Regulation of SmEXPA13 expression by SmMYB1R1-L enhances salt tolerance in Salix matsudana Koidz.

Expansins, cell wall proteins, play a significant role in plant stress resistance. Our previous study confirmed the expression of the expansin gene SmEXPA13 from Salix matsudana Koidz. enhanced salt tolerance of plants. This report presented an assay that the expression of SmEXPA13 was higher in the salt-resistant willow variety 9901 than in the salt-sensitive variety Yanjiang. In order to understand the possible reasons, a study of the regulation process was conducted. Despite being cloned from both varieties, SmEXPA13 and its promotor showed no significant differences in the structure and sequence. A transcription factor (TF), SmMYB1R1-L, identified through screening the yeast library of willow cDNA, was found to regulate SmEXPA13. Yeast one-hybrid (Y1H) assay confirmed that SmMYB1R1-L could bind to the MYB element at the -520 bp site on the SmEXPA13 promotor. A dual-luciferase reporter assay also demonstrated that SmMYB1R1-L could greatly activate SmEXPA13 expression. The willow calli with over-expression of SmMYB1R1-L exhibited better physiological performance than the wild type under salt stress. Further testing the expression of SmMYB1R1-L displayed it significantly higher in 9901 willow than that in Yanjiang under salt stress. In conclusion, the high accumulation of SmMYB1R1-L in 9901 willow under salt stress led to the high expression of SmEXPA13, resulting in variations in salt stress resistance among willow varieties. The SmMYB1R1-L/SmEXPA13 cascade module in willow offers a new perspective on plant resistance mechanisms.

Ultra-wide field and new wide field composite retinal image registration with AI-enabled pipeline and 3D distortion correction algorithm.

PURPOSE: This study aimed to compare a new Artificial Intelligence (AI) method to conventional mathematical warping in accurately overlaying peripheral retinal vessels from two different imaging devices: confocal scanning laser ophthalmoscope (cSLO) wide-field images and SLO ultra-wide field images. METHODS: Images were captured using the Heidelberg Spectralis 55-degree field-of-view and Optos ultra-wide field. The conventional mathematical warping was performed using Random Sample Consensus-Sample and Consensus sets (RANSAC-SC). This was compared to an AI alignment algorithm based on a one-way forward registration procedure consisting of full Convolutional Neural Networks (CNNs) with Outlier Rejection (OR CNN), as well as an iterative 3D camera pose optimization process (OR CNN + Distortion Correction [DC]). Images were provided in a checkerboard pattern, and peripheral vessels were graded in four quadrants based on alignment to the adjacent box. RESULTS: A total of 660 boxes were analysed from 55 eyes. Dice scores were compared between the three methods (RANSAC-SC/OR CNN/OR CNN + DC): 0.3341/0.4665/4784 for fold 1-2 and 0.3315/0.4494/4596 for fold 2-1 in composite images. The images composed using the OR CNN + DC have a median rating of 4 (out of 5) versus 2 using RANSAC-SC. The odds of getting a higher grading level are 4.8 times higher using our OR CNN + DC than RANSAC-SC (p < 0.0001). CONCLUSION: Peripheral retinal vessel alignment performed better using our AI algorithm than RANSAC-SC. This may help improve co-localizing retinal anatomy and pathology with our algorithm.

Accuracy and Time Comparison Between Side-by-Side and Artificial Intelligence Overlayed Images.

