Plant Methods最新文献

Genomic Selection (GS) predicts traits using genome-wide markers, speeding up genetic progress and enhancing breeding efficiency. Recent emphasis has been placed on deep learning models to enhance prediction accuracy. However, current deep learning models focus on learning specific phenotypes for the given task, overlooking the inter-correlations among different phenotypes. In response, we introduce MtCro, a multi-task learning approach that simultaneously captures diverse plant phenotypes within a shared parameter space. Extensive experiments reveal that MtCro outperforms mainstream models, including DNNGP and SoyDNGP, with performance gains of 1-9% on the Wheat2000 dataset, 1-8% on Wheat599, and 1-3% on Maize8652. Furthermore, comparative analysis shows a consistent 2-3% improvement in multi-phenotype predictions, emphasizing the impact of inter-phenotype correlations on accuracy. By leveraging multi-task learning, MtCro efficiently captures diverse plant phenotypes, enhancing both model training efficiency and prediction accuracy, ultimately accelerating the progress of plant genetic breeding. Our code is available on https://github.com/chaodian12/mtcro .

Background: D. giganteiformis subsp. pontederae and D. superbus subsp. superbus are protected or critically endangered species in several European regions; therefore, developing an efficient in vitro micropropagation protocol is essential for germplasm conservation and recultivation purposes.

Results: After germination, one-nodal segments of both species were transferred onto several MS media supplemented with 3% sucrose and different types of cytokinins (at a concentration of 4.5 µM) alongside 0.54 µM 1-naphthaleneacetic acid (NAA) for the multiplication phase for 3 weeks. The shoot clusters were subsequently transferred onto elongation medium (plant growth regulator-free MS medium) for 3 weeks. Individual shoots separated from the shoot clusters were cultured on MS medium supplemented with 0.54 µM NAA and 2% sucrose for 3 weeks for rooting. Taking into account the effects and after-effects of cytokinins, we found that the most suitable cytokinin for D. giganteiformis subsp. pontederae was N-(2-isopentenyl)-adenine (2-iP), while for D. superbus subsp. superbus it was meta-topolin (mT).

Conclusions: In vitro micropropagation methods were developed for two endangered Dianthus species (D. giganteiformis subsp. pontederae and D. superbus subsp. superbus) by determining the optimal type of cytokinin to be used during the multiplication phase. The protocols are designed to produce large quantities of propagation material for recultivation, educational, and research purposes within three months.

Background: Allergic contact dermatitis and chronic actinic dermatitis are frequently observed among florists and gardeners due to exposure to potentially allergenic plants and plant products. Tulipalin A, an alpha-methylene-gamma-butyrolactone, is a common allergen synthesized by Tulipa genera, but its natural occurrence across Plantae remains unexplored.

Results: Here, we demonstrated the secondary electrospray ionization coupled Orbitrap mass spectrometry (SESI-Orbitrap MS) methodology for quantifying tulipalin A release from plants upon injury. By outlining temperature treatment, homogenization strategies and plant organ distribution, we show that processing flower samples stored at room temperature using a garlic press yielded the highest tulipalin A release upon injury. Via real-time monitoring, tulipalin A release was demonstrated to occur immediately upon homogenization. Next, the biosynthesis of tulipalin A across spring flowers was landscaped. Highlighting Rosa, Gerbera, Neapolitanum, Ranunculus, Othocalis, Muscari, Galanthus, Tulipa and Alstroemeria to release detectable amounts of tulipalin A upon injury. Tulipalin A was predominantly released from the Tulipa and Alstroemeria species, both belonging to the Liliales order, as stated in previous clinical and research studies.

Conclusions: In conclusion, a rapid method using the SESI-Orbitrap MS is reported to detect and track tulipalin A synthesis across plant organs and outline its cross-species distribution. Our methodology can be easily adapted for mapping other volatile plant defense metabolites and identify potentially allergenic plants. By addressing these aspects, we can ensure a safer work environment for florists and gardeners.

