Search Results (799)

Downy mildew caused by Plasmopara viticola is an important disease in grape production, particularly in the highly susceptible, widely cultivated Vitis vinifera L. Breeding for disease resistance is an effective solution, and V. vinifera intraspecific crosses can yield progeny with both disease resistance and high quality. To assess the potential of intraspecific recurrent selection in V. vinifera (IRSV) in improving grapevine resistance to downy mildew and to analyze the pattern of disease resistance inheritance, the disease-resistant variety Ecolly was selected as one of the parents and crossed with Cabernet Sauvignon, Marselan, and Dunkelfelder, respectively, creating three reciprocal combinations, resulting in 1657 hybrid F1 progenies. The primary results are as follows: (1) significant differences in disease resistance among grape varieties and, significant differences in disease resistance between different vintages of the same variety were found; (2) the leaf downy mildew resistance levels of F1 progeny of different hybrid combinations conformed to a skewed normal distribution and showed some maternal dominance; (3) the degree of leaf bulbous elevation was negatively correlated with the level of leaf downy mildew resistance, and the correlation coefficient with the level of field resistance was higher; (4) five progenies with higher levels of both field and in vitro disease resistance were obtained. Intraspecific hybridization can improve the disease resistance of offspring through super-parent genetic effects, and Ecolly can be used as breeding material for recurrent hybridization to obtain highly resistant varieties. Full article

Precisely recognizing diseases in tomato leaves plays a crucial role in enhancing the health, productivity, and quality of tomato crops. However, disease identification methods that rely on single-mode information often face the problems of insufficient accuracy and weak generalization ability. Therefore, this paper proposes a tomato leaf disease recognition framework FCMNet based on multimodal fusion, which combines tomato leaf disease image and text description to enhance the ability to capture disease characteristics. In this paper, the Fourier-guided Attention Mechanism (FGAM) is designed, which systematically embeds the Fourier frequency-domain information into the spatial-channel attention structure for the first time, enhances the stability and noise resistance of feature expression through spectral transform, and realizes more accurate lesion location by means of multi-scale fusion of local and global features. In order to realize the deep semantic interaction between image and text modality, a Cross Vision–Language Alignment module (CVLA) is further proposed. This module generates visual representations compatible with Bert embeddings by utilizing block segmentation and feature mapping techniques. Additionally, it incorporates a probability-based weighting mechanism to achieve enhanced multimodal fusion, significantly strengthening the model’s comprehension of semantic relationships across different modalities. Furthermore, to enhance both training efficiency and parameter optimization capabilities of the model, we introduce a Multi-strategy Improved Coati Optimization Algorithm (MSCOA). This algorithm integrates Good Point Set initialization with a Golden Sine search strategy, thereby boosting global exploration, accelerating convergence, and effectively preventing entrapment in local optima. Consequently, it exhibits robust adaptability and stable performance within high-dimensional search spaces. The experimental results show that the FCMNet model has increased the accuracy and precision by 2.61% and 2.85%, respectively, compared with the baseline model on the self-built dataset of tomato leaf diseases, and the recall and F1 score have increased by 3.03% and 3.06%, respectively, which is significantly superior to the existing methods. This research provides a new solution for the identification of tomato leaf diseases and has broad potential for agricultural applications. Full article

Congenital Chagas disease (CCD) is caused when Trypanosoma cruzi crosses the placental barrier during pregnancy and reaches the fetus, which can lead to serious consequences in the developing fetus. Current treatment is carried out with nifurtimox or benznidazole, but their effectiveness is limited, and they cause side effects, requiring the search for new therapeutic strategies. In this sense, many studies have demonstrated the potential of different compounds of the Copaifera genus in the control of parasitic diseases. Here, we aimed to evaluate the effect of oleoresin (OR) and leaf hydroalcoholic extract (LHE) of Copaifera multijuga on Trypanosoma cruzi infection in human villous trophoblast cells (BeWo line) and human placenta explants. Treatment with both compounds reduced invasion, proliferation, and release of trypomastigotes. Furthermore, OR and LHE affected the trypomastigotes and amastigote morphology, compromising their ability to invade and proliferate in BeWo cells, respectively. Also, treatment with OR decreased ROS production in infected BeWo cells, while LHE induced an increase. In addition, both compounds induced pro-inflammatory and anti-inflammatory cytokine production. In human placental explants, both compounds also decreased T. cruzi infection, in addition to inducing the production of pro-inflammatory cytokines. Thus, both OR and LHE of C. multijuga control T. cruzi infection at the human maternal–fetal interface, highlighting the possible therapeutic potential of these compounds for the treatment of CCD. Full article