BACKGROUND AND OBJECTIVE: The purpose of this study was to evaluate the accuracy and the time to find a lesion, taken in different platforms, color fundus photographs and infrared scanning laser ophthalmoscope images, using the traditional side-by-side (SBS) colocalization technique to an artificial intelligence (AI)-assisted technique. PATIENTS AND METHODS: Fifty-three pathological lesions were studied in 11 eyes. Images were aligned using SBS and AI overlaid methods. The location of each color fundus lesion on the corresponding infrared scanning laser ophthalmoscope image was analyzed twice, one time for each method, on different days, for two specialists, in random order. The outcomes for each method were measured and recorded by an independent observer. RESULTS: The colocalization AI method was superior to the conventional in accuracy and time (P < .001), with a mean time to colocalize 37% faster. The error rate using AI was 0% compared with 18% in SBS measurements. CONCLUSIONS: AI permitted a more accurate and faster colocalization of pathologic lesions than the conventional method. [Ophthalmic Surg Lasers Imaging Retina 2023;54:108-113.].

The regulatory module MdBZR1-MdCOL6 mediates brassinosteroid- and light-regulated anthocyanin synthesis in apple.

The anthocyanin content is an important indicator of the nutritional value of most fruits, including apple (Malus domestica). Anthocyanin synthesis is coordinately regulated by light and various phytohormones. In this study on apple, we revealed the antagonistic relationship between light and brassinosteroid (BR) signaling pathways, which is mediated by BRASSINAZOLE-RESISTANT 1 (MdBZR1) and the B-box protein MdCOL6. The exogenous application of brassinolide inhibited the high-light-induced anthocyanin accumulation in red-fleshed apple seedlings, whereas increases in the light intensity decreased the endogenous BR content. The overexpression of MdBZR1 inhibited the anthocyanin synthesis in apple plants. An exposure to a high-light intensity induced the degradation of dephosphorylated MdBZR1, resulting in functional impairment. MdBZR1 was identified as an upstream repressor of MdCOL6, which promotes anthocyanin synthesis in apple plants. Furthermore, MdBZR1 interacts with MdCOL6 to attenuate its ability to activate MdUFGT and MdANS transcription. Thus, MdBZR1 negatively regulates MdCOL6-mediated anthocyanin accumulation. Our study findings have clarified the molecular basis of the integration of light and BR signals during the regulation of anthocyanin biosynthesis, which is an important process influencing fruit quality.

ACCURATE REGISTRATION BETWEEN ULTRA-WIDE-FIELD AND NARROW ANGLE RETINA IMAGES WITH 3D EYEBALL SHAPE OPTIMIZATION.

The Ultra-Wide-Field (UWF) retina images have attracted wide attentions in recent years in the study of retina. However, accurate registration between the UWF images and the other types of retina images could be challenging due to the distortion in the peripheral areas of an UWF image, which a 2D warping can not handle. In this paper, we propose a novel 3D distortion correction method which sets up a 3D projection model and optimizes a dense 3D retina mesh to correct the distortion in the UWF image. The corrected UWF image can then be accurately aligned to the target image using 2D alignment methods. The experimental results show that our proposed method outperforms the state-of-the-art method by 30%.

Physiological analysis and transcriptome sequencing of a delayed-green leaf mutant 'Duojiao' of ornamental crabapple (Malus sp.).

Malus spectabilis 'Duojiao' is a spontaneous delayed-green leaf color mutant of M. spectabilis 'Riversii' and has chloroplasts with irregularly arranged vesicles and indistinct stromal lamellae. The yellow leaves of mutant have less chlorophyll (Chl), carotenoids, and flavonoids. Measurement of photosynthetic gas exchange indicated that the mutant has lower photosynthetic activity than 'Riversii' plants. Transcriptome sequencing with the Illumina platform was used to characterize differences in gene expression between the leaves of plants with yellow and green colors and elucidate the molecular mechanisms responsible for variation in leaf color in ornamental crabapple. In the comparison group of mutant yellow leaves and the maternal green leaves, 1848 differentially significant expressed genes (DEGs) were annotated by transcriptomic analysis. Many DEGs and transcription factors were identified related to chloroplast development, Chl synthesis and degradation, photosynthesis, carotenoid biosynthesis, flavonoid biosynthesis and other pathways related to plant leaf color formation. Among these, the Chl biosynthesis-related coproporphyrinogen gene, oxidative decarboxylase gene, and Chl a oxygenase gene were down-regulated, indicating that Chl biosynthesis was blocked. GLK1, which regulates chloroplast development, was down-regulated in yellow leaves. Parallel experiments showed that the content of the Chl synthesis precursors, protoporphyrinogen IX, chlorophyllide a, and chlorophyllide b and the activity of chlorophyllogen III oxidase and chlorophyllide a oxygenase in the yellow leaves of 'Duojiao' were lower than those in the green leaves of 'Riversii'. Thus, leaf color formation is greatly affected by Chl metabolism and chloroplast development. The reliability of the RNA-sequencing data was confirmed by quantitative real-time PCR analysis with 12 selected DEGs. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-022-01248-7.

