfa was hampered by lacking reliable information on the autotetraploid genome and molecular markers linked to important agronomic traits. We herein reported the de novo assembly of the allele-aware chromosome-level genome of Zhongmu-4, a cultivar widely cultivated in China, and a comprehensive database of genomic variations based on resequencing of 220 germplasms. Approximate 2.74 Gb contigs (N50 of 2.06 Mb), accounting for 88.39% of the estimated genome, were assembled, and 2.56 Gb contigs were anchored to 32 pseudo-chromosomes. A total of 34,922 allelic genes were identified from the allele-aware genome. We observed the expansion of gene families, especially those related to the nitrogen metabolism, and the increase of repetitive elements including transposable elements, which probably resulted in the increase of Zhongmu-4 genome compared with Medicago truncatula. Population structure analysis revealed that the accessions from Asia and South America had relatively lower genetic diversity than those from Europe, suggesting that geography may influence alfalfa genetic divergence during local adaption. Genome-wide association studies identified 101 single nucleotide polymorphisms (SNPs) associated with 27 agronomic traits. Two candidate genes were predicted to be correlated with fall dormancy and salt response. We believe that the allele-aware chromosome-level genome sequence of Zhongmu-4 combined with the resequencing data of the diverse alfalfa germplasms will facilitate genetic research and genomics-assisted breeding in variety improvement of alfalfa.

One application of DNA-informed breeding, which has potential to increase the effectiveness of traditional breeding methods, is the use of DNA-based diagnostic tests to estimate genetic potential of breeding individuals. In sweet cherry (Prunus avium L.), cracked or soft fruit are major industry challenges. Recent research detected two quantitative trait loci (QTLs) for fruit cracking and firmness differing in trait levels associated with QTL haplotypic variation. Also, a DNA test for cracking (Pav-G5Crack-SSR), using two simple sequence repeat (SSR) markers, was previously developed but not yet validated on breeding germplasm. In addition to SSR markers, single nucleotide polymorphism (SNP) markers can be used for developing locus-specific DNA tests and run as simple assays such as high-resolution melting (HRM). The objective of this research was to develop and evaluate the predictiveness of DNA tests for fruit cracking and firmness in sweet cherry. Unselected seedlings from pedigree-connected families were screened with the Pav-G5Crack-SSR DNA test. DNA tests were also created from four SNP markers with HRM assays, using two years of cracking and firmness data for evaluation. Pav-G5Crack-SSR explained 12–15% of the cracking phenotypic variance, while Pav-G1Crack-SNP and Pav-G5Crack-SNP (which targeted the same QTL as Pav-G5Crack-SSR) together explained 16%–30% of the cracking phenotypic variance. Pav-G1Firm-SNP and Pav-G3Firm-SNP together explained 22%–28% of the firmness phenotypic variance. All three DNA tests can be implemented in breeding programs to enhance effectiveness in breeding for decreased cracking incidence and increased fruit firmness in sweet cherry.

Abstract Alfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

Breeding for decreased fruit cracking incidence and increased fruit firmness in sweet cherry creates an attractive alternative to variable results from cultural management practices. DNA-informed breeding increases its efficiency, yet upstream research is needed to identify the genomic regions associated with the trait variation of a breeding-relevant magnitude, as well as to identify the parental sources of favorable alleles. The objectives of this research were to identify the quantitative trait loci (QTLs) associated with fruit cracking incidence and firmness, estimate the effects of single nucleotide polymorphism (SNP) haplotypes at the detected QTLs, and identify the ancestral source(s) of functional haplotypes. Fruit cracking incidence and firmness were evaluated for multiple years on 259 unselected seedlings representing 22 important breeding parents. Phenotypic data, in conjunction with genome-wide genotypic data from the RosBREED cherry 6K SNP array, were used in the QTL analysis performed via Pedigree-Based Analysis using the FlexQTL™ software, supplemented by a Genome-Wide Association Study using the BLINK software. Haplotype analysis was conducted on the QTLs to identify the functional SNP haplotypes and estimate their phenotypic effects, and the haplotypes were tracked through the pedigree. Four QTLs (two per trait) were consistent across the years and/or both analysis methods and validated the previously reported QTLs. qCrack-LG1.1m (the label given to a consistent QTL for cracking incidence on chromosome 1) explained 2–15.1% of the phenotypic variance, while qCrack-LG5.1m, qFirm-LG1.2m, and qFirm-LG3.2m explained 7.6–13.8, 8.8–21.8, and 1.7–10.1% of the phenotypic variance, respectively. At each QTL, at least two SNP haplotypes had significant effects and were considered putative functional SNP haplotypes. Putative low-cracking SNP haplotypes were tracked to an unnamed parent of ‘Emperor Francis’ and ‘Schmidt’ and unnamed parents of ‘Napoleon’ and ‘Hedelfingen,’ among others, and putative high-firmness haplotypes were tracked to an unnamed parent of ‘Emperor Francis’ and ‘Schmidt,’ an unnamed grandparent of ‘Black Republican,’ ‘Rube,’ and an unknown parent of ‘Napoleon.’ These four stable QTLs can now be targeted for DNA test development, with the goal of translating information discovered here into usable tools to aid in breeding decisions.

