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Drought is a major environmental constraint limiting global peanut productivity. Wild peanut species, characterized by greater genetic diversity, represent valuable resources for improving drought resilience in cultivated peanut. However, the molecular mechanisms underpinning drought tolerance in wild peanut species remain largely unexplored. This study evaluated the drought tolerance of three wild-type peanut accessions from two different species, Arachis dardani GK12946, Arachis dardani V7215, and Arachis ipaënsis K30076. Physiological measurements such as fresh weight and dry weight revealed statistically non-significant differences between drought-stressed and well-watered conditions, indicating strong inherent drought tolerance. Transcriptome analysis revealed that 3272, 3648, and 1181 genes in leaf samples of A. dardani GK12946, A. dardani V7215, and A. ipaënsis K30076 were differentially expressed, respectively. In root samples, 3014, 3472, and 2033 genes were differentially expressed in the same accessions. Notably, differentially expressed genes (DEGs) and set intersection (Venn) analysis suggests A. dardani V7215 exhibited the highest number of DEGs (1155) uniquely expressed in leaves, and 899 DEGs uniquely expressed in roots, suggesting accession-specific gene expression. Gene Ontology enrichment revealed that upregulated genes were associated with abiotic stress responses, temperature stimulus, heat stress, and DNA-binding transcription factor activity. Co-expression network analysis using WGCNA identified key drought-responsive modules, enriched for GO terms like stress regulation, protein folding, as well as GST family amino acid metabolic processes. Overall, this study provides comprehensive insights into the molecular basis of drought tolerance in wild peanut accessions. Our findings establish a valuable resource for functional genomics and crop improvement under water-limited conditions.

Biofuels can help reduce dependence on petroleum-based fuels, and peanut oil is a potentially valuable biofuel source. This study estimates the carbon intensity (CI) of peanut oil production in Texas using the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. Both the Argonne National Laboratory (ANL) and California (CA) versions of GREET were employed to calculate CI values across various scenarios. Six pathways were developed considering farming, transportation, oil extraction, and land use change processes. These scenarios varied based on peanut varieties (High Oil and Conventional Oil content), irrigation methods (irrigated or dryland), and locations (Stephenville, Dilley, and Vernon): (1) Stephenville Dryland var. High Oil, (2) Stephenville Fully Irrigated var. High Oil, (3) Vernon Limited Irrigated var. High Oil/Rye cover crop, (4) Vernon Limited Irrigated var. Conventional Oil, (5) Vernon Limited Irrigated var. Conventional Oil/Rye cover crop, (6) Dilley Fully Irrigated var. High Oil. The CI values of these scenarios were compared with those of soybean oil. According to the ANL-GREET model results, the highest CI was observed in the Dryland scenario, though it remains lower than that of soybean oil. The lowest CI was found in the Vernon Span 17 Rye Irrigated scenario. The CA-GREET model results indicated the lowest CI for Dilley and the highest for Stephenville Dryland. The high oil yield in Dilley (1.25 tons/acre) significantly reduced the CI compared to the yield in Stephenville Dryland (0.25 tons/acre). These findings suggest that peanut oil is a promising addition to the currently available biofuel options.

Aims: The plant and soil microbiome serve as a reservoir of beneficial endophytic bacteria, including plant-growth-promoting (PGP) Bacillus subtilis, which enhances nutrient acquisition and protects plants against environmental stresses. We isolated novel bacteria from cultivated peanut plants selected from agricultural fields that survived a season of water scarcity and high temperatures. Experiments were conducted to determine whether plant survival was partially attributable to the presence of beneficial microbes that could be harnessed for future biotechnology applications. Methods and Results: Seven bacterial isolates of Bacillus spp. were identified through 16S rRNA sequencing, revealing close affiliations to B. subtilis, B. safensis, and B. velezensis. Growth curve analysis and colony morphology characterization revealed distinct growth patterns across different media types, while phytohormone production assays demonstrated variable indole-3-acetic acid (IAA) synthesis among isolates. When applied as seed biopriming agents to two hybrid peanut varieties, bacterial inoculation significantly enhanced root surface area and root tip development, with B. subtilis-TAM84A showing the most pronounced effects on ‘Schubert’ roots. In addition, vegetative growth assessments indicated increased branch numbers and plant height, particularly with treatments with B. velezensis strains TAM6B and TAM61A, and a consortium of all isolates. Under drought conditions, inoculated plants exhibited delayed wilting and improved recovery after rehydration, indicating enhanced drought resilience. Conclusions: Several local Bacillus strains recovered from drought-tolerant peanut plants showed improved growth and drought tolerance in greenhouse-grown peanut plants. Ongoing field studies aim to evaluate the potential of regionally adapted microbial populations as soil amendments during planting. Impact Statement: This study demonstrates that local strains of Bacillus isolated from drought-resistant peanut plants possess significant potential as bioinoculants to improve growth and drought tolerance in potted peanut plants. This work provides a foundation for utilizing regionally adapted microbial populations to address agricultural challenges related to water scarcity.