Self-Supervised Rigid Registration for Multimodal Retinal Images.

The ability to accurately overlay one modality retinal image to another is critical in ophthalmology. Our previous framework achieved the state-of-the-art results for multimodal retinal image registration. However, it requires human-annotated labels due to the supervised approach of the previous work. In this paper, we propose a self-supervised multimodal retina registration method to alleviate the burdens of time and expense to prepare for training data, that is, aiming to automatically register multimodal retinal images without any human annotations. Specially, we focus on registering color fundus images with infrared reflectance and fluorescein angiography images, and compare registration results with several conventional and supervised and unsupervised deep learning methods. From the experimental results, the proposed self-supervised framework achieves a comparable accuracy comparing to the state-of-the-art supervised learning method in terms of registration accuracy and Dice coefficient.

EESANet: edge-enhanced self-attention network for two-dimensional phase unwrapping.

In this paper, we first propose a quantitative indicator to measure the amount of prior information contained in the wrapped phase map. Then, Edge-Enhanced Self-Attention Network is proposed for two-dimensional phase unwrapping. EESANet adopts a symmetrical en-decoder architecture and uses self-designed Serried Residual Blocks as its basic block. We add Atrous Spatial Pyramid Pooling and Positional Self-Attention to the network to obtain the long-distance dependency in phase unwrapping, and we further propose Edge-Enhanced Block to enhance the effective edge features of the wrapped phase map. In addition, weighted cross-entropy loss function is employed to overcome the category imbalance problem. Experiments show that our method has higher precision, stronger robustness and better generalization than the state-of-the-art.

Transcription factor McWRKY71 induced by ozone stress regulates anthocyanin and proanthocyanidin biosynthesis in Malus crabapple.

In plants, anthocyanins and proanthocyanidins (PAs) play important roles in plant resistance to abiotic stress. In this study, ozone (O3) treatments caused the up-regulation of Malus crabapple structural genes McANS, McCHI, McANR and McF3H, which promoted anthocyanin and PA accumulation. We identified the WRKY transcription factor (TF) McWRKY71 by screening differentially expressed genes (DEGs) that were highly expressed in response to O3 stress from an RNA sequencing (RNA-seq) analysis. Overexpressing McWRKY71 increased the resistance of 'Orin' apple calli to O3 stress and promoted the accumulation of anthocyanins and PAs, which facilitated reactive oxygen species scavenging to further enhance O3 tolerance. Biochemical and molecular analyses showed that McWRKY71 interacted with McMYB12 and directly bound the McANR promoter to participate in the regulation of PA biosynthesis. These findings provide new insights into the WRKY TFs mechanisms that regulate the biosynthesis of secondary metabolites, which respond to O3 stress, in Malus crabapple.

Two-Step Registration on Multi-Modal Retinal Images via Deep Neural Networks.

