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Deep convolutional neural networks (DCNNs) have exhibited excellent feature extraction and detail reconstruction capabilities for single image super-resolution (SISR). Nevertheless, most previous DCNN-based methods do not fully utilize the complementary strengths between feature maps, channels, and pixels. Therefore, it hinders the ability of DCNNs to represent abundant features. To tackle the aforementioned issues, we present a Cascaded Visual Attention Network for SISR called CVANet, which simulates the visual attention mechanism of the human eyes to focus on the reconstruction process of details. Specifically, we first designed a trainable feature attention module (FAM) for feature-level attention learning. Afterward, we introduce a channel attention module (CAM) to reinforce feature maps under channel-level attention learning. Meanwhile, we propose a pixel attention module (PAM) that adaptively selects representative features from the previous layers, which are utilized to generate a high-resolution image. Satisfactory, our CVANet can effectively improve the resolution of images by exploring the feature representation capabilities of different modules and the visual perception properties of the human eyes. Extensive experiments with different methods on four benchmarks demonstrate that our CVANet outperforms the state-of-the-art (SOTA) methods in subjective visual perception, PSNR, and SSIM.The code will be made available https://github.com/WilyZhao8/CVANet. [ABSTRACT FROM AUTHOR]

Automation is increasingly at the forefront of transportation research, with the potential to bring fully autonomous vehicles to our roads in the coming years. This comprehensive survey provides a holistic look at the essential components and cutting-edge technologies that are driving the development and implementation of autonomous driving. It starts by evaluating two critical system architectures that are fundamental to the operation of autonomous vehicles: the layered and end-to-end structures. It then examines the critical areas of scene perception and localization, emphasizing the importance of sensor technologies. These technologies are vital for tasks such as object detection and semantic segmentation, which allow vehicles to understand and navigate their environment. A special focus is given to the complex topic of object detection, along with suggestions for how it can be enhanced. The survey then proceeds to provide detailed discussions on path planning, trajectory prediction, and decision-making processes. These elements are crucial for the smooth navigation of autonomous vehicles, and the survey highlights the role of artificial intelligence (AI) and machine learning in these processes. Overall, the survey presents the rapid progress in the field of autonomous driving, offering a comprehensive assessment of the technologies and innovations that are essential for moving toward a safe and efficient autonomous future. [ABSTRACT FROM AUTHOR]

Owing to the various object types and scales, complicated backgrounds, and similar appearance between tissues in medical images, it is difficult to extract some valuable information from different medical images. In this paper, we propose a context aware network with dual-stream pyramid (CANet) for medical image segmentation, which comprises a dual-stream pyramid module and an encoder–decoder module with context aware. Concretely, the dual-stream pyramid captures numerous complementary features at different layers by adopting multi-resolution input versions and multi-scale convolutional units, which is conductive to learning the local detail features in various scales. The encoder–decoder module with context aware progressively concatenates semantic features of the encoder branch with high-level features of the decoder branch in an efficient manner, which aims to suppress complicated backgrounds and highlight the most attractive object in medical images. Quantitative and qualitative experiments demonstrate that our CANet favorably performs against 13 state-of-the-art object segmentation methods on three publicly available medical image segmentation datasets. The code will be released at: https://github.com/Xie-Xiwang/BSPC2022_CANet. • We propose a context aware network with dual-stream pyramid (CANet) for medical image segmentation, which comprises a dual-stream pyramid module (L-shaped module) and an encoder–decoder network with context aware module (U-shaped module). • A novel dual-stream pyramid strategy is designed by multi-resolution input versions and multi-scale convolutional units. Compared with existing medical image segmentation methods, our method avoids the loss of local detail from the raw image and considers object scale variability. Furthermore, our method establishes the complementary relationship between local detail features and multi-scale features. • An encoder–decoder network with context aware is proposed by introducing a chain residual pooling module, which effectively suppresses complex background and provides discriminative feature representation for the decoding branch. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN)-based self-supervised visual representation learning (SSL) is a long-standing problem achieving significant successes with traditional handcrafted pretext tasks and contrastive learning. However, existing SSL methods typically suffer from high computational overhead and poor performance due to sluggish convergence speeds and poor detail extraction capabilities. In this work, to address these issues and improve the robustness, we provide a new self-supervised architecture for incorporating a single-branch backbone with hybrid tasks into the representation learning process. Specifically, our method takes advantage of features from both intra- and inter-images by using discrete montage images. Then a single backbone with a novel A daptive D ecouple C onfusion (ADC) module is proposed to improve the feature extraction capabilities and alleviate the confusion regions in montage images. Besides, both concatenated discrete vectors and patch-based global average pooled vectors in latent space are utilized to learn local detailed features and maintain semantic consistency simultaneously. Moreover, our method is optimized by hybrid tasks and enjoys faster convergence speed due to these improvements. Extensive experiments on several datasets demonstrate the effectiveness and robustness of our method. The proposed method has 2.0% improved in linear classification to the conventional single-branch methods. [Display omitted] • A new framework SSH that leverages concatenated discrete and PGAP vectors is proposed. • An A daptive D ecouple C onfusion (ADC) module is proposed to avoid degenerate performance in existing jigsaw-based methods. • We demonstrate the advantages of the above contributions through extensive experiments. [ABSTRACT FROM AUTHOR]

