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Rice false smut caused by Ustilaginoidea virens has become one of the most important diseases of rice. Mycoviruses are viruses that can infect fungi with the potential to control fungal diseases. However, little is known about the biocontrol role of hypoviruses in U. virens. In this study, we revealed that the hypovirulence-associated U. virens strain Uv325 was co-infected by four novel mycoviruses from three lineages, designated Ustilaginoidea virens RNA virus 16 (UvRV16), Ustilaginoidea virens botourmiavirus virus 8 (UvBV8), Ustilaginoidea virens botourmiavirus virus 9 (UvBV9), and Ustilaginoidea virens narnavirus virus 13 (UvNV13), respectively. The U. virens strain co-infected by four mycoviruses showed slower growth rates, reduced conidial yield, and attenuated pigmentation. We demonstrated that UvRV16 was not only the major factor responsible for the hypovirulent phenotype in U. vriens, but also able to prevent U. virens to accumulate more mycotoxin, thereby weakening the inhibitory effects on rice seed germination and seedling growth. Additionally, we indicated that UvRV16 can disrupt the antiviral response of U. virens by suppressing the transcriptional expression of multiple genes involved in autophagy and RNA silencing. In conclusion, our study provided new insights into the biological control of rice false smut.

Huanglongbing (HLB) is a destructive citrus bacterial disease caused by Candidatus Liberibacter asiaticus ( Ca .Las) and cannot be cured by current pesticides. Root lesion and Tylenchulus semipenetrans juveniles were observed in HLB-affected citrus tree roots. We hypothesize that root treatment with fosthiazate (FOS) and Cupric-Ammonium Complex (CAC) will improve the root growth and inhibit HLB. CAC is a broad spectrum fungicide and can promote growth of crops. FOS kills Tylenchulus semipenetrans and protects roots from damage by harmful bacteria such as Ca .Las. After 90 days of combination treatment of FOS and CAC through root drenches, the citrus grew new roots and its leaves changed their color to green. The inhibition rate of Ca .Las reached more than 90%. During treatment process, the chlorophyll content and the root vitality increased 396 and 151%, respectively, and starch accumulation decreased by 88%. Transmission electron microscopy (TEM) and plant tissue dyeing experiments showed that more irregular swollen starch granules existed in the chloroplast thylakoid system of the HLB-infected leaves. This is due to the blocking of their secretory tissue by starch. TEM and flow cytometry experiments in vitro showed the synergistic effects of FOS and CAC. A transcriptome analysis revealed that the treatment induced the differential expression of the genes which involved 103 metabolic pathways. These results suggested that the cocktail treatment of FOS and CAC may effectively kill various pathogens including Ca .Las on citrus root and thus effectively control HLB.

Motion blur is a common problem in the field of surveillance scenarios, and it obstructs the acquisition of valuable information. Thanks to the success of deep learning, a sequence of CNN-based architecture has been designed for image deblurring and has made great progress. As another type of neural network, transformers have exhibited powerful deep representation learning and impressive performance based on high-level vision tasks. Transformer-based networks leverage self-attention to capture the long-range dependencies in the data, yet the computational complexity is quadratic to the spatial resolution, which makes transformers infeasible for the restoration of high-resolution images. In this article, we propose an efficient transformer-based deblurring network, named MIMO-Uformer, for vehicle-surveillance scenarios. The distinct feature of the MIMO-Uformer is that the basic-window-based multi-head self-attention (W-MSA) of the Swin transformer is employed to reduce the computational complexity and then incorporated into a multi-input and multi-output U-shaped network (MIMO-UNet). The performance can benefit from the operation of multi-scale images by MIMO-UNet. However, most deblurring networks are designed for global blur, while local blur is more common under vehicle-surveillance scenarios since the motion blur is primarily caused by local moving vehicles. Based on this observation, we further propose an Intersection over Patch (IoP) factor and a supervised morphological loss to improve the performance based on local blur. Extensive experiments on a public and a self-established dataset are carried out to verify the effectiveness. As a result, the deblurring behavior based on PSNR is improved at least 0.21 dB based on GOPRO and 0.74 dB based on the self-established datasets compared to the existing benchmarks.

