Three new endophytic Apiospora species (Apiosporaceae, Amphisphaeriales) from China.

Apiospora species are widely distributed fungi with diverse lifestyles, primarily functioning as plant pathogens, as well as exhibiting saprophytic and endophytic behaviors. This study reports the discovery of three new species of Apiospora, namely A.gongcheniae, A.paragongcheniae, and A.neogongcheniae, isolated from healthy Poaceae plants in China. These novel species were identified through a multi-gene phylogenetic analysis. The phylogenetic analysis of the combined ITS, LSU, tef1, and tub2 sequence data revealed that the three new species formed a robustly supported clade with A.garethjonesii, A.neogarethjonesii, A.setostroma, A.subrosea, A.mytilomorpha, and A.neobambusae. Detailed descriptions of the newly discovered species are provided and compared with closely related species to enhance our understanding of the genus Apiospora.

XTransCT: ultra-fast volumetric CT reconstruction using two orthogonal x-ray projections for image-guided radiation therapy via a transformer network.

Objective.The aim of this study was to reconstruct volumetric computed tomography (CT) images in real-time from ultra-sparse two-dimensional x-ray projections, facilitating easier navigation and positioning during image-guided radiation therapy.Approach.Our approach leverages a voxel-sapce-searching Transformer model to overcome the limitations of conventional CT reconstruction techniques, which require extensive x-ray projections and lead to high radiation doses and equipment constraints.Main results.The proposed XTransCT algorithm demonstrated superior performance in terms of image quality, structural accuracy, and generalizability across different datasets, including a hospital set of 50 patients, the large-scale public LIDC-IDRI dataset, and the LNDb dataset for cross-validation. Notably, the algorithm achieved an approximately 300% improvement in reconstruction speed, with a rate of 44 ms per 3D image reconstruction compared to former 3D convolution-based methods.Significance.The XTransCT architecture has the potential to impact clinical practice by providing high-quality CT images faster and with substantially reduced radiation exposure for patients. The model's generalizability suggests it has the potential applicable in various healthcare settings.

QUIZ: An arbitrary volumetric point matching method for medical image registration.

Rigid pre-registration involving local-global matching or other large deformation scenarios is crucial. Current popular methods rely on unsupervised learning based on grayscale similarity, but under circumstances where different poses lead to varying tissue structures, or where image quality is poor, these methods tend to exhibit instability and inaccuracies. In this study, we propose a novel method for medical image registration based on arbitrary voxel point of interest matching, called query point quizzer (QUIZ). QUIZ focuses on the correspondence between local-global matching points, specifically employing CNN for feature extraction and utilizing the Transformer architecture for global point matching queries, followed by applying average displacement for local image rigid transformation.We have validated this approach on a large deformation dataset of cervical cancer patients, with results indicating substantially smaller deviations compared to state-of-the-art methods. Remarkably, even for cross-modality subjects, it achieves results surpassing the current state-of-the-art.

A statistical deformation model-based data augmentation method for volumetric medical image segmentation.

The accurate delineation of organs-at-risk (OARs) is a crucial step in treatment planning during radiotherapy, as it minimizes the potential adverse effects of radiation on surrounding healthy organs. However, manual contouring of OARs in computed tomography (CT) images is labor-intensive and susceptible to errors, particularly for low-contrast soft tissue. Deep learning-based artificial intelligence algorithms surpass traditional methods but require large datasets. Obtaining annotated medical images is both time-consuming and expensive, hindering the collection of extensive training sets. To enhance the performance of medical image segmentation, augmentation strategies such as rotation and Gaussian smoothing are employed during preprocessing. However, these conventional data augmentation techniques cannot generate more realistic deformations, limiting improvements in accuracy. To address this issue, this study introduces a statistical deformation model-based data augmentation method for volumetric medical image segmentation. By applying diverse and realistic data augmentation to CT images from a limited patient cohort, our method significantly improves the fully automated segmentation of OARs across various body parts. We evaluate our framework on three datasets containing tumor OARs from the head, neck, chest, and abdomen. Test results demonstrate that the proposed method achieves state-of-the-art performance in numerous OARs segmentation challenges. This innovative approach holds considerable potential as a powerful tool for various medical imaging-related sub-fields, effectively addressing the challenge of limited data access.

