Kansas State University

Allele mining of crop pangenomes can enable the identification of novel variants for trait improvement, increase crop genetic diversity, and purge deleterious mutations around fixed genomic regions. Sorghum, a C4 cereal crop domesticated in the tropics, was selected for reduced plant height and maturity to develop combine-harvestable and photoperiod-insensitive US grain sorghums. To breed semi-dwarf US grain sorghum hybrids, public and private sector programs mostly used the dw3-ref allele, which produces undesirable height revertants (frequency of 0.1–0.3%) due to uneven crossing over at the 882 bp tandem duplication region. Although the dw3-ref allele produces revertants, US sorghum breeding programs continued using this allele in the absence of identified allelic variants that suppress revertants. In this study, we leveraged a sorghum pangenome resource (resequenced sorghum association panel and a global diversity panel of 1661 lines) to identify seven loss-of-function variants in the Dw3 gene using the SnpEff variant calling prediction. We validated the Segaolane dw3 loss-of-function variant, resulting from a 137 bp deletion in the third exon, to suppress revertant production. Segaolane NAM family RILs with the dw3-ref allele produced revertants while no revertants were observed in RILs with the Segaolane dw3 allele. The availability of resequencing data enabled the designing of haplotype-based markers detecting the Segaolane stable dw3 allele for marker-assisted trait introgression into elite sorghum breeding lines. This research mining new stable-dwarfing dw3 alleles demonstrated the application of sorghum pan-genome for trait improvement and developing marker-assisted breeding strategies.

Determining which statistical methods are appropriate for data is both user and data dependent and prone to change as new methodology becomes available. This process encompasses model ideation, model selection, and determining appropriate use of statistical methods. Literature on models for animal movement emerging in the past two decades has yielded a rich collection of statistical methods garnering much deserved positive attention. Among such efforts, there is limited investigation of the broader place for simple machine learning methodology in animal movement modeling. We propose a bagged (i.e., bootstrap aggregated) animal movement model using simple, off-the-shelf machine learning algorithms. The model is intuitive, retains statistical inference about characteristics of animal movement (i.e., estimated from model-based summary statistics), and only requires knowledge of elementary statistical and machine learning analysis to understand. We show by simulation that our model can provide unbiased estimates of pertinent characteristics of animal movement (e.g., daily displacement) in the presence of large and realistic location error. We believe that increasing accessible literature on simple machine learning animal movement models provides valuable pedagogical and practical support for researchers using statistical models to study animal movement.

Hessian fly (HF), Mayetiola destructor (Say), is a serious pest of wheat (Triticum aestivum L.) worldwide. Growing resistant cultivars is the most effective and economical approach for HF management. Previous screening of 176 wheat accessions from Pakistan only identified Parvaz‐94 with high resistance to HF biotype Great Plains (GP), a predominant biotype in the US Great Plains. To determine the quantitative trait loci (QTL) for HF resistance in Parvaz‐94, we evaluated a population of 178 recombinant inbred lines from Parvaz‐94 × Cadenza for resistance to HF biotype GP and constructed a high‐density linkage map with 3469 single‐nucleotide polymorphisms generated by genotyping‐by‐sequencing. Two QTLs (QHf.hwwg‐1AS.2 and QHf.hwwg‐6BS.2) were identified on the short arms of chromosomes 1A and 6B, respectively. QHf.hwwg‐1AS.2 was mapped to a 4.0 Mb interval (4.6–8.6 Mb) on the chromosome arm 1AS, and QHf.hwwg‐6BS.2 was localized to a 5.4 Mb interval (2.3–7.7 Mb) on the chromosome arm 6BS based on International Wheat Genome Sequencing Consortium RefSeq v2.1 reference genome. Kompetitive allele‐specific PCR markers were developed for both QTL. The marker K6B_7697506, tightly linked to QHf.hwwg‐6BS.2, was validated in three diversity panels of 610 winter wheat accessions from the major US winter wheat growing states and can be used for marker‐assisted selection of QHf.hwwg‐6BS.2 in breeding programs.

Chickpeas (Cicer arietinum L.) are globally valued legume known for their affordability, nutritional significance, and health benefits. They are rich in protein, fiber, vitamins, and minerals such as iron, zinc, folate, and magnesium. This review comprehensively explores the chemical composition of chickpeas and their functional properties, focusing on macronutrients, micronutrients, phytochemicals, and antinutritional factors. It also delves into the potential health benefits of bioactive compounds and peptides derived from chickpeas, highlighting their roles in various physiological functions and applications. The exceptional technofunctional properties of chickpea proteins, including gel formation, texture enhancement, emulsification, and fat/water binding, make them ideal ingredients for diverse food products. Their versatility allows for use in various forms (isolates, concentrates, textured proteins), contributing to the development of a wide range of plant‐based foods, nutritional supplements, and gluten‐free options. While chickpeas contain some antinutrients like phytates, lectins, and enzyme inhibitors, effective processing methods can significantly reduce their potential negative effects. This review provides valuable insights, offering the novel contributions and an enhanced understanding it brings to the scientific community and food industry. By bridging compositional data with physiological implications, the review reinforces the pivotal role of chickpeas as a dietary component and enriches the existing scientific literature on this essential legume.

