Search Results (3,697)

Unique genes refer to genes specific to a particular organism and play crucial roles in the biological functions, evolutionary processes, and adaptations to external environments. However, the roles of unique genes in plant pathogenic fungi remain largely unexplored. In this study, we identified a novel unique gene in the rice blast fungus Magnaporthe oryzae, named MoUNG (M. oryzae unique gene), through T-DNA insertion mutagenesis. The disruption of the MoUNG promoter region in the T-DNA insertion mutant (T30-104) led to an almost loss of MoUNG expression. MoUNG has no functional domains and lacks homologues in other organism. It is highly expressed during the early-infection stage between 16 and 32 h post-inoculation (HPI), in contrast to its expression in mycelia and at the later infection stage of 48 HPI. Notably, attempts to knock out MoUNG were unsuccessful, so we examined the T30-104 mutant and found it showed significantly reduced growth, conidiation, and pathogenicity. Introducing the full-length MoUNG with its promoter into T30-104 restored these phenotypic defects. Additionally, subcellular localization assays revealed that MoUNG exhibits a dot-like distribution within the cytoplasm of mycelium, conidium, appressorium, and invasive hypha. Furthermore, knock-down of MoUNG produced results similar to those observed with the insertion mutation. In conclusion, we identified a novel unique gene MoUNG in M. oryzae and demonstrated its involvement in growth, conidiation, and pathogenicity. Full article

Developing accurate, non-destructive, and automated methods for monitoring the phenotypic traits of rapeseed is crucial for improving yield and quality in modern agriculture. We used a line laser binocular stereo vision technology system to obtain the three-dimensional (3D) point cloud data of different rapeseed varieties (namely Qinyou 7, Zheyouza 108, and Huyou 039) at the seedling stage, and the phenotypic traits of rapeseed were extracted from those point clouds. After pre-processing the rapeseed point clouds with denoising and segmentation, the plant height, leaf length, leaf width, and leaf area of the rapeseed in the seedling stage were extracted by a series of algorithms and were evaluated for accuracy with the manually measured values. The following results were obtained: the R² values for plant height data between the extracted values of the 3D point cloud and the manually measured values reached 0.934, and the RMSE was 0.351 cm. Similarly, the R² values for leaf length of the three kinds of rapeseed were all greater than 0.95, and the RMSEs for Qinyou 7, Zheyouza 108, and Huyou 039 were 0.134 cm, 0.131 cm, and 0.139 cm, respectively. Regarding leaf width, R² was greater than 0.92, and the RMSEs were 0.151 cm, 0.189 cm, and 0.150 cm, respectively. Further, the R² values for leaf area were all greater than 0.98 with RMSEs of 0.296 cm², 0.231 cm² and 0.259 cm², respectively. The results extracted from the 3D point cloud are reliable and have high accuracy. These results demonstrate the potential of 3D point cloud technology for automated, non-destructive phenotypic analysis in rapeseed breeding programs, which can accelerate the development of improved varieties. Full article

Plant genomics have progressed significantly due to advances in information technology, but phenotypic measurement technology has not kept pace, hindering plant breeding. As maize is one of China’s three main grain crops, accurately measuring plant height is crucial for assessing crop growth and productivity. This study addresses the challenges of plant segmentation and inaccurate plant height extraction in maize populations under field conditions. A three-dimensional dense point cloud was reconstructed using the structure from motion–multi-view stereo (SFM-MVS) method, based on multi-view image sequences captured by an unmanned aerial vehicle (UAV). To improve plant segmentation, we propose a column space approximate segmentation algorithm, which combines the column space method with the enclosing box technique. The proposed method achieved a segmentation accuracy exceeding 90% in dense canopy conditions, significantly outperforming traditional algorithms, such as region growing (80%) and Euclidean clustering (75%). Furthermore, the extracted plant heights demonstrated a high correlation with manual measurements, with R² values ranging from 0.8884 to 0.9989 and RMSE values as low as 0.0148 m. However, the scalability of the method for larger agricultural operations may face challenges due to computational demands when processing large-scale datasets and potential performance variability under different environmental conditions. Addressing these issues through algorithm optimization, parallel processing, and the integration of additional data sources such as multispectral or LiDAR data could enhance its scalability and robustness. The results demonstrate that the method can accurately reflect the heights of maize plants, providing a reliable solution for large-scale, field-based maize phenotyping. The method has potential applications in high-throughput monitoring of crop phenotypes and precision agriculture. Full article

