Author: slquinlan

Plant Phenomics. 2025 Oct 27;7(4):100126. doi: 10.1016/j.plaphe.2025.100126. eCollection 2025 Dec.

ABSTRACT

Unmanned aerial systems (UAS) are reliable tools for field phenotyping, enabling rapid, large-scale, and cost-effective data collection to support breeding programs. However, many UAS-based approaches rely on manual data processing, limiting scalability and efficiency. This study presents a fully automated pipeline for high-throughput phenotyping (HTP) of peanut crop architectural traits, including canopy height (CH), growth habit (GH), and mainstem prominence (MP) by integrating UAS imagery, a vision foundation model-Segment Anything Model (SAM), and convolutional neural networks (CNN). SAM auto-mask generator mode was used to identify field extent and orientation, while SAM interactive mode enabled individual plot segmentation using auto-generated point prompts. Terrain points automatically sampled near each plot were used to model the ground surface and compute the canopy height model, allowing CH estimations at the plot level. CH estimations showed strong agreement with manual measurements (R² ​= ​0.78, RMSE ​= ​3 ​cm, MAPE ​= ​10 ​%). For MP and GH estimation, three pre-trained CNN models (AlexNet, ResNet18, and EfficientNet-B0) were evaluated, with AlexNet achieving the highest accuracy (89 ​% for GH, 83 ​% for MP). To assess the feasibility of using these HTP-derived estimations in plant breeding, quantitative trait loci (QTL) analysis was performed, identifying major-effect loci associated with these traits. The results were consistent with conventional QTL mapping methods, demonstrating that UAS-based phenotyping provides reliable trait data for genetic studies in peanut breeding. Overall, our deep learning-based data processing workflow minimizes manual efforts, providing an efficient and scalable approach that can accelerate genetic studies and trait selection in large-scale breeding programs.

PMID:42040996 | PMC:PMC13109298 | DOI:10.1016/j.plaphe.2025.100126

Plant Dis. 2026 Apr 27. doi: 10.1094/PDIS-01-26-0221-SC. Online ahead of print.

ABSTRACT

Cotton bacterial blight (CBB), caused by Xanthomonas citri pv. malvacearum (Xcm), reemerged in U.S. cotton production without evidence of new pathogen races. To test whether resistant cotton can act as an inoculum source, we conducted replicated field trials over two seasons using Xcm strains with distinct spontaneous antibiotic resistance markers facilitating source tracking from inoculated resistant and susceptible seed embedded within rows of susceptible and uninoculated plants. Xcm was recovered from uninoculated susceptible plants in both years, originating from both resistant and susceptible sources, with recovery highest early in the season and declining over time. Most isolations were from asymptomatic leaves, and rare seed-to-seed transmission from resistant plants was confirmed (0.13%). These findings provide field-based evidence that resistant cotton can asymptomatically harbor Xcm and potentially contributing to pathogen persistence.

PMID:42037266 | DOI:10.1094/PDIS-01-26-0221-SC

Epidemics. 2026 Apr 9;55:100914. doi: 10.1016/j.epidem.2026.100914. Online ahead of print.

ABSTRACT

SARS-CoV-2, the virus responsible for COVID-19, emerged in late 2019 and rapidly spread worldwide. Inferring transmission direction between epidemiologically linked cases is an important component of outbreak investigation, yet symptom-onset-based heuristics can become unreliable when onset differences are small due to incubation variability, reporting noise, and asymptomatic infections. Using a detailed contact-tracing dataset, we develop Pseudo-Time Reconstruction for Epidemic (PTRE), a network-informed analytical framework that integrates symptom timing, individual-level information, and contact network structure to induce a relative ordering of cases. We apply this framework to regional COVID-19 contact-tracing data and evaluate directional discrimination across onset-gap regimes. We further validate PTRE through simulation studies with known ground-truth transmission directions on diverse network topologies. Our results show a regime-dependent pattern: PTRE aligns with onset-based ordering when temporal separation is large, while structural connectivity provides a complementary directional signal when onset differences are minimal. These findings highlight both the potential and the limitations of integrating network information to enhance directional discrimination under temporal ambiguity.

PMID:41985381 | DOI:10.1016/j.epidem.2026.100914

Plant J. 2026 Apr;126(1):e70856. doi: 10.1111/tpj.70856.