Frequent corn leaf diseases and pests pose serious threats to agricultural production. Traditional manual detection methods suffer from significant limitations in both performance and efficiency. To address this, the present paper proposes a novel biotic condition screening (BCS) model for the detection of corn leaf diseases and pests, called BCS_YOLO, based on the You Only Look Once version 11n (YOLOv11n). The proposed model enables accurate detection and classification of various corn leaf pathologies and pest infestations under challenging agricultural field conditions. It achieves this thanks to three key newly designed modules—a Self-Perception Coordinated Global Attention (SPCGA) module, a High/Low-Frequency Feature Enhancement (HLFFE) module, and a Local Attention Enhancement (LAE) module. The SPCGA module improves the model’s ability to perceive fine-grained targets by fusing multiple attention mechanisms. The HLFFE module adopts a frequency domain separation strategy to strengthen edge delineation and structural detail representation in affected areas. The LAE module effectively improves the model’s discrimination ability between targets and backgrounds through local importance calculation and intensity adjustment mechanisms. Conducted experiments show that BCS_YOLO achieves 78.4%, 73.7%, 76.0%, and 82.0% in $precision$, recall, $F1score$, and $mAP@50$, respectively, representing corresponding improvements of 3.0%, 3.3%, 3.2%, and 4.6% compared to the baseline model (YOLOv11n), while also outperforming the mainstream object detection models. In summary, the proposed BCS_YOLO model provides a practical and scalable solution for efficient detection of corn leaf diseases and pests in complex smart-agriculture scenarios, demonstrating significant theoretical and application value. Full article

Brown spot, caused by Alternaria alternata, is the most important leaf fungal disease threatening kiwifruit production in China, and it is typically controlled through the application of fungicides, such as procymidone and difenoconazole. To date, fungicide resistance development has not yet been systematically reported for the pathogen of kiwifruit. A total of 135 single-conidium A. alternata isolates were collected from different cities in Zhejiang Province, China. Alternaria alternata developed prevailing resistance to procymidone and initial resistance to difenoconazole, with resistance frequencies of 60.7 and 13.3%, respectively. Positive cross-resistance was observed between procymidone and iprodione but not between procymidone and difenoconazole, tebuconazole, prochloraz, pydiflumetofen, pyraclostrobin, or thiophanate-methyl. Moreover, no cross-resistance was observed between difenoconazole and all other tested fungicides, including the two other demethylation inhibitors, tebuconazole and prochloraz. A fitness penalty was not detected in procymidone-resistant (Pro^R) or difenoconazole-resistant (Dif^R) isolates. However, double-resistant (Pro^R Dif^R) isolates had a fitness penalty, showing significantly decreased sporulation, germination, and pathogenicity. The P894L single point mutation, caused by the change from CCA to CTA at the 894th codon of Os1, was detected in Pro^R isolates. Molecular dynamic simulation showed that the P894L mutation significantly decreased the inhibitory activity of procymidone against AaOs1 in A. alternata. These results provide insight into the development and characteristics of fungicide resistance, offering guidance for the study and management of kiwifruit diseases. Full article