Identification of peanut cultivars for distinct phenotypic or genotypic traits whether using visual characterization or laboratory analysis requires substantial expertise, time, and resources. A less subjective and more precise method is needed for identification of peanut germplasm throughout the value chain. In this proof-of-principle study, the accuracy of Raman spectroscopy (RS), a non-invasive, non-destructive technique, in peanut phenotyping and identification is explored. We show that RS can be used for highly accurate peanut phenotyping via surface scans of peanut leaves and the resulting chemometric analysis: On average 94% accuracy in identification of peanut cultivars and breeding lines was achieved. Our results also suggest that RS can be used for highly accurate determination of nematode resistance and susceptibility of those breeding lines and cultivars. Specifically, nematode-resistant peanut cultivars can be identified with 92% accuracy, whereas susceptible breeding lines were identified with 81% accuracy. Finally, RS revealed substantial differences in biochemical composition between resistant and susceptible peanut cultivars. We found that resistant cultivars exhibit substantially higher carotenoid content compared to the susceptible breeding lines. The results of this study show that RS can be used for quick, accurate, and non-invasive identification of genotype, nematode resistance, and nutrient content. Armed with this knowledge, the peanut industry can utilize Raman spectroscopy for expedited breeding to increase yields, nutrition, and maintaining purity levels of cultivars following release.

The cultivated peanut (Arachis hypogaea L.) is a legume consumed worldwide in the form of oil, nuts, peanut butter, and candy. Improving peanut production and nutrition will require new technologies to enable novel trait development. Clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR–Cas9) is a powerful and versatile genome-editing tool for introducing genetic changes for studying gene expression and improving crops, including peanuts. An efficient in vivo transient CRISPR–Cas9- editing system using protoplasts as a testbed could be a versatile platform to optimize this technology. In this study, multiplex CRISPR–Cas9 genome editing was performed in peanut protoplasts to disrupt a major allergen gene with the help of an endogenous tRNA-processing system. In this process, we successfully optimized protoplast isolation and transformation with green fluorescent protein (GFP) plasmid, designed two sgRNAs for an allergen gene, Ara h 2, and tested their efficiency by in vitro digestion with Cas9. Finally, through deep-sequencing analysis, several edits were identified in our target gene after PEG-mediated transformation in protoplasts with a Cas9 and sgRNA-containing vector. These findings demonstrated that a polyethylene glycol (PEG)-mediated protoplast transformation system can serve as a rapid and effective tool for transient expression assays and sgRNA validation in peanut.

Sclerotinia blight caused by Sclerotinia minor (Jagger) is a significant threat to peanut production; therefore varietal improvement toward this disease is needed. To date, there have been no reported quantitative trait locus (QTL) associated with Sclerotinia blight resistance in peanut. Hence, the objective of this study was to identify QTLs for Sclerotinia blight resistance. A total of 90 F 2:6 recombinant inbred lines, derived from a released cultivar Tamrun OL07 and a breeding line Tx964117, were used as mapping population and field experiments were conducted in 2010, 2012 and 2018 at the Texas A&M AgriLife Research and Extension Center at Stephenville, Texas. A genetic map was developed using 1211 SNP markers based on double digest restriction-site associated DNA sequencing (ddRAD-Seq). In total, seven QTLs were identified, two QTLs from 2010 and five QTLs from 2018, with LOD score values of 3.2 to 7.2 and explaining 6.6–25.6% phenotypic variance. Among these QTLs, three were detected in common by WinQTLCart and R/qtl. Interestingly, one of the QTLs coincides with a previously reported peanut Leaf spot resistance QTL. The findings from this study not only provide insights into disease resistant QTLs in peanut but can also be used as potential targets for breeding programs to enhance Sclerotinia blight resistance through molecular breeding.