ABSTRACT

Caterpillars and beetles are among the most economically damaging defoliating insects, and their economic damage is predicted to increase in the coming decades. Hence the use of genetically derived resistance to supplement other pest control strategies is warranted. In soybean (Glycine max (L.) Merr.), a major determinant for resistance is the quantitative trait locus, QTL-M. Glyma07g14530, the gene underlying QTL-M, encodes a feeding-inducible flavonol 3-O-glycosyltransferase (F3GlcT or UGT78D2) that glucosylates kaempferol, as well as quercetin, myricetin, and isorhamnetin. The resistant allele has a premature stop codon in it, thus preventing the glucosylation and sequestration of flavonols in the vacuole, leading to a concomitant accumulation of proanthocyanidins and manifestation of resistance. Expressing the dominant (susceptible) allele in resistant plants restores susceptibility, and silencing the susceptible allele results in resistance. The discovery and characterization of GmF3GlcT helps clarify the role of flavonoids in resistance to leaf-chewing insects and facilitates the development of insect-resistant cultivars that ultimately can lower production costs and reduce insecticide applications.

PMID:41985162 | PMC:PMC13082823 | DOI:10.1111/tpj.70856

Evol Appl. 2026 Mar 19;19(3):e70225. doi: 10.1111/eva.70225. eCollection 2026 Mar.

ABSTRACT

Genetic rescue is the increase in individual or population fitness caused by new genetic variation. It typically involves the deliberate movement of genetically diverse individuals into small and isolated populations to reduce inbreeding and maladaptation while enhancing evolutionary potential. Despite growing interest, gaps in theory and empirical evidence have limited the wider application of genetic rescue in conservation biology. To address these gaps, we put together this Special Issue on Genetic Rescue. The issue assembles 21 original papers that evaluate or apply genetic rescue and related approaches in conservation. The contributions include empirical studies and simulations spanning diverse animal and plant species across varied ecological and socio-environmental contexts, alongside perspectives and syntheses. Collectively, they demonstrate the value of integrating population genomics and evolutionary biology into conservation, identify opportunities and limitations of genetic rescue, and underscore its potential to improve resilience, adaptive capacity, and the long-term persistence of biodiversity.

PMID:42015951 | PMC:PMC13093423 | DOI:10.1111/eva.70225

Plant Dis. 2026 Apr 9. doi: 10.1094/PDIS-09-25-1919-RE. Online ahead of print.

ABSTRACT

Vegetable production in Georgia faces significant challenges from different diseases and pests and one of them is root-knot nematodes (Meloidogyne spp.). In a recent statewide survey in major vegetable-growing regions in Georgia, two Meloidogyne species, guava root-knot nematode (M. enterolobii) and peach root-knot nematode (M. floridensis), were detected for the first time in addition to the commonly occurring southern root-knot nematode, M. incognita. Despite their emerging significance, comparative data on the reproductive capabilities of these recently detected species on key vegetable crops are lacking. This study evaluated the reproduction potential of M. enterolobii, M. floridensis, and M. incognita on eight economically important vegetable crops in Georgia: beet, broccoli, cabbage, cantaloupe, pepper, snap bean, squash, and tomato. Reproduction factor (final population/initial inoculum), galling index, and number of eggs per gram of root were quantified for each species in each vegetable crop. All Meloidogyne species reproduced in the tested vegetable crops in this study. In most experiments, M. enterolobii exhibited significantly higher Rf, galling indices, and egg counts per gram of root compared to M. incognita and M. floridensis across the majority of tested vegetable crops, with the exception of squash and tomato. On these two crops, all three species showed comparable values for the measured parameters. These findings highlight the high reproductive capacity and pathogenic potential of M. enterolobii on a broad range of vegetable hosts. Based on these findings, we hypothesize that in mixed-species populations within infested vegetable fields, M. enterolobii may become the predominant species due to its superior reproductive performance.

PMID:41955121 | DOI:10.1094/PDIS-09-25-1919-RE

J Vis Exp. 2026 Mar 20;(229). doi: 10.3791/69753.

ABSTRACT

Phytophthora is an oomycete genus that contains numerous destructive plant pathogens, among which is the broad-host-range species P. palmivora. Functional genomics of Phytophthora has been constrained by the limited availability of robust, easy-to-implement transformation methods. Among the available approaches, Agrobacterium-mediated transformation (AMT) is particularly attractive because it requires minimal specialized equipment and often produces stable transformants with single-copy gene insertions. Most AMT protocols for fungal and oomycete transformation use minimal medium (MM) salts in Agrobacterium induction and co-cultivation media. Preparing these media typically involves making and mixing multiple stock solutions, with some containing numerous components at different concentrations. The process is time-consuming and error-prone, therefore leading to inconsistent transformation success. Here, we present a streamlined, simple, and reproducible AMT protocol for P. palmivora that uses commercially available Murashige and Skoog (MS) basal medium to prepare the Agrobacterium induction and co-cultivation media. This protocol has also been successfully applied to transform P. capsici and may be applied to other Phytophthora spp. as well.