Husk has multiple functions such as protecting ears from diseases, infection, and dehydration during development. Additionally, husks comprised of fewer, shorter, thinner, and narrower layers allow faster moisture evaporation of kernels prior to harvest. Intensive studies have been conducted to identify appropriate husk architecture by understanding the genetic basis of related traits, including husk length, husk layer number, husk thickness, and husk width. However, marker-assisted selection is inefficient because the identified quantitative trait loci and associated genetic loci could only explain a small proportion of total phenotypic variation. Genomic selection (GS) has been used successfully on many species including maize on other traits. Thus, the potential of using GS for husk traits to directly identify superior inbred lines, without knowing the specific underlying genetic loci, is well worth exploring. In this study, we compared four GS models on a maize association population with 498 inbred lines belonging to four subpopulations, including 27 lines in stiff stalk, 67 lines in non-stiff stalk, 193 lines in tropical-subtropical, and 211 lines in mixture subpopulations. Genomic Best Linear Unbiased Prediction with principal components as cofactor, performed the best and was selected to examine the impact of interaction between sampling proportions and subpopulations. We found that predictions on inbred lines in a subpopulation were benefited from excluding individuals from other subpopulations for training if the training population within the subpopulation was large enough. Husk thickness exhibited the highest prediction accuracy among all husk traits. These results gave strategic insight to improve husk architecture.

The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. In this review, we discuss the lesson learned from GS and phenomics in six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, and groundnut, and their implementation schemes are discussed after assessing their impact in the breeding programs. Here, the status of the adoption of genomics and phenomics is provided for those crops, with a complete GS overview. GS’s progress until 2020 is discussed in detail, and relevant information and links to the source codes are provided for implementing this technology into plant breeding programs, with most of the examples from wheat breeding programs. Detailed information about various phenotyping tools is provided to strengthen the field of phenomics for a plant breeder in the coming years. Finally, we highlight the benefits of merging genomic selection, phenomics, and machine and deep learning that have resulted in extraordinary results during recent years in wheat, rice, and soybean. Hence, there is a potential for adopting these technologies into crops like the common bean, chickpea, and groundnut. The adoption of phenomics and GS into different breeding programs will accelerate genetic gain that would create an impact on food security, realizing the need to feed an ever-growing population.

Unknown genetic architecture makes it difficult to characterize the genetic basis of traits and associated molecular markers because of the complexity of small effect quantitative trait loci (QTLs), environmental effects, and difficulty in phenotyping. Seedling emergence of wheat (Triticum aestivum L.) from deep planting, has a poorly understood genetic architecture, is a vital factor affecting stand establishment and grain yield, and is historically correlated with coleoptile length. This study aimed to dissect the genetic architecture of seedling emergence while accounting for correlated traits using one multi-trait genome-wide association study (MT-GWAS) model and three single-trait GWAS (ST-GWAS) models. The ST-GWAS models included one single-locus model [mixed-linear model (MLM)] and two multi-locus models [fixed and random model circulating probability unification (FarmCPU) and Bayesian information and linkage-disequilibrium iteratively nested keyway (BLINK)]. We conducted GWAS using two populations. The first population consisted of 473 varieties from a diverse association mapping panel phenotyped from 2015 to 2019. The second population consisted of 279 breeding lines phenotyped in 2015 in Lind, WA, with 40,368 markers. We also compared the inclusion of coleoptile length and markers associated with reduced height as covariates in our ST-GWAS models. ST-GWAS found 107 significant markers across 19 chromosomes, while MT-GWAS found 82 significant markers across 14 chromosomes. The FarmCPU and BLINK models, including covariates, were able to identify many small effect markers while identifying large effect markers on chromosome 5A. By using multi-locus model breeding, programs can uncover the complex nature of traits to help identify candidate genes and the underlying architecture of a trait, such as seedling emergence.

Abstract Background Dual-purpose cattle are more adaptive to environmental challenges than single-purpose dairy or beef cattle. Balance among milk, reproductive, and mastitis resistance traits in breeding programs is therefore more critical for dual-purpose cattle to increase net income and maintain well-being. With dual-purpose Xinjiang Brown cattle adapted to the Xinjiang Region in northwestern China, we conducted genome-wide association studies (GWAS) to dissect the genetic architecture related to milk, reproductive, and mastitis resistance traits. Phenotypic data were collected for 2410 individuals measured during 1995–2017. By adding another 445 ancestors, a total of 2855 related individuals were used to derive estimated breeding values for all individuals, including the 2410 individuals with phenotypes. Among phenotyped individuals, we genotyped 403 cows with the Illumina 150 K Bovine BeadChip. Results GWAS were conducted with the FarmCPU (Fixed and random model circulating probability unification) method. We identified 12 markers significantly associated with six of the 10 traits under the threshold of 5% after a Bonferroni multiple test correction. Seven of these SNPs were in QTL regions previously identified to be associated with related traits. One identified SNP, BovineHD1600006691, was significantly associated with both age at first service and age at first calving. This SNP directly overlapped a QTL previously reported to be associated with calving ease. Within 160 Kb upstream and downstream of each significant SNP identified, we speculated candidate genes based on functionality. Four of the SNPs were located within four candidate genes, including CDH2, which is linked to milk fat percentage, and GABRG2, which is associated with milk protein yield. Conclusions These findings are beneficial not only for breeding through marker-assisted selection, but also for genome editing underlying the related traits to enhance the overall performance of dual-purpose cattle.

