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Remote sensing image (RSI) super-resolution (SR) is an efficient and low-cost technique to achieve high-resolution and high-quality reconstruction images. The quality of RSI SR reconstruction is affected by the prior information contained in the degradation model. Therefore, studying how to incorporate more RSI degradation prior into the degradation model is crucial. This article presents an approach to design the degradation model by extracting degradation factors from the perspective of remote sensing imaging mechanisms. It includes two aspects: simulating the atmospheric scattering effect through RGB channel weights downsampling and the comprehensive degradation effect of the remote sensing imaging platform through combined blurring. Furthermore, we proposed a high-performance RSI SR network based on degradation mechanism (RSN-DM), which includes a degrader D and a generator G , to employ remote sensing prior fully. We conducted experiments on the UC Merced Land-Use and WPU-RESIS45 datasets, demonstrating that our proposed method is effective. Our method achieves state-of-the-art (SOTA) performance in quantitative evaluation and visual quality. Finally, we apply the proposed degradation model to other networks to further validate the model's effectiveness. Therefore, the degradation model proposed in this paper can enhance the performance of remote sensing image super-resolution techniques in practical applications. • Proposed novel degradation model for remote sensing image super-resolution, considering scattering and platform effects. • An unsupervised super-resolution network matching the degradation model is proposed. • The super-resolution results of the proposed method have advantages in terms of evaluation metrics and visual fidelity. [ABSTRACT FROM AUTHOR]

UDP- l -rhamnose (UDP-Rha) is an important sugar donor for glycosylation of various cell molecules in plant. Rhamnosides are widely present in different plant tissues and play important biological roles under different developmental or environmental conditions. However, enzymes involved in UDP-Rha biosynthesis and their encoding genes have been identified in few plants, which limits the functional analysis of plant rhamnosides. Here, two UDP-Rha biosynthesis genes, named PpRHM1 (2028 bp) and PpRHM2 (2016 bp), were isolated and characterized from Prunus persica , which is rich sources of flavonol rhamnosides. Both recombinant RHM proteins can catalyze the transformation from UDP- d -glucose (UDP-Glc) to UDP-Rha, which was confirmed by LC-MS and formation of flavonol rhamnosides. Biochemical analysis showed that both recombinant RHM proteins preferred alkaline conditions in pH range of 8.0–9.0 and had optimal reaction temperature between 25 and 30 °C. PpRHM1 showed the better UDP-Glc substrate affinity with K m of 360.01 μM. Gene expression analysis showed different transcript levels of both RHMs in all plant tissues tested, indicating the involvement of rhamnosides in various tissues in plant. Such results provide better understanding of UDP-Rha biosynthesis in fruit tree and may be helpful for further investigation of various rhamnose derivatives and their biological functions. Image 1 • Two UDP-rhamnose synthase genes were first cloned and identified in peach. • PpRHM1 and PpRHM2 can catalyze UDP-glucose to UDP-rhamnose. • Biochemical characteristics of PpRHM1 and PpRHM2 were analyzed. [ABSTRACT FROM AUTHOR]

Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future. [ABSTRACT FROM AUTHOR]