Climate change is making droughts more frequent, which is a major problem for crop yield, especially for crops that are vulnerable to drought, such as common buckwheat (Fagopyrum esculentum). Drought stress affects negatively on physiological and biochemical processes of plants, leading to reduced yields. This study addresses the knowledge gap regarding effective strategies to mitigate drought-induced damage and enhance productivity in buckwheat. We hypothesized that iron oxide nanoparticles (Fe3O4 NPs) and rice husk biochar could improve drought tolerance in buckwheat by modulating its physiological and biochemical responses. To test this, buckwheat plants were grown under well-watered (80% of field capacity, FC) and drought (40% of FC) conditions following a completely randomized design (CRD) with three replications. Results showed that the application of 50 g/kg rice husk biochar and 400 ppm Fe3O4 NPs, either separately or in combination, significantly enhanced the yield and improved key physiological and biochemical traits, including relative water content, photosynthetic rate, stomatal conductance, chlorophyll content, and antioxidant activity. The combination of Fe3O4 NPs and rice husk biochar led to improvements the plants’ relative water content, photosynthetic rate, chlorophyll levels, membrane stability index, proline, antioxidant activity (DPPH), and seed yield by 22.37, 17.11, 43.05, 16.07, 43.75, 8.59, and 50.87%, respectively compared to untreated drought plants. Moreover, this treatment reduced oxidative stress indicators such as hydrogen peroxide and malondialdehyde by 31.09 and 38.19%, respectively. These results show that Fe3O4 NPs, when combined with rice husk biochar, significantly improve drought tolerance in common buckwheat, providing a viable strategy to increase crop yields in water-limited environments. In view of climate change, this study emphasises the potential of combining biochar with nanomaterials for sustainable agricultural practices.

Viral pathogens adversely affect wheat (Triticum aestivum L.) development and are responsible for significant wheat yield losses. Barley yellow dwarf virus (BYDV) is one of the most serious worldwide virus threats to cereal crops. Soil‐borne wheat mosaic virus (SBWMV) has been present in the Great Plains and responsible for wheat damage for over a century. Identification of additional sources of genetic resistance is paramount to combat the potential damage from these viruses. We constructed a panel of 269 winter wheat cultivars and breeding lines to assess the resistance to naturally occurring BYDV and SBWMV in a Kansas nursery. These lines were sequenced using exome and promoter capture identifying over 640,000 variants for association analysis with visual disease severity ratings. We found 10 and seven significant regions affecting resistance to BYDV and SBWMV, respectively. These regions include the Bdv2 and Sbwm1 loci, as well as novel loci affecting virus resistance. Most of the novel associations are rare, with effect sizes ranging from 5% to 22%. We performed a survey of the viral population present in the disease nursery, which confirmed the presence of both BYDV and SBWMV and revealed differences in virus population from year to year. Additionally, it suggested that co‐infections of multiple viruses are common, demonstrating the need for breeding lines harboring resistance to multiple viruses. Deployment of these novel genetic resistance regions in combination with existing resistance loci should allow for increased resistance and potentially more sustainable viral control and reduce the risks associated with wheat yield loss due to these viruses.

The undesirable consequences of climate change on crop yields threaten the resiliency of farmers' livelihoods in climate‐vulnerable regions. Assessing the resilience of agrifood systems to climate and non‐climate hazards helps identify solutions for ensuring the sustainability of farming households. The literature review indicates that a knowledge gap remains in interpreting outputs generated by procedures under various study‐specific conditions. A review of selected articles from 1547 documents on resilience among Senegalese farmers identified relevant indices representing farmers' resilience from nine studies, resulting in 83 observations for the resilience index and control variables. This study utilized spatial meta‐data and survival regression analysis to examine the effects of regional interactions, shock types, and factor selection on measured resilience through the following phases: (1) Organizing the meta‐data, (2) specifying eight meta‐regression models to assess bias from regional data variations and the interaction effect of sample size, (3) converting meta‐data to survival data to analyze resilience failure exposure and time‐to‐event failure, and (4) regressing the shock types and agroecological zone conditions on the outcomes from phase three. The results indicated that the “climate hazard” shock, “COVID‐19” shock, and “seed diversity effect” were the primary contributors to the highest failure of resilience capacity. The spatial lag significantly affected resilience magnitude. Accounting for the spatial lag changed the negative effect to a positive effect for variables representing different shock types. For example, when accounting for the spatial lag, the impact of “climate hazard” and “other shock sources” shifted compared to the “COVID‐19” shock, indicating that their influence on resilience capacity changed direction. The effect of shock‐type variables on resilience failure exposure was significant, regardless of whether the shock sources remained constant or changed. The findings emphasize the need for policy considerations regarding measurement procedures, regional factors, and shock‐specific interventions to avoid overestimation or underestimation of resilience. For instance, resilience measurement procedures should be improved by distinguishing between permanent and temporary shocks, as well as by considering the vulnerability of interacting regions in comparison to isolated regions. Failure to incorporate these factors may result in an overestimation of resilience for “non‐climate” shocks.