Soil metal(loid) pollution is a threat to ecological and environmental safety. The vegetation recovery in mining areas is of great significance for protecting soil resources. In this study, (1) we first gathered four types of soils to analyse their contamination degree, including tailings mud (TM), wasteland soil (TS) very near TM, as well as non-rhizosphere soils of pepper (PF) and maize (MF) in a farmland downstream from the TM (about 5 km). Geo-accumulation and potential ecological risk indices indicated that the soil samples were mainly polluted by antimony (Sb), arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), and copper (Cu) to different degrees. Leachates of TM resulted in increased Sb, As, and Cd accumulation in TS. (2) Then, we sampled six local plants growing in the TS to assess the possibilities of using these plants as recovery vegetation in TS, of which Persicaria maackiana (Regel) Nakai ex T. Mori absorbed relatively high Sb concentrations in the leaves and roots. (3) After that, we collected rhizosphere soil and tissue samples from eight crops on the above farmland to assess their capacities as recovering vegetation of contaminated farmland soil, of which the fruits of maize accumulated the lowest concentrations of most monitored metal(loid)s (except for Pb). Further, we compared the differences in the bacterial community structure of MF, PF, TM, and TS to assess capacities of cultivating pepper and maize to improve soil microbial community structure. The MF displayed the best characteristics regarding the following attributes: (1) the highest concentrations of OMs and total P; (2) the highest OTU numbers and diversity of bacteria; and (3) the lowest abundance of bacteria with potentially pathogenic and stress-tolerant phenotypes. Full article

Background: Cellular senescence is a biological process with a dual role in organismal health. While transient senescence supports tissue repair and acts as a tumor-suppressive mechanism, the chronic accumulation of senescent cells contributes to aging and the progression of age-related diseases. Senotherapeutics, including senolytics, which selectively eliminate senescent cells, and senomorphics, which modulate the senescence-associated secretory phenotype (SASP), have emerged as promising strategies for managing age-related pathologies. Among these, polyphenols, a diverse group of plant-derived bioactive compounds, have gained attention for their potential to modulate cellular senescence. Methods: This review synthesizes evidence from in vitro, in vivo, and clinical studies on the senolytic and senomorphic activities of bioactive polyphenols, including resveratrol, kaempferol, apigenin, and fisetin. The analysis focuses on their molecular mechanisms of action and their impact on fundamental aging-related pathways. Results: Polyphenols exhibit therapeutic versatility by activating SIRT1, inhibiting NF-κB, and modulating autophagy. These compounds demonstrate a dual role, promoting the survival of healthy cells while inducing apoptosis in senescent cells. Preclinical evidence indicates their capacity to reduce SASP-associated inflammation, restore tissue homeostasis, and attenuate cellular senescence in various models of aging. Conclusions: Polyphenols represent a promising class of senotherapeutics for mitigating age-related diseases and promoting healthy lifespan extension. Further research should focus on clinical validation and the long-term effects of these compounds, paving the way for their development as therapeutic agents in geriatric medicine. Full article

Soybeans (Glycine max (L.) Merr.) are a multifunctional crop that contributes significantly to global food security, economic development, and agricultural sustainability. Genomic selection (GS) is widely used in plant breeding, which can effectively reduce breeding costs and shorten the breeding cycle compared to traditional breeding methods. In this study, Hyper-seq technology was used to gather data on 104,728 single nucleotide polymorphism (SNP) sites from 420 natural populations of soybean that were chosen as experimental materials. Furthermore, three years’ worth of phenotypic data on the population’s main stem node count were gathered for this investigation. Comparative analysis was used to assess the validity and accuracy of a number of GS models, including Ridge Regression Best Linear Unbiased Prediction (RRBLUP), Genomic Best Linear Unbiased Prediction (GBLUP), and various Bayesian techniques (Bayesian_A, Bayesian_B, Bayesian_C, Bayesian_RR, Bayesian_LOOS, and Bayesian_RKHS). Each model’s performance was compared using fivefold cross-validation. The research findings indicate that the data obtained by Hyper-seq technology is particularly useful for breeding experiments, including genome-wide selection. The most accurate of them is Bayesian_A, whereas the one with the quickest computational efficiency is GBLUP. Using Hyper-seq technology requires integrating at least 15,000 SNPs to guarantee the model’s stability. It is also important to note that, even if 153 Hyper-seq datasets are 50% less expensive than 153 Whole Genome Sequencing datasets, the difference in prediction accuracy between the two datasets is less than 4%. This discovery further validates the reliability and efficacy of Hyper-seq technology within the domain of genome-wide selection breeding. Full article