Background: The study aimed to investigate the association between nonalcoholic fatty liver disease (NAFLD) and ischemic stroke events after revascularization in patients with Moyamoya disease (MMD). Methods: This study prospectively enrolled 275 MMD patients from September 2020 to December 2021. Patients with alcoholism and other liver diseases were excluded. NAFLD was confirmed by CT imaging or abdominal ultrasonography. Stroke events and modified Rankin Scale (mRS) scores at the latest follow-up were compared between the two groups. Results: A total of 275 patients were enrolled in the study, among which 65 were diagnosed with NAFLD. Univariate logistic regression analysis showed that NAFLD (P = 0.029) was related to stroke events. Multivariate logistic regression analysis showed that NAFLD is a predictor of postoperative stroke in MMD patients (OR = 27.145, 95% CI = 2.031–362.81, P = 0.013). Kaplan-Meier analysis showed that compared with MMD patients with NAFLD, patients in the control group had a longer stroke-free time (P = 0.004). Univariate Cox analysis showed that NAFLD (P = 0.016) was associated with ischemic stroke during follow-up in patients with MMD. Multivariate Cox analysis showed that NAFLD was an independent risk factor for stroke in patients with MMD (HR = 10.815, 95% CI = 1.259–92.881, P = 0.030). Furthermore, fewer patients in the NAFLD group had good neurologic status (mRS score ≤ 2) than the control group (P = 0.005). Conclusion: NAFLD was an independent risk factor for stroke in patients with MMD after revascularization and worse neurological function outcomes. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) super-resolution (SR) is a classical computer vision task that aims to accomplish the conversion of images from lower to higher resolutions. With the booming development of deep learning (DL) technology, more and more researchers are dedicated to the research of image SR techniques based on DL and have made remarkable progress. However, no scholar has provided a comprehensive review of the field. As a response, in this paper we aim to supply a comprehensive summary of the DL-based SR techniques for HSI, including upsampling frameworks, upsampling methods, network design, loss functions, representative works with different strategies, and future directions, in which we design several sets of comparative experiments for the advantages and limitations of two-dimensional convolution and three-dimensional convolution in the field of HSI SR and analyze the experimental results in depth. In addition, the paper also briefly discusses the secondary foci such as common datasets, evaluation metrics, and traditional SR algorithms. To the best of our knowledge, this paper is the first review on DL-based HSI SR. [ABSTRACT FROM AUTHOR]

Moyamoya disease (MMD) is a rare cerebrovascular disease characterized by stenosis or occlusion of the internal carotid artery, thus leading to ischaemic and haemorrhagic strokes. Although genetic studies have identified ring finger protein 213 (RNF213) as a susceptibility gene, the low disease penetrance suggests that a secondary trigger, such as infection, may initiate disease onset. This study aimed to characterize the innate immune cell profile of peripheral blood mononuclear cells (PBMCs) of MMD patients via mass cytometry (CyTOF). Blood samples from 10 MMD patients and 10 healthy controls were analysed, with a focus on natural killer (NK) cells, monocytes, and dendritic cells (DCs). The results revealed significant changes in the NK and monocyte subpopulations in MMD patients; specifically, there was a decrease in the CD56dimCD16− NK03 subset and an increase in CD163high classical monocytes, thus indicating compromised microbial defences and heightened inflammation. Additionally, significant changes were observed in DC subpopulations, including an increase in CCR7+ mature DCs and a decrease in CD141+ and CD1c+ DCs. Overactivation of the TLR/MyD88/NF-κB pathway was observed in most innate immune cells, thus indicating its potential role in disease progression. These findings provide novel insights into immune dysfunction in MMD and highlight potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Object detection in remote sensing images (RSIs) requires the locating and classifying of objects of interest, which is a hot topic in RSI analysis research. With the development of deep learning (DL) technology, which has accelerated in recent years, numerous intelligent and efficient detection algorithms have been proposed. Meanwhile, the performance of remote sensing imaging hardware has also evolved significantly. The detection technology used with high-resolution RSIs has been pushed to unprecedented heights, making important contributions in practical applications such as urban detection, building planning, and disaster prediction. However, although some scholars have authored reviews on DL-based object detection systems, the leading DL-based object detection improvement strategies have never been summarized in detail. In this paper, we first briefly review the recent history of remote sensing object detection (RSOD) techniques, including traditional methods as well as DL-based methods. Then, we systematically summarize the procedures used in DL-based detection algorithms. Most importantly, starting from the problems of complex object features, complex background information, tedious sample annotation that will be faced by high-resolution RSI object detection, we introduce a taxonomy based on various detection methods, which focuses on summarizing and classifying the existing attention mechanisms, multi-scale feature fusion, super-resolution and other major improvement strategies. We also introduce recognized open-source remote sensing detection benchmarks and evaluation metrics. Finally, based on the current state of the technology, we conclude by discussing the challenges and potential trends in the field of RSOD in order to provide a reference for researchers who have just entered the field. [ABSTRACT FROM AUTHOR]