Volumetric tumor tracking from a single cone-beam X-ray projection image enabled by deep learning.

Radiotherapy serves as a pivotal treatment modality for malignant tumors. However, the accuracy of radiotherapy is significantly compromised due to respiratory-induced fluctuations in the size, shape, and position of the tumor. To address this challenge, we introduce a deep learning-anchored, volumetric tumor tracking methodology that employs single-angle X-ray projection images. This process involves aligning the intraoperative two-dimensional (2D) X-ray images with the pre-treatment three-dimensional (3D) planning Computed Tomography (CT) scans, enabling the extraction of the 3D tumor position and segmentation. Prior to therapy, a bespoke patient-specific tumor tracking model is formulated, leveraging a hybrid data augmentation, style correction, and registration network to create a mapping from single-angle 2D X-ray images to the corresponding 3D tumors. During the treatment phase, real-time X-ray images are fed into the trained model, producing the respective 3D tumor positioning. Rigorous validation conducted on actual patient lung data and lung phantoms attests to the high localization precision of our method at lowered radiation doses, thus heralding promising strides towards enhancing the precision of radiotherapy.

Integrated physiological and omics analyses reveal the mechanism of beneficial fungal Trichoderma sp. alleviating cadmium toxicity in tobacco (Nicotiana tabacum L.).

Cadmium (Cd) is a highly toxic heavy metal and readily accumulates in tobacco, which imperils public health via Cd exposure from smoking. Beneficial microbes have a pivotal role in promoting plant growth, especially under environmental stresses such as heavy metal stresses. In this study, we introduced a novel fungal strain Trichoderma nigricans T32781, and investigated its capacity to alleviate Cd-induced stress in tobacco plants through comprehensive physiological and omics analyses. Our findings revealed that T32781 inoculation in soil leads to a substantial reduction in Cd-induced growth inhibition. This was evidenced by increased plant height, enhanced biomass accumulation, and improved photosynthesis, as indicated by higher values of key photosynthetic parameters, including the maximum quantum yield of photosystem Ⅱ (Fv/Fm), stomatal conductance (Gs), photosynthetic rate (Pn) and transpiration rate (Tr). Furthermore, element analysis demonstrated that T. nigricans T32781 inoculation resulted in a remarkable reduction of Cd uptake by 62.2% and a 37.8% decrease in available soil Cd compared to Cd-stressed plants without inoculation. The protective role of T32781 extended to mitigating Cd-induced oxidative stress by improving antioxidant enzyme activities of superoxide dismutase (SOD), peroxidase (POD), and ascorbate peroxidase (APX). Metabolic profiling of tobacco roots identified 43 key metabolites, with notable contributions from compounds like nicotinic acid, succinic acid, and fumaric acid in reducing Cd toxicity in T32781-inoculated plants. Additionally, rhizosphere microbiome analysis highlighted the promotion of beneficial microbes, including Gemmatimonas and Sphingomonas, by T32781 inoculation, which potentially contributed to the restoration of plant growth under Cd exposure. In summary, our study demonstrated that T. nigricans T32781 effectively alleviated Cd stress in tobacco plants by reducing Cd uptake, alleviating Cd-induced oxidative stress, influencing plant metabolite and modulating the microbial composition in the rhizosphere. These findings offer a novel perspective and a promising candidate strain for enhancing Cd tolerance and prohibiting its accumulation in plants to reduce health risks associated with exposure to Cd-contaminated plants.

Volumetric feature points integration with bio-structure-informed guidance for deformable multi-modal CT image registration.

Objective.Medical image registration represents a fundamental challenge in medical image processing. Specifically, CT-CBCT registration has significant implications in the context of image-guided radiation therapy (IGRT). However, traditional iterative methods often require considerable computational time. Deep learning based methods, especially when dealing with low contrast organs, are frequently entangled in local optimal solutions.Approach.To address these limitations, we introduce a registration method based on volumetric feature points integration with bio-structure-informed guidance. Surface point cloud is generated from segmentation labels during the training stage, with both the surface-registered point pairs and voxel feature point pairs co-guiding the training process, thereby achieving higher registration accuracy.Main results.Our findings have been validated on paired CT-CBCT datasets. In comparison with other deep learning registration methods, our approach has improved the precision by 6%, reaching a state-of-the-art status.Significance.The integration of voxel feature points and bio-structure feature points to guide the training of the medical image registration network has achieved promising results. This provides a meaningful direction for further research in medical image registration and IGRT.