Precision agriculture (PA) is a farm management data-driven technology that enhances production with efficient resource usage. Existing PA methods rely on data processing, highlighting the need for a portable computing device for real-time, infield decisions. Raspberry Pi, a cost-effective multi-OS single-board computer, addresses this gap. However, information on Raspberry Pi’s use in PA remains limited. This review consolidates details on Raspberry Pi versions, sensors, devices, algorithm deployment, and PA applications. A systematic literature review of three academic databases (Scopus, Web of Science, IEEE Xplore) yielded 84 (as of 22 November 2024) articles based on four research questions and screening criteria (exclusion and inclusion). Narrative synthesis and subgroup analysis were used to synthesize the results. Findings suggest Raspberry Pi can be a central unit to control sensors, enabling cost-effective automated decision support for PA, particularly in plant disease detection, site-specific weed management, plant phenotyping, biomass estimation, and irrigation systems. Despite focusing on these areas, further research is essential on other PA applications such as livestock monitoring, UAV-based applications, and farm management software. Additionally, Raspberry Pi can be used as a valuable learning tool for students, researchers, and farmers and can promote PA adoption globally, helping stakeholders realize its potential. Full article

Maize, the world’s most widely cultivated food crop, is critical in global food security. Low temperatures significantly hinder maize seedling growth, development, and yield formation. Efficient and accurate assessment of maize seedling quality under cold stress is essential for selecting cold-tolerant varieties and guiding field management strategies. However, existing evaluation methods lack a multimodal approach, resulting in inefficiencies and inaccuracies. This study combines phenotypic extraction technologies with a convolutional neural network–long short-term memory (CNN–LSTM) deep learning model to develop an advanced grading system for maize seedling quality. Initially, 27 quality indices were measured from 3623 samples. The RAGA-PPC model identified seven critical indices: plant height (x₁), stem diameter (x₂), width of the third spreading leaf (x₁₁), total leaf area (x₁₂), root volume (x₁₇), shoot fresh weight (x₂₂), and root fresh weight (x₂₃). The CNN–LSTM model, leveraging CNNs for feature extraction and LSTM for temporal dependencies, achieved a grading accuracy of 97.57%, surpassing traditional CNN and LSTM models by 1.28% and 1.44%, respectively. This system identifies phenotypic markers for assessing maize seedling quality, aids in selecting cold-tolerant varieties, and offers data-driven support for optimising maize production. It provides a robust framework for evaluating seedling quality under low-temperature stress. Full article

Among living organisms, higher animals primarily use a combination of vocal and non-verbal cues for communication. In other species, however, chemical signaling holds a central role. The chemical and biological activity of the molecules produced by the organisms themselves and the existence of receptors/targeting sites that allow recognition of such molecules leads to various forms of responses by the producer and recipient organisms and is a fundamental principle of such communication. Chemical language can be used to coordinate processes within one species or between species. Chemical signals are thus information for other organisms, potentially inducing modification of their behavior. Additionally, this conversation is influenced by the external environment in which organisms are found. This review presents examples of chemical communication among microorganisms, between microorganisms and plants, and between microorganisms and animals. The mechanisms and physiological importance of this communication are described. Chemical interactions can be both cooperative and antagonistic. Microbial chemical signals usually ensure the formation of the most advantageous population phenotype or the disadvantage of a competitive species in the environment. Between microorganisms and plants, we find symbiotic (e.g., in the root system) and parasitic relationships. Similarly, mutually beneficial relationships are established between microorganisms and animals (e.g., gastrointestinal tract), but microorganisms also invade and disrupt the immune and nervous systems of animals. Full article