Genomic-assisted breeding has transitioned from theoretical concepts to practical applications in breeding. Genomic selection (GS) predicts genomic breeding values (GEBV) using dense genetic markers. Single-stage models predict GEBVs from phenotypic observations in one step, fully accounting for the entire variance-covariance structure among genotypes, but face computational challenges. Two-stage models, preferred for their simplicity and efficiency, first calculate adjusted genotypic means accounting for spatial variation within each environment, then use these means to predict GEBVs. However, unweighted (UNW) two-stage models assume independent errors among adjusted means, neglecting correlations among estimation errors. Here, we show that fully-efficient two-stage models perform similarly to UNW models for randomized complete block designs but substantially better for augmented designs. Our simulation studies demonstrate the impact of the fully-efficient methodology on prediction accuracy across different implementations and scenarios. Incorporating non-additive effects and augmented designs significantly improved accuracy, emphasizing the synergy between design and model strategy. Consistent performance requires the estimation error covariance to be incorporated into a random effect (Full_R model) rather than into the residuals. Our results suggest that the fully-efficient methodology, particularly the Full_R model, should be more prevalent, especially as GS increases the appeal of sparse designs. We also provide a comprehensive theoretical background and open-source R code, enhancing understanding and facilitating broader adoption of fully-efficient two-stage models in GS. Here, we offer insights into the practical applications of fully-efficient models and their potential to increase genetic gain, demonstrating a $13.80\%$ improvement after five selection cycles when moving from UNW to Full_R models.

Throughout their lifetime, trees store valuable environmental information within their wood. Unlocking this information requires quantitative analysis, in most cases of the surface of wood. The conventional pathway for high-resolution digitization of wood surfaces and segmentation of wood features requires several manual and time consuming steps. We present a semi-automated high-throughput pipeline for sample preparation, gigapixel imaging, and analysis of the anatomy of the end-grain surfaces of discs and increment cores. The pipeline consists of a collaborative robot (Cobot) with sander for surface preparation, a custom-built open-source robot for gigapixel imaging (Gigapixel Woodbot), and a Python routine for deep-learning analysis of gigapixel images. The robotic sander allows to obtain high-quality surfaces with minimal sanding or polishing artefacts. It is designed for precise and consistent sanding and polishing of wood surfaces, revealing detailed wood anatomical structures by applying consecutively finer grits of sandpaper. Multiple samples can be processed autonomously at once. The custom-built open-source Gigapixel Woodbot is a modular imaging system that enables automated scanning of large wood surfaces. The frame of the robot is a CNC (Computer Numerical Control) machine to position a camera above the objects. Images are taken at different focus points, with a small overlap between consecutive images in the X-Y plane, and merged by mosaic stitching, into a gigapixel image. Multiple scans can be initiated through the graphical application, allowing the system to autonomously image several objects and large surfaces. Finally, a Python routine using a trained YOLOv8 deep learning network allows for fully automated analysis of the gigapixel images, here shown as a proof-of-concept for the quantification of vessels and rays on full disc surfaces and increment cores. We present fully digitized beech discs of 30-35 cm diameter at a resolution of 2.25  $\mu$ m, for which we automatically quantified the number of vessels (up to 13 million) and rays. We showcase the same process for five 30 cm length beech increment cores also digitized at a resolution of 2.25  $\mu$ m, and generated pith-to-bark profiles of vessel density. This pipeline allows researchers to perform high-detail analysis of anatomical features on large surfaces, test fundamental hypotheses in ecophysiology, ecology, dendroclimatology, and many more with sufficient sample replication.

Background: Rice blast is one of the most destructive diseases in rice cultivation, significantly threatening global food security. Timely and precise detection of rice panicle blast is crucial for effective disease management and prevention of crop losses. This study introduces ConvGAM, a novel semantic segmentation model leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM). This design aims to enhance feature extraction and focus on critical image regions, addressing the challenges of detecting small and complex disease patterns in UAV-captured imagery. Furthermore, the model incorporates advanced loss functions to handle data imbalances effectively, supporting accurate classification across diverse disease severities.

Results: The ConvGAM model, leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM), achieves outstanding performance in feature extraction, crucial for detecting small and complex disease patterns. Quantitative evaluation demonstrates that the model achieves an overall accuracy of 91.4%, a mean IoU of 79%, and an F1 score of 82% on the test set. The incorporation of Focal Tversky Loss further enhances the model's ability to handle imbalanced datasets, improving detection accuracy for rare and severe disease categories. Correlation coefficient analysis across disease severity levels indicates high consistency between predictions and ground truth, with values ranging from 0.962 to 0.993. These results confirm the model's reliability and robustness, highlighting its effectiveness in rice panicle blast detection under challenging conditions.

Conclusion: The ConvGAM model demonstrates strong qualitative advantages in detecting rice panicle blast disease. By integrating advanced feature extraction with the ConvNeXt-Large backbone and GAM, the model achieves precise detection and classification across varying disease severities. The use of Focal Tversky Loss ensures robustness against dataset imbalances, enabling accurate identification of rare disease categories. Despite these strengths, future efforts should focus on improving classification accuracy and adapting the model to diverse environmental conditions. Additionally, optimizing model parameters and exploring advanced data augmentation techniques could further enhance its detection capabilities and expand its applicability to broader agricultural scenarios.