Ring artifacts correction for computed tomography image using unsupervised contrastive learning.

Objective.Computed tomography (CT) is a widely employed imaging technology for disease detection. However, CT images often suffer from ring artifacts, which may result from hardware defects and other factors. These artifacts compromise image quality and impede diagnosis. To address this challenge, we propose a novel method based on dual contrast learning image style transformation network model (DCLGAN) that effectively eliminates ring artifacts from CT images while preserving texture details.Approach. Our method involves simulating ring artifacts on real CT data to generate the uncorrected CT (uCT) data and transforming them into strip artifacts. Subsequently, the DCLGAN synthetic network is applied in the polar coordinate system to remove the strip artifacts and generate a synthetic CT (sCT). We compare the uCT and sCT images to obtain a residual image, which is then filtered to extract the strip artifacts. An inverse polar transformation is performed to obtain the ring artifacts, which are subtracted from the original CT image to produce a corrected image.Main results.To validate the effectiveness of our approach, we tested it using real CT data, simulated data, and cone beam computed tomography images of the patient's brain. The corrected CT images showed a reduction in mean absolute error by 12.36 Hounsfield units (HU), a decrease in root mean square error by 18.94 HU, an increase in peak signal-to-noise ratio by 3.53 decibels (dB), and an improvement in structural similarity index by 9.24%.Significance.These results demonstrate the efficacy of our method in eliminating ring artifacts and preserving image details, making it a valuable tool for CT imaging.

An unsupervised dual contrastive learning framework for scatter correction in cone-beam CT image.

PURPOSE: Cone-beam computed tomography (CBCT) is widely utilized in modern radiotherapy; however, CBCT images exhibit increased scatter artifacts compared to planning CT (pCT), compromising image quality and limiting further applications. Scatter correction is thus crucial for improving CBCT image quality. METHODS: In this study, we proposed an unsupervised contrastive learning method for CBCT scatter correction. Initially, we transformed low-quality CBCT into high-quality synthetic pCT (spCT) and generated forward projections of CBCT and spCT. By computing the difference between these projections, we obtained a residual image containing image details and scatter artifacts. Image details primarily comprise high-frequency signals, while scatter artifacts consist mainly of low-frequency signals. We extracted the scatter projection signal by applying a low-pass filter to remove image details. The corrected CBCT (cCBCT) projection signal was obtained by subtracting the scatter artifacts projection signal from the original CBCT projection. Finally, we employed the FDK reconstruction algorithm to generate the cCBCT image. RESULTS: To evaluate cCBCT image quality, we aligned the CBCT and pCT of six patients. In comparison to CBCT, cCBCT maintains anatomical consistency and significantly enhances CT number, spatial homogeneity, and artifact suppression. The mean absolute error (MAE) of the test data decreased from 88.0623 ± 26.6700 HU to 17.5086 ± 3.1785 HU. The MAE of fat regions of interest (ROIs) declined from 370.2980 ± 64.9730 HU to 8.5149 ± 1.8265 HU, and the error between their maximum and minimum CT numbers decreased from 572.7528 HU to 132.4648 HU. The MAE of muscle ROIs reduced from 354.7689 ± 25.0139 HU to 16.4475 ± 3.6812 HU. We also compared our proposed method with several conventional unsupervised synthetic image generation techniques, demonstrating superior performance. CONCLUSIONS: Our approach effectively enhances CBCT image quality and shows promising potential for future clinical adoption.

Three new species of Trichoderma (Hypocreales, Hypocreaceae) from soils in China.

Trichoderma spp. are diverse fungi with wide distribution. In this study, we report on three new species of Trichoderma, namely T.nigricans, T.densissimum and T.paradensissimum, collected from soils in China. Their phylogenetic position of these novel species was determined by analyzing the concatenated sequences of the second largest nuclear RNA polymerase subunit encoding gene (rpb2) and the translation elongation factor 1- alpha encoding gene (tef1). The results of the phylogenetic analysis showed that each new species formed a distinct clade: T.nigricans is a new member of the Atroviride Clade, and T.densissimum and T.paradensissimum belong to the Harzianum Clade. A detailed description of the morphology and cultural characteristics of the newly discovered Trichoderma species is provided, and these characteristics were compared with those of closely related species to better understand the taxonomic relationships within the Trichoderma.

Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT.

Objective: To develop a contrast learning-based generative (CLG) model for the generation of high-quality synthetic computed tomography (sCT) from low-quality cone-beam CT (CBCT). The CLG model improves the performance of deformable image registration (DIR). Methods: This study included 100 post-breast-conserving patients with the pCT images, CBCT images, and the target contours, which the physicians delineated. The CT images were generated from CBCT images via the proposed CLG model. We used the Sct images as the fixed images instead of the CBCT images to achieve the multi-modality image registration accurately. The deformation vector field is applied to propagate the target contour from the pCT to CBCT to realize the automatic target segmentation on CBCT images. We calculate the Dice similarity coefficient (DSC), 95 % Hausdorff distance (HD95), and average surface distance (ASD) between the prediction and reference segmentation to evaluate the proposed method. Results: The DSC, HD95, and ASD of the target contours with the proposed method were 0.87 ± 0.04, 4.55 ± 2.18, and 1.41 ± 0.56, respectively. Compared with the traditional method without the synthetic CT assisted (0.86 ± 0.05, 5.17 ± 2.60, and 1.55 ± 0.72), the proposed method was outperformed, especially in the soft tissue target, such as the tumor bed region. Conclusion: The CLG model proposed in this study can create the high-quality sCT from low-quality CBCT and improve the performance of DIR between the CBCT and the pCT. The target segmentation accuracy is better than using the traditional DIR.

First report of Fusarium concentricum as a causal agent of Fusarium leaf blotch on pecan (Carya illinoinensis) in Southeast China.

The economically important nut crop pecan (Carya illinoinensis (Wangenh.) K. Koch) is seriously affected by increasing incidence of fungal disease worldwide (Xiao et al 2021). The top leaves of the pecan variety 'Pawnee' in the orchard of Zhejiang A&F University, Zhejiang, China were damaged by massive dark brown plaques in summer to autumn 2021. The causal agent was isolated from leaves with target plaques following the steps: sterilized with 70% alcohol (30 s × 2), rinsed with sterilized water (3 ×) before and after 5% sodium hypochlorite (30 s), excised the plaques, and placed on PDA medium at 28℃ in a dark incubator for 3-d. The mycelium on the edge of each colony was transferred to fresh SNA medium in dark for 2 weeks to induce conidia formation. A few conidia-germinated mycelia were transferredand inoculated on new plates containing fresh PDA medium to obtain the purified cultures. Koch's postulates were applied to validate the pathogenicity of the purified isolates. Non-woundedly healthy leaves (disinfected with 5% sodium hypochlorite) of 'Pawnee' seedlings were inoculated with 5 mm 7-d old purified cultures. Dark-brown spots appeared on the leaves 2 days post inoculation at 25℃. The spots became larger accompanied by partially cracking and slight deformation of inoculated leaves from day 2 to day 4, while the control leaves remained asymptomatic. A re-isolated strain ZJ-6 from these infected leaves was identified as the pathogenic isolate with the same symptom as the previous one. Morphologically, aerial mycelia of the pathogenic isolate ZJ-6 cashmere and white. The reverse of colony orange. The edge of the colony appeared gradually thinner, the aerial mycelia loose and flocky, and the matrix mycelium whitened. Hyphae were septate, translucent with smooth wall and 1.47-7.14 µm in width. Microconidia (n = 20) obovoid to fusoid, mainly with 0-septate, 4.45-7.78×4.79-16.25 µm. Macroconidia (n = 20) sickle, mainly with 3-5 septa, 5.56-10.28×56.67-114.54 µm. Simultaneous of monophialidic and polyphialidic conidiophores. Conidiophore width 1.47-3.68 µm, slightly smaller than vegetative hyphae. The morphological characteristics matched with previous descriptions of Fusarium species (Nirenberg and O'Donnell 1998; Wang et al 2013). The identity of ZJ-6 was confirmed by phylogenetic reconstruction using the concatenated sequences of the ATP citrate lyase (ACL1), Calmodulin (CaM), the internal transcribed spacer (ITS) rDNA region, ribosomal RNA gene (LSU), the largest subunit of DNA-dependent RNA polymerase II (RPB1), partial translation elongation factor-1 alpha (TEF) and ß-Tubulin (TUB). To this end, the genomic DNA of ZJ-6 was extracted by the M5 hipermix-MF859 (Mei5 Biotechnology) and submitted to GenBank under the accession numbers OP933646, OP933647, OP925890, OP925889, OP933396, OP933648, and OP933397, respectively. The obtained sequences of ZJ-6 were used for nucleotide BLAST against thetandard databases, respectively, and the strains with sequence identification values above 98% were selected to construct multiple alignment for building a phylogenetic tree. This analyses allowed the identification of ZJ-6 as Fusarium concentricum Nirenberg & O'Donnell, a species with few reports that can cause serious damage to the fruits and branches of other hosts (Hasan et al 2020; Huda-Shakirah et al 2020; Wang et al 2013). This is the first report of pathogenic F. concentricum on pecan in Southeast China that caused no harvest of infected plants.