Field studies were conducted from 2007 to 2010 to evaluate the response of peanut cultivars to different fungicides, application timings, and methods. Overall, fungicides reduced Sclerotinia blight incidence and increased pod yields when applied to susceptible and partially resistant cultivars. Disease suppression was greater when full fungicide rates were applied preventatively; however, yields between fungicide treated plots were similar. Lower levels of disease and higher yields were achieved with the partially resistant cultivar Tamrun OL07 compared to the susceptible cultivars Flavor Runner 458 and Tamrun OL 02. Despite possessing improved resistance Tamrun OL07 responded to all fungicide applications. While similar levels of disease control were achieved with broadcast or banded applications made during the day or at night, the yield response for the different application methods was inconsistent among years. A negative relationship (slope = −73.8; R 2 = 0.73 ; P < 0.01 ) was observed between final disease incidence ratings and yield data from studies where a fungicide response was observed. These studies suggest that both boscalid and fluazinam are effective at controlling Sclerotinia blight in peanuts. Alternative management strategies such as nighttime and banded applications could allow for lower fungicide rates to be used; however, additional studies are warranted.

Traditionally, psychomotor skills training for nursing students involves didactic instruction followed by procedural review and practice with a task trainer, manikin, or classmates. This article describes a novel method of teaching psychomotor skills to associate degree and baccalaureate nursing students, Cooperative Learning Simulation Skills Training (CLSST), in the context of nasogastric tube insertion using a deliberate practice-to-mastery learning model. Student dyads served as operator and student learner. Automatic scoring was recorded in the debriefing log. Student pairs alternated roles until they achieved mastery, after which they were assessed individually. Median checklist scores of 100% were achieved by students in both programs after one practice session and through evaluation. Students and faculty provided positive feedback regarding this educational innovation. CLSST in a deliberate practice-to-mastery learning paradigm offers a novel way to teach psychomotor skills in nursing curricula and decreases the instructor-to-student ratio. [Traditionally, psychomotor skills training for nursing students involves didactic instruction followed by procedural review and practice with a task trainer, manikin, or classmates. This article describes a novel method of teaching psychomotor skills to associate degree and baccalaureate nursing students, Cooperative Learning Simulation Skills Training (CLSST), in the context of nasogastric tube insertion using a deliberate practice-to-mastery learning model. Student dyads served as operator and student learner. Automatic scoring was recorded in the debriefing log. Student pairs alternated roles until they achieved mastery, after which they were assessed individually. Median checklist scores of 100% were achieved by students in both programs after one practice session and through evaluation. Students and faculty provided positive feedback regarding this educational innovation. CLSST in a deliberate practice-to-mastery learning paradigm offers a novel way to teach psychomotor skills in nursing curricula and decreases the instructor-to-student ratio. [ J Nurs Educ . 2015;54(3, Suppl.):S47–S51.]

The Alaska Hare (Lepus othus Merriam 1900) is the largest lagomorph in North America but remains one of the most poorly studied terrestrial mammals on the continent. Its current distribution is restricted to western Alaska south of the Brooks Range, but historical accounts from north of the Brooks Range (the North Slope) have led to confusion over its past, present, and predicted future distributions. To determine if L. othus occurs or historically occurred on the North Slope, we surveyed museum collections, vetted observational accounts, and produced a spatial distribution model based on the resulting georeferenced records. We located a historic specimen long presumed lost that suggests the occurrence of L. othus on the North Slope as recently as the late 1800s. We also uncovered evidence of L. othus and (or) Mountain Hare (Lepus timidus Linnaeus 1758) on several islands in the Bering Sea, raising the possibility of recurring gene flow between these closely related species across seasonal ice connecting Asia and North America. While our results paint a more complete picture of the current distribution of L. othus, persistent uncertainties surrounding its taxonomic status and potential northward range shift onto lands reserved for oil and gas development call for additional study.

Natural language processing (NLP) and named entity recognition (NER) techniques are applied to collections of newspaper articles from four cities in the U.S. Southwest. The results are used to generate a network of water management institutions that reflect public perceptions of water management and the structure of water management in these areas. This structure can be highly centralized or fragmented; in the latter case, multiple peer institutions exist that may cooperate or be in conflict. This is reflected in the public discourse of the water consumers in these areas and can, we contend, impact the potential responses of management agencies to challenges of water supply and quality and, in some cases, limit their effectiveness. Flagstaff, AZ, Tucson, AZ, Las Vegas, NV, and the Grand Valley, CO, are examined, including more than 110,000 articles from 2004–2012. Documents are scored by association with water topics, and phrases likely to be institutions are extracted via custom NLP and NER algorithms; those institutions associated with water-related documents are used to form networks via document co-location. The Grand Valley is shown to have a markedly different structure, which we contend reflects the different historical trajectory of its development and its current state, which includes multiple institutions of roughly equal scope and size. These results demonstrate the utility of using NLP and NER methods to understanding the structure and variation of water management systems.