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BackgroundThe relationship between gut microbiota and hematologic malignancies has attracted considerable attention. As research progresses, it has become increasingly clear that the composition of gut microbiota may influence the onset and progression of hematologic malignancies. However, our understanding of this association remains limited.MethodsIn our study, we classified gut microbiota into five groups based on information at the phylum, class, order, family, and genus levels. Subsequently, we obtained data related to common hematologic malignancies from the IEU Open GWAS project. We then employed a bidirectional Mendelian Randomization (MR) approach to determine whether there is a causal relationship between gut microbiota and hematologic malignancies. Additionally, we conducted bidirectional MR analyses to ascertain the directionality of this causal relationship.ResultsThrough forward and reverse MR analyses, we found the risk of lymphoid leukemia was significantly associated with the abundance of phylum Cyanobacteria, order Methanobacteriales, class Methanobacteria, family Peptococcaceae, family Methanobacteriaceae, and genera Lachnospiraceae UCG010, Methanobrevibacter, Eubacterium brachy group, and Butyrivibrio. The risk of myeloid leukemia was significantly associated with the abundance of phylum Actinobacteria, phylum Firmicutes, order Bifidobacteriales, order Clostridiales, class Actinobacteria, class Gammaproteobacteria, class Clostridia, family Bifidobacteriaceae, and genera Fusicatenibacter, Eubacterium hallii group, Blautia, Collinsella, Ruminococcus gauvreauii group, and Bifidobacterium. The risk of Hodgkin lymphoma was significantly associated with the abundance of family Clostridiales vadinBB60 group, genus Peptococcus, and genus Ruminococcaceae UCG010. The risk of malignant plasma cell tumor was significantly associated with the abundance of genera Romboutsia and Eubacterium rectale group. The risk of diffuse large B-cell lymphoma was significantly associated with the abundance of genera Erysipelatoclostridium and Eubacterium coprostanoligenes group. The risk of mature T/NK cell lymphomas was significantly associated with the abundance of phylum Verrucomicrobia, genus Ruminococcaceae UCG013, genus Lachnoclostridium, and genus Eubacterium rectale group. Lastly, the risk of myeloproliferative neoplasms was significantly associated with the abundance of genus Coprococcus 3 and Eubacterium hallii group.ConclusionOur study provided new evidence for the causal relationship between gut microbiota and hematologic malignancies, offering novel insights and approaches for the prevention and treatment of these tumors.

Cercospora leaf blight (CLB), caused by Cercospora cf. flagellaris, C. kikuchii, and C. cf. sigesbeckiae, is a significant soybean [Glycine max (L.) Merr.] disease in regions with hot and humid conditions causing yield loss in the United States and Canada. There is limited information regarding resistant soybean cultivars, and there have been marginal efforts to identify the genomic regions underlying resistance to CLB. A Genome-Wide Association Study was conducted using a diverse panel of 460 soybean accessions from maturity groups III to VII to identify the genomic regions associated to the CLB disease. These accessions were evaluated for CLB in different regions of the southeastern United States over 3 years. In total, the study identified 99 Single Nucleotide Polymorphism (SNPs) associated with the disease severity and 85 SNPs associated with disease incidence. Across multiple environments, 47 disease severity SNPs and 23 incidence SNPs were common. Candidate genes within 10 kb of these SNPs were involved in biotic and abiotic stress pathways. This information will contribute to the development of resistant soybean germplasm. Further research is warranted to study the effect of pyramiding desirable genomic regions and investigate the role of identified genes in soybean CLB resistance.

BackgroundOur study examined the global, national, and regional trends in the incidence, mortality, and disability-adjusted life years (DALYs) associated with older people’s acute myeloid leukemia (AML) over a 30 years period. AML, which predominantly affects individuals aged 60–89, is known for its severity and unfavorable prognosis. By providing insights into the growing burden of AML, our research highlights the urgent need for effective interventions and support at various levels.MethodsIn this study, we analyzed older people with AML aged 60–89 using the Global Burden of Disease (GBD) database for 2019. Our goal was to assess trends and characteristics by examining the incidence rate, mortality rate, DALYs, and estimated annual percentage change (EAPC). We aimed to provide a comprehensive understanding of the disease’s trajectory and development.ResultsIn 2019, the older age group of 60 to 89 years reported 61,559 new cases of AML, with the corresponding number of deaths being 53,620, and the estimated DALYs standing at 990,656. Over the last 30 years, the incidence rate of AML in this age bracket increased by 1.67 per 100,000 people, the mortality rate rose by 1.57 per 100,000 people, and the rate of DALYs, indicative of disease burden, climbed by 1.42 per 100,000 people. High Socio-demographic Index (SDI) regions, particularly high-income North America and Australia, had the highest incidence rates. Germany had the highest incidence rate among the 204 countries analyzed, while Monaco reported the highest mortality and DALY rates. Smoking, high body mass index, occupational exposure to benzene, and formaldehyde were identified as significant risk factors associated with mortality from older people with AML in 2019.ConclusionOur study showed that the incidence, mortality, and DALY rates of AML in the older population were strongly correlated with the SDI, and these rates have been steadily increasing. This had become an increasingly serious global health issue, particularly in areas with a high SDI. We highlighted the urgency to focus more on this disease and called for the prompt implementation of appropriate preventive and control measures.