2D/3D Non-Rigid Image Registration via Two Orthogonal X-ray Projection Images for Lung Tumor Tracking.

Two-dimensional (2D)/three-dimensional (3D) registration is critical in clinical applications. However, existing methods suffer from long alignment times and high doses. In this paper, a non-rigid 2D/3D registration method based on deep learning with orthogonal angle projections is proposed. The application can quickly achieve alignment using only two orthogonal angle projections. We tested the method with lungs (with and without tumors) and phantom data. The results show that the Dice and normalized cross-correlations are greater than 0.97 and 0.92, respectively, and the registration time is less than 1.2 seconds. In addition, the proposed model showed the ability to track lung tumors, highlighting the clinical potential of the proposed method.

Sinogram domain metal artifact correction of CT via deep learning.

PURPOSE: Metal artifacts can significantly decrease the quality of computed tomography (CT) images. This occurs as X-rays penetrate implanted metals, causing severe attenuation and resulting in metal artifacts in the CT images. This degradation in image quality can hinder subsequent clinical diagnosis and treatment planning. Beam hardening artifacts are often manifested as severe strip artifacts in the image domain, affecting the overall quality of the reconstructed CT image. In the sinogram domain, metal is typically located in specific areas, and image processing in these regions can preserve image information in other areas, making the model more robust. To address this issue, we propose a region-based correction of beam hardening artifacts in the sinogram domain using deep learning. METHODS: We present a model composed of three modules: (a) a Sinogram Metal Segmentation Network (Seg-Net), (b) a Sinogram Enhancement Network (Sino-Net), and (c) a Fusion Module. The model starts by using the Attention U-Net network to segment the metal regions in the sinogram. The segmented metal regions are then interpolated to obtain a sinogram image free of metal. The Sino-Net is then applied to compensate for the loss of organizational and artifact information in the metal regions. The corrected metal sinogram and the interpolated metal-free sinogram are then used to reconstruct the metal CT and metal-free CT images, respectively. Finally, the Fusion Module combines the two CT images to produce the result. RESULTS: Our proposed method shows strong performance in both qualitative and quantitative evaluations. The peak signal-to-noise ratio (PSNR) of the CT image before and after correction was 18.22 and 30.32, respectively. The structural similarity index measure (SSIM) improved from 0.75 to 0.99, and the weighted peak signal-to-noise ratio (WPSNR) increased from 21.69 to 35.68. CONCLUSIONS: Our proposed method demonstrates the reliability of high-accuracy correction of beam hardening artifacts.

First report of Colletotrichum plurivorum causing anthracnose on pecan (Carya illinoinensis) in China.