Accurate segmentation of adrenal glands from CT images is essential for enhancing computer-aided diagnosis and surgical planning. However, the small size, irregular shape, and proximity to surrounding tissues make this task highly challenging. This study introduces a novel pipeline that significantly improves the segmentation of left and right adrenal glands by integrating advanced pre-processing techniques and a robust post-processing framework. Utilising a 2D UNet architecture with various backbones (VGG16, ResNet34, InceptionV3), the pipeline leverages test-time augmentation (TTA) and targeted removal of unconnected regions to enhance accuracy and robustness. Our results demonstrate a substantial improvement, with a 38% increase in the Dice similarity coefficient for the left adrenal gland and an 11% increase for the right adrenal gland on the AMOS dataset, achieved by the InceptionV3 model. Additionally, the pipeline significantly reduces false positives, underscoring its potential for clinical applications and its superiority over existing methods. These advancements make our approach a crucial contribution to the field of medical image segmentation.

Background and Objectives Fundamentally it is important to understand starch granule initiation and deposition of starch molecules during the growth of granules. The objective of this study was to investigate the morphology, composition, and structure of starches in sorghum from fifth day post‐anthesis (DPA) until the maturity (25 DPA). Findings The minimal size of sorghum starch for showing Maltese cross was 4 µm. The average size of starches on 5 DPA was 3.2 µm, and most starches did not exhibit Maltese cross. Amylose content was low (13.0%) on 5 DPA and increased to 31% on 25 DPA. The size of amylose was long with a peak at DP 1771 on 5 DPA and changed during the starch biosynthesis. The short‐chain amylopectin proportion significantly increased on 25 DPA. Conclusions The low amylose content and high proportion of long‐chain amylose might be favorable for the initial sorghum starch formation. The starch polymers were less radially oriented in the primary starches. Amylopectin in the periphery of a large sorghum starch was more branched than that of the inner part. Significance and Novelty The variations in the amylose length distributions and orientation of starch polymers provide new information on starch biosynthesis.

Diaphragm hyperaemia and regional blood flow distribution are impaired with ageing, potentially consequent to altered vascular structure and/or diminished vasomotor function. Evidence from locomotory skeletal muscle suggests that age‐related diaphragm vasomotor dysfunction may be related to a blunted endothelium‐mediated vasodilatation, decreased nitric oxide (NO) bioavailability and/or augmented reactive oxygen species (ROS) generation. We hypothesized that, in the medial costal diaphragm with old age, there would be fewer feed arteries (FAs) and impaired vasomotor function, via endothelium‐specific mechanisms, in first‐order (1A) arterioles. In young (Y) and old (O) Fischer‐344 rats, the number of medial costal diaphragm FAs was quantified. 1A arterioles (117–220 µm) were isolated, cannulated and pressurized via hydrostatic reservoirs. Thereafter endothelium‐dependent (via ACh) vasodilatory responses were assessed. In a separate set of arterioles, ACh‐mediated dilatation was assessed before and after treatment with the superoxide dismutase mimetic Tempol (100 µm) and Tempol plus the hydrogen peroxide (H2O2) scavenger catalase (100 U/ml). The average number of medial costal FAs was lower in the rat diaphragm with old age (p = 0.001). Endothelium‐ and nitric oxide synthase (NOS)‐dependent vasodilatation was 21% lower in medial costal 1A arterioles from O rats (p < 0.001). Tempol decreased ACh‐mediated vasodilatation of medial costal 1A arterioles from Y and O rats but did not eliminate age‐related differences. Tempol plus catalase further decreased ACh‐mediated vasodilatation in O but not Y vessels. In the medial costal diaphragm vasculature, ageing is associated with (1) arterial rarefaction, (2) impaired endothelium‐dependent vasodilatation via NOS‐ and ROS‐dependent mechanisms and (3) increased reliance on ROS‐mediated vasodilatation. image Key points Old age blunts the hyperaemic response and alters regional blood flow distribution in the diaphragm. The effect of ageing on vascular structure and function in respiratory skeletal muscle is unknown. In young and old Fischer‐344 rats of both sexes, we quantified the number of feed arteries (FAs) and assessed the vasoreactivity of first‐order (1A) arterioles in the medial costal diaphragm. The number of medial costal diaphragm FAs was lower in old rats. In 1A arterioles endothelium‐dependent vasodilatation was blunted, and reactive oxygen species (ROS)‐mediated vasodilatory signalling was greater in old rats. We found no evidence of sex differences in diaphragm macrovascular structure, endothelial function or ROS‐mediated signalling in young or old rats. Our findings in the diaphragm vasculature with ageing provide a mechanistic basis for the age‐related deficits in diaphragm blood flow capacity. Therapeutic interventions targeting the diaphragm vasculature to improve perfusion and oxygen delivery may reduce the burden of age‐related diaphragm dysfunction.