Protein and sugar content are important seed quality traits in soybean because they improve the value and sustainability of soy food and feed products. Thus, identifying Quantitative Trait Loci (QTL) for soybean seed protein and sugar content can benefit plant breeders and the soybean market by accelerating the breeding process via marker-assisted selection. For this study, a population of recombinant inbred lines (RILs) was developed from a cross between R08-3221 (high protein and low sucrose) and R07-2000 (high sucrose and low protein). Phenotypic data for protein content were taken from the F2:4 and F2:5 generations. The DA7250 NIR analyzer and HPLC instruments were used to analyze total seed protein and sucrose content. Genotypic data were generated using analysis via the SoySNP6k chip. A total of four QTLs were identified in this study. Two QTLs for protein content were located on chromosomes 11 and 20, and two QTLs associated with sucrose content were located on chromosomes 14 and. 11, the latter of which co-localized with detected QTLs for protein, explaining 10% of the phenotypic variation for protein and sucrose content in soybean seed within the study population. Soybean breeding programs can use the results to improve soybean seed quality.

The sucrose and Alanine (Ala) content in edamame beans significantly impacts the sweetness flavor of edamame-derived products as an important attribute to consumers’ acceptance. Unlike grain-type soybeans, edamame beans are harvested as fresh beans at the R6 to R7 growth stages when beans are filled 80-90% of the pod capacity. The genetic basis of sucrose and Ala contents in fresh edamame beans may differ from those in dry seeds. To date, there is no report on the genetic basis of sucrose and Ala contents in the edamame beans. In this study, a genome-wide association study was conducted to identify single nucleotide polymorphisms (SNPs) related to sucrose and Ala levels in edamame beans using an association mapping panel of 189 edamame accessions genotyped with a SoySNP50K BeadChip. A total of 43 and 25 SNPs was associated with sucrose content and Ala content in the edamame beans, respectively. Four genes (Glyma.10g270800, Glyma.08g137500, Glyma.10g268500, and Glyma.18g193600) with known effects on the process of sucrose biosynthesis and 37 novel sucrose-related genes were characterized. Three genes (Gm17g070500, Glyma.14g201100 and Glyma.18g269600) with likely relevant effects in regulating Ala content and 22 novel Ala-related genes were identified. In addition, by summarizing the phenotypic data of edamame beans from three locations in two years, three PI accessions (PI 532469, PI 243551, and PI 407748) were selected as the high sucrose and high Ala parental lines for the perspective breeding of sweet edamame varieties. Thus, the beneficial alleles, candidate genes, and selected PI accessions identified in this study will be fundamental to develop edamame varieties with improved consumers’ acceptance, and eventually promote edamame production as a specialty crop in the United States.

The widespread adoption of genetically modified (GM) dicamba-tolerant (DT) soybean was followed by numerous reports of off-target dicamba damage and yield losses across most soybean-producing states. In this study, a subset of the USDA Soybean Germplasm Collection consisting of 382 genetically diverse soybean accessions originating from 15 countries was used to identify genomic regions associated with soybean response to off-target dicamba exposure. Accessions were genotyped with the SoySNP50K BeadChip and visually screened for damage in environments with prolonged exposure to off-target dicamba. Two models were implemented to detect significant marker-trait associations: the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and a model that allows the inclusion of population structure in interaction with the environment (G×E) to account for variable patterns of genotype responses in different environments. Most accessions (84%) showed a moderate response, either moderately tolerant or moderately susceptible, with approximately 8% showing tolerance and susceptibility. No differences in off-target dicamba damage were observed across maturity groups and centers of origin. Both models identified significant associations in regions of chromosomes 10 and 19. The BLINK model identified additional significant marker-trait associations on chromosomes 11, 14, and 18, while the G×E model identified another significant marker-trait association on chromosome 15. The significant SNPs identified by both models are located within candidate genes possessing annotated functions involving different phases of herbicide detoxification in plants. These results entertain the possibility of developing non-GM soybean cultivars with improved tolerance to off-target dicamba exposure and potentially other synthetic auxin herbicides. Identification of genetic sources of tolerance and genomic regions conferring higher tolerance to off-target dicamba may sustain and improve the production of other non-DT herbicide soybean production systems, including the growing niche markets of organic and conventional soybean.

