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Echinochloa crus-galli (L.) P. Beauv (common name: barnyard grass) is a major weed in rice-growing areas and has evolved resistance to multiple herbicides. Florpyrauxifen-benzyl (trade name Rinskor) is a novel synthetic auxin herbicide that was approved in China in 2017 and is widely used in rice production to control resistant weeds, including barnyard grass. We identified a florpyrauxifