Search Results (258)

It is widely accepted in prokaryotic systematics that a 95–96% ANI (average nucleotide identity) value is equivalent to 70% dDDH (digital DNA–DNA hybridization) value in prokaryotic systematics. However, we recently found that a 70% dDDH value was equivalent to an approximately 96.7% ANIm value in the genus Micromonospora based on a correlation analysis between dDDH and ANIm from a total of 1770 pairs of type Micromonospora strains (60 type strains). Therefore, we proposed that 96.7% ANIm (ANI based on the MUMmer algorithm) value could act as the threshold value in delineating Micromonospora species. Meanwhile, the taxonomic status of an actinobacterial strain HUAS LYJ1^T, isolated from the rhizosphere soil of Camellia oleifera, was determined by using a polyphasic method. A 16S rRNA gene sequence analysis indicated that strain HUAS LYJ1^T shared the highest similarity to Micromonospora wenchangensis CCTCC AA 2012002^T (99.3%). Phylogenetic trees based on 16S rRNA gene and whole-genome sequences demonstrated that strain HUAS LYJ1^T was most closely related to M. wenchangensis CCTCC AA 2012002^T. However, the ANIm value between them was 95.77%, below the 96.7% cut-off point recommended above; the dDDH value between them was 63.2%, also far below the 70% threshold value in delineating bacterial species. Based on these molecular data, as well as phenotypic and chemotaxonomical features, it is concluded that strain HUAS LYJ1^T represents a novel Micromonospora species, for which the name Micromonospora cynarisoli sp. nov. is proposed. In addition, it was found that the ANIm and dDDH values of Micromonospora haikouensis DSM 45626^T, Micromonospora harpali NEAU-JC6^T and Micromonospora oryzae DSM 102119^T were 97.21–97.86% and dDDH 73.9–79.6%, respectively, above the 96.6% ANIm and 70% dDDH threshold value in delineating Micromonospora species. Consequently, according to rule 42 of the International Committee on Systematics of Prokaryote Code, we propose that M. harpali Fang et al. 2015 and M. oryzae Kittiwongwattana et al. 2015 are later heterotypic synonyms of M. haikouensis Xie et al. 2012. Full article

Euproctis pseudoconspersa is a devastating pest in Camellia oleifera plantations, necessitating the development of sustainable molecular intervention strategies. This study targeted the chitin synthase A gene (EpCHSA) to evaluate and compare the RNA interference (RNAi) efficacy of dsRNA synthesized via the T7 in vitro transcription system and the Escherichia coli HT115 (DE3) expression system. The EpCHSA gene (2199 bp ORF) was cloned and characterized, exhibiting peak expression during the fourth-instar stage, and predominantly in the head tissues of fifth-instar larvae. Bioassays demonstrated that larvae fed with 500 ng/μL in vitro synthesized dsRNA exhibited continuous gene silencing for five days, reaching a maximum efficiency of 68.1%. Conversely, treatment with 100× concentrated bacterial broth (5000 ng/μL) elicited a superior silencing effect of 79.3% within 24 h. Furthermore, the bacterial treatment group reached a 14-day mortality rate of 46.66%, significantly higher than the in vitro group (38.33%). Both methods induced severe phenotypic abnormalities, including molting failure and pupal malformation. These findings conclude that the E. coli expression system offers a cost-effective and highly potent platform for dsRNA production. This research provides a critical technical foundation and promising application prospects for the field-scale management of E. pseudoconspersa utilizing RNAi-based biopesticides. Full article

This study involves the design of an integrated machine dedicated to the core processes of classifying, shelling, and cleaning to address the critical drawbacks of existing Camellia oleifera fruit processing equipment, including the high manual labor requirement, low operating efficiency, unsatisfactory shelling and cleaning performance, and severe camellia seed damage. The classifying system employed a slat drum structure, and response surface methodology (RSM) was utilized to determine and optimize its operating parameters: spiral blade speed: 20 rpm; drum speed: 10 rpm; and rise angle: 9.6°. The shelling system employed a horizontal flexible structure, and polyurethane was the core material. We determined through single-factor experiments that the shelling drum rotation speed was 200 rpm. For the cleaning system, a composite mode integrating drum screening and friction separation was adopted, and single-factor experiments further determined the optimal operating parameters: cleaning drum rotation speed: 20 rpm; friction conveyor shaft rotation speed: 150 rpm; and cleaning inclination angle: 25°. The performance test verified that the integrated machine achieved outstanding results: the shelling rate reached 97.52%, the camellia seed breakage rate did not exceed 2.42%, the impurity content rate did not exceed 1.99%, the loss rate was less than 3.66%, and the processing capacity reached 2614 kg/h. Full article