The availability of high-dimensional molecular markers has allowed plant breeding programs to maximize their efficiency through the genomic prediction of a phenotype of interest. Yield is a complex quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture information and its interaction with molecular markers via covariance structures for enhancing predictive ability across breeding scenarios. A total of 797 soybean lines derived from 367 unique bi-parental populations were genotyped using the Illumina BARCSoySNP6K and tested for yield during 5 years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including the GBLUP model (M1), the reaction norm model (M2) including the interaction between molecular markers and the environment (G×E), an extended version of M2 that also includes soil type (S), and the interaction between soil type and molecular markers (G×S) (M3), and a parsimonious version of M3 which discards the G×E term (M4). Four cross-validation scenarios simulating progeny testing and line selection of tested–untested genotypes (TG, UG) in observed–unobserved environments [OE, UE] were implemented (CV2 [TG, OE], CV1 [UG, OE], CV0 [TG, UE], and CV00 [UG, UE]). Across environments, the addition of G×S interaction in M3 decreased the amount of variability captured by the environment (−30.4%) and residual (−39.2%) terms as compared to M1. Within environments, the G×S term in M3 reduced the variability captured by the residual term by 60 and 30% when compared to M1 and M2, respectively. M3 outperformed all the other models in CV2 (0.577), CV1 (0.480), and CV0 (0.488). In addition to the Pearson correlation, other measures were considered to assess predictive ability and these showed that the addition of soil texture seems to structure/dissect the environmental term revealing its components that could enhance or hinder the predictability of a model, especially in the most complex prediction scenario (CV00). Hence, the availability of soil texture information before the growing season could be used to optimize the efficiency of a breeding program by allowing the reconsideration of field experimental design, allocation of resources, reduction of preliminary trials, and shortening of the breeding cycle.

Southern root-knot nematode [SRKN, Meloidogyne incognita (Kofold & White) Chitwood] is a plant-parasitic nematode challenging to control due to its short life cycle, a wide range of hosts, and limited management options, of which genetic resistance is the main option to efficiently control the damage caused by SRKN. To date, a major quantitative trait locus (QTL) mapped on chromosome (Chr.) 10 plays an essential role in resistance to SRKN in soybean varieties. The confidence of discovered trait-loci associations by traditional methods is often limited by the assumptions of individual single nucleotide polymorphisms (SNPs) always acting independently as well as the phenotype following a Gaussian distribution. Therefore, the objective of this study was to conduct machine learning (ML)-based genome-wide association studies (GWAS) utilizing Random Forest (RF) and Support Vector Machine (SVM) algorithms to unveil novel regions of the soybean genome associated with resistance to SRKN. A total of 717 breeding lines derived from 330 unique bi-parental populations were genotyped with the Illumina Infinium BARCSoySNP6K BeadChip and phenotyped for SRKN resistance in a greenhouse. A GWAS pipeline involving a supervised feature dimension reduction based on Variable Importance in Projection (VIP) and SNP detection based on classification accuracy was proposed. Minor effect SNPs were detected by the proposed ML-GWAS methodology but not identified using Bayesian-information and linkage-disequilibrium Iteratively Nested Keyway (BLINK), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Enriched Compressed Mixed Linear Model (ECMLM) models. Besides the genomic region on Chr. 10 that can explain most of SRKN resistance variance, additional minor effects SNPs were also identified on Chrs. 10 and 11. The findings in this study demonstrated that overfitting in GWAS may lead to lower prediction accuracy, and the detection of significant SNPs based on classification accuracy limited false-positive associations. The expansion of the basis of the genetic resistance to SRKN can potentially reduce the selection pressure over the major QTL on Chr. 10 and achieve higher levels of resistance.

Weeds can cause significant yield losses and will continue to be a problem for agricultural production due to climate change. Dicamba is widely used to control weeds in monocot crops, especially genetically engineered dicamba-tolerant (DT) dicot crops, such as soybean and cotton, which has resulted in severe off-target dicamba exposure and substantial yield losses to non-tolerant crops. There is a strong demand for non-genetically engineered DT soybeans through conventional breeding selection. Public breeding programs have identified genetic resources that confer greater tolerance to off-target dicamba damage in soybeans. Efficient and high throughput phenotyping tools can facilitate the collection of a large number of accurate crop traits to improve the breeding efficiency. This study aimed to evaluate unmanned aerial vehicle (UAV) imagery and deep-learning-based data analytic methods to quantify off-target dicamba damage in genetically diverse soybean genotypes. In this research, a total of 463 soybean genotypes were planted in five different fields (different soil types) with prolonged exposure to off-target dicamba in 2020 and 2021. Crop damage due to off-target dicamba was assessed by breeders using a 1–5 scale with a 0.5 increment, which was further classified into three classes, i.e., susceptible (≥3.5), moderate (2.0 to 3.0), and tolerant (≤1.5). A UAV platform equipped with a red-green-blue (RGB) camera was used to collect images on the same days. Collected images were stitched to generate orthomosaic images for each field, and soybean plots were manually segmented from the orthomosaic images. Deep learning models, including dense convolutional neural network-121 (DenseNet121), residual neural network-50 (ResNet50), visual geometry group-16 (VGG16), and Depthwise Separable Convolutions (Xception), were developed to quantify crop damage levels. Results show that the DenseNet121 had the best performance in classifying damage with an accuracy of 82%. The 95% binomial proportion confidence interval showed a range of accuracy from 79% to 84% (p-value ≤ 0.01). In addition, no extreme misclassifications (i.e., misclassification between tolerant and susceptible soybeans) were observed. The results are promising since soybean breeding programs typically aim to identify those genotypes with ‘extreme’ phenotypes (e.g., the top 10% of highly tolerant genotypes). This study demonstrates that UAV imagery and deep learning have great potential to high-throughput quantify soybean damage due to off-target dicamba and improve the efficiency of crop breeding programs in selecting soybean genotypes with desired traits.

The efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield, achieved in 1 year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials (PTs) due to their large population, complex genetic behavior, and high genotype-environment interaction. The goal of this study was to investigate the performance of selecting superior soybean breeding lines using image-based secondary traits by comparing them with the selection of breeders. A total of 11,473 progeny rows (PT) were planted in 2018, of which 1,773 genotypes were selected for the preliminary yield trial (PYT) in 2019, and 238 genotypes advanced for the advanced yield trial (AYT) in 2020. Six agronomic traits were manually measured in both PYT and AYT trials. A UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons. A group of image features was extracted to develop the secondary crop traits for selection. Results show that the soybean seed yield of the selected genotypes by breeders was significantly higher than that of the non-selected ones in both yield trials, indicating the superiority of the breeder's selection for advancing soybean yield. A least absolute shrinkage and selection operator model was used to select soybean lines with image features and identified 71 and 76% of the selection of breeders for the PT and PYT. The model-based selections had a significantly higher average yield than the selection of a breeder. The soybean yield selected by the model in PT and PYT was 4 and 5% higher than those selected by breeders, which indicates that the UAV-based high-throughput phenotyping system is promising in selecting high-yield soybean genotypes.

Abstract Background Salinity is an abiotic stress that negatively affects soybean [Glycine max (L.) Merr.] seed yield. Although a major gene for salt tolerance was identified and consistently mapped to chromosome (Chr.) 3 by linkage mapping studies, it does not fully explain genetic variability for tolerance in soybean germplasm. In this study, a genome-wide association study (GWAS) was performed to map genomic regions for salt tolerance in a diverse panel of 305 soybean accessions using a single nucleotide polymorphism (SNP) dataset derived from the SoySNP50K iSelect BeadChip. A second GWAS was also conducted in a subset of 234 accessions using another 3.7 M SNP dataset derived from a whole-genome resequencing (WGRS) study. In addition, three gene-based markers (GBM) of the known gene, Glyma03g32900, on Chr. 3 were also integrated into the two datasets. Salt tolerance among soybean lines was evaluated by leaf scorch score (LSS), chlorophyll content ratio (CCR), leaf sodium content (LSC), and leaf chloride content (LCC). Results For both association studies, a major locus for salt tolerance on Chr. 3 was confirmed by a number of significant SNPs, of which three gene-based SNP markers, Salt-20, Salt14056 and Salt11655, had the highest association with all four traits studied. Also, additional genomic regions on Chrs. 1, 8, and 18 were found to be associated with various traits measured in the second GWAS using the WGRS-derived SNP dataset. Conclusions A region identified on Chr. 8 was identified to be associated with all four traits and predicted as a new minor locus for salt tolerance in soybean. The candidate genes harbored in this minor locus may help reveal the molecular mechanism involved in salt tolerance and to improve tolerance in soybean cultivars. The significant SNPs will be useful for marker-assisted selection for salt tolerance in soybean breeding programs.

This review summarizes the history and current state of the known genetic basis for soybean resistance to Soybean mosaic virus (SMV), and examines how the integration of molecular markers has been utilized in breeding for crop improvement. SVM causes yield loss and seed quality reduction in soybean based on the SMV strain and the host genotype. Understanding the molecular underpinnings of SMV–soybean interactions and the genes conferring resistance to SMV has been a focus of intense research interest for decades. Soybean reactions are classified into three main responses: resistant, necrotic, or susceptible. Significant progress has been achieved that has greatly increased the understanding of soybean germplasm diversity, differential reactions to SMV strains, genotype–strain interactions, genes/alleles conferring specific reactions, and interactions among resistance genes and alleles. Many studies that aimed to uncover the physical position of resistance genes have been published in recent decades, collectively proposing different candidate genes. The studies on SMV resistance loci revealed that the resistance genes are mainly distributed on three chromosomes. Resistance has been pyramided in various combinations for durable resistance to SMV strains. The causative genes are still elusive despite early successes in identifying resistance alleles in soybean; however, a gene at the Rsv4 locus has been well validated.

Edamame is a food-grade soybean [Glycine max (L.) Merr.] that is harvested immature between the R6 and R7 reproductive stages. To be labeled as a premium product, the edamame market demands large pod size and intense green color. A staggered harvest season is critical for the commercial industry to post-harvest process the crop in a timely manner. Currently, there is little information to assist in predicting the optimum time to harvest edamame when the pods are at their collective largest size and greenest color. The objectives of this study were to assess the impact of cultivar, planting date, and harvest date on edamame color, pod weight, and a newly minted Edamame Harvest Quality Index combining both aforementioned factors. And to predict edamame harvest quality based on phenological stages, thermal units, and planting dates. We observed that pod color and weight depended on the cultivar, planting date, and harvest date combination. Our results also indicated that edamame quality is increased with delayed planting dates and that quality was dependent on harvest date with a quadratic negative response to delaying harvest. Maximum quality depended on cultivar and planting and harvest dates, but it remained stable for an interval of 18–27 days around the peak. Finally, we observed that the number of days between R1 and harvest was consistently identified as a key factor driving edamame quality by both stepwise regression and neural network analysis. These research results will help define a planting and harvest strategy for edamame production in Arkansas and the United States Mid-South.

