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Objective To quantify the setup errors for the different anatomical sites of patients who received intensity-modulated radiotherapy (IMRT) with linear accelerator on-board kilovolt fan beam CT(kV-FBCT) as non-isocenter IGRT and megavolt cone beam CT (MV-CBCT) as isocenter IGRT. Methods A retrospective analysis was performedon 70 patients who underwent radiotherapy, kV-FBCT, and/or MV-CBCT scans after each routine setup prior to IMRT. The average displacement (M), systematic error (Σ), and random error (б) at different treatment sites in the left-right, anterior-posterior, and cranial-caudal directions were calculated according to the individual displacements. The formula 2.5Σ+0.7б was used to estimate the PTV margin in respective direction. For each single patient, the root mean square in three directions was used as 3D displacement. Results A total of 1130 displacements were recorded in the 70 patients. The PTV margin was estimated to be 1.9-3.1 mm in head and neck cancer, 2.8-5.1 mm in thoracic cancer, 4.6-5.1 mm in breast cancer, 3.0-5.5 mm in upper abdominal cancer, and 3.5-6.8 mm in pelvic tumor. For the 3D mean displacements, the head and neck, thoracic, breast, upper abdominal, and pelvic cancer were 2.4±1.0, 4.0±1.6, 4.1±2.0, 4.6±2.1, and 4.6±2.1 mm, respectively. The average 3D displacement obtained by kV-FBCT and MV-CBCT were 4.1 and 3.4 mm, respectively (P=0.212). Conclusion The quantitative setup-error data can be obtained using linear accelerator on-board FBCT, and the non-isocenter IGRT induced set-up error cannot be negligible.

ObjectiveWine grapes are severely affected by leafroll disease, which affects their growth, and reduces the quality of the color, taste, and flavor of wine. Timely and accurate diagnosis of leafroll disease severity is crucial for preventing and controlling the disease, improving the wine grape fruit quality and wine-making potential. Unmanned aerial vehicle (UAV) remote sensing technology provides high-resolution images of wine grape vineyards, which can capture the features of grapevine canopies with different levels of leafroll disease severity. Deep learning networks extract complex and high-level features from UAV remote sensing images and perform fine-grained classification of leafroll disease infection severity. However, the diagnosis of leafroll disease severity is challenging due to the imbalanced data distribution of different infection levels and categories in UAV remote sensing images.MethodA novel method for diagnosing leafroll disease severity was developed at a canopy scale using UAV remote sensing technology and deep learning. The main challenge of this task was the imbalanced data distribution of different infection levels and categories in UAV remote sensing images. To address this challenge, a method that combined deep learning fine-grained classification and generative adversarial networks (GANs) was proposed. In the first stage, the GANformer, a Transformer-based GAN model was used, to generate diverse and realistic virtual canopy images of grapevines with different levels of leafroll disease severity. To further analyze the image generation effect of GANformer. The t-distributed stochastic neighbor embedding (t-SNE) to visualize the learned features of real and simulated images. In the second stage, the CA-Swin Transformer, an improved image classification model based on the Swin Transformer and channel attention mechanism was used, to classify the patch images into different classes of leafroll disease infection severity. CA-Swin Transformer could also use a self-attention mechanism to capture the long-range dependencies of image patches and enhance the feature representation of the Swin Transformer model by adding a channel attention mechanism after each Transformer layer. The channel attention (CA) mechanism consisted of two fully connected layers and an activation function, which could extract correlations between different channels and amplify the informative features. The ArcFace loss function and instance normalization layer was also used to enhance the fine-grained feature extraction and downsampling ability for grapevine canopy images. The UAV images of wine grape vineyards were collected and processed into orthomosaic images. They labeled into three categories: healthy, moderate infection, and severe infection using the in-field survey data. A sliding window method was used to extract patch images and labels from orthomosaic images for training and testing. The performance of the improved method was compared with the baseline model using different loss functions and normalization methods. The distribution of leafroll disease severity was mapped in vineyards using the trained CA-Swin Transformer model.Results and DiscussionsThe experimental results showed that the GANformer could generate high-quality virtual canopy images of grapevines with an FID score of 93.20. The images generated by GANformer were visually very similar to real images and could produce images with different levels of leafroll disease severity. The T-SNE visualization showed that the features of real and simulated images were well clustered and separated in two-dimensional space, indicating that GANformer learned meaningful and diverse features, which enriched the image dataset. Compared to CNN-based deep learning models, Transformer-based deep learning models had more advantages in diagnosing leafroll disease infection. Swin Transformer achieved an optimal accuracy of 83.97% on the enhanced dataset, which was higher than other models such as GoogLeNet, MobileNetV2, NasNet Mobile, ResNet18, ResNet50, CVT, and T2TViT. It was found that replacing the cross entropy loss function with the ArcFace loss function improved the classification accuracy by 1.50%, and applying instance normalization instead of layer normalization further improved the accuracy by 0.30%. Moreover, the proposed channel attention mechanism, named CA-Swin Transformer, enhanced the feature representation of the Swin Transformer model, achieved the highest classification accuracy on the test set, reaching 86.65%, which was 6.54% higher than using the Swin Transformer on the original test dataset. By creating a distribution map of leafroll disease severity in vineyards, it was found that there was a certain correlation between leafroll disease severity and grape rows. Areas with a larger number of severe leafroll diseases caused by Cabernet Sauvignon were more prone to have missing or weak plants.ConclusionsA novel method for diagnosing grapevine leafroll disease severity at a canopy scale using UAV remote sensing technology and deep learning was proposed. This method can generate diverse and realistic virtual canopy images of grapevines with different levels of leafroll disease severity using GANformer, and classify them into different classes using CA-Swin Transformer. This method can also map the distribution of leafroll disease severity in vineyards using a sliding window method, and provides a new approach for crop disease monitoring based on UAV remote sensing technology.

Phenotypes resulting from mutations in genetic model organisms can help reveal candidate genes for evolutionarily important phenotypic changes in related taxa. Although testing candidate gene hypotheses experimentally in nonmodel organisms is typically difficult, ontology-driven information systems can help generate testable hypotheses about developmental processes in experimentally tractable organisms. Here, we tested candidate gene hypotheses suggested by expert use of the Phenoscape Knowledgebase, specifically looking for genes that are candidates responsible for evolutionarily interesting phenotypes in the ostariophysan fishes that bear resemblance to mutant phenotypes in zebrafish. For this, we searched ZFIN for genetic perturbations that result in either loss of basihyal element or loss of scales phenotypes, because these are the ancestral phenotypes observed in catfishes (Siluriformes). We tested the identified candidate genes by examining their endogenous expression patterns in the channel catfish, Ictalurus punctatus. The experimental results were consistent with the hypotheses that these features evolved through disruption in developmental pathways at, or upstream of, brpf1 and eda/edar for the ancestral losses of basihyal element and scales, respectively. These results demonstrate that ontological annotations of the phenotypic effects of genetic alterations in model organisms, when aggregated within a knowledgebase, can be used effectively to generate testable, and useful, hypotheses about evolutionary changes in morphology.