Point cloud segmentation is necessary for obtaining highly precise morphological traits in plant phenotyping. Although a huge development has occurred in point cloud segmentation, the segmentation of point clouds from complex plant leaves still remains challenging. Rapeseed leaves are critical in cultivation and breeding, yet traditional two-dimensional imaging is susceptible to reduced segmentation accuracy due to occlusions between plants. The current study proposes the use of binocular stereo-vision technology to obtain three-dimensional (3D) point clouds of rapeseed leaves at the seedling and bolting stages. The point clouds were colorized based on elevation values in order to better process the 3D point cloud data and extract rapeseed phenotypic parameters. Denoising methods were selected based on the source and classification of point cloud noise. However, for ground point clouds, we combined plane fitting with pass-through filtering for denoising, while statistical filtering was used for denoising outliers generated during scanning. We found that, during the seedling stage of rapeseed, a region-growing segmentation method was helpful in finding suitable parameter thresholds for leaf segmentation, and the Locally Convex Connected Patches (LCCP) clustering method was used for leaf segmentation at the bolting stage. Furthermore, the study results show that combining plane fitting with pass-through filtering effectively removes the ground point cloud noise, while statistical filtering successfully denoises outlier noise points generated during scanning. Finally, using the region-growing algorithm during the seedling stage with a normal angle threshold set at 5.0/180.0* M_PI and a curvature threshold set at 1.5 helps to avoid the under-segmentation and over-segmentation issues, achieving complete segmentation of rapeseed seedling leaves, while the LCCP clustering method fully segments rapeseed leaves at the bolting stage. The proposed method provides insights to improve the accuracy of subsequent point cloud phenotypic parameter extraction, such as rapeseed leaf area, and is beneficial for the 3D reconstruction of rapeseed. Full article

Aims: The functional traits of twigs and leaves are closely related to the ability of plants to cope with heterogeneous environments. The analysis of the characteristics of twigs and leaves and leaf thermal dissipation in riparian plants is of great significance for exploring the light energy allocation and ecological adaptation strategies of plant leaves in heterogeneous habitats. However, there are few studies on the correlation between the twig–leaf characteristics of riparian plants and their heat dissipation in light heterogeneous environments. Methods: In this study, the riparian plant Hippophae rhamnoides in Taohe National Wetland Park was the research object. According to the differences in the canopy light environment of the H. rhamnoides population, three habitat gradients were set: I, the full sight zone; II, the moderate shade zone; and III, the canopy cover zone. We studied the relationship between the twig–leaf characteristics of H. rhamnoides and leaf thermal dissipation in a heterogeneous light environment. Important Findings: The results are as follows: from the full sight zone to the canopy cover zone, the population characteristics and the twig, leaf, and photosynthetic fluorescence physiological characteristics of H. rhamnoides demonstrated significant changes (p < 0.05). In the full sight zone, H. rhamnoides tended to have thick leaves with a smaller SLA on short and thick twigs, and the light energy absorbed by the leaves accounted for a higher proportion of thermal dissipation. In the moderate shade zone, H. rhamnoides tended to grow many thin leaves with high SLA on long and thick twigs, and the proportion of light energy absorbed by the leaves for heat dissipation was lower than that in the full sight zone. In the canopy cover zone, H. rhamnoides tended to grow a few large and thick leaves with a low SLA on slender and long twigs, and the proportion of light energy absorbed by the leaves for heat dissipation was the lowest. There was a significant correlation between the twig–leaf and leaf heat dissipation of H. rhamnoides in the three habitats (p < 0.05). The co-variation of plant branches and leaves and the timely adjustment of thermal dissipation in photoheterogeneous habitats reflect the phenotypic plasticity mechanism and self-protection strategy of riparian plants in adapting to heterogeneous environments. Full article