• We developed a cost-effective method for precision 2D stage error calibration. • Images of an arbitrarily textured plane are captured and analysed in the method. • A modified bundle adjustment algorithm was developed for global optimisation. • 3 μm accuracy within a 100 mm range was demonstrated using experiments. Positioning errors are a primary performance indicator of precision stages. Calibration of stage positioning errors usually requires expensive, specialised instruments or artefacts. In this paper, we introduce a simple and cost-effective, machine vision-based method for stage error calibration. The novelty is that we used an arbitrarily textured plane, for example an array of coins, as the imaging target, as opposed to costly precision machined artefacts. Positioning errors were extracted from overlapped texture images using a fast and globally optimised feature-matching algorithm. A computer simulation demonstrates that the method is accurate and numerically stable in terms of sensing noise. Trials with a coordinate measuring machine reveal that the method can achieve sub-pixel accuracy, with a maximum measurement error of approximately 3 μm within a 100 mm range. [ABSTRACT FROM AUTHOR]

Wedelolactone, a chemical compound extracted from Wedelia calendulacea or Eclipta alba , has been reported to regulate key steps in inflammation. However, the effects of wedelolactone on fungal keratitis are not known. Hence, we aimed to characterize the impact of wedelolactone in Aspergillus fumigatus keratitis. Aspergillus fumigatus was used to establish an in vivo mouse model of fungal keratitis and an in vitro model of THP-1 macrophages. Mice and THP-1 macrophages were pre-treated with wedelolactone. Clinical evaluation, myeloperoxidase (MPO) assay, neutrophil staining, western blot and quantitative polymerase chain reaction (qRT-PCR) were used to assess the effect of wedelolactone on A. fumigatus infection. Therapeutic effect of natamycin treatment with or without wedelolactone was measured via slit lamp microscopy. We confirmed that wedelolactone attenuated the infiltration of neutrophils and decreased MPO level at earlier time points in mice with A. fumigatus keratitis. Pre-treatment with wedelolactone decreased pro-inflammatory cytokine interleukin 1 beta (IL-1β) maturation by inhibiting caspase-1 activity. Combined with natamycin, wedelolactone protected corneal transparency in mouse with fungal keratitis. Present findings indicated that wedelolactone reduced host immune responses by attenuating neutrophil recruitment and IL-1β maturation in Aspergillus fumigatus keratitis. Wedelolactone combined with an antifungal medicine could be a potential therapy for reducing lesion severity in fungal keratitis. • Wedelolactone played a protective role to cornea in mouse early fungal keratitis (FK). • Wedelolactone impaired IL-1β maturation against A. fumigatus infection by caspase-1. • Wedelolactone combined with an antifungal medicine may be a potential therapy to FK. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging techniques are widely used for rapid, efficient, and non-destructive identification of wheat varieties. However, the interference of noise in hyperspectral images and the underutilization of spatial information by most methods are two challenging issues in identifying wheat varieties. In this paper, we present a new approach called Cost-sensitive K-Nearest Neighbor using Hyperspectral imaging (CSKNN) to address these issues. First, we fuse 128 bands acquired by hyperspectral imaging equipment to obtain hyperspectral images of wheat grains, and employ a central regionalization strategy to extract the region of interest. We then use a smoothing denoising strategy to remove noise from the hyperspectral images and improve the saliency of the object grains. Furthermore, we consider the characteristics of different bands and use linear discriminant analysis to compress features, reducing intra-class differences and increasing inter-class differences. Finally, we propose a Cost-sensitive KNN for training and testing of wheat varieties. Our experiments on different strains and varieties of wheat datasets in the same region show that our CSKNN achieves high classification accuracies of 98.09% and 97.45%, outperforming state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) based methods are widely used in identifying seeds varieties with high accuracy. However, the excessive spectral bands in HSI may contain redundant information and degrade the model performance. To address this challenge, we propose a novel feature extraction method called low rank tensor approximation (LRTA). Unlike traditional methods, LRTA extracts joint discriminative information of all wheat seeds from hyperspectral images in a 3-order tensor form, preserving intrinsic information. Our model has three key steps: extracting the region of interest from hyperspectral images and presenting average spectral information in tensor form, using LRTA to extract jointly discriminative information, and feeding this information into a classifier to identify seeds varieties. Experiments on our proposed dataset show that the proposed method has 4% improved to the conventional methods on average. Our method shows promise for improving the accuracy of seeds identification while reducing the dimensionality of HSI. [ABSTRACT FROM AUTHOR]

Abstract Purpose Maresin1, a lipid mediator derived from polyunsaturated fatty acids, has been shown to suppress the inflammatory response in various inflammatory diseases. However, its effects in fungal keratitis are still uncertain. In this study, we investigated the role of maresin1 (MaR1) in Aspergillus fumigatus keratitis of the eye in a mouse model. Methods Mouse corneas were infected with A. fumigatus by corneal intrastromal injection. Two hours after infection, maresin1 (5 ng/5 μl) was delivered by subconjunctival injection. Then, topical administration of maresin1 (5 ng/3 μl) was applied to mouse corneas twice a day from day 1 to day 5. The development of FK lesions, the production of chemokines, the production of inflammation cytokines and the levels of p-GSK3β were measured via slit-lamp biomicroscope, quantitative polymerase chain reaction (qRT-PCR) and western blot. The presence of neutrophils in the cornea was detected by immunofluorescence staining and myeloperoxidase. The effect of maresin1 on A. fumigatus stimulated mouse macrophage RAW264.7 cells was assessed via PCR and Western blot. Results In our study, administration of maresin1 reduced the severity of fungal keratitis with infiltration of fewer neutrophils and reduced levels of the chemokine CXCL1, while the anti-inflammatory cytokines such as IL-10 were enhanced compared with the PBS group. Additionally, in vitro studies showed that treatment with maresin1 inhibited the production of the chemokine CXCL1 and enhanced IL-10 levels in A. fumigatus stimulated RAW264.7 mouse macrophages. Moreover, levels of p-GSK3β increased after maresin1 treatment in A. fumigatus stimulated RAW264.7 cells. Conclusion Taken together, these findings demonstrate that treatment with maresin1 moderates corneal inflammation through reducing neutrophil recruitment and levels of the chemokine CXCL1 and enhancing the anti-inflammatory cytokine IL-10 in A. fumigatus keratitis. Additionally, maresin1 alters levels of GSK3β phosphorylation to regulate CXCL1 and IL-10 expression in response to A. fumigatus infection. Topical administration of maresin1 may emerge as a novel anti-inflammatory molecule and has a protective role in A. fumigatus keratitis. Highlights • Maresin1 alleviates corneal lesions caused by Aspergillus fumigatus in C57BL/6 mouse. • Maresin1 increases p-GSK3β levels to regulate CXCL1 and IL-10 expression in response to Aspergillus fumigatus infection. • Maresin1 inhibits neutrophil recruitment into corneal stroma in mouse Aspergillus fumigatus keratitis. [ABSTRACT FROM AUTHOR]

