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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]

Siamese-architecture-based self-supervised learning usually suffers from relatively high computational consumption and unsatisfactory performance because of its slow convergence and poor feature extraction capability. To alleviate these issues, we propose a self-supervised method, called SSL 2 , that is both efficient and effective. Specifically, a global and local sampling method is proposed to increase the number of samples while maintaining connections between semantic features. More significantly, SSL 2 maintains low computational complexity and facilitates the establishment of mapping relationships between global comprehensive and local detailed features compared with other methods. In addition, an i nformation r etainer p rojection h ead (IRPH) is proposed to further balance the information between detailed inconsistency and semantic consistency. Finally, hybrid tasks are embedded into SSL 2 to optimize the model so that it can effectively leverage the data provided by global and local sampling. Extensive qualitative and quantitative evaluations of various types of benchmarks illustrate that SSL 2 outperforms existing self-supervised frameworks in commonly used computer vision tasks. Specifically, SSL 2 achieved satisfactory performance with linear classification on ImageNet, outperforming MoCo-v2 by 2.2% with fewer calculations, and it also achieved competitive results compared with other state-of-the-art methods. • A new single-branch self-supervised learning framework is designed. • A novel sampling strategy is designed to make full use of the information. • A head is designed to balance detailed inconsistency and semantic consistency. • Well-balanced hybrid loss functions are introduced. [ABSTRACT FROM AUTHOR]

• Use the new model (the 3D-3T model) to estimate plant transpiration. • The Normalized Difference Ratio Index (NDRI) is proposed for the first time. • The 2D-3T model was converted to the 5P-3T model. • The temperature of the canopy was accurately obtained by a portable thermal imager. Crop transpiration is the main dissipation way of agricultural water. Accurate and effective estimation of crop transpiration is of great significance for the study of the carbon-water cycle relationship and efficient water-saving irrigation. This study uses infrared thermal imaging technology to accurately extract the average temperature of the plant canopy as a key input for the new model (three-dimensional temperature (3D-3T) model), which is an extension of the three-temperature (3T) model and applied to estimate the canopy transpiration rate of typical citrus trees (variety named Guoqing No. 1) from 8:00 to 18:00 on May 7 to 11, 2021. And a processing method for extracting green plants in visible-light images based on the Normalized Difference Ratio Index (NDRI) is proposed for the first time, which can distinguish green plants from other ground objects, backgrounds, and shadows in the visible-light image, and then help to accurately extract the average canopy temperature from infrared thermal images. For this experiment, the canopy structure characteristics of the selected experimental subjects and the measurement methods used were considered, so the 3D-3T model was converted to a 5-point three-temperature model (5P-3T model) to estimate the canopy transpiration rate of citrus trees and evaluate the plant water status, and then the sap flow measurement was used for validation. The results showed that: (1) The 3T model overestimated the transpiration rate of the citrus tree, while the 5P-3T model underestimated it. The estimated values of the 5P-3T model have a good correlation with the measured values of the packaged stem sap flow gauge method, with a determination coefficient (R2) of 0.72 and root mean square error (RMSE) of 0.35 mm/d. (2) In this model, the plant transpiration transfer coefficient (h at ) can evaluate the water shortage of plants, where the value of h at increased with the aggravation of plant water stress. (3) The citrus trees have lower transpiration rates under water stress, ranging from 0.06 to 0.12 mm/h in the daytime (from 8-10 May 2021). The model used in this study only needs the input of temperature information and net radiation, and would not depend on aerodynamic resistance. The method in this paper can realize the non-contact and non-invasive measurement of crop transpiration, and it provides a feasible way to use a portable thermal imager to accurately obtain the temperature for estimating plant canopy transpiration rate. [ABSTRACT FROM AUTHOR]

Ideal wheat grain quality detection systems aim for rapid and precise recognition of flaws. Computer vision and machine learning have been widely studied as potential alternatives to human inspection. Numerous deep learning-based detection and classification methods have been proposed in the last few years. However, these methods suffer from poor performance and extremely long time consumption due to the weak feature extraction ability and high computational overhead. To solve these challenges, we present the first comprehensive study and analysis of wheat grain detection using improved YOLOv5. Specifically, we design a machine vision system and construct a Wheat Grain Detection Benchmark (WGDB) including 1746 images with 7844 bounding boxes, all of which have been independently classified. Utilizing this dataset, we conduct a comprehensive study of the most advanced objection detection methods. In addition, we propose a Wheat Grain Detection Network (called WGNet) trained on this benchmark as a baseline, aiming to solve the degradation issues by employing sparse network pruning and a hybrid attention module. Extensive experiments demonstrate the limitations of existing methods and the improved performance of our method, which achieves state-of-the-art precision with the fastest inference speed. The constructed benchmark and the improved experiments shed light on future research in wheat grain detection. The dataset and code will be available at WGNet. • We construct a large-scale wheat grain detection benchmark. • We conduct a comprehensive study with the constructed dataset. • We propose a light-weight Wheat Grain Detection network by employing sparse network pruning and a hybrid attention module. [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]