Background/Objectives: DNA damage response (DDR) is a highly conserved and complex signal transduction network required for preserving genome integrity. DNA repair pathways downstream of DDR include the tyrosyl-DNA phosphodiesterase1 (TDP1) enzyme that hydrolyses the phosphodiester bond between the tyrosine residue of topoisomerase I (TopI) and 3′-phosphate end of DNA. A small TDP1 subfamily, composed of TDP1α and TDP1β, is present in plants. The aim of this work was to investigate the role of the two TDP1 genes in the DDR context. Methods: A series of Arabidopsis thaliana DDR single and double mutants defective in the sog1, e2fb, pol2A, atm, and atr genes, treated with the genotoxic agents camptothecin (CPT, inhibitor of TopI) and NSC120686 (NSC, inhibitor of TDP1), were used. These compounds were specifically used due to their known impact on the TDP1 function. The effect of the treatments was assessed via phenotypic analyses that included germination percentage, speed, and seedling growth. Subsequently, the expression of the TDP1α and TDP1β genes was monitored through qRT-PCR. Results: Overall, the gathered data indicate that the atm mutant was highly sensitive to NSC120686, both phenotypically and concerning the TDP1α gene expression profiles. Alternatively, the upregulation of TDP1β in e2fb, pol2a, and atr supports its implication in the replication stress response. Conclusions: The current study demonstrates that genotoxic stress induced by CPT and NSC has a genotype-dependent effect reflected by a differential expression of TDP1 genes and early phenotypic development. Full article

Exploring the relationships between plant phenotypes and genetic information requires advanced phenotypic analysis techniques for precise characterization. However, the diversity and variability of plant morphology challenge existing methods, which often fail to generalize across species and require extensive annotated data, especially for 3D datasets. This paper proposes a zero-shot 3D leaf instance segmentation method using RGB sensors. It extends the 2D segmentation model SAM (Segment Anything Model) to 3D through a multi-view strategy. RGB image sequences captured from multiple viewpoints are used to reconstruct 3D plant point clouds via multi-view stereo. HQ-SAM (High-Quality Segment Anything Model) segments leaves in 2D, and the segmentation is mapped to the 3D point cloud. An incremental fusion method based on confidence scores aggregates results from different views into a final output. Evaluated on a custom peanut seedling dataset, the method achieved point-level precision, recall, and F1 scores over 0.9 and object-level mIoU and precision above 0.75 under two IoU thresholds. The results show that the method achieves state-of-the-art segmentation quality while offering zero-shot capability and generalizability, demonstrating significant potential in plant phenotyping. Full article

Accurate leaf segmentation and counting are critical for advancing crop phenotyping and improving breeding programs in agriculture. This study evaluates YOLOv11-based models for automated leaf detection and segmentation across spring barley, spring wheat, winter wheat, winter rye, and winter triticale. The key focus is assessing whether a unified model trained on a combined multi-crop dataset can outperform crop-specific models. Results show that the unified model achieves superior performance in bounding box tasks, with mAP@50 exceeding 0.85 for spring crops and 0.7 for winter crops. Segmentation tasks, however, reveal mixed results, with individual models occasionally excelling in recall for winter crops. These findings highlight the benefits of dataset diversity in improving generalization, while emphasizing the need for larger annotated datasets to address variability in real-world conditions. While the combined dataset improves generalization, the unique characteristics of individual crops may still benefit from specialized training. Full article

Blueberries are a relatively recently domesticated species, primarily bred through hybridization. Mutation breeding, which uses chemical or physical treatment to increase plant mutation, has not yet been applied to blueberries. This study introduces a mutation breeding strategy for the highbush blueberry cultivar Vaccinium corymbosum. We established a high-efficiency regeneration protocol, which was applied to leaves and stems exposed to gamma irradiation using ⁶⁰Co-γ rays at doses of 10, 20, 40, 80, and 120 gray (Gy), to increase the efficiency of mutated cells to develop into adventitious shoots. We determined that the median lethal dose (LD₅₀) was approximately 56 Gy for leaf explants and 80 Gy for stem explants. Phenotypic variations, including changes in leaf color and growth characteristics, which may be due to altered plant response to environmental factors, were successfully observed in the first-generation (M1) plants. The height of M1 plants quantitatively decreased with increasing irradiation doses. To evaluate the mutants induced by each irradiation dose, whole-genome resequencing was conducted on individuals from each dose group, revealing significant genomic alterations at the 80 Gy dose. This approach provides a valuable reference for future blueberry breeding programs aimed at enhancing genetic diversity and improving cultivar performance. Full article