Consumption of edamame (vegetable soybeans) has increased significantly in the U.S. over the last 20 years. Although market demand has been increasing, most edamame is still imported from Asian countries. A team of multistate plant-breeding programs in the mid-Atlantic and Southeast U.S. has focused on developing new breeding lines that grow well in the U.S. and deliver what domestic growers, processors and consumers need and expect from their edamame. In our study, sensory evaluation was used to identify edamame genotypes and sensory attributes preferred by consumers to support breeding selection criteria. In the first year (reported as our “screening study”), 20 edamame genotypes were grown in three locations: Newport, AR, and Blacksburg and Painter, VA. In the second year (reported as our “validation study”), 10 edamame genotypes selected after our screening study were grown in Blacksburg and Painter, VA, Portageville, MO, and Stoneville, MS. In both years of research, untrained participants (adults; vegetable consumers not allergic to soy; N ≥ 50) used a traditional 9-point acceptability (hedonic) scale (1 = “dislike extremely”; 9 = “like extremely”) to evaluate overall-liking, aroma, appearance, taste, and texture, and a 5-point scale (1 = “not sweet,” 5 = “extremely sweet”) to evaluate sweetness intensity. Next, participants used a check-all-that-apply (CATA) list of selected sensory terms to describe the sensory characteristics of each edamame sample. Overall acceptability of edamame genotypes was significantly different among all genotypes (p < 0.05). Samples described as “bitter,” “sour” (flavor) or “starchy” (texture) were associated with lower acceptability scores while “salty” and “sweet” (flavor) were correlated with higher acceptability. Sensory data from the screening study were used to select the best genotypes by use of a defined decision process based on the consumer data. The validation study tested the selection decisions and further supported the genotype choices. Sensory evaluation is a powerful tool to direct breeders to improve market acceptability and develop new edamame genotypes. Both screening and validation studies illustrate the significant role of consumer sensory data in support of genotypes targeted for domestic (U.S.) production.

Soybean [Glycine max (L.) Merr.] is a crop of great interest worldwide. Exploring molecular approaches to increase yield genetic gain has been one of the main challenges for soybean breeders and geneticists. Agronomic traits such as maturity, plant height, and seed weight have been found to contribute to yield. In this study, a total of 250 soybean accessions were genotyped with 10,259 high-quality SNPs postulated from genotyping by sequencing (GBS) and evaluated for grain yield, maturity, plant height, and seed weight over three years. A genome-wide association study (GWAS) was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model. Genomic selection (GS) was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that 20, 31, 37, and 23 SNPs were significantly associated with maturity, plant height, seed weight, and yield, respectively; Many SNPs were mapped to previously described maturity and plant height loci (E2, E4, and Dt1) and a new plant height locus was mapped to chromosome 20. Candidate genes were found in the vicinity of the two SNPs with the highest significant levels associated with yield, maturity, plant height, seed weight, respectively. A 11.5-Mb region of chromosome 10 was associated with both yield and seed weight. Overall, the accuracy of GS was dependent on the trait, year, and population structure, and high accuracy indicates that these agronomic traits can be selected in molecular breeding through GS. The SNP markers identified in this study can be used to improve yield and agronomic traits through the marker-assisted selection and GS in breeding programs.

Soybean is sensitive to flooding stress that may result in poor seed quality and significant yield reduction. Soybean production under flooding could be sustained by developing flood-tolerant cultivars through breeding programs. Conventionally, soybean tolerance to flooding in field conditions is evaluated by visually rating the shoot injury/damage due to flooding stress, which is labor-intensive and subjective to human error. Recent developments of field high-throughput phenotyping technology have shown great potential in measuring crop traits and detecting crop responses to abiotic and biotic stresses. The goal of this study was to investigate the potential in estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights. The flooding injury score (FIS) of 724 soybean breeding plots was taken visually by breeders when soybean showed obvious injury symptoms. Aerial images were taken on the same day using a five-band multispectral and an infrared (IR) thermal camera at 20, 50, and 80 m above ground. Five image features, i.e., canopy temperature, normalized difference vegetation index, canopy area, width, and length, were extracted from the images at three flight heights. A deep learning model was used to classify the soybean breeding plots to five FIS ratings based on the extracted image features. Results show that the image features were significantly different at three flight heights. The best classification performance was obtained by the model developed using image features at 20 m with 0.9 for the five-level FIS. The results indicate that the proposed method is very promising in estimating FIS for soybean breeding.

The wide adoption of dicamba-tolerant (DT) soybean has led to numerous cases of off-target dicamba damage to non-DT soybean and dicot crops. This study aimed to develop a method to differentiate soybean response to dicamba using unmanned-aerial-vehicle-based imagery and machine learning models. Soybean lines were visually classified into three classes of injury, i.e., tolerant, moderate, and susceptible to off-target dicamba. A quadcopter with a built-in RGB camera was used to collect images of field plots at a height of 20 m above ground level. Seven image features were extracted for each plot, including canopy coverage, contrast, entropy, green leaf index, hue, saturation, and triangular greenness index. Classification models based on artificial neural network (ANN) and random forest (RF) algorithms were developed to differentiate the three classes of response to dicamba. Significant differences for each feature were observed among classes and no significant differences across fields were observed. The ANN and RF models were able to precisely distinguish tolerant and susceptible lines with an overall accuracy of 0.74 and 0.75, respectively. The imagery-based classification model can be implemented in a breeding program to effectively differentiate phenotypic dicamba response and identify soybean lines with tolerance to off-target dicamba damage.