Multi-modal retinal image registration plays an important role in the ophthalmological diagnosis process. The conventional methods lack robustness in aligning multi-modal images of various imaging qualities. Deep-learning methods have not been widely developed for this task, especially for the coarse-to-fine registration pipeline. To handle this task, we propose a two-step method based on deep convolutional networks, including a coarse alignment step and a fine alignment step. In the coarse alignment step, a global registration matrix is estimated by three sequentially connected networks for vessel segmentation, feature detection and description, and outlier rejection, respectively. In the fine alignment step, a deformable registration network is set up to find pixel-wise correspondence between a target image and a coarsely aligned image from the previous step to further improve the alignment accuracy. Particularly, an unsupervised learning framework is proposed to handle the difficulties of inconsistent modalities and lack of labeled training data for the fine alignment step. The proposed framework first changes multi-modal images into a same modality through modality transformers, and then adopts photometric consistency loss and smoothness loss to train the deformable registration network. The experimental results show that the proposed method achieves state-of-the-art results in Dice metrics and is more robust in challenging cases.

Perspective Distortion Correction for Multi-Modal Registration between Ultra-Widefield and Narrow-Angle Retinal Images.

Multi-modal retinal image registration between 2D Ultra-Widefield (UWF) and narrow-angle (NA) images has not been well-studied, since most existing methods mainly focus on NA image alignment. The stereographic projection model used in UWF imaging causes strong distortions in peripheral areas, which leads to inferior alignment quality. We propose a distortion correction method that remaps the UWF images based on estimated camera view points of NA images. In addition, we set up a CNN-based registration pipeline for UWF and NA images, which consists of the distortion correction method and three networks for vessel segmentation, feature detection and matching, and outlier rejection. Experimental results on our collected dataset shows the effectiveness of the proposed pipeline and the distortion correction method.

Robust Content-Adaptive Global Registration for Multimodal Retinal Images Using Weakly Supervised Deep-Learning Framework.

Multimodal retinal imaging plays an important role in ophthalmology. We propose a content-adaptive multimodal retinal image registration method in this paper that focuses on the globally coarse alignment and includes three weakly supervised neural networks for vessel segmentation, feature detection and description, and outlier rejection. We apply the proposed framework to register color fundus images with infrared reflectance and fluorescein angiography images, and compare it with several conventional and deep learning methods. Our proposed framework demonstrates a significant improvement in robustness and accuracy reflected by a higher success rate and Dice coefficient compared with other methods.

A Convolutional Neural Network Pipeline For Multi-Temporal Retinal Image Registration.

A sequence of images is usually captured to observe the change of health status in medical diagnosis. However, an image sequence taken over year usually suffers from severe deformation, making it time-consuming for physicians to match corresponding patterns. In this paper, we propose a coarse-to-fine pipeline for retinal image registration based on convolutional neural network. By leveraging the three components of the pipeline: feature matching, outlier rejection, and local registration, we recover the deformation and accurately align multi-temporal image sequences. Experimental results show that the proposed network is robust to severe deformation as well as illumination and contrast variations. With the proposed registration pipeline, the change of image patterns over time can be identified through visual analysis.

Artificial Intelligence for Automated Overlay of Fundus Camera and Scanning Laser Ophthalmoscope Images.

Purpose: The purpose of this study was to evaluate the ability to align two types of retinal images taken on different platforms; color fundus (CF) photographs and infrared scanning laser ophthalmoscope (IR SLO) images using mathematical warping and artificial intelligence (AI). Methods: We collected 109 matched pairs of CF and IR SLO images. An AI algorithm utilizing two separate networks was developed. A style transfer network (STN) was used to segment vessel structures. A registration network was used to align the segmented images to each. Neither network used a ground truth dataset. A conventional image warping algorithm was used as a control. Software displayed image pairs as a 5 × 5 checkerboard grid composed of alternating subimages. This technique permitted vessel alignment determination by human observers and 5 masked graders evaluated alignment by the AI and conventional warping in 25 fields for each image. Results: Our new AI method was superior to conventional warping at generating vessel alignment as judged by masked human graders (P < 0.0001). The average number of good/excellent matches increased from 90.5% to 94.4% with AI method. Conclusions: AI permitted a more accurate overlay of CF and IR SLO images than conventional mathematical warping. This is a first step toward developing an AI that could allow overlay of all types of fundus images by utilizing vascular landmarks. Translational Relevance: The ability to align and overlay imaging data from multiple instruments and manufacturers will permit better analysis of this complex data helping understand disease and predict treatment.