Camellia oleifera is an economically vital woody oil crop. Its productivity and oil quality are severely compromised by various diseases. Implementing pixel-level lesion segmentation within complex field environments is crucial for advancing precision plant protection. Despite recent progress, existing segmentation methods struggle with three primary challenges: semantic ambiguity arising from evolving pathological stages, blurred boundaries due to overlapping lesions, and the high omission rate of micro-lesions. To address these issues, this paper presents TB-DLossNet (Text-Conditioned Boundary-Aware Network with Dynamic Loss Reweighting), a novel segmentation framework based on semantic-visual multi-modal fusion. Leveraging VMamba as the visual backbone, the proposed model innovatively integrates BERT-encoded structured text as an auxiliary modality to resolve visual ambiguities through cross-modal semantic guidance. Furthermore, a boundary enhancement branch is incorporated alongside a multi-scale deep supervision strategy to mitigate boundary displacement and ensure the topological continuity of lesion structures. To tackle the detection of small-scale targets, we designed a dynamic weight loss function conditioned on lesion area, significantly bolstering the model’s sensitivity to minute pathological features. Additionally, to alleviate the scarcity of high-quality data, we curated a comprehensive multi-modal dataset encompassing seven typical diseases of Camellia oleifera. Experimental results demonstrate that TB-DLossNet achieves a Mean Intersection over Union (mIoU) of 87.02%, outperforming the state-of-the-art unimodal VMamba and multimodal Lvit by 4.9% and 2.59%, respectively. Qualitative evaluations confirm that our model exhibits lower false-negative rates and superior boundary-fitting precision in heterogeneous field scenarios. Finally, generalization tests on an apple disease dataset further validate the robustness and transferability of the proposed framework. Full article

Camellia oleifera Abel, recognized as a woody oil-producing tree species, possesses considerable economic significance. To improve the breeding efficiency of C. oleifera, it is crucial to elucidate the genetic foundation underlying the mechanisms regulating fruit traits. In this study, a total of 6,252,197 high-quality single-nucleotide polymorphisms (SNPs) were identified from 109 germplasm accessions. Through genetic structure analysis, these accessions were categorized into two distinct populations. The average fixation index (Fst) was found to be 0.0153, indicating weak population differentiation. The genome-wide association analysis (GWAS) identified 157 significant loci. From these loci, 110 candidate genes were selected, which were associated with disease resistance, reproduction, development, and RNA biosynthesis. Twenty-three genes were involved in metabolic pathways, including genetic information-processing protein families, metabolic protein families, terpenoids, and polyketides. The identification of gene loci closely related to fruit traits not only provides genetic data for studying the molecular mechanisms of fruit traits but also offers new research avenues for molecular breeding of C. oleifera. Full article

Colletotrichum fructicola is the predominant pathogenic agent responsible for anthracnose in Camellia oleifera. RGS2 is a GTPase-activating protein that negatively regulates G-protein signaling by inactivating Gα subunits. In this study, we characterized the ortholog of CfRGS2 in C. fructicola to explore its pathogenic roles. Seven canonical RGS genes were identified through BLASTp and keyword searches. Conserved domains and subcellular localizations were predicted bioinformatically. A CfRGS2 knockout mutant was generated via overlap-PCR and PEG-mediated transformation, verified by PCR, and complemented by reintroducing the wild-type gene. Phenotypic characterization showed that the growth rates of mutants ΔCfrgs2-1 and ΔCfrgs2-2 were significantly reduced compared with those of the wild-type and complemented strains. On both PDA and minimal medium, the mutant strains exhibited significantly smaller colony diameters of 3.3 cm and 3.1 cm, respectively, relative to the control strains. Moreover, conidiation in the mutants was only 4% of that in the wild-type and complemented strains, and appressorium formation was reduced to 6%, with statistical analyses confirming high significance. Under cell wall stress induced by 400 μg/mL Congo red, the growth inhibition rates of ΔCfrgs2-1 and ΔCfrgs2-2 were 44% and 48%, respectively, significantly higher than those of the control strains. Pathogenicity assays demonstrated that the mutants failed to induce lesions on unwounded leaves and caused 47% and 30% smaller lesion areas on wounded apple fruits, respectively. In summary, C. fructicola possesses seven canonical RGS proteins that regulate G-protein signaling, among which CfRgs2 is implicated in growth, conidiation, the stress response to cell wall perturbation, and virulence. Full article