Flavonoids give plants their rich colors and play roles in a number of physiological processes. In this study, we identified a novel colorless maize mutant showing reduced pigmentation throughout the whole life cycle by EMS mutagenesis. E183K mutation in maize chalcone synthase C2 (ZmC2) was mapped using MutMap strategy as the causal for colorless, which was further validated by transformation in Arabidopsis. We evaluated transcriptomic and metabolic changes in maize first sheaths caused by the mutation. The downstream biosynthesis was blocked while very few genes changed their expression pattern. ZmC2-E183 site is highly conserved in chalcone synthase among Plantae kingdom and within species’ different varieties. Through prokaryotic expression, transient expression in maize leaf protoplasts and stable expression in Arabidopsis, we observed that E183K and other mutations on E183 could cause almost complete protein aggregation of chalcone synthase. Our findings will benefit the characterization of flavonoid biosynthesis and contribute to the body of knowledge on protein aggregation in plants.

Regeneration of injured peripheral nerves is an extremely complex process. Nogo-A (neurite outgrowth inhibitor-A) inhibits axonal regeneration by interacting with Nogo receptor in the myelin sheath of the central nervous system (CNS). The aim of this study was to investigate the effects of Nogo-A and its receptor on the repair of sciatic nerve injury in rats. Sprague-Dawley rats (n=96) were randomly divided into 4 groups: control group (control), sciatic nerve transection group (model), immediate repair group (immediate repair), and delayed repair group (delayed repair). The rats were euthanized 1 week and 6 weeks after operation. The injured end tissues of the spinal cord and sciatic nerve were obtained. The protein expressions of Nogo-A and Nogo-66 receptor (NgR) were detected by immunohistochemistry. The protein expressions of Nogo-A, NgR, and Ras homolog family member A (RhoA) were detected by western blot. At 1 week after operation, the pathological changes in the immediate repaired group were less, and the protein expressions of Nogo-A, NgR, and RhoA in the spinal cord and sciatic nerve tissues were decreased (P

Abstract Genome‐wide association studies (GWAS) have developed into a powerful and ubiquitous tool for the investigation of complex traits. In large part, this was fueled by advances in genomic technology, enabling us to examine genome‐wide genetic variants across diverse genetic materials. The development of the mixed model framework for GWAS dramatically reduced the number of false positives compared with naïve methods. Building on this foundation, many methods have since been developed to increase computational speed or improve statistical power in GWAS. These methods have allowed the detection of genomic variants associated with either traditional agronomic phenotypes or biochemical and molecular phenotypes. In turn, these associations enable applications in gene cloning and in accelerated crop breeding through marker assisted selection or genetic engineering. Current topics of investigation include rare‐variant analysis, synthetic associations, optimizing the choice of GWAS model, and utilizing GWAS results to advance knowledge of biological processes. Ongoing research in these areas will facilitate further advances in GWAS methods and their applications.

Genomic selection (GS) is transforming the field of plant breeding and implementing models that improve prediction accuracy for complex traits is needed. Analytical methods for complex datasets traditionally used in other disciplines represent an opportunity for improving prediction accuracy in GS. Deep learning (DL) is a branch of machine learning (ML) which focuses on densely connected networks using artificial neural networks for training the models. The objective of this research was to evaluate the potential of DL models in the Washington State University spring wheat breeding program. We compared the performance of two DL algorithms, namely multilayer perceptron (MLP) and convolutional neural network (CNN), with ridge regression best linear unbiased predictor (rrBLUP), a commonly used GS model. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat nested association mapping (NAM) population planted from 2014–2016 growing seasons. We predicted five different quantitative traits with varying genetic architecture using cross-validations (CVs), independent validations, and different sets of SNP markers. Hyperparameters were optimized for DL models by lowering the root mean square in the training set, avoiding model overfitting using dropout and regularization. DL models gave 0 to 5% higher prediction accuracy than rrBLUP model under both cross and independent validations for all five traits used in this study. Furthermore, MLP produces 5% higher prediction accuracy than CNN for grain yield and grain protein content. Altogether, DL approaches obtained better prediction accuracy for each trait, and should be incorporated into a plant breeder’s toolkit for use in large scale breeding programs.

Abstract Southern leaf blight (SLB) and northern leaf blight (NLB) are the two major foliar diseases limiting maize production worldwide. Upon previous study with the nested association mapping (NAM) population, which consist of 5,000 recombinant inbred lines from 25 parents crossed with B73, we expanded the phenotyping environments from the United States (US) to China, and increased the marker densities from 1106 to 7386 SNPs for linkage mapping, and from 1.6 to 28.5 million markers for association mapping. We identified 49 SLB and 48 NLB resistance-related unique QTLs in linkage mapping, and multiple loci in association mapping with candidate genes involved in known plant disease-resistance pathways. Furthermore, an independent natural population with 282 diversified inbred lines were sequenced for four candidate genes selected based on their biological functions. Three of them demonstrated significant associations with disease resistance. These findings provided valuable resources for further implementations to develop varieties with superior resistance for NLB and SLB.