Citrus Huanglongbing (HLB), caused by the phloem-inhibiting bacterium Candidatus Liberibacter asiaticus (CLas), is the most devastating citrus disease, intimidating citrus production worldwide. Although commercially cultivated citrus cultivars are vulnerable to CLas infection, HLB-tolerant attributes have, however, been observed in certain citrus varieties, suggesting a possible pathway for identifying innate defense regulators that mitigate HLB. By adopting transcriptome and small RNAome analysis, the current study compares the responses of HLB-tolerant lemon (Citrus limon L.) with HLB-susceptible Shatangju mandarin (Citrus reticulata Blanco cv. Shatangju) against CLas infection. Transcriptome analysis revealed significant differences in gene expression between lemon and Shatangju. A total of 1751 and 3076 significantly differentially expressed genes were identified in Shatangju and lemon, respectively. Specifically, CLas infected lemon tissues demonstrated higher expressions of genes involved in antioxidant enzyme activity, protein phosphorylation, carbohydrate, cell wall, and lipid metabolism than Shatangju. Wet-lab experiments further validated these findings, demonstrating increased antioxidant enzyme activity in lemon: APX (35%), SOD (30%), and CAT (64%) than Shatangju. Conversely, Shatangju plants exhibited higher levels of oxidative stress markers like H2O2 (44.5%) and MDA content (65.2%), alongside pronounced ion leakage (11.85%), than lemon. Moreover, microscopic investigations revealed that CLas infected Shatangju phloem exhibits significantly more starch and callose accumulation than lemon. Furthermore, comparative sRNA profiles revealed the potential defensive regulators for HLB tolerance. In Shatangju, increased expression of csi-miR166 suppresses the expression of disease-resistant proteins, leading to inadequate defense against CLas. Conversely, reduced expression of csi-miR166 in lemon plants enables them to combat HLB by activating disease-resistance proteins. The above findings indicate that when infected with CLas, lemon exhibits stronger antioxidative activity and higher expression of disease-resistant genes, contributing to its enhanced tolerance to HLB. In contrast, Shatangju shows lower antioxidative activity, reduced expression of disease-resistant genes, significant ion leakage, and extensive callose deposition, possibly related to damage to plant cell structure and blockage of phloem sieve tubes, thereby promoting the development of HLB symptoms.

Although several protocols for genetic transformation of citrus have been published, it is highly desirable to further improve its efficiency. Here we report treatments of Agrobacterium cells and citrus explants prior to and during co-cultivation process to enhance transformation efficiency using a commercially used rootstock ‘Carrizo’ citrange [Citrus sinensis (L.) Osb. × Poncirius trifoliata (L.) Raf.] as a model plant. We found explants from light-grown seedlings exhibited higher transformation efficiency than those from etiolated seedlings. We pre-cultured Agrobacterium cells in a 1/10 MS, 0.5 g/L 2-(N-morpholino) ethanesulfonic acid (MES) and 100 µM acetosyringone liquid medium for 6 h at 25 °C before used to infect citrus explants. We incubated epicotyl segments in an MS liquid medium containing 13.2 µM 6-BA, 4.5 µM 2,4-D, 0.5 µM NAA for 3 h at 25 °C prior to Agrobacterium infection. In the co-cultivation medium, we added 30 µM paclobutrazol and 10 µM lipoic acid. Each of these treatments significantly increased the efficiencies of transformation up to 30.4% (treating Agrobacterium with acetosyringone), 31.8% (treating explants with cytokinin and auxin), 34.9% (paclobutrazol) and 38.6% (lipoic acid), respectively. When the three treatments were combined, we observed that the transformation efficiency was enhanced from 11.5% to 52.3%. The improvement of genetic transformation efficiency mediated by these three simple treatments may facilitate more efficient applications of transgenic and gene editing technologies for functional characterization of citrus genes and for genetic improvement of citrus cultivars.

The most serious citrus postharvest diseases are citrus green mould caused by Penicillium digitatum and citrus blue mould caused by Penicillium italicum. Marine microorganisms have become a new measure in the control of these diseases. In this study, we isolated 56 marine actinomycetes, and strain AM-4 isolated from Drupa granulata exhibited the strongest antagonistic activity against P. digitatum. Molecular, morphological, cultural and physio-biochemical analysis identified strain AM-4 as Streptomyces chumphonensis. Strain AM-4 also exhibited antagonistic activity against 11 other phytopathogens, with an inhibition zone width ranging from 0.65 ± 0.04 to 2.35 ± 0.02 cm. The mycelial growth inhibition rates of the fermentation liquid of strain AM-4 against P. digitatum and P. italicum were 66.23% and 61.42%, respectively, and the conidial germination inhibition rates were 65.77% and 62.13%, respectively. The relative control efficacy of the gradient-diluted fermentation extracts of strain AM-4 against citrus green mould caused by P. digitatum ranged from 77.95% to 92.91% at 5 days post inoculation and 56.30% to 87.22% 10 days after inoculation. The relative control efficacy of the gradient diluted fermentation extracts of strain AM-4 against citrus blue mould caused by P. italicum ranged from 61.32% to 84.91% 5 days after inoculation and from 51.34% to 80.92% 10 days after inoculation. The relative control efficacy of the fermentation extracts of strain AM-4 was even better than prochloraz against citrus green mould caused by prochloraz-resistant P. digitatum. Therefore, as a biological control strain, AM-4 has a good prospect for development and utilization potential.