The aim of this study was to investigate the effects of exogenous gibberellin (GA₃) and uniconazole (S₃₃₀₇) on the growth of C. oleifera spring shoots, and ultimately to seek ways to improve the quality of its panicles. Five-year-old ‘Huaxin’ trees were sprayed with 2400 mg/L GA₃ or 800 mg/L S₃₃₀₇ at the leaf expansion stage. Growth parameters, physiological indicators, and endogenous hormone levels were measured. The results showed that GA₃ significantly enhanced shoot extension and internode lengthening, whereas S₃₃₀₇ treatment exhibited the opposite inhibitory effects. GA₃ treatments increased the content of soluble sugar, enhanced the activity of SOD and POD, decreased the activity of CAT, decreased MDA accumulation, indicating that membrane lipid peroxidation was alleviated. The results of endogenous hormone analysis indicated that GA₃ and S₃₃₀₇ reduced the concentration of ABA and IAA, increased the content of tZ, and altered the distribution of endogenous GA₃. Overall, GA₃ enhanced spring shoot growth by regulating endogenous hormone balance, enhancing nutrient accumulation and antioxidant capacity. These results provide the theoretical and technical basis for the high-quality spike formation and high-yield of cultivation of C. oleifera. Full article

The harvesting of Camellia oleifera fruit in hilly areas faces core problems such as low manual efficiency, poor terrain adaptability of existing machinery, and severe emissions and noise from traditional equipment. This study designed a crawler chassis utilizing a permanent magnet synchronous motor-driven hydraulic system. The research integrated kinematic modeling and resistance calculations for parameter matching, followed by AMESim dynamic simulations and motor calibration experiments. Finally, comprehensive field tests were conducted to evaluate the prototype. The results indicate the chassis achieves a maximum travel speed >1.5 m∙s⁻¹, a climbing angle of 41.4°, and a turning radius of 0.72 m, with noise levels strictly below 80 dB(A). Significantly, dynamic power characteristic tests under actual vibration harvesting conditions revealed that the 45 kW motor maintains a rapid response with ample power reserve. The input power exhibited a distinct square-wave pattern synchronized with hydraulic valve commands, peaking at 18.1 kW during vibration bursts. These findings confirm the system’s stability under coupled driving and harvesting loads. This design offers a viable, low-noise solution for electrifying and intelligently upgrading Camellia oleifera harvesting equipment in complex terrains. Full article

To investigate how insect hair morphology influences pollination effectiveness, this study examined four common wild pollinators in Camellia oleifera plantations: two bee species (Colletes gigas and Apis cerana) and two hornet species (Vespa velutina and Vespa soror). We systematically measured hair length, hair density, and pollen loads on four body regions (head, thorax, abdomen, and legs). The results indicated that the following: (1) C. gigas possessed significantly longer and denser hairs across all body parts, especially on the legs, compared to the other three species. (2) Both the pollen load per body part and the total pollen load were markedly higher in C. gigas than in the other pollinators. The two hornet species did not differ significantly from A. cerana in pollen load, and even exceeded it in certain traits such as head hair length. (3) Correlation analysis revealed a significant positive relationship between total pollen load and both hair length (ρ = 0.545, p < 0.01) and hair density (ρ = 0.391, p < 0.01). Pollen loads on different body regions were also strongly positively correlated, suggesting functional synergy across the insect’s surface. Leg pollen load correlated positively with head and leg hair length, but negatively with head hair density. Notably, leg hair length and density showed a unique positive correlation, highlighting region-specific morphological adaptation. (4) Cluster analysis separated C. gigas from the other three species, which grouped together. In conclusion, hair length and density—particularly on the legs—are key morphological traits underpinning pollen-carrying efficiency in these pollinators. C. gigas demonstrates superior hair morphology and pollen-carrying performance, supporting its role as an effective pollinator of C. oleifera. This study provides a trait-based framework for identifying dominant pollinators and underscores that evaluating species with complex ecological roles, such as hornets, requires integrating morphological traits with broader behavioral and community contexts. Full article