Tissue extracellular matrix (ECM) is a structurally and compositionally unique microenvironment within which native cells can perform their natural biological activities. Cells grown on artificial substrata differ biologically and phenotypically from those grown within their native tissue microenvironment. Studies examining human tissue ECM structures and the biology of human tissue cells in their corresponding tissue ECM are lacking. Such investigations will improve our understanding about human pathophysiological conditions for better clinical care. We report here human normal breast tissue and invasive ductal carcinoma tissue ECM structural features. For the first time, a hydrogel was successfully fabricated using whole protein extracts of human normal breast ECM. Using immunofluorescence staining of type I collagen (Col I) and machine learning of its fibrous patterns in the polymerized human breast ECM hydrogel, we have defined the microstructural characteristics of the hydrogel and compared the microstructures with those of other native ECM hydrogels. Importantly, the ECM hydrogel supported 3D growth and cell-ECM interaction of both normal and cancerous mammary epithelial cells. This work represents further advancement toward full reconstitution of the human breast tissue microenvironment, an accomplishment that will accelerate the use of human pathophysiological tissue-derived matrices for individualized biomedical research and therapeutic development.

Abstract Background Soybean (Glycine max [L.] Merr.) is one of the most important oil and protein crops. Ever-increasing soybean consumption necessitates the improvement of varieties for more efficient production. However, both correlations among different traits and genetic interactions among genes that affect a single trait pose a challenge to soybean breeding. Results To understand the genetic networks underlying phenotypic correlations, we collected 809 soybean accessions worldwide and phenotyped them for two years at three locations for 84 agronomic traits. Genome-wide association studies identified 245 significant genetic loci, among which 95 genetically interacted with other loci. We determined that 14 oil synthesis-related genes are responsible for fatty acid accumulation in soybean and function in line with an additive model. Network analyses demonstrated that 51 traits could be linked through the linkage disequilibrium of 115 associated loci and these links reflect phenotypic correlations. We revealed that 23 loci, including the known Dt1, E2, E1, Ln, Dt2, Fan, and Fap loci, as well as 16 undefined associated loci, have pleiotropic effects on different traits. Conclusions This study provides insights into the genetic correlation among complex traits and will facilitate future soybean functional studies and breeding through molecular design.

Species in the genus Auricularia play important roles for people’s food and nutrition especially Auricularia cornea and A. heimuer. To understand their evolutionary history, genome structure, and population-level genetic variation, we performed a high-quality genome sequencing of Auricularia cornea and the corresponding comparative genomic analysis. The genome size of A. cornea was similar to Auricularia subglabra, but 1.5 times larger than that of A. heimuer. Several factors were responsible for genome size variation including gene numbers, repetitive elements, and gene lengths. Phylogenomic analysis revealed that the estimated divergence time between A. heimuer and other Auricularia is ∼79.1 million years ago (Mya), while the divergence between A. cornea and A. subglabra occurred in ∼54.8 Mya. Population genomic analysis also provided insight into the demographic history of A. cornea and A. heimuer, indicating that their populations fluctuated over time with global climate change during Marine Isotope Stage 5-2. Moreover, despite the highly similar external morphologies of A. cornea and A. heimuer, their genomic properties were remarkably different. The A. cornea genome only shared 14% homologous syntenic blocks with A. heimuer and possessed more genes encoding carbohydrate-active enzymes and secondary metabolite biosynthesis proteins. The cross-taxa transferability rates of simple sequence repeat (SSR) and insertion or deletion (InDel) markers within the genus Auricularia were also lower than that previously observed for species within the same genus. Taken together, these results indicate a high level of genetic differentiation between these two Auricularia species. Consequently, our study provides new insights into the genomic evolution and genetic differentiation of Auricularia species that will facilitate future genetic breeding.

Agaricus bisporus distributed in the Tibetan Plateau of China has high-stress resistance that is valuable for breeding improvements. However, its evolutionary history, specialization, and adaptation to the extreme Tibetan Plateau environment are largely unknown. Here, we performed de novo genome sequencing of a representative Tibetan Plateau wild strain ABM and comparative genomic analysis with the reported European strain H97 and H39. The assembled ABM genome was 30.4 Mb in size, and comprised 8,562 protein-coding genes. The ABM genome shared highly conserved syntenic blocks and a few inversions with H97 and H39. The phylogenetic tree constructed by 1,276 single-copy orthologous genes in nine fungal species showed that the Tibetan Plateau and European A. bisporus diverged ∼5.5 million years ago. Population genomic analysis using genome resequencing of 29 strains revealed that the Tibetan Plateau population underwent significant differentiation from the European and American populations and evolved independently, and the global climate changes critically shaped the demographic history of the Tibetan Plateau population. Moreover, we identified key genes that are related to the cell wall and membrane system, and the development and defense systems regulated A. bisporus adapting to the harsh Tibetan Plateau environment. These findings highlight the value of genomic data in assessing the evolution and adaptation of mushrooms and will enhance future genetic improvements of A. bisporus.