Boosting Feature Matching Accuracy with Pairwise Affine Estimation.

Local image feature matching lies in the heart of many computer vision applications. Achieving high matching accuracy is challenging when significant geometric difference exists between the source and target images. The traditional matching pipeline addresses the geometric difference by introducing the concept of support region. Around each feature point, the support region defines a neighboring area characterized by estimated attributes like scale, orientation, affine shape, etc. To correctly assign support region is not an easy job, especially when each feature is processed individually. In this paper, we propose to estimate the relative affine transformation for every pair of to-be-compared features. This "tailored" measurement of geometric difference is more precise and helps improve the matching accuracy. Our pipeline can be incorporated into most existing 2D local image feature detectors and descriptors. We comprehensively evaluate its performance with various experiments on a diversified selection of benchmark datasets. The results show that the majority of tested detectors/descriptors gain additional matching accuracy with proposed pipeline.

How does Malus crabapple resist ozone? Transcriptomics and metabolomics analyses.

Ozone (O3), an oxidizing toxic air pollutant, is ubiquitous in industrialized and developing countries. To understand the effects of O3 exposure on apple (Malus) and to explore its defense mechanisms, we exposed 'Hongjiu' crabapple to O3 and monitored its responses using physiological, transcriptomics, and metabolomics analyses. Exposure to 300 nL L-1 O3 for 3 h caused obvious damage to the leaves of Malus crabapple, affected chlorophyll and anthocyanin contents, and activated antioxidant enzymes. The gene encoding phospholipase A was highly responsive to O3 in Malus crabapple. McWRKY75 is a key transcription factor in the response to O3 stress, and its transcript levels were positively correlated with those of flavonoid-related structural genes (McC4H, McDFR, and McANR). The ethylene response factors McERF019 and McERF109-like were also up-regulated by O3. Exogenous methyl jasmonate (MeJA) decreased the damaging effects of O3 on crabapple and was most effective at 200 µmol L -1. Treatments with MeJA altered the metabolic pathways of crabapple under O3 stress. In particular, MeJA activated the flavonoid metabolic pathway in Malus, which improved its resistance to O3 stress.

Long noncoding RNA MALAT1 downregulates cardiac transient outward potassium current by regulating miR-200c/HMGB1 pathway.

The dysregulation of long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) participates in the remodeling of electrophysiological/ion channel in cardiomyocytes during arrhythmia. The lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is reported to be highly expressed in myocardial ischemia-reperfusion injury and offsets cardioprotective effects of fentanyl. However, the roles of MALAT1 and its related miRNAs during arrhythmia are poorly understood. In this study, the overexpression of MALAT1 was firstly indicated in cardiomyocytes from arrhythmic model rats. After downregulation of MALAT1 by RNA interference, transient outward potassium current (Ito), peak current density, and the levels of Kv4.2 and Kv4.3 channel proteins were increased in rat cardiomyocytes. Then, miR-200c was predicted and convinced to be a direct target of MALAT1, and high-mobility group box 1 (HMGB1) was verified to be a target of miR-200c during arrhythmia. HMGB1 expression reduced by the knockdown of MALAT1 was further decreased by miR-200c overexpression. In addition, cardiac Ito, peak current density, and the levels of Kv4.2 and Kv4.3 in arrhythmic model rats were detected to be negatively correlated with the expression of HMGB1, and to be positively with miR-200c expression. Taken together, these results suggested that MALAT1 may act as a competing endogenous RNA for miR-200c to upregulate the expression of HMGB1 and downregulate cardiac Ito.