We aimed to establish a rapid and precise method for identifying and quantifying multi-species vegetable oil (corn oil, olive oil (OLO), soybean oil, and sunflower oil (SUO)) adulterations in camellia oil (CAO), using soft ionization by chemical reaction in transfer–high-resolution mass spectrometry (SICRIT-HRMS) and machine learning methods. The results showed that SICRIT-HRMS could effectively characterize the volatile profiles of pure and adulterated CAO samples, including binary, ternary, quaternary, and quinary adulteration systems. The low m/z region (especially 100–300) exhibited importance to oil classification in multiple feature-selection methods. For qualitative detection, binary classification models based on convolutional neural networks (CNN), Random Forest (RF), and gradient boosting trees (GBT) algorithms showed high accuracies (98.70–100.00%) for identifying CAO adulteration under no dimensionality reduction (NON), principal component analysis (PCA), and uniform manifold approximation and projection (UMAP) strategies. The RF algorithm exhibited relatively high accuracy (96.25–99.45%) in multiclass classification. Moreover, the five models, including CNN, RF, support vector machines (SVM), logistic regression (LR), and GBT, exhibited different performances in distinguishing pure and adulterated CAO. Among 1093 blind oil samples, under NON, PCA, and UMAP: 10, 5, and 67 samples were misclassified by CNN model; 6, 7, and 41 samples were misclassified by RF model; 8, 9, and 82 samples were misclassified by SVM model; 17, 18, and 78 samples were misclassified by LR model; 7, 9, and 43 samples were misclassified by GBT model. For quantitative prediction, the PCA-CNN model performed optimally in predicting adulteration levels in CAO, especially with respect to OLO and SUO, exhibiting a high coefficient of determination for calibration ($R_C^2$, 0.9664–0.9974) and coefficient of determination for prediction ($R_p^2$, 0.9599–0.9963) values, low root mean square error of calibration (RMSEC, 0.9–5.3%) and root mean square error of prediction (RMSEP, 1.1–5.8%) values, and RPD (5.0–16.3) values greater than 3.0. These results indicate that SICRIT-HRMS combined with machine learning can rapidly and accurately identify and quantify multi-species vegetable oil adulterations in CAO, which provides a reference for developing non-targeted and high-throughput detection methods in edible oil authenticity. Full article

Camellia oleifera, a woody oil crop unique to China, plays a crucial role in alleviating the global pressure of edible oil supply and maintaining ecological security. However, it remains challenging to accurately forecast Camellia oleifera yield in complex landscapes using only remote sensing data. The aim of this study is to develop an interpretable deep learning model, namely Shapley Additive Explanations–guided Attention–long short-term memory (SALSTM), for estimating Camellia oleifera yield by integrating an improved spectral bloom index and environmental parameters. The study area is located in Hengyang City in Hunan Province. Sentinel-2 imagery, meteorological observation from 2019 to 2023, and topographic data were collected. First, an improved spectral bloom index (ISBI) was constructed as a proxy for flowering density, while average temperature, precipitation, accumulated temperature, and wind speed were selected to represent environmental regulation variables. Second, a SALSTM model was designed to capture temporal dynamics from multi-source inputs, in which the LSTM module extracts time-dependent information and an attention mechanism assigns time-step-wise weights. Feature-level importance derived from SHAP analysis was incorporated as a guiding prior to inform attention distribution across variable dimensions, thereby enhancing model transparency. Third, model performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). The result show that the constructed SALSTM model achieved strong predictive performance in predicting Camellia oleifera yield in Hengyang City (RMSE = 0.5738 t/ha, R² = 0.7943). Feature importance analysis results reveal that ISBI weight > 0.26, followed by average temperature and precipitation from flowering to fruit stages, these features are closely associated with C. oleifera yield. Spatially, high-yield zones were mainly concentrated in the central–southern hilly regions throughout 2019–2023, In contrast, low-yield zones were predominantly distributed in the northern and western mountainous areas. Temporally, yield hotspots exhibited a gradual increasing while low-yield zones showed mild fluctuations. This framework provides an effective and transferable approach for remote sensing-based yield estimation of flowering and fruit-bearing crops in complex landscapes. Full article

This study investigates the bacterial community structure and diversity across different root compartments (non-rhizosphere soil, rhizosphere soil, rhizosphere, and endosphere) of Camellia oleifera and their associations with three cultivars (‘Huashuo’, ‘Huajin’, ‘Huaxin’). High-throughput sequencing and bioinformatics analyses were performed to characterize the bacterial communities. A total of 22 phyla, 59 classes, 155 orders, 268 families, 523 genera, 929 species, and 2045 operational taxonomic units (OTUs) were identified. Alpha diversity indices (Shannon, Simpson, Chao1) showed no statistically significant differences among the three cultivars, but varied significantly across root compartments. The rhizosphere exhibited the highest bacterial diversity and richness, which was significantly higher than that in the endosphere. At the phylum level, Proteobacteria, Chloroflexi, Actinobacteriota, Acidobacteriota, Firmicutes, and Bacteroidetes dominated the communities. Significant differences were observed in the relative abundance of dominant genera (e.g., Proteus, actinomycetes) among varieties and root compartments. PCoA analysis revealed that ‘Huaxin’ had a distinct bacterial community structure compared to ‘Huashuo’ and ‘Huajin’, while the endosphere was separated from other compartments. Interaction network analysis indicated that most bacterial interactions were positive, with Colidextribacter, Uliginosibacterium, and Aliidongia showing the highest centrality, suggesting their key roles in maintaining community stability. This study provides novel insights into the distribution patterns and driving factors of root-associated bacteria in C. oleifera, laying a theoretical foundation for future research on disease control and quality improvement of this crop. Full article