Wheat leaf rust (Lr), caused by Puccinia triticina Eriks. (Pt), is one of the most important diseases affecting wheat production worldwide. Using resistant wheat cultivars is the most economic and environmentally friendly way to control leaf rust. The Chinese wheat cultivar Xinong1163-4 has shown good resistance to Lr in field trials. To identify the genetic basis of Lr resistance in Xinong1163-4, 195 recombinant inbred lines (RILs) from the Xinong1163-4/Thatcher cross were phenotyped for Lr severity in three environments: the 2017/2018, 2018/2019, and 2019/2020 growing seasons in Baoding, Hebei Province. Bulked segregant analysis and simple sequence repeat markers were then used to identify the quantitative trait loci (QTLs) for Lr adult plant resistance (APR) in the population. As a result, six QTLs were detected, designated as QLr.hbau-1BL.1, QLr.hbau-1BL.2, and QLr.hbau-1BL.3. These QTLs were predicted to be novel. QLr.hbau-4BL, QLr.hbau-4BL.1, and QLr.hbau-3A were identified at similar physical positions to previously reported QTLs. Based on chromosome positions and molecular marker testing, QLr.hbau-1BL.3 shares similar flanking markers with Lr46. Lr46 is a non-race-specific APR gene for leaf rust, stripe rust, and powdery mildew. Similarly, QLr.hebau-4BL showed resistance to multiple diseases, including leaf rust, stripe rust, Fusarium head blight, and powdery mildew. The QTLs identified in this study, as well as their closely linked markers, can potentially be used for marker-assisted selection in wheat breeding. Full article

Tomato (Solanum lycopersicum) is an essential vegetable crop cultivated worldwide, but its production is highly vulnerable to tomato yellow leaf curl disease (TYLCD), which is transmitted by whiteflies (Bemisia tabaci). Management strategies typically focus on controlling either the virus or its vector. This study evaluates the effectiveness of multi-disease and insect-resistant tomato lines, developed by the World Vegetable Center (WorldVeg), which integrate Ty-1/Ty-3 genes for virus resistance and WF2-10 and WF3-09 genes for whitefly resistance. Virus accumulation, whitefly settling behavior, and adult mortality were assessed among multi-resistant lines, a Ty-resistant line, a whitefly-resistant line, and a susceptible check using preference bioassays, controlled inoculation experiments, and acylsugar quantification. Multi-resistant lines exhibited significantly higher acylsugar concentrations, reduced whitefly preference for settling, and increased whitefly adult mortality. Additionally, these lines displayed less severe disease symptoms and lower virus accumulation over time than Ty-resistant, whitefly-resistant, and susceptible controls. These findings highlight the superior efficacy of combined virus and vector resistance in mitigating tomato yellow leaf curl Thailand virus (TYLCTHV) transmission. This research underscores the importance of integrated genetic resistance as a key element of sustainable integrated pest management strategies, offering an environmentally friendly solution for safeguarding global tomato production. Full article

Monoterpene indole alkaloids (MIAs) exhibit diverse structures and pharmacological effects. Annotating MIAs in herbal medicines remains challenging when using liquid chromatography combined with high-resolution mass spectrometry (LC-HRMS). This study introduced a new annotation strategy employing LC-HRMS to efficiently identify MIAs in herbal medicines. Briefly, MS² spectra under multiple collision energies (MCEs/MS²) helped capture high-quality product ions across a range of mass-to-charge (m/z) values, revealing key MS² features such as diagnostic product ions (DPIs), characteristic cleavages (CCs), and neutral/radical losses (NLs/RLs). Next, feature-based molecular networking (FBMN) was created to map the structural relationships among MIAs across large MS datasets. Potential MIAs were then graded and annotated through systematic comparison with known biosynthetic pathways (BPs), derived skeletons, and their characteristic substituents. The MCEs/MS²-FBMN/BPs workflow was first applied to annotate MIAs in the alkaloids from the leaf of Alstonia scholaris (ALAS), a new botanical drug for respiratory diseases. A total of 229 MIAs were systematically annotated and classified, forming a solid basis for future clinical research on ALAS. This study offers an effective strategy that enhances the structural annotation of MIAs within complex herbal medicines. Full article