Edamame is a food-grade soybean that is harvested at the green-immature stage (R6) and sold fresh or frozen for consumption after steaming or boiling. Limited studies have been conducted on high-temperature sterilization of edamame in cans and on acid preservation of edamame. The objectives of this study were to evaluate the color retention and texture of edamame when pasteurized in acidic brine as compared to boiling, to assess the effect of sucrose and turmeric addition on acid-treated edamame, and to characterize varietal differences when edamame lines were pasteurized in an acidic brine at either R6 or R8 stage. All studies were conducted using industry-standard processing conditions for acid-preservation of food in glass containers. The results of this research indicated that acid processing caused losses in intensity of green color and hue, but much smaller than those reported when heat-processing edamame in cans. We also observed a small and borderline significant increase in texture (p = 0.0790) in acid-preserved samples. In addition, we found that green color is positively affected by the addition of turmeric to the brine, but not of sucrose. Finally, the varieties R07-589 (red-brown seed coat) and R09-345 (black seed coat) acid-processed at R8 had color and texture similar to canned in-kind substitute products. In conclusion, acid-preservation, and addition of turmeric to immature edamame, helped maintain an acceptable quality of the processed products.

Abstract Background Identification of the quantitative trait locus (QTL) underlying salt tolerance is a prerequisite for marker-assisted selection in the salt-tolerant breeding process. Methods In this study, the recombinant inbred lines derived from the salt-tolerant elite soybean cultivar ‘Jidou 12’ and the salt-sensitive elite cultivar ‘Ji NF 58’ were used to identify the QTL associated with salt tolerance, using both salt tolerance rating (STR) and leaf chlorophyll content (SPAD) as indicators. Results A major salt-tolerant QTL, which was flanked by SSR markers GMABAB and Barcsoyssr_03_1421 on chromosome 3, was identified based on single-marker regression, single trait composite interval mapping, and multiple interval mapping analysis. For STR, the LOD ranged from 19.8 to 20.1; R2 ranged from 44.3 to 44.7%; and the additive effect ranged from 0.876 to 0.885 among the three mapping methods. For SPAD, the LOD ranged from 10.6 to 11.0; R2 ranged from 27.0 to 27.6%; and the additive effect ranged from 1.634 to 1.679 among the three mapping methods. Conclusions In this study, a major QTL conditioning salt tolerance on chromosome 3 was identified. The DNA markers closely associated with the QTLs might be useful in marker-assisted selection for soybean salt tolerance improvement in Huanghuaihai, China.

Soybean is the second largest source of oil worldwide. Developing soybean varieties with high levels of oleic acid is a primary goal of the soybean breeders and industry. Edible oils containing high level of oleic acid and low level of linoleic acid are considered with higher oxidative stability and can be used as a natural antioxidant in food stability. All developed high oleic acid soybeans carry two alleles; GmFAD2-1A and GmFAD2-1B. However, when planted in cold soil, a possible reduction in seed germination was reported when high seed oleic acid derived from GmFAD2-1 alleles were used. Besides the soybean fatty acid desaturase (GmFAD2-1) subfamily, the GmFAD2-2 subfamily is composed of five members, including GmFAD2-2A, GmFAD2-2B, GmFAD2-2C, GmFAD2-2D, and GmFAD2-2E. Segmental duplication of GmFAD2-1A/GmFAD2-1B, GmFAD2-2A/GmFAD2-2C, GmFAD2-2A/GmFAD2-2D, and GmFAD2-2D/GmFAD2-2C have occurred about 10.65, 27.04, 100.81, and 106.55 Mya, respectively. Using TILLING-by-Sequencing+ technology, we successfully identified 12, 8, 10, 9, and 19 EMS mutants at the GmFAD2-2A, GmFAD2-2B, GmFAD2-2C, GmFAD2-2D, and GmFAD2-2E genes, respectively. Functional analyses of newly identified mutants revealed unprecedented role of the five GmFAD2-2A, GmFAD2-2B, GmFAD2-2C, GmFAD2-2D, and GmFAD2-2E members in controlling the seed oleic acid content. Most importantly, unlike GmFAD2-1 members, subcellular localization revealed that members of the GmFAD2-2 subfamily showed a cytoplasmic localization, which may suggest the presence of an alternative fatty acid desaturase pathway in soybean for converting oleic acid content without substantially altering the traditional plastidial/ER fatty acid production.