• Apple MdUGT75B1 and MdUGT71B1 were identified as key UGTs in flavonol biosynthesis. • MdUGT75B1 was flavonol-specific UGT with UDP-Gal preference. • MdUGT71B1 exhibited UDP-Glc preference toward broader flavonoid substrates. Apple is rich in flavonol glycosides, which are believed to contribute to putative health benefits associated with apple consumption. Glycosylation, catalyzed by uridine diphospho-glycosyltransferases (UGTs), is the last step in flavonol biosynthesis, which confers molecular stability and solubility to the flavonol. In the present study, the involvement of two UGT s, MdUGT75B1 and MdUGT71B1 , in flavonol biosynthesis in apple was investigated. The major flavonols are quercetin 3- O -glycosides, and UV-B and blue light treatment significantly enhanced the accumulation of quercetin 3- O -galactoside, quercetin 3- O -glucoside, and kaempferol 3- O -galactoside. Transcript levels of MdUGT75B1 and MdUGT71B1 in fruit subjected to different treatments were correlated well with flavonol accumulation. MdUGT75B1 showed flavonol-specific activity with a preference for UDP-galactose as the sugar donor, while MdUGT71B1 using UDP-glucose exhibited a wider substrate acceptance. Thus, MdUGT75B1 and MdUGT71B1 are key UGTs involved in flavonol biosynthesis and may have important roles in regulating accumulation of these health-promoting bioactive compounds in apple. [ABSTRACT FROM AUTHOR]

Chemokine ligand 26 (CCL26) is a member of the eotaxin family. It works by interacting exclusively with chemokine receptor 3 (CCR3) and acts as an eosinophil-selective chemoattractant. There is an emerging role for eotaxins in autoimmune diseases. Studies have reported that chemokine ligand 11 (CCL11) and CCL26 are upregulated in patients with neuromyelitis optica spectrum disorder (NMOSD) during remission, CCL26 levels appear to be decreased in relapsing-remitting multiple sclerosis (RRMS), whereas CCL26 levels are significantly increased in secondary progressive multiple sclerosis (SPMS), indicating that CCL26 participates in the pathogenesis of multiple sclerosis (MS). We investigated the levels of CCL26, CCR3 and claudin-5 (a marker of changes in BBB (blood-brain barrier) permeability) at different stages of experimental autoimmune encephalomyelitis (EAE) to explore the underlying immune mechanisms of EAE. Our results showed that the levels of CCL26 and CCR3 in EAE rats were significantly increased compared with those in the control group. The levels of CCL26 in the serum and in brain tissues as well as the protein expression of CCR3 in brain tissues were positively correlated with the inflammatory scores of brain tissues from EAE rats and were negatively correlated with the protein expression of claudin-5. We concluded that CCL26, which in turn binds to the receptor CCR3, showed pro-inflammatory effects and aggravated tissue damage involving BBB impairment, especially in the acute stage of EAE. Our study uncovers another possible immunopathological mechanism of MS and provides a possible target for immune therapy. Unlabelled Image • CCL26 and CCR3 showed pro-inflammatory effects at the acute stage of EAE. • The expression of CCL26 and CCR3 in serum and brain correlated with the severity of EAE. • Inflammatory effects of CCL26 and CCR3 caused the damage of BBB combining with the study of Claudin-5. [ABSTRACT FROM AUTHOR]

• A convolutional fusion model with joint channel and spatial attention was built. • Different perceptive fields were adopted in multi-branch structure to get features. • The particular perceptual loss with adjusted image was designed for image matching. • 4 traditional and 9 deep learning-based methods were used to demonstrate the model. • Images with high fidelity and salient feature were friendly to human visual system. Infrared and visible image fusion can synthesize complementary features of salient objects and texture details which are important for all-weather detection and other tasks. Nowadays, the deep learning based unsupervised fusion solutions are preferred and have obtained good results since the reference images for fusion tasks are not available. In the existing methods, some prominent features are missing in the fused images and the visual vitality needs to be improved. From this thought, attention mechanism is introduced to the fusion network. Especially, channel dimension and spatial dimension attention are jointed to supplement each other for feature extraction. Multiple attention branches emphasize on multi-scale features to complete the encoding. Skip connections are added to learn residual information. The multi-layer perceptual loss, the structure similarity loss and the content loss together construct the strong constraints for training. Comparative experiments with subjective and objective evaluations on 4 traditional and 9 deep learning based methods demonstrate the advantages of the proposed model. [ABSTRACT FROM AUTHOR]