Pecan (Carya illinoinensis) is an economically important nut crop worldwide (Xiao et al 2021). Anthracnose symptoms were found on pecan fruits and leaves in plantations in Anhui and Jiangsu provinces, China in August 2019. Irregular, dark brown or black spotted lesions firstly appeared on the surface and inside of fruits, and spread to all leaves. The symptoms resulted in 30% to 50% leaf drop and nearly a half of fruit decay in almost all trees of the susceptible cv. Wichita. The causal agent were isolated from fruits with target symptoms following the steps: surface disinfected with 75% ethanol (2×, 30 s), rinsed with sterile deionized water (3×), ~ 0.5 cm small fragments of the fruits excised and plated on potato dextrose agar (PDA) medium and incubated at 28 °C in dark for 3-d. Mycelium of each colony was picked and incubated on fresh PDA at 25 °C with a 12-hour light/dark cycle for 6-d to induce conidia formation. One 5-mm hyphal plug produced from each single spore isolate was transferred onto fresh PDA to obtain the pure cultures. Koch's postulates was employed for pathogenicity determination of the isolates. Non-wounded healthy leaves from seedlings of the disease susceptible cv. Pawnee were disinfected with 1% NaClO and inoculated with 5-mm 5-d hyphal of each isolate at 25 ℃. Tiny lesion spots were visible on the leaves after 2 days post inoculation (DPI) with isolate W-6 (the only pathogenic one among all isolates), and expanded over time until to the leaves withered, while the control leaves and leaves inoculated with other isolates remained asymptomatic. The pathogenicity of W-6 were confirmed using leaves and fruits of living Pawnee trees growing in Linglong Mountain Plantation, Lin'an, Hangzhou, China (119°38'51″E, 30°12'39″N, elevation: 119m). Three experimental replicates were conducted separately with three bio-replicates for all pathogenetic testing. The same symptoms were observed on both detached leaves and leaves and fruits of living trees.. The colony of W-6 have round cottony mycelium with complete edges and showed the fastest growth rate 3 - 4 DPI. After 7 DPI, white aerial mycelium turned yellowish brown and formed Acervulus in the mycelium. Conidia (n=50) one-celled, 12.0 - 20.0 µm × 3.5 to 6.0 µm width. Hyaline cylindrical with slightly rounded ends and two or three large guttulate at the centre. Most Acervulus dark brown and slightly irregular in shape, 12.70 × 18.79 µm (n=10). Setae were dark brown in color with average length around 34.10 µm (n=10). These characteristics matched previous descriptions of Colletotrichum orchidearum species complex, including C. plurivorum (Damm et al 2019). The identity of W-6 was confirmed by multi-locus phylogenetic analysis using the internal transcribed spacer (ITS) rDNA region and partial sequences of the conserved genes glyceraldehyde-3-phosphate dehydrogenase (GAPDH), actin (ACT), beta-tubulin 2 (TUB2), and chitin synthase (CHS). The sequences of W-6 were used for Basic Local Alignment Search Tool (BLAST) against NCBI GenBank and the sequences with 100% identity to that of W-6 were achieved, respectively. The concatenated sequences of the ACT-CHS-GAPDH-ITS-TUB2 was used for building a phylogenetic tree. The molecular analyses allowed the identification of the pathogen as C. plurivorum. It was known that 9 of the 11 Colletotrichum species causing pecan anthrax worldwide were reported in southern China (Brenneman 1989; Oh et al 2021). This is the first report of C. plurivorum as causal agent of pecan in China.

An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.

The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography-guided adaptive radiation therapy. A deep unsupervised learning model was introduced to map breast's tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord from planning computed tomography to cone-beam CT. To improve the traditional image registration method's performance, we used a regional deformable framework based on the narrow-band mapping, which can mitigate the effect of the image artifacts on the cone-beam CT. We retrospectively selected 373 anonymized cone-beam CT volumes from 111 patients with breast cancer. The cone-beam CTs are divided into three sets. 311 / 20 / 42 cone-beam CT images were used for training, validating, and testing. The manual contour was used as reference for the testing set. We compared the results between the reference and the model prediction for evaluating the performance. The mean Dice between manual reference segmentations and the model predicted segmentations for breast tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord were 0.78 ± 0.09, 0.90 ± 0.03, 0.88 ± 0.04, 0.94 ± 0.03, 0.95 ± 0.02, and 0.77 ± 0.07, respectively. The results demonstrated a good agreement between the reference and the proposed contours. The proposed deep learning-based regional deformable model technique can automatically propagate contours for breast cancer adaptive radiotherapy. Deep learning in contour propagation was promising, but further investigation was warranted.

First report of Curvularia muehlenbeckiae, the causal agent of Curvularia leaf spot on pecan (Carya illinoinensis) in China.