Self-supervised visual representation learning focuses on capturing comprehensive features via exploiting the unlabeled datasets. However, existing contrastive learning based SSL frameworks are subjected to higher computational consumption and unsatisfactory performance. To handle these issues, we present a novel single-branch SSL method that incorporates an adaptive feature selection and activation module and a progressive cascade dimensionality reduction module, called APNet. Specifically, our method first fully exploits the unlabeled datasets and extracts intra- and inter-image information via introducing montage image. In addition, a novel adaptive feature selection and activation module is designed to generate the most comprehensive features. Besides, a progressive cascade dimensionality reduction module is proposed to capture the most representative features from latent vectors through cascade dimensionality increasing–decreasing operations. Extensive experiments have demonstrated the robustness and effectiveness of APNet. Specifically, APNet exceeds MoCo-v3 by 3.1% on the ImageNet-100 dataset, and consumes only half of the calculation. Code is available at https://github.com/AI-TYQ/APNet. • We embed feature selection and dimensionality reduction modules into SSL framework. • We design an attention module that selects and activates representative features. • We introduce a dimensionality reduction module to retain discriminative features. • We demonstrate the advantages of the above contributions through experiments. [Display omitted] [ABSTRACT FROM AUTHOR]

Complete surgical resection, accessible therapeutic targets and effective tyrosine kinase inhibitors (TKIs) have not completely cured gastrointestinal stromal tumours (GISTs), with most patients suffering from residual tumours and recurrence. The existence of nerve infiltration in GIST provides a way for tumour cells to escape local resection and systemic targeted therapy, which may challenge the previous understanding of its behaviour patterns and inspire the development of more radical excision and more precise targeted therapy. Moreover, tumour dormancy has emerged as a major cause of drug resistance and tumour relapse. Among these pathways, the nerve-tumour regulatory axis GDNF-GFRA1 is activated in GISTs, assists tumour cells in achieving dormancy and protects them from apoptosis under environmental stress by enhancing autophagic flux. The concrete mechanism is that the GDNF-regulating interaction between GFRA1 and the lysosomal calcium channel MCOLN1 activates Ca2+-dependent TFEB signalling. Activated TFEB transcriptionally regulates intracellular lysosome levels, which could achieve feedback upregulation of cellular autophagy flux during TKI treatment. This dormancy-transition axis fills parts of the mechanistic vacancy before the onset of secondary mutations, and strategies for TKIs combined with targeting GFRA1-dependent autophagy have distinct promise as prospective clinical therapies. [ABSTRACT FROM AUTHOR]

Gastric Cancer (GC) is the fifth leading cause of cancer-related death in the world, and in urgent need of specific therapeutic targets to acquire prominent effectiveness. T-cell immunoglobulin and immunoreceptor tyrosine–based inhibitory motif (ITIM) domain (TIGIT) and programmed cell death protein 1 (PD-1) are identified to be abnormally overexpressed in various types of cancers including GC. This study aimed to investigate whether TIGIT and PD-1 could serve as potential prognostic biomarkers for GC. Firstly, TCGA GC dataset analysis and correlation analysis were utilized to inspect the relationship between expression of TIGIT, PD-1 and CD8 + T cells in GC and adjacent normal tissues. Then, flow cytometry was used to verify the data after collecting the peripheral blood, GC and adjacent normal tissues from 150 GC patients. Lastly, quantitative RT-PCR was performed to detect the expression of CD155, CD113, CD112 and TIGIT in six human GC cell lines and 631 GC patients in KM Plotter Database to conduct prognostic analysis. As results, we found that TIGIT and PD-1 were upregulated in GC tissues with high CD8 + T cells infiltration, while correlation analysis indicated they were in high-positive correlation. In addition, the flow cytometry analysis further showed that the high-expression of TIGIT in tumor microenvironment of GC could suppress the function of infiltrative CD8 + T cells, which leads to the escape of GC cells from immune killing. Furthermore, CD155 and CD112 were found abnormally upregulated in GC tissues and cell lines and the high expression of CD155, CD112 and TIGIT demonstrated poor prognosis results. In conclusion, these results provided potential therapeutic targets and prognostic biomarkers for treatment of GC in clinic. [ABSTRACT FROM AUTHOR]