Capturing the comprehensive information of various sizes and shapes of images in the same convolution layer is typically a challenging task in computer vision. There are two main kinds of methods for capturing those features. The first uses the inception structure and its variants. The second utilizes larger convolution kernels on specific layers or stacks with more convolution blocks. However, these methods can result in computationally intensive or vanishing gradients. In this paper, to accommodate feature distributions with different sizes, shapes and reduce computational cost, we propose a width- and depth-aware module named the WD-module to match feature distributions. Moreover, the proposed WD-module consumes less computational cost and parameters compared with traditional residual convolution layers. To verify the effectiveness of our proposed method, a size- and shape-aware backbone network named S2A-Net was built, which was obtained by stacking the WD-modules. By visualizing heat maps and features, the proposed S2A-Net can adapt to objects with different sizes and shapes in visual recognition tasks and learn more comprehensive characteristics. Experimental results show that the proposed method has higher accuracy in image recognition and outperforms other state-of-the-art networks with the same numbers of layers. • A width-aware and deep-aware module is build. • A size-aware and shape-aware network for visual recognition is build. • Compared with other state-of-the-art methods, the proposed method can achieve better results while consume less parameters and FLOPs. [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]

• A error analyzing toolbox for VIS is proposed, bining false prediction into 7 types. • TIVE weights error type's damage to mAP and evaluates over different temporal length. • We investigate the spatial and temporal localization abilities of popular VIS models. In this paper, we introduce TIVE, a T oolbox for I dentifying V ideo instance segmentation E rrors. By directly operating output prediction files, TIVE defines isolated error types and weights each type's damage to mAP, for the purpose of distinguishing model characters. By decomposing localization quality in spatial–temporal dimensions, model's potential drawbacks on spatial segmentation and temporal association can be revealed. TIVE can also report mAP over instance temporal length for real applications. We conduct extensive experiments by the toolbox to further illustrate how spatial segmentation and temporal association affect each other. We expect the analysis of TIVE can give the researchers more insights, guiding the community to promote more meaningful explorations for video instance segmentation. The proposed toolbox is available at https://github.com/wenhe-jia/TIVE. [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]

• The characteristics of rainfall erosivity in the Huaihe River Basin were analyzed. • 87% rainfall erosivity concentrated during May – September in the Huaihe River Basin. • Rainfall erosivity in the Huaihe River Basin was highest in the upper basin. • Climatic indexes were correlated with rainfall erosivity with different lag times. • Some advice was provide for local human activities based on soil erosion prevention. Rainfall erosivity (RE) is an important factor in the soil erosion process, which cannot be altered by human intervention alone. The Huaihe River Basin (HRB) is a large agricultural watershed, suffering severe water erosion, investigating the spatio-temporal variation of RE is essential for local soil erosion prevention. The linear regression, Yue-Pilon method, and the Hurst exponent were used in analyzing the spatio-temporal variation within the HRB during 1960–2018. The slip correlation analysis and F-test were applied to obtain the correlation between climatic indices [the Arctic Oscillation (AO), the Southern Oscillation (SOI), the North Pacific Index (NPI), and the Niño 4SST index (SST)] and RE in the HRB. Results revealed that the annual average RE were 4280, 5061, 4068, 4886, and 4089 MJ mm hm−2 h−1 within the HRB, upper-HRB, middle-HRB, lower-HRB, and the Yishusi River Watershed, respectively. The annual average RE will increase within the upper-HRB and lower-HRB and decrease within the middle-HRB and the Yishusi River Watershed in the future. The seasonal average RE was ranked as summer > autumn = spring > winter in the HRB. The spatiotemporal difference was significant in the HRB, and different sub-regions exhibited a different trend in seasonal RE, except for the winter RE that increased significantly in all sub-regions (p < 0.05). The highest monthly RE occurred in July, with RE during May–September accounting for approximately 87% of the annual RE in the HRB. The AO and NPI had significant correlations with RE (p < 0.05) both on the annual and monthly scales with different lag times. The monthly AO, SOI, SST, and NPI were significantly correlated with RE in the long term with different lag times in different months (p < 0.05). These findings could provide potential predictive factors for RE prediction and help prevent soil erosion within the HRB. [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]

Atrial fibrillation (AF) is a common arrhythmia, and its incidence increases with age. Many methods have been developed to identify AF, including both the hand-picked features by experts and the recent emerging artificial intelligent (AI) methods. As the traditional hand-picked features have almost reached the boundary of their capability, the AI methods have shown their great potentials to achieve high accuracy for the AF identification. However, some common AI methods, especially deep learning methods, do not provide good properties of interpretability, making it difficult to explore the internal relationship between input and prediction results. In addition, most of the reported methods are only for the intra-patient test of AF and Non-AF. In this study, we try to develop an AF detector based on a twin-attentional convolutional long short-term memory neural network (TAC-LSTM), which can not only generate results with high accuracy but also enable a human-friendly function to provide the possible explanations of the automated extracted features by AI. TAC-LSTM was applied to extract multi-domain features of ECG signals for AF detection and to mine the influence of different input segments on the final prediction results. Finally, the proposed method is validated on the MIT-BIH Atrial Fibrillation Database (AFDB) with intra-patient test and inter-patient test and the results also have shown that multi-domain features extracted by TAC-LSTM can provide more useful information. Collectively, TAC-LSTM can be used for clinicians as an auxiliary diagnostic tool. • A novel deep neural network with multi-domain input layers is proposed. • CNN and LSTM are employed to extract short-term and long-term dependence features. • In AF detection, multi-domain information is characterized by complementary. • Fusing signal time and time-frequency domain information improves model performance. • Our method mines the relationship between the input segment and prediction results. [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]