Maize production in Fuxin City, Liaoning Province, China, is threatened by northern corn leaf blight (NCLB) and Fusarium stalk rot, with straw return under conservation tillage exacerbating the NCLB severity by 20% in local fields. This study evaluated the efficacy of combining difenoconazole, a commonly used fungicide, with a Trichoderma bioagent for disease control in straw-incorporated soils. Field trials in Fuxin showed that applying 300 g/ha difenoconazole with 1.5 L/ha Trichoderma fermentate achieved superior results: a 72.4% reduction in the NCLB disease index and a stalk rot incidence of only 0.61%. These outcomes significantly outperformed single-component treatments like difenoconazole alone (56.2% NCLB suppression) or other fungicides (e.g., carbendazim, triadimefon). The combined treatment also outperformed the single treatments with biocontrol agent (67.1% NCLB inhibition). The results highlight the synergistic potential of integrating chemical and biological agents to manage residue-borne diseases, offering a practical strategy for sustainable disease control in conservation agriculture systems with straw return in Liaoning, China. Full article

Rootstocks are vital in cucumber production. Although figleaf gourd (Cucurbita ficifolia) is among the species used, its application remains limited due to the perception that white-seeded pumpkin (C. maxima × C. moschata) offers superior commercial traits. This perception is partly due to the insufficient collection and evaluation of local figleaf gourd germplasm, which has obscured its potential as a rootstock. Based on prior screening, four wild figleaf gourd genotypes from Yunnan Province were selected and compared with seven commercial white-seeded pumpkin rootstocks. Scions grafted onto figleaf gourd exhibited vegetative growth (stem diameter, plant height, and leaf area) and fruit morphology (length, diameter, biomass, and surface bloom) comparable to the top-performing white-seeded pumpkin genotypes. Fruits from figleaf gourd rootstocks also displayed comparable or significantly higher nutritional quality, including vitamin C, total soluble solids, soluble sugars, and proteins. Notably, figleaf gourd itself showed significantly greater intrinsic resistance to Fusarium wilt than white-seeded pumpkin. When used as a rootstock, it protected the scion from pathogen stress by triggering a stronger antioxidant response (higher SOD and POD activity) and mitigating cellular damage (lower MDA levels and electrolyte leakage). These results provide evidence that these figleaf gourd genotypes are not merely viable alternatives but are high-performing rootstocks, particularly in enhancing nutritional value and providing elite disease resistance. Full article

Crop diseases pose a significant threat to agricultural productivity and global food security. Timely and accurate disease identification is crucial for improving crop yield and quality. While most existing deep learning-based methods focus primarily on image datasets for disease recognition, they often overlook the complementary role of textual features in enhancing visual understanding. To address this problem, we proposed a cross-modal data fusion via a vision-language model for crop disease recognition. Our approach leverages the Zhipu.ai multi-model to generate comprehensive textual descriptions of crop leaf diseases, including global description, local lesion description, and color-texture description. These descriptions are encoded into feature vectors, while an image encoder extracts image features. A cross-attention mechanism then iteratively fuses multimodal features across multiple layers, and a classification prediction module generates classification probabilities. Extensive experiments on the Soybean Disease, AI Challenge 2018, and PlantVillage datasets demonstrate that our method outperforms state-of-the-art image-only approaches with higher accuracy and fewer parameters. Specifically, with only 1.14M model parameters, our model achieves a 98.74%, 87.64% and 99.08% recognition accuracy on the three datasets, respectively. The results highlight the effectiveness of cross-modal learning in leveraging both visual and textual cues for precise and efficient disease recognition, offering a scalable solution for crop disease recognition. Full article

This work proposes an end-to-end solution for leaf segmentation, disease detection, and damage quantification, specifically focusing on citrus crops. The primary motivation behind this research is to enable the early detection of phytosanitary problems, which directly impact the productivity and profitability of Spanish and Portuguese agricultural developments, while ensuring environmentally safe management practices. It integrates an onboard computing module for Unmanned Aerial Vehicles (UAVs) using a Raspberry Pi 4 with Global Positioning System (GPS) and camera modules, allowing the real-time geolocation of images in citrus croplands. To address the lack of public data, a comprehensive database was created and manually labelled at the pixel level to provide accurate training data for a deep learning approach. To reduce annotation effort, we developed a custom automation algorithm for pixel-wise labelling in complex natural backgrounds. A SegNet architecture with a Visual Geometry Group 16 (VGG16) backbone was trained for the semantic, pixel-wise segmentation of citrus foliage. The model was successfully integrated as a modular component within a broader system architecture and was tested with UAV-acquired images, demonstrating accurate disease detection and quantification, even under varied conditions. The developed system provides a robust tool for the efficient monitoring of citrus crops in precision agriculture. Full article