Purple seed stain (PSS) of soybean (Glycine max (L.) Merr.) is a prevalent seed disease. It results in poor seed quality and reduced seed lot market grade, and thus undermines value of soybean worldwide. The objectives of this research were to evaluate the reaction of selected soybean genotypes collected from 15 countries representing maturity groups (MGs) III, IV, and V to PSS, and to identify new sources of resistance to PSS based on three years of evaluation of natural field infection by Cercospora spp. in the Mississippi Delta of the U. S. In this study, 42 soybean genotypes were evaluated in 2010, 2011, and 2012. Seventeen lines including six MG III (PI 88490, PI 504488, PI 417361, PI 548298, PI 437482, and PI 578486), seven MG IV (PI 404173, PI 346308, PI 355070, PI 416779, PI 80479, PI 346307, and PI 264555), and four MG V (PI 417567, PI 417420, PI 381659, and PI 407749) genotypes had significantly lower percent seed infection by Cercospora spp. than the susceptible checks and other genotypes evaluated (P ≤ 0.05). These genotypes of soybean can be used in developing soybean cultivars or germplasm lines with resistance to PSS and for genetic mapping of PSS resistance genes. In addition, among these 17 lines with different levels of resistance to PSS, nine soybean genotypes (PI 417361, PI 504488, PI 88490, PI 346308, PI 416779, PI 417567, PI 381659, PI 417567, and PI 407749) were previously reported as resistant to Phomopsis seed decay. Therefore, they could be useful in breeding programs to develop soybean cultivars with improved resistance to both seed diseases.

Vegetable soybean (Edamame) blanching with hot water/steam is an energy- and water- intensive process that may also result in compromised product quality. The effectiveness of infrared (IR) heating to dry and simultaneously blanch Edamame was investigated at heating intensities of 11.06, 8.43 and 6.99 kW/m2. Temperature, weight, texture, and green intensity of heated samples for various durations were determined. In general, product weight decreased during IR heating. The largest weight reduction (9.5 %) was achieved after 100 s of heating at the highest IR heating intensity. Hardness was reduced alongside treatment duration, reaching the lowest values (11172.9–10847 N) at 100 or 120 s despite heating intensity. The highest green intensity was recorded (0.33) for treatments at 100 or 120 s. The new process combined drying and blanching into one step which potentially improves processing efficiency and product quality.

Soybean seed composition has a profound impact on its market value and commercial use as an important commodity. Increases in oil and protein content have been historically pursued by breeders and genetic engineers; consequently, rapid methods for their quantification are well established. The interest in complete carbohydrate profiles in mature seeds, on the other hand, has recently increased due to numerous attempts to redirect carbohydrates into oil and protein or to offer specialty seed with a specific sugar profile to meet animal nutritional requirements. In this work, a sequential protocol for quantifying reserve and structural carbohydrates in soybean seed was developed and validated. Through this procedure, the concentrations of soluble sugars, sugar alcohols, starch, hemicellulose, and crystalline cellulose can be determined in successive steps from the same starting material using colorimetric assays, LC–MS/MS, and GC–MS. The entire workflow was evaluated using internal standards to estimate the recovery efficiency. Finally, it was successfully applied to eight soybean genotypes harvested from two locations, and the resulting correlations of carbohydrate and oil or protein are presented. This methodology has the potential not only to guide soybean cultivar optimization processes but also to be expanded to other crops with only slight modifications.

This study examines two different strategies with respect to managing the order in which information about genetically modified (GM) technology would reach and impact consumers of edamame, often referred to as the "vegetable soybean". Edamame are soybeans harvested while the beans are young and soft. We capture consumers' willingness to pay (WTP) for unlabeled edamame, non-GM edamame, and GM edamame using a non-hypothetical random nth price auction. We elicit consumers' valuation for each edamame product before and after introducing information, and test two strategies where the order of providing positive and negative information is reversed. The results suggest that negative information affects WTP to a much greater extent than positive information. Hence a strategy to proactively deal with eventual negative press about GM technology did not lead to a different result than a strategy that would react to or attempt to thwart negative information with positive information at a later date. These findings suggest that it would be difficult to introduce new GM edamame as edible products in the market as marginally negative preconceptions about GM at the time of the experiment were easier to reinforce with negative information than to combat with positive information about GM.

Zao5241 is an elite soybean [Glycine max (L.) Merr.] line and backbone parent. In this study, we employed iTRAQ to analyze the proteomes and protein expression profiles of Zao5241 during leaf development. We identified 1,245 proteins in all experiments, of which only 45 had been previously annotated. Among overlapping proteins between three biological replicates, 598 proteins with 2 unique peptides identified were reliably quantified. The protein datasets were classified into 36 GO functional terms, and the photosynthesis term was most significantly enriched. A total of 113 proteins were defined as being differentially expressed during leaf development; 41 proteins were found to be differently expressed between two and four week old leaves, and 84 proteins were found to be differently expressed between two and six week old leaves, respectively. Cluster analysis of the data revealed dynamic proteomes. Proteins annotated as electron carrier activity were greatly enriched in the peak expression profiles, and photosynthesis proteins were negatively modulated along the whole time course. This dataset will serve as the foundation for a systems biology approach to understanding photosynthetic development.