Mycoviruses are viruses that infect fungi, yeasts, and oomycetes and can replicate and multiply within them. They are widely distributed in plant- and animal-pathogenic fungi. In this study, we identified a novel positive-sense single-stranded RNA (+ ssRNA) mycovirus from Ustilaginoidea virens strain Uv339, the causal agent of rice false smut (RSF), and this virus was named \"Ustilaginoidea virens narnavirus 5\" (UvNV5). Sequence analysis revealed that UvNV5 has a complete genome length of 2091 nt and contains a single open reading frame (ORF) (nt 131–2038) encoding a 635-amino-acid (aa) RNA-dependent RNA polymerase (RdRp) with a molecular mass of 71.8 kDa. BLASTp analysis revealed that the RdRp of UvNV5 shares only 38.22% amino acid sequence identity with that of Ustilaginoidea virens narnavirus virus 13, its closest relative. Phylogenetic analysis indicated that UvNV5 clustered within a branch alongside members of the class Amabiliviricetes . In summary, UvNV5 is a novel mycovirus of the class Amabiliviricetes that infects U. virens .

Graph neural networks (GNNs) demonstrate their effectiveness in facilitating node classification and a range of graph-based tasks. However, recent studies have revealed that GNNs can be vulnerable to various adversarial attacks. Despite various defense strategies, ranging from attack-agnostic defenses to attack-oriented defenses that have been proposed to mitigate the impact of adversarial attacks on graph data, effectively learning attack-agnostic graph representation remains an open challenge. This paper introduces a novel information Competition-based framework for Graph Neural Networks (i.e., iC-GNN, e.g., iC-GCN, iC-GAT, etc.) to enhance the robustness of GNNs against various adversarial attacks in node classifications. Through the use of graph reconstruction and low-rank approximation, our approach learns diversified graph representations to collaboratively perform node classifications. Meanwhile, mutual information constraints are utilized on different graph representations to ensure diversity and competition in graph features. The experimental results indicate that within the proposed framework, iC-GCN outperforms other graph defense frameworks in countering a wide range of targeted and non-targeted adversarial attacks in both evasion and poisoning training scenarios. Additionally, this concept has been extended to encompass other widely utilized GNN models like iC-GAT and iC-SAGE. All iC-GNN models demonstrate effective defense capabilities, demonstrating comparable resilience to adversarial attacks. This underscores the superiority and scalable nature of the iC-GNN framework, providing opportunities for a variety of graph learning applications.

Graph neural networks (GNNs) demonstrate their effectiveness in facilitating node classification and a range of graph-based tasks. However, recent studies have revealed that GNNs can be vulnerable to various adversarial attacks. Despite various defense strategies, ranging from attack-agnostic defenses to attack-oriented defenses that have been proposed to mitigate the impact of adversarial attacks on graph data, effectively learning attack-agnostic graph representation remains an open challenge. This paper introduces a novel information Competition-based framework for Graph Neural Networks (i.e., iC -GNN, e.g., iC -GCN, iC -GAT, etc.) to enhance the robustness of GNNs against various adversarial attacks in node classifications. Through the use of graph reconstruction and low-rank approximation, our approach learns diversified graph representations to collaboratively perform node classifications. Meanwhile, mutual information constraints are utilized on different graph representations to ensure diversity and competition in graph features. The experimental results indicate that within the proposed framework, iC -GCN outperforms other graph defense frameworks in countering a wide range of targeted and non-targeted adversarial attacks in both evasion and poisoning training scenarios. Additionally, this concept has been extended to encompass other widely utilized GNN models like iC -GAT and iC -SAGE. All iC -GNN models demonstrate effective defense capabilities, demonstrating comparable resilience to adversarial attacks. This underscores the superiority and scalable nature of the iC -GNN framework, providing opportunities for a variety of graph learning applications.

The genome of the soybean pathogen Phytophthora sojae contains nearly 400 genes encoding candidate effector proteins carrying the host cell entry motif RXLR-dEER. Here, we report a broad survey of the transcription, variation, and functions of a large sample of the P. sojae candidate effectors. Forty-five (12%) effector genes showed high levels of polymorphism among P. sojae isolates and significant evidence for positive selection. Of 169 effectors tested, most could suppress programmed cell death triggered by BAX, effectors, and/or the PAMP INF1, while several triggered cell death themselves. Among the most strongly expressed effectors, one immediate-early class was highly expressed even prior to infection and was further induced 2- to 10-fold following infection. A second early class, including several that triggered cell death, was weakly expressed prior to infection but induced 20- to 120-fold during the first 12 h of infection. The most strongly expressed immediate-early effectors could suppress the cell death triggered by several early effectors, and most early effectors could suppress INF1-triggered cell death, suggesting the two classes of effectors may target different functional branches of the defense response. In support of this hypothesis, misexpression of key immediate-early and early effectors severely reduced the virulence of P. sojae transformants.