The pecan (Carya illinoinensis) industry is largely affected by the increased incidence of diseases (Xiao et al 2021). Leaf spot symptoms were identified in an orchard of cultivar Pawnee pecan trees at Zhejiang A&F University, Zhejiang, China in August 2020. Small black spots occurred on the veins and edges of the leaves and nearby tissues turned yellow and slightly deformed (May to July). The spots (0.5-1.5cm) spread to all leaves with 25% to 40% leaf drop occurring in almost all trees between August and October. The causal pathogen was isolated from leaves with target symptoms using the following method: surface sterilized with 70% alcohol (2×, 30 s), rinsed with sterilized water (3×), leaf spots excised and placed on PCA media, and left to incubate at 28℃ in the dark for 3-d. Mycelium on the edge of each clone was excised and incubated on fresh oatmeal agar medium with a 12-hour light/dark cycle for 7-d to obtain conidia. Single spore isolates were germinated on PDA medium under the same conditions as previously described, one 5-mm hyphal plug was transferred to fresh PCA media to obtain the pure cultures. The pathogenicity of the isolates were verified using Koch's postulates. Non-wounded healthy leaves (disinfected with 1% NaClO) of cv. Pawnee (disease susceptible) were obtained from seedlings grown in green-house at 26 ℃ and inoculated with 5-mm hyphal plugs and a conidia-hyphae suspension (~106/mL) containing one-week old purified cultures. After 3-15 days post-inoculation, small black spots appeared on the leaves inoculated with isolate P-6 (the only pathogenic isolate from the leaf spots in the orchard) and grew larger until the whole leaf wilted while the control leaves remained asymptomatic. The experiment was repeated two times with two bio-replicates each run. Finally, the pathogen was re-isolated from infected leaves, which showed the same symptoms as the previous isolate. Aerial mycelia of P-6 turned from white to gray and substrate mycelia from brown to black. Colonies had a fimbriate margin before mycelia filled the medium. Hyphae were septate, branched, brown or black, smooth wall and 1.4-10 µm in width. Conidiophore single-branch, dark brown, curved or straight, 1.93-5.51×44.12-104.41 µm width, conidiogenous cells 6.66-16.67 µm (terminal) and 8.82-23.33 µm (intercalary) length, mono- to polytretic, proliferating sympodially. Conidia (n=20) four cells, 15.7 - 25.7 µm × 7.1 - 11.4 µm wdth, swelling and curving from the basal cell to the third. The bending angle was 5° to 80°. The middle two cells were brown and usually verruculose, the basal and apical cells paler and less ornamented. No sexual morph observed. The morphological characteristics matched previous descriptions of Curvularia species (Madrid et al 2014). The identity of P-6 was confirmed by phylogenetic reconstruction using the concatenated sequences of ITS rDNA, partial GAPDH, LSU, TEF-1α and RPB2 regions (Raza et al 2019). The genomic DNA of P-6 was extracted by the M5 hipermix-MF859 (Mei5 Biotechnology). The sequences of P-6 were used for nucleotide BLAST against the Standard databases and model strains were selected to construct the concatenated sequences of GAPDH-ITS-LSU-TEF-1α-RPB2 for building a phylogenetic tree. This analysis identified P-6 as a strain of C. muehlenbeckiae, a species with few reports other than in gramineous crops (Raza et al 2019; Chen et al 2021; Cui et al 2020; Ni et al 2016). This is the first report of C. muehlenbeckiae on pecan in China and worldwide.

Verification of TRI3 Acetylation of Trichodermol to Trichodermin in the Plant Endophyte Trichoderma taxi.