Background: Crossover interactions stemming from phenotypic plasticity complicate selection decisions when evaluating hybrid maize with superior grain yield and consistent performance. Consequently, a two-year, region-wide investigation of 45 hybrids maize across Nepal was performed with the aim of disclosing both site and wide adapted hybrids. Utilizing an innovative "ProbBreed" package, based on Bayesian probability analysis of randomized complete block designs with three replicated trials at each station, this study substantively streamlines hybrids maize selection.

Results: This finding revealed substantial genetic, environmental, and interactive influences on grain yield (p < 0.05). Among the hybrids, DKC9149 (8.8 tons/ha) emerged as the elite with probability coefficient of (0.39), followed by NK6607(0.35 & 8.6 tons/ha). Joint probability analysis identified RMH1899 super (0.23 & 8.3 tons/ha), followed by RMH 666 (0.15 & 8.4 tons/ha) and Uttam 121 (0.11 & 8.6 tons/ha), all of which accounted for overall environmental conditions. Additionally, over the years, DKC 9149, NK 6607(0.18 & 8.6 tons/ha), GK 3254(0.18 & 8.5 tons/ha), Shann 111(0.12 & 8.4 tons/ha), Sweety 1(0.13 & 8.4 tons/ha), and ADV 756(0.10 & 8.2 tons/ha) consistently demonstrated superior performance and stability. Delving with site specific recommendations include Nepalgunj: RMH 9999(8.5 tons/ha), NK 6607(8.6 tons/ha); Parwanipur: DKC 9149, MM 2033(8.5 tons/ha); Rampur: ADV 756, DKC 9149, MM 2929(8.6 tons/ha); and Tarahara: GK 3254(8.5 tons/ha), NK 6607(8.6 tons/ha), Uttam 121.

Conclusion: Thus, Selected hybrids are predicted to outperform within the recommended domain. Over and above, integrating genomic information into Bayesian models expected to enhance prediction accuracy and expedite breeding progress.

Background: Quantifying plant transpiration via thermal imaging is desirable for applications in agriculture, plant breeding, and plant science. However, thermal imaging under natural non-steady state conditions is currently limited by the difficulty of quantifying thermal properties of leaves, especially specific heat capacity (C_p). Existing literature offers only rough estimates of C_p and lacks simple and accurate methods to determine it.

Results: We developed a non-invasive method to quantify k (the product of leaf thickness (lt), leaf density(ρ), and C_p), by fitting a leaf energy balance model to a leaf temperature (T_leaf) transient during and after a ~ 10 s light pulse. C_p was then estimated by dividing k by lt*ρ. Using this method, we quantified C_p for 13 horticultural and tropical plant species, and explored the relationship between C_p and leaf water content, specific leaf area and T_leaf response rate during the light pulse. Values of C_p ranged between 3200-4000 J kg^-1 K^-1, and were positively correlated with leaf water content. In species with very thick leaves, such as Phalaenopsis amabilis, we found leaf thickness to be a major factor in the temperature response to a short light pulse.

Conclusions: Our method allows for easy determination of leaf C_p of different species, and may help pave the way to apply more accurate thermal imaging under natural non-steady state conditions.

Background: Plant cell walls are made of a complex network of interacting polymers that play a critical role in plant development and responses to environmental changes. Thus, improving plant biomass and fitness requires the elucidation of the structural organization of plant cell walls in their native environment. The ¹³C-based multi-dimensional solid-state nuclear magnetic resonance (ssNMR) has been instrumental in revealing the structural information of plant cell walls through 2D and 3D correlation spectral analyses. However, the requirement of enriching plants with ¹³C limits the applicability of this method. To our knowledge, there is only a very limited set of methods currently available that achieve high levels of ¹³C-labeling of plant materials using ¹³CO_2, and most of them require large amounts of ¹³CO₂ in larger growth chambers.

Results: In this study, a simplified protocol for ¹³C-labeling of plant materials is introduced that allows ca 60% labeling of the cell walls, as quantified by comparison with commercially labeled samples. This level of ¹³C-enrichment is sufficient for all conventional 2D and 3D correlation ssNMR experiments for detailed analysis of plant cell wall structure. The protocol is based on a convenient and easy setup to supply both ¹³C-labeled glucose and ¹³CO₂ using a vacuum-desiccator. The protocol does not require large amounts of ¹³CO₂.

Conclusion: This study shows that our ¹³C-labeling of plant materials can make the accessibility to ssNMR technique easy and affordable. The derived high-resolution 2D and 3D correlation spectra are used to extract structural information of plant cell walls. This helps to better understand the influence of polysaccharide-polysaccharide interaction on plant performance and allows for a more precise parametrization of plant cell wall models.