Camellia oleifera oil (CO) is an important edible oil with excellent nutritional value. Recently, there has been an increasing market demand for oils with distinct flavor profiles. However, the formation mechanisms of characteristic aromas in CO remain unclear. Therefore, this study investigated the effects of roasting (170 °C, 0–30 min) on free amino acids, soluble sugars, and volatile components in camellia seeds and the corresponding oils. To further elucidate the generation mechanisms of flavor compounds in CO, reaction systems simulating the Maillard reaction and lipid oxidation were constructed. The results show strong correlations between volatile compounds and both soluble sugars and free amino acids during roasting. The key flavor precursors identified included arginine, glutamic acid, glycine, histidine, leucine, phenylalanine, and lysine, as well as sucrose and glucose. The simulated systems indicated that the flavor compounds in CO were mainly derived from the Maillard reaction and lipid oxidation, with significant interactions enhancing its unique flavor. This study potentially provides scientific guidance for the production and flavor control of fragrant CO. Full article

Acidic agricultural soils are frequently challenged by co-occurring heavy metal contamination and greenhouse gas (GHG) emissions. While biochar is widely used for integrated remediation, the specific role of silicon (Si) in modulating its effectiveness in cadmium (Cd) stabilization and nitrous oxide (N₂O) mitigation remains insufficiently understood. This study evaluated the co-remediation efficacy of two types of high-Si (bamboo leaves, ML; rice straw, RS) and two types of low-Si (Camellia oleifera leaves, CL; Camellia oleifera shells, CS) biochar, produced at 450 °C, within a Cd-contaminated and nitrogen-fertilized acidic soil. Results from a 90-day incubation showed that while all biochar effectively immobilized Cd, the low-Si CL biochar exhibited a superior stabilization efficiency of 66.2%. This enhanced performance was attributed to its higher soil organic carbon (SOC) and moderate dissolved organic carbon (DOC) release, which facilitated robust Cd²⁺ sorption and complexation. In contrast, high-Si biochar was more effective in mitigating cumulative N₂O emissions (up to 67.8%). This mitigation was strongly associated with an elevated abundance of the nosZ gene (up to 48.1%), which catalyzes the terminal step of denitrification. Soil pH and DOC were identified as pivotal drivers regulating both Cd bioavailability and N₂O dynamics. Collectively, low-Si biochar is preferable for Cd stabilization in acidic soils, whereas high-Si biochar is more effective at elevating pH and reducing N₂O emissions. These findings emphasize that optimizing co-remediation outcomes necessitates a targeted approach, selecting biochar based on the specific contamination profile and desired environmental benefits. Full article

To address the challenges of inefficient Camellia oleifera fruits harvesting in hilly and mountainous regions due to the difficulty of using large machinery, a handheld vibration harvesting device for Camellia oleifera fruits was designed. Based on the vibration-induced detachment process of Camellia oleifera fruits, a single-pendulum dynamic model of the “fruit-branch” system was established and solved to calculate the tangential acceleration required for fruit detachment. The key factors influencing harvesting efficiency were identified as vibration frequency, amplitude, height, and duration. Using ANSYS, modal response and harmonic response analyses were conducted on a 3D model of the Camellia oleifera tree to determine the operational parameters ensuring branch acceleration meets the fruit detachment. Furthermore, a rigid-flexible coupling simulation system integrating the harvesting device and Camellia oleifera tree was developed on the ADAMS. This analysis revealed the variation patterns of branch acceleration with respect to vibration frequency and clamping height, thereby validating the rationality of the dynamic model and the feasibility of the device. Finally, an orthogonal experiment was designed using Design-Expert 13, and multi-objective optimization analysis was performed on the device’s working parameters based on the experimental data. The aforementioned research identified the optimal working parameter combination and actual harvesting performance of the handheld vibration harvesting device: when the vibration frequency is 14 Hz, vibration height is 980 mm, and vibration duration is 13 s, the fruit picking rate reaches 95.22%. The harvesting efficiency of this device is significantly higher than manual picking methods, fully meeting the requirements for efficient Camellia oleifera fruit harvesting. Full article