The mini core collection (MCC) has been established by streamlining core collection (CC) chosen from China National Genebank including 23,587 soybean (Glycine max) accessions by morphological traits and simple sequence repeat (SSR) markers. Few studies have been focused on the maturity that has been considered as one of the most critical traits for the determination of the adaptation-growing region of the soybean. In the current study, two hundred and ninty-nine accessions of MCC planted for two years at four locations namely in Heihe, Harbin, Jining and Wuhan cities in China were used to assess the variation of maturity in MCC and identify the integrated effect of 4 E loci on flowering and maturity time in soybean. Forty-two North American varieties served as references of maturity groups (MG). Each accession in MCC was classified by comparing with the MG references in the days from VE (emergence) and physiological maturity (R7). The results showed that MCC covered a large range of MGs from MG000 to MGIX/X. Original locations and sowing types were revealed as the major affecting factors for maturity groups of the MCC accessions. The ratio of the reproductive period to the vegetative period (R/V) varied among MCC accessions. Genotyping of 4 maturity genes (i.e. E1, E2, E3 and E4) in 228 accessions indicated that recessive alleles e1, e2, e3 and e4 promoted earlier flowering and shortened the maturity time with different effects, while the dominate alleles were always detected in accessions with longer maturity. The allelic combinations determined the diversification of soybean maturity groups and adaptation to different regions. Our results indicated that the maturity of Chinese soybean MCC showed genetic diversities in phenotype and genotype, which provided information for further MG classification, geographic adaptation analysis of Chinese soybean cultivars, as well as developing new soybean varieties with adaptation to specific regions.

High-throughput RNA-seq technology has provided an unprecedented opportunity to reveal the very complex structures of transcriptomes. However, it is an important and highly challenging task to assemble vast amounts of short RNA-seq reads into transcriptomes with alternative splicing isoforms. In this study, we present a novel de novo assembler, BinPacker, by modeling the transcriptome assembly problem as tracking a set of trajectories of items with their sizes representing coverage of their corresponding isoforms by solving a series of bin-packing problems. This approach, which subtly integrates coverage information into the procedure, has two exclusive features: 1) only splicing junctions are involved in the assembling procedure; 2) massive pell-mell reads are assembled seemingly by moving a comb along junction edges on a splicing graph. Being tested on both real and simulated RNA-seq datasets, it outperforms almost all the existing de novo assemblers on all the tested datasets, and even outperforms those ab initio assemblers on the real dog dataset. In addition, it runs substantially faster and requires less memory space than most of the assemblers. BinPacker is published under GNU GENERAL PUBLIC LICENSE and the source is available from: http://sourceforge.net/projects/transcriptomeassembly/files/BinPacker_1.0.tar.gz/download. Quick installation version is available from: http://sourceforge.net/projects/transcriptomeassembly/files/BinPacker_binary.tar.gz/download.

Cercospora leaf blight (CLB), caused by Cercospora cf. flagellaris, C. kikuchii, and C. cf. sigesbeckiae, is a significant soybean [Glycine max (L.) Merr.] disease in regions with hot and humid conditions causing yield loss in the United States and Canada. There is limited information regarding resistant soybean cultivars, and there have been marginal efforts to identify the genomic regions underlying resistance to CLB. A Genome-Wide Association Study was conducted using a diverse panel of 460 soybean accessions from maturity groups III to VII to identify the genomic regions associated to the CLB disease. These accessions were evaluated for CLB in different regions of the southeastern United States over 3 years. In total, the study identified 99 Single Nucleotide Polymorphism (SNPs) associated with the disease severity and 85 SNPs associated with disease incidence. Across multiple environments, 47 disease severity SNPs and 23 incidence SNPs were common. Candidate genes within 10 kb of these SNPs were involved in biotic and abiotic stress pathways. This information will contribute to the development of resistant soybean germplasm. Further research is warranted to study the effect of pyramiding desirable genomic regions and investigate the role of identified genes in soybean CLB resistance. [ABSTRACT FROM AUTHOR]

The availability of high-dimensional molecular markers has allowed plant breeding programs to maximize their efficiency through the genomic prediction of a phenotype of interest. Yield is a complex quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture information and its interaction with molecular markers via covariance structures for enhancing predictive ability across breeding scenarios. A total of 797 soybean lines derived from 367 unique bi-parental populations were genotyped using the Illumina BARCSoySNP6K and tested for yield during 5 years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including the GBLUP model (M1), the reaction norm model (M2) including the interaction between molecular markers and the environment (G×E), an extended version of M2 that also includes soil type (S), and the interaction between soil type and molecular markers (G×S) (M3), and a parsimonious version of M3 which discards the G×E term (M4). Four cross-validation scenarios simulating progeny testing and line selection of tested-untested genotypes (TG, UG) in observed-unobserved environments [OE, UE] were implemented (CV2 [TG, OE], CV1 [UG, OE], CV0 [TG, UE], and CV00 [UG, UE]). Across environments, the addition of G×S interaction in M3 decreased the amount of variability captured by the environment (-30.4%) and residual (-39.2%) terms as compared to M1. Within environments, the G×S term in M3 reduced the variability captured by the residual term by 60 and 30% when compared to M1 and M2, respectively. M3 outperformed all the other models in CV2 (0.577), CV1 (0.480), and CV0 (0.488). In addition to the Pearson correlation, other measures were considered to assess predictive ability and these showed that the addition of soil texture seems to structure/dissect the environmental term revealing its components that could enhance or hinder the predictability of a model, especially in the most complex prediction scenario (CV00). Hence, the availability of soil texture information before the growing season could be used to optimize the efficiency of a breeding program by allowing the reconsideration of field experimental design, allocation of resources, reduction of preliminary trials, and shortening of the breeding cycle. [ABSTRACT FROM AUTHOR]