High-yield and high-quality of milk are the primary goals of dairy production. Understanding the genetic architecture underlying these milk-related traits is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we measured five milk production and quality traits in Holstein cattle population from China. These traits included milk yield, fat, and protein. We used the estimated breeding values as dependent variables to conduct the genome-wide association studies (GWAS). Breeding values were estimated through pedigree relationships by using a linear mixed model. Genotyping was carried out on the individuals with phenotypes by using the Illumina BovineSNP150 BeadChip. The association analyses were conducted by using the fixed and random model Circulating Probability Unification (FarmCPU) method. A total of ten single-nucleotide polymorphisms (SNPs) were detected above the genome-wide significant threshold (p < 4.0 × 10−7), including six located in previously reported quantitative traits locus (QTL) regions. We found eight candidate genes within distances of 120 kb upstream or downstream to the associated SNPs. The study not only identified the effect of DGAT1 gene on milk fat and protein, but also discovered novel genetic loci and candidate genes related to milk traits. These novel genetic loci would be an important basis for molecular breeding in dairy cattle.

Aerial imagery has the potential to advance high-throughput phenotyping for agricultural field experiments. This potential is currently limited by the difficulties of identifying pixels of interest (POI) and performing plot segmentation due to the required intensive manual operations. We developed a Python package, GRID (GReenfield Image Decoder), to overcome this limitation. With pixel-wise K-means cluster analysis, users can specify the number of clusters and choose the clusters representing POI. The plot grid patterns are automatically recognized by the POI distribution. The local optima of POI are initialized as the plot centers, which can also be manually modified for deletion, addition, or relocation. The segmentation of POI around the plot centers is initialized by automated, intelligent agents to define plot boundaries. A plot intelligent agent negotiates with neighboring agents based on plot size and POI distributions. The negotiation can be refined by weighting more on either plot size or POI density. All adjustments are operated in a graphical user interface with real-time previews of outcomes so that users can refine segmentation results based on their knowledge of the fields. The final results are saved in text and image files. The text files include plot rows and columns, plot size, and total plot POI. The image files include displays of clusters, POI, and segmented plots. With GRID, users are completely liberated from the labor-intensive task of manually drawing plot lines or polygons. The supervised automation with GRID is expected to enhance the efficiency of agricultural field experiments.

Fusarium Head Blight (FHB) has emerged in spring wheat production in Pacific Northwest during the last decade due to factors including climate changes, crop rotations, and tillage practices. A breeding population with 170 spring wheat lines was established and screened over a 2-year period in multiple locations for FHB incidence (INC), severity (SEV), and deposition of the mycotoxin, deoxynivalenol (DON). A genome-wide association study suggested that the detectable number of genetic loci and effects are limited for marker-assisted selection. In conjunction with the success of breeding on FHB resistance in other programs, genomic selection (GS) was suggested as a better option. To evaluate the prediction accuracy of GS in the current breeding population, we conducted a variety of validations by varying proportions of testing populations and cohorts based on both FHB resistance and market class, including soft white spring (SWS), hard white spring (HWS), and hard red spring (HRS). We found that INC had higher heritability, higher correlation across years and locations, and higher prediction accuracy than SEV and DON. Prediction accuracy varied among the scenarios that restricted the testing population to a certain cohort. For a small set of newly developed or introduced lines (

Soft white wheat is used in domestic and foreign markets for various end products requiring specific quality profiles. Phenotyping for end-use quality traits can be costly, time-consuming and destructive in nature, so it is advantageous to use molecular markers to select experimental lines with superior traits. An association mapping panel of 469 soft white winter wheat cultivars and advanced generation breeding lines was developed from regional breeding programs in the U.S. Pacific Northwest. This panel was genotyped on a wheat-specific 90 K iSelect single nucleotide polymorphism (SNP) chip. A total of 15,229 high quality SNPs were selected and combined with best linear unbiased predictions (BLUPs) from historical phenotypic data of the genotypes in the panel. Genome-wide association mapping was conducted using the Fixed and random model Circulating Probability Unification (FarmCPU). A total of 105 significant marker-trait associations were detected across 19 chromosomes. Potentially new loci for total flour yield, lactic acid solvent retention capacity, flour sodium dodecyl sulfate sedimentation and flour swelling volume were also detected. Better understanding of the genetic factors impacting end-use quality enable breeders to more effectively discard poor quality germplasm and increase frequencies of favorable end-use quality alleles in their breeding populations.

Preharvest sprouting (PHS), the germination of grain on the mother plant under cool and wet conditions, is a recurring problem for wheat farmers worldwide. α-amylase enzyme produced during PHS degrades starch resulting in baked good with poor end-use quality. The Hagberg-Perten Falling Number (FN) test is used to measure this problem in the wheat industry, and determines how much a farmer's wheat is discounted for PHS damage. PHS tolerance is associated with higher grain dormancy. Thus, breeding programs use germination-based assays such as the spike-wetting test to measure PHS susceptibility. Association mapping identified loci associated with PHS tolerance in U.S. Pacific Northwest germplasm based both on FN and on spike-wetting test data. The study was performed using a panel of 469 white winter wheat cultivars and elite breeding lines grown in six Washington state environments, and genotyped for 15,229 polymorphic markers using the 90k SNP Illumina iSelect array. Marker-trait associations were identified using the FarmCPU R package. Principal component analysis was directly and a kinship matrix was indirectly used to account for population structure. Nine loci were associated with FN and 34 loci associated with PHS based on sprouting scores. None of the QFN.wsu loci were detected in multiple environments, whereas six of the 34 QPHS.wsu loci were detected in two of the five environments. There was no overlap between the QTN detected based on FN and PHS, and there was little correlation between the two traits. However, both traits appear to be PHS-related since 19 of the 34 QPHS.wsu loci and four of the nine QFN.wsu loci co-localized with previously published dormancy and PHS QTL. Identification of these loci will lead to a better understanding of the genetic architecture of PHS and will help with the future development of genomic selection models.