PMID:41941429 | DOI:10.3791/69753

FEBS J. 2026 Mar 31. doi: 10.1111/febs.70528. Online ahead of print.

ABSTRACT

Serine hydroxymethyltransferase (SHMT) is a conserved enzyme in folate-mediated one-carbon metabolism, where it contributes to nucleotide biosynthesis, methylation capacity, and cellular stress responses. Amino acid polymorphisms of soybean SHMT8 are known to affect the resistance of soybean to its primary pathogen, the soybean cyst nematode (SCN). A set of SHMT8 variants from ethyl methanesulfonate (EMS)-mutagenized soybean populations has been identified with varying resistance phenotypes, but their biochemical consequences remain poorly understood. Here, we use biochemical and structural studies to assess the impacts of the A149T variant on soybean SHMT8. Despite the conservative nature of the substitution, A149T reduces folate binding, pyridoxal-5′-phosphate-dependent catalysis, and thermal stability. High-resolution crystal structures (1.9-2.3 Å resolution) reveal only very minor structural changes. However, while the usual tetrameric assembly of the enzyme is retained at the high protein concentration in crystals, multiple other methods including a 2.9 Å cryo-electron microscopy (cryo-EM) structure show that the A149T variant is predominantly a dimer. Significant structural changes in the dimer are consistent with the observed biochemical impacts of the variant and help explain the well-known reduction in activity associated with dimerization of SHMT in other systems. We also find destabilization of the tetrameric assembly in other SHMT8 variants associated with changes in SCN resistance, suggesting that weakened oligomerization may be a common consequence of such mutations. Together, these results highlight quaternary structure as a critical determinant of SHMT8 activity and stability and suggest a potential mechanistic link between enzyme biochemistry and soybean defense.

PMID:41914311 | DOI:10.1111/febs.70528

Antimicrob Agents Chemother. 2026 Mar 30:e0168925. doi: 10.1128/aac.01689-25. Online ahead of print.

ABSTRACT

Annually, aspergillosis, candidiasis, cryptococcosis, and mucormycosis result in approximately 1,500,000, 650,000, 120,000, and 59,000 deaths, respectively. Mortality rates among patients receiving antifungal drug treatment range from 30% to 90%. Therefore, there is an urgent need to improve the efficacy of antifungal drug therapies against infections by these high-priority fungal diseases. Aspergillus fumigatus, Candida albicans, Cryptococcus neoformans, and Rhizopus delemar are the most common causative pathogens. We have previously developed DectiSomes, which are liposomes loaded with antifungal drugs and coated with the carbohydrate recognition domains of mouse Dectin-1 and/or Dectin-2. We demonstrated that the murine DectiSomes efficiently bound and killed these pathogens growing in vitro and/or in mouse disease models. With the plan to move DectiSomes into the clinic with the human Dectin orthologs, we were concerned that the significant sequence divergence between mouse and human Dectin-1 and Dectin-2 carbohydrate recognition domains could have altered pathogen specificity. Herein, we compared the functionality of the human and mouse Dectin-1 and Dectin-2 orthologs in targeting DectiSomes to these pathogens. Binding and growth inhibition data on A. fumigatus and C. neoformans supported their functional similarity, while results with C. albicans and R. delemar indicated some functional divergence. Despite these differences, our results demonstrate that both human and mouse DectiSomes are effective at binding and killing all four diverse fungal pathogens.

PMID:41910313 | DOI:10.1128/aac.01689-25

Front Plant Sci. 2026 Mar 13;17:1733525. doi: 10.3389/fpls.2026.1733525. eCollection 2026.

ABSTRACT

Drought is the most damaging abiotic stress affecting soybean production, with variable rainfall contributing significantly to year-to-year yield variability. Breeding efforts aim to develop cultivars with stable and competitive yields under both drought and non-drought stressed conditions. However, drought tolerance in soybean is a highly complex trait, influenced by diverse physiological, morphological, genetic factors and environments. Identifying genotypes with improved drought tolerance is challenging because traditional phenotyping methods for drought tolerance are subjective and time-consuming. Furthermore, quantitative trait loci (QTLs) associated with drought tolerance typically exhibit small effects and limited consistency across environments and populations. These challenges highlight the need for improved methodologies to identify and evaluate promising sources of genetic variation. This review summarizes the current state of drought tolerance breeding in soybean and discusses recent advances in remote sensing, transcriptomics, proteomics, and metabolomics aimed at enhancing drought tolerance research and cultivar development in soybean.

PMID:41907757 | PMC:PMC13021458 | DOI:10.3389/fpls.2026.1733525