Oxidative stress and inflammatory responses often occur concomitantly, and they are key causative factors in various human and animal diseases. Evidence suggests that mulberry leaf protein (MLP) may have potential antioxidant and anti-inflammatory properties, but there are significant challenges in enhancing their bioactivities. In this study, MLP was enzymatically hydrolyzed using papain, protamex, alkaline protease, trypsin, and neutral protease, followed by comprehensive evaluation of the antioxidant capacity, anti-inflammatory properties, and cytotoxicity of the hydrolysates. Our findings revealed that some enzymes significantly enhanced the peptide production and antioxidant activity of MLP (p < 0.01), and its activity was positively correlated with the degree of hydrolysis. Among the five hydrolysates, neutral protease hydrolysate (NeuH) exhibited the best antioxidant properties, with free radical scavenging rates of 71.58 ± 0.42% (ABTS), 26.38 ± 0.15% (OH), and 73.91 ± 0.37% (DPPH) at a concentration of 0.1 mg/mL. In addition, NeuH significantly suppressed IL-6 secretion (p < 0.01) and downregulated mRNA expression of IL-6, iNOS, and COX-2 inflammatory markers. This study not only establishes a correlation between enzymatic parameters and MLP biological functions but also demonstrates the potential of optimized MLP hydrolysates, particularly NeuH, as valuable natural antioxidant and anti-inflammatory ingredients for functional foods or nutraceuticals aimed at mitigating oxidative stress and inflammation-related disorders. Full article

Citrus tatter leaf virus (CTLV), a major pathogen threatening global citrus production, remains poorly characterized in terms of its regional genetic diversity and evolutionary dynamics. To address this gap, we conducted a comprehensive population genetic analysis of CTLV in Zhejiang Province, China, using 181 coat protein (CP) gene sequences—the largest regional CTLV dataset to date. Our analyses uncovered substantial genetic diversity among Zhejiang CTLV isolates. Phylogenetic reconstructions revealed that these isolates span multiple clades, closely aligning with global CTLV population structures, indicative of recurrent viral introductions and extensive regional circulation. Population structure analyses revealed significant genetic differentiation driven by geography, with Jinhua isolates forming a distinct cluster, and by host species, with Citrus reticulata ‘Criton’ isolates diverging from those in other citrus varieties. Selection pressure analysis indicated that while most CP polymorphic sites were under purifying selection, several clade-specific codons showed signatures of positive selection. These results offer new insights into CTLV’s population structure and localized evolutionary trajectories, enhancing our understanding of its regional adaptation and informing strategies for disease management and control of this globally significant pathogen. Full article

The genus Xanthomonas (family Xanthomonadaceae) comprises 39 validly published species and is associated with a broad host range, infecting hundreds of monocot and dicot plants worldwide. While many Xanthomonas species are notorious for causing leaf spot and blight diseases in major agricultural crops, less attention has been given to their impact on ornamental plants. In Hawaii and other key production regions, xanthomonads have posed persistent threats to popular ornamentals in the Araceae and Araliaceae families. This review synthesizes the evolving phylogenetic and taxonomic framework of Xanthomonas strains isolated from Araceae and Araliaceae, highlighting recent advances enabled by multilocus sequence analysis and whole genome sequencing. We discuss the reclassification of key pathovars, unresolved phylogenetic placements, and the challenges of pathovar delineation within these plant families. Additionally, we examine current knowledge of molecular determinants of pathogenicity, including gene clusters involved in exopolysaccharide and lipopolysaccharide biosynthesis, flagellar assembly, cell-wall-degrading enzymes, and secretion systems (types II, III, and VI). Comparative genomics and functional studies reveal that significant gaps remain in our understanding of the genetic basis of host adaptation and virulence in these xanthomonads. Addressing these knowledge gaps will be crucial for developing effective diagnostics and management strategies for bacterial diseases in ornamental crops. Full article