Plant cell wall formation is a complex, coordinated and developmentally regulated process. Cellulose is the most dominant constituent of plant cell walls. Because of its paracrystalline structure, cellulose is the main determinant of mechanical strength of plant tissues. As the most abundant polysaccharide on earth, it is also the focus of cellulosic biofuel industry. To reduce culm lodging in wheat and for improved ethanol production, delineation of the variation for stem cellulose content could prove useful. We present results on the analysis of the stem cellulose content of 288 diverse wheat accessions and its genome-wide association study (GWAS). Cellulose concentration ranged from 35 to 52% (w/w). Cellulose content was normally distributed in the accessions around a mean and median of 45% (w/w). Genome-wide marker-trait association study using 21,073 SNPs helped identify nine SNPs that were associated (p < 1E-05) with cellulose content. Four strongly associated (p < 8.17E-05) SNP markers were linked to wheat unigenes, which included β-tubulin, Auxin-induced protein 5NG4, and a putative transmembrane protein of unknown function. These genes may be directly or indirectly involved in the formation of cellulose in wheat culms. GWAS results from this study have the potential for genetic manipulation of cellulose content in bread wheat and other small grain cereals to enhance culm strength and improve biofuel production.

Harnessing diversity from germplasm collections is more feasible today because of the development of lower-cost and higher-throughput genotyping methods. However, the cost of phenotyping is still generally high, so efficient methods of sampling and exploiting useful diversity are needed. Genomic selection (GS) has the potential to enhance the use of desirable genetic variation in germplasm collections through predicting the genomic estimated breeding values (GEBVs) for all traits that have been measured. Here, we evaluated the effects of various scenarios of population genetic properties and marker density on the accuracy of GEBVs in the context of applying GS for wheat ( L.) germplasm use. Empirical data for adult plant resistance to stripe rust ( f. sp. ) collected on 1163 spring wheat accessions and genotypic data based on the wheat 9K single nucleotide polymorphism (SNP) iSelect assay were used for various genomic prediction tests. Unsurprisingly, the results of the cross-validation tests demonstrated that prediction accuracy increased with an increase in training population size and marker density. It was evident that using all the available markers (5619) was unnecessary for capturing the trait variation in the germplasm collection, with no further gain in prediction accuracy beyond 1 SNP per 3.2 cM (∼1850 markers), which is close to the linkage disequilibrium decay rate in this population. Collectively, our results suggest that larger germplasm collections may be efficiently sampled via lower-density genotyping methods, whereas genetic relationships between the training and validation populations remain critical when exploiting GS to select from germplasm collections.

The growth and maturity of cattle body size affect not only feed efficiency, but also productivity and longevity. Dissecting the genetic architecture of body size is critical for cattle breeding to improve both efficiency and productivity. The volume and weight of body size are indicated by several measurements. Among them, Heart Girth (HG) and Hip Height (HH) are the most important traits. They are widely used as predictors of body weight (BW). Few association studies have been conducted for HG and HH in cattle focusing on single growth stage. In this study, we extended the Genome-wide association studies to a full spectrum of four growth stages (6-, 12-, 18-, and 24-months after birth) in Chinese Holstein heifers. The whole genomic single nucleotide polymorphisms (SNPs) were obtained from the Illumina BovineSNP50 v2 BeadChip genotyped on 3,325 individuals. Estimated breeding values (EBVs) were derived for both HG and HH at the four different ages and analyzed separately for GWAS by using the Fixed and random model Circuitous Probability Unification (FarmCPU) method. In total, 27 SNPs were identified to be significantly associated with HG and HH at different growth stages. We found 66 candidate genes located nearby the associated SNPs, including nine genes that were known as highly related to development and skeletal and muscular growth. In addition, biological function analysis was performed by Ingenuity Pathway Analysis and an interaction network related to development was obtained, which contained 16 genes out of the 66 candidates. The set of putative genes provided valuable resources and can help elucidate the genomic architecture and mechanisms underlying growth traits in dairy cattle.

A three-dimensional (3D) finite-element model (FEM), including quasi-static stamping, sequential coupling for electromagnetic forming (EMF) and springback, was established to analyze the springback calibration by electromagnetic force. Results show that the tangential stress at the sheet bending region is reduced, and even the direction of tangential stress at the bending region is changed after EMF. The springback can be significantly reduced with a higher discharge voltage. The simulation results are in good agreement with the experiment results, and the simulation method has a high accuracy in predicting the springback of quasi-static stamping and electromagnetic forming.