• Mutated DNA repair genes and immune stress lead to multiple mutations in tumors. • TCR sequences revealed antitumor immune response difference among individuals. • Infiltrating innate immune cells contributed to TCR multiplicity in mouse melanoma. • Anti-tumor effect was stronger in lung metastasis than subcutaneous melanoma. Melanoma is amongst the most aggressive malignant tumors. The purpose of this study is to detect the tumor microenvironment systematically using multi-omics analyses and to propose strategies for precision medicine. Multiple factors of tumor microenvironment contribute to the drug resistance and immune surveillance failure. Here we analyzed genome mutations and characterized the immune state of tumor microenvironments in mouse melanoma by whole exome sequencing (WES) and RNA sequencing (RNA-Seq) approaches. Somatic mutation analysis revealed 35.1% novel mutations in mouse tumors when compared with B16F10 cell line, provided a basis for multi-site sequencing for accurate neoantigen selection. Mutation cluster, gene expression comparison, and gene ontology (GO) analyses by R packages proved DNA repair damage, inflammation, slower cell division, and metabolic change in tumor microenvironment. Further analyses of T-cell receptor (TCR) sequences, immune signaling pathway activation, tumor infiltrated immune cells and chemokine expression revealed extensive difference of antitumor immune response among individuals. Our study revealed the characteristics of tumor microenvironment with mouse melanoma model, suggested the need of comprehensive genome mutations and personal immune state analyses for cancer precision medicine. [ABSTRACT FROM AUTHOR]

• Nerolidol protected the corneas from infection by inhibiting the growth of Aspergillus fumigatus. • Nerolidol played a protective role to corneal transparency in mouse A. fumigatus keratitis. • Nerolidol impaired LOX-1/IL-1β signaling in mice corneas and HCECs infected by A. fumigatus. Nerolidol, a naturally occurring sesquiterpene has both anti-microbial and anti-inflammatory properties. The current study aims to investigate the antifungal and the anti-inflammatory effects of nerolidol against mouse Aspergillus fumigatus (A. fumigatus) keratitis. The minimum inhibitory concentration (MIC) and cytotoxicity tests were used to study the antifungal ability. For in vivo and in vitro studies, the mouse corneas and the human corneal epithelial cells (HCECs) infected with A. fumigatus spores were intervented with nerolidol or phosphate buffer saline (PBS). Thereafter, the effect of the nerolidol on the response against inflammation was analyzed using the following parameters: recruitment of the neutrophils or macrophages and the expression of the lectin-type oxidized low density lipoprotein receptor-1 (LOX-1) and interleukin 1β (IL-1β). Techniques used were the slit lamp, immunofluorescence, myeloperoxidase (MPO) detection, quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot. Nerolidol directly inhibits the growth of A. fumigatus. The administration of nerolidol reduced the severity of fungal keratitis with infiltration of fewer inflammatory cells and reduced levels of the LOX-1, as well the anti-inflammatory cytokines such as IL-1β were reduced compared with the PBS group. Additionally, in vitro studies showed that treatment with nerolidol inhibited the production of the LOX-1 / IL-1β levels in A. fumigatus stimulated HCECs. Nerolidol attenuated the A. fumigatus keratitis inflammatory response by inhibiting the growth of A. fumigatus , reducing the recruitment of the neutrophils and the macrophages, and inhibiting the LOX-1/ IL-1β signaling. [ABSTRACT FROM AUTHOR]