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10 results for “Wheat powdery mildew”
A kinase fusion protein from Aegilops longissima confers resistance to wheat powdery mildew
<p>Many disease resistance genes have been introgressed into wheat from its wild relatives. However, reduced recombination within the introgressed segments hinders cloning of the introgressed genes. Here, we report the identification of wheat powdery mildew resistance gene <em>Pm13</em> originated from from <em>Aegilops longissima</em> using a method combining physical mapping with radiation-induced chromosomal aberrations and transcriptome sequencing analysis of ethyl methanesulfonate (EMS)-induced loss-of-function mutants. <em>Pm13</em> encodes a kinase fusion protein, designated MLKL-K, with an N-terminal domain of mixed lineage kinase domain-like protein (MLKL_NTD domain) and a C-terminal serine/threonine kinase domain bridged by a brace. The resistance function of <em>Pm13</em> is validated by transient and stable transgenic complementation assays. Transient over-expression analyses in <em>Nicotiana benthamiana</em> leaves and wheat protoplasts reveal that the fragment Brace-Kinase<sub>122-476</sub> of MLKL-K is capable of inducing cell death, which is dependent on a functional kinase domain and the three α-helices in the brace region close to the N-terminus of the kinase domain.</p>
Wheat Powdery Mildew Image Dataset
<p>The dataset consists of wheat leaf images collected using a mobile phone, specifically focused on capturing diseased areas. These images are annotated with labels indicating the presence of diseases, suitable for training and testing a YOLO (You Only Look Once) object detection model. The labeled dataset aims to enable the model to accurately identify and classify diseased regions in similar images.</p> <h2>Description of the data and file structure</h2> <h4>1. Images Folder:</h4> <ul> <li> <p>Structure: The Images folder contains three subfolders: train, test, and val. Each subfolder contains image files in formats such as JPEG or PNG.</p> </li> <ul> <li> <p>train: Contains images used for training the YOLO model.</p> </li> <li> <p>test: Contains images used for testing the model's performance.</p> </li> <li> <p>val: Contains images used for validating the model during training, helping to tune hyperparameters and prevent overfitting.</p> </li> </ul> </ul> <h4>2. Labels Folder:</h4> <ul> <li> <p>Structure: The Labels folder mirrors the structure of the Images folder, with subfolders named train, test, and val. Each subfolder contains YOLO-format label files.</p> </li> <ul> <li> <p>train: Contains label files corresponding to the training images.</p> </li> <li> <p>test: Contains label files for the testing images.</p> </li> <li> <p>val: Contains label files for the validation images.</p> </li> </ul> </ul> <h4>3. YOLO Label Format:</h4> <ul> <li> <p>Contents: Each label file corresponds to an image and contains information in the YOLO format, which includes:</p> </li> <ul> <li> <p>Class ID: An integer representing the class label (e.g., a specific disease).</p> </li> <li> <p>Bounding Box Coordinates: Four numbers representing the center x, center y, width, and height of the bounding box, all normalized between 0 and 1.</p> </li> <li> <p>File Naming Convention: The label files are named identically to their corresponding images, except for the file extension (e.g., image1.jpg and image1.txt).</p> </li> </ul> </ul> <h4>4. Usage and Application:</h4> <ul> <li> <p>Model Training and Validation: The dataset can be used to train and validate YOLO object detection models. The clear separation into train, test, and validation sets supports robust model evaluation and helps prevent data leakage.</p> </li> <li> <p>Model Testing: The test set is used to evaluate the model's performance on unseen data, providing an unbiased measure of its generalization capability.</p> </li> <li> <p>Model Tuning: The validation set helps fine-tune model parameters and assess performance during the training process.</p> </li> </ul>
A kinase fusion protein from Aegilops longissima confers resistance to wheat powdery mildew
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Data + scripts for master thesis "Investigation into thermal adaptation of wheat powdery mildew caused by Blumeria graminis f. sp. tritici in Europe and the Middle East"
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Expression data in wheat (T. aestivum L.) near isogenic lines in response to powdery mildew infection
GEO Series GSE27320. Triticum aestivum. 12 samples. Type: Expression profiling by array.
Wheat response to powdery mildew infection and heat stress
GEO Series GSE27339. Triticum aestivum. 18 samples. Type: Expression profiling by array; Non-coding RNA profiling by high throughput sequencing.
Genome-wide sequencing small RNAs in response to powdery mildew and heat stress in wheat
GEO Series GSE27327. Triticum aestivum. 6 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Transcriptomic analysis of the effect of silicon on wheat plants infected or uninfected with powdery mildew
GEO Series GSE12936. Triticum aestivum. 12 samples. Type: Expression profiling by array.
2023 NGN - Management strategies for wheat powdery mildew in southern NSW Data
<p>The 2023 NGN project <em>“Management strategies for wheat powdery mildew in southern NSW”</em> focused on validating cost-effective fungicide resistance management strategies for wheat powdery mildew (WPM), a disease capable of causing up to 25% yield loss in Australia. WPM is particularly problematic in western, lower-rainfall areas of NSW during wetter-than-average seasons and under irrigation, especially as many commonly grown wheat varieties are poorly resistant. The dataset includes trial results from two NSW research sites—Balldale and irrigated Hillston—and is organised by site and trial name. Excel spreadsheets contain original data and derived calculations, such as plant density, with keys to explain units and codes. A PDF report outlines the experimental design, treatment summaries, and environmental conditions affecting trial interpretation. Assessments were conducted using GDM ARM software, and each data sheet includes contextual information such as crop growth stage, assessment details, and explanatory notes. This project was supported by GRDC and made possible through the contributions of growers and partners, including FAR Australia and AgGrow Agronomy and Research.</p>
Development of molecular markers linked to powdery mildew resistance gene Pm4b by combining SNP discovery from transcriptome sequencing data with bulked segretant analysis (BSR-seq) in wheat
GEO Series GSE108697. Triticum aestivum. 2 samples. Type: Genome variation profiling by high throughput sequencing.
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