To compare with quasi-static uniaxial tensioning, researchers designed an electromagnetic uniaxial tension method using a runway coil. However, the requirements to obtain a uniformly deformed sample and the ways the stress changes on the sample using a runway coil have not been studied in the past. In this study, a three-dimensional (3D) sequential coupling method was developed to analyze the factors affecting on-sheet deformation inhomogeneity under electromagnetic uniaxial tension. Two main process parameters, comprising the die type and the relative position of the coil and sheet, were evaluated. Under the optimal parameters, the experiment and simulation both obtained uniformly deformed samples with different discharge conditions, and the simulation method had a high accuracy in modeling the deformation process. The stress state of the sample is approximately unidirectional tensile stress before 240 μs. After 240 μs, the three main stresses showed significant oscillations.

Originating in a tropical climate, maize has faced great challenges as cultivation has expanded to the majority of the world's temperate zones. In these zones, frost and cold temperatures are major factors that prevent maize from reaching its full yield potential. Among 30 elite maize inbred lines adapted to northern China, we identified two lines of extreme, but opposite, freezing tolerance levels—highly tolerant and highly sensitive. During the seedling stage of these two lines, we used RNA-seq to measure changes in maize whole genome transcriptome before and after freezing treatment. In total, 19,794 genes were expressed, of which 4,550 exhibited differential expression due to either treatment (before or after freezing) or line type (tolerant or sensitive). Of the 4,550 differently expressed genes, 948 exhibited differential expression due to treatment within line or lines under freezing condition. Analysis of gene ontology found that these 948 genes were significantly enriched for binding functions (DNA binding, ATP binding, and metal ion binding), protein kinase activity, and peptidase activity. Based on their enrichment, literature support, and significant levels of differential expression, 30 of these 948 genes were selected for quantitative real-time PCR (qRT-PCR) validation. The validation confirmed our RNA-Seq-based findings, with squared correlation coefficients of 80% and 50% in the tolerance and sensitive lines, respectively. This study provided valuable resources for further studies to enhance understanding of the molecular mechanisms underlying maize early freezing response and enable targeted breeding strategies for developing varieties with superior frost resistance to achieve yield potential.

Most human diseases and agriculturally important traits are complex. Dissecting their genetic architecture requires continued development of innovative and powerful statistical methods. Corresponding advances in computing tools are critical to efficiently use these statistical innovations and to enhance and accelerate biomedical and agricultural research and applications. The genome association and prediction integrated tool (GAPIT) was first released in 2012 and became widely used for genome-wide association studies (GWAS) and genomic prediction. The GAPIT implemented computationally efficient statistical methods, including the compressed mixed linear model (CMLM) and genomic prediction by using genomic best linear unbiased prediction (gBLUP). New state-of-the-art statistical methods have now been implemented in a new, enhanced version of GAPIT. These methods include factored spectrally transformed linear mixed models (FaST-LMM), enriched CMLM (ECMLM), FaST-LMM-Select, and settlement of mixed linear models under progressively exclusive relationship (SUPER). The genomic prediction methods implemented in this new release of the GAPIT include gBLUP based on CMLM, ECMLM, and SUPER. Additionally, the GAPIT was updated to improve its existing output display features and to add new data display and evaluation functions, including new graphing options and capabilities, phenotype simulation, power analysis, and cross-validation. These enhancements make the GAPIT a valuable resource for determining appropriate experimental designs and performing GWAS and genomic prediction. The enhanced R-based GAPIT software package uses state-of-the-art methods to conduct GWAS and genomic prediction. The GAPIT also provides new functions for developing experimental designs and creating publication-ready tabular summaries and graphs to improve the efficiency and application of genomic research.

False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days.

To study the factors that affect the mechanical properties of materials, double-pass hot compression tests were performed under different deformation parameters using a Gleeble-3500 thermo-simulation machine. The static softening behavior of 5754 aluminium alloy during testing was analyzed by the 0.2% offset-stress method. The results show that the static softening fraction was greatly influenced by deformation parameters and rapidly increased with increasing delay time, strain rate and deformation temperature. In addition, a mesoscopic cellular automaton (CA) model was employed to simulate the microstructural evolution of the static recrystallization (SRX) during the double-pass hot compression test of the 5754 aluminium alloy. The results show that the SRX nuclei first formed along the grain boundaries, where the energy was sufficient, and deformation parameters had a significant influence on the SRX of the 5754 aluminium alloy. The recrystallized volume fraction increased with increasing temperature, strain rate, and delay time among deformation stages. The mean recrystallized grain size increased with increasing deformation temperature and delay time. However, the mean grain size was slightly reduced with an increase in the strain rate from 0.1 s−1 to 1 s−1 at constant temperature and delay time.

Wheat (Triticumaestivum L.) is a major cereal crop grown across a wide range of environments, but its productivity around the world is challenged by various biotic and abiotic factors. Wheat landraces from around the world are a source of unexploited genetic diversity that can be essential for modern wheat-breeding programs in search of resistance to abiotic stresses like freezing tolerance. This genetic diversity study of 553 winter wheat landraces based on single-nucleotide polymorphisms (SNPs) revealed separate clusters of landraces related to the latitude of origin. Linkage block analysis revealed genomic regions with specific alleles skewed towards landraces from higher latitudes, suggesting that migration to higher latitudes resulted in the fixing of specific alleles. Electrolyte leakage was used to measure the tolerance of freezing to −14 °C, −16 °C, and −18 °C of 192 landraces. There was a significant negative correlation between latitude and electrolyte leakage, with an R2 value of 0.14, (p < 0.0001), in a regression analysis indicating greater freezing tolerance in landraces from higher latitudes. Genome-wide association studies identified regions in chromosomes 4A and 6A associated with higher latitudes and freezing tolerance, respectively. Landraces with freezing tolerance may be useful in developing new germplasm as novel sources of greater cold hardiness.