Trichodermin, a trichothecene first isolated in Trichoderma species, is a sesquiterpenoid antibiotic that exhibits significant inhibitory activity to the growth of many pathogenic fungi such as Candida albicans, Rhizoctonia solani, and Botrytis cinerea by inhibiting the peptidyl transferase involved in eukaryotic protein synthesis. Trichodermin has also been shown to selectively induce cell apoptosis in several cancer cell lines and thus can act as a potential lead compound for developing anticancer therapeutics. The biosynthetic pathway of trichodermin in Trichoderma has been identified, and most of the involved genes have been functionally characterized. An exception is TRI3, which encodes a putative acetyltransferase. Here, we report the identification of a gene cluster that contains seven genes expectedly involved in trichodermin biosynthesis (TRI3, TRI4, TRI6, TRI10, TRI11, TRI12, and TRI14) in the trichodermin-producing endophytic fungus Trichoderma taxi. As in Trichoderma brevicompactum, TRI5 is not included in the cluster. Functional analysis provides evidence that TRI3 acetylates trichodermol, the immediate precursor, to trichodermin. Disruption of TRI3 gene eliminated the inhibition to R. solani by T. taxi culture filtrates and significantly reduced the production of trichodermin but not of trichodermol. Both the inhibitory activity and the trichodermin production were restored when native TRI3 gene was reintroduced into the disruption mutant. Furthermore, a His-tag-purified TRI3 protein, expressed in Escherichia coli, was able to convert trichodermol to trichodermin in the presence of acetyl-CoA. The disruption of TRI3 also resulted in lowered expression of both the upstream biosynthesis TRI genes and the regulator genes. Our data demonstrate that T. taxi TRI3 encodes an acetyltransferase that catalyzes the esterification of the C-4 oxygen atom on trichodermol and thus plays an essential role in trichodermin biosynthesis in this fungus.

Biogeography and Ecology of Magnaporthales: A Case Study.

The order Magnaporthales belongs to Sordariomycetes, Ascomycota. Magnaporthales includes five families, namely Ceratosphaeriaceae, Pseudohalonectriaceae, Ophioceraceae, Pyriculariaceae, and Magnaporthaceae. Most Magnaporthales members are found in Poaceae plants and other monocotyledonous herbaceous plants ubiquitously as plant pathogens or endophytic fungi, and some members are found in decaying wood or dead grass as saprophytic fungi. Therefore, studying the biogeography and ecology of Magnaporthales is of great significance. Here, we described the biodiversity of endophytic Magnaporthales fungi from Poaceae at three latitudes in China and conducted a meta-analysis of the geography and ecology of Magnaporthales worldwide. We found that Magnaporthales is a dominant order in the endophytic fungi of Poaceae. More than half of the endophytic Magnaporthales fungi have a taxonomically uncertain placement. Notably, few endophytic fungi are grouped in the clusters with known saprophytic or pathogenic Magnaporthales fungi, indicating that they may have saprophytic and parasitic differentiation in nutritional modes and lifestyles. The meta-analysis revealed that most species of Magnaporthales have characteristic geographical, host, and tissue specificity. The geographical distribution of the three most studied genera, namely Gaeumannomyces, Magnaporthiopsis, and Pyricularia, in Magnaporthales may depend on the distribution of their hosts. Therefore, studies on the endophytic fungal Magnaporthales from monocotyledonous plants, including Poaceae, in middle and low latitudes will deepen our understanding of the biogeography and ecology of Magnaporthales.

The Genomes of the Allohexaploid Echinochloa crus-galli and Its Progenitors Provide Insights into Polyploidization-Driven Adaptation.

The hexaploid species Echinochloa crus-galli is one of the most detrimental weeds in crop fields, especially in rice paddies. Its evolutionary history is similar to that of bread wheat, arising through polyploidization after hybridization between a tetraploid and a diploid species. In this study, we generated and analyzed high-quality genome sequences of diploid (E. haploclada), tetraploid (E. oryzicola), and hexaploid (E. crus-galli) Echinochloa species. Gene family analysis showed a significant loss of disease-resistance genes such as those encoding NB-ARC domain-containing proteins during Echinochloa polyploidization, contrary to their significant expansionduring wheat polyploidization, suggesting that natural selection might favor reduced investment in resistance in this weed to maximize its growth and reproduction. In contrast to the asymmetric patterns of genome evolution observed in wheat and other crops, no significant differences in selection pressure were detected between the subgenomes in E. oryzicola and E. crus-galli. In addition, distinctive differences in subgenome transcriptome dynamics during hexaploidization were observed between E. crus-galli and bread wheat. Collectively, our study documents genomic mechanisms underlying the adaptation of a major agricultural weed during polyploidization. The genomic and transcriptomic resources of three Echinochloa species and new insights into the polyploidization-driven adaptive evolution would be useful for future breeding cereal crops.