In order to predict flow behavior and find the optimum hot working processing parameters for 5754 aluminum alloy, the experimental flow stress data obtained from the isothermal hot compression tests on a Gleeble-3500 thermo-simulation apparatus, with different strain rates (0.1–10 s–1) and temperatures (300–500 °C), were used to construct the constitutive models of the strain-compensation Arrhenius (SA) and back propagation (BP) artificial neural network (ANN). In addition, an optimized BP–ANN model based on the genetic algorithm (GA) was established. Furthermore, the predictability of the three models was evaluated by the statistical indicators, including the correlation coefficient (R) and average absolute relative error (AARE). The results showed that the R of the SA model, BP–ANN model, and ANN–GA model were 0.9918, 0.9929, and 0.9999, respectively, while the AARE of these models was found to be 3.2499–5.6774%, 0.0567–5.4436% and 0.0232–1.0485%, respectively. The prediction error of the SA model was high at 400 °C. It was more accurate to use the BP–ANN model to determine the flow behavior compared to the SA model. However, the BP–ANN model had more instability at 300 °C and a true strain in the range of 0.4–0.6. When compared with the SA model and BP–ANN model, the ANN–GA model had a more efficient and more accurate prediction ability during the whole deformation process. Furthermore, the dynamic softening characteristic was analyzed by the flow curves. All curves showed that 5754 aluminum alloy showed the typical rheological characteristics. The flow stress rose rapidly with increasing strain until it reached a peak. After this, the flow stress remained constant, which demonstrates a steady flow softening phenomenon. Besides, the flow stress and the required variables to reach the steady state deformation increased with increasing strain rate and decreasing temperature.

Complex trait dissection through association mapping provides a powerful complement to traditional linkage analysis. The genetic structure of an association mapping panel can be estimated by genomewide background markers and subsequently accounted for in association analysis. Deciding the number of background markers is a common issue that needs to be addressed in many association mapping studies. We first showed that the adequacy of markers in relationship estimation influences the maximum likelihood of the model explaining phenotypic variation and demonstrated this influence with a series of computer simulations with different trait architectures. Analyses and computer simulations were then conducted using two different data sets: one from a diverse set of maize ( L.) inbred lines with a complex population structure and familial relatedness, and the other from a group of crossbred dogs. Our results showed that the likelihood-based model-fitting approach can be used to quantify the robustness of genetic relationships derived from molecular marker data. We also found that kinship estimation was more sensitive to the number of markers used than population structure estimation in terms of model fitting, and a robust estimate of kinship for association mapping with diverse germplasm requires a certain amount of background markers (e.g., 300–600 biallelic markers for the simulated pedigree materials, >1000 single nucleotide polymorphisms or 100 simple sequence repeats [SSRs] for the diverse maize panel, and about 100 SSRs for the canine panel). Kinship construction with subsets of the whole marker panel and subsequent model testing with multiple phenotypic traits could provide ad hoc information on whether the number of markers is sufficient to quantify genetic relationships among individuals.

Plant architecture is a key factor for high productivity maize because ideal plant architecture with an erect leaf angle and optimum leaf orientation value allow for more efficient light capture during photosynthesis and better wind circulation under dense planting conditions. To extend our understanding of the genetic mechanisms involved in leaf-related traits, three connected recombination inbred line (RIL) populations including 538 RILs were genotyped by genotyping-by-sequencing (GBS) method and phenotyped for the leaf angle and related traits in six environments. We conducted single population quantitative trait locus (QTL) mapping and joint linkage analysis based on high-density recombination bin maps constructed from GBS genotype data. A total of 45 QTLs with phenotypic effects ranging from 1.2% to 29.2% were detected for four leaf architecture traits by using joint linkage mapping across the three populations. All the QTLs identified for each trait could explain approximately 60% of the phenotypic variance. Four QTLs were located on small genomic regions where candidate genes were found. Genomic predictions from a genomic best linear unbiased prediction (GBLUP) model explained 45±9% to 68±8% of the variation in the remaining RILs for the four traits. These results extend our understanding of the genetics of leaf traits and can be used in genomic prediction to accelerate plant architecture improvement.

Genome-Wide Association Studies shed light on the identification of genes underlying human diseases and agriculturally important traits. This potential has been shadowed by false positive findings. The Mixed Linear Model (MLM) method is flexible enough to simultaneously incorporate population structure and cryptic relationships to reduce false positives. However, its intensive computational burden is prohibitive in practice, especially for large samples. The newly developed algorithm, FaST-LMM, solved the computational problem, but requires that the number of SNPs be less than the number of individuals to derive a rank-reduced relationship. This restriction potentially leads to less statistical power when compared to using all SNPs. We developed a method to extract a small subset of SNPs and use them in FaST-LMM. This method not only retains the computational advantage of FaST-LMM, but also remarkably increases statistical power even when compared to using the entire set of SNPs. We named the method SUPER (Settlement of MLM Under Progressively Exclusive Relationship) and made it available within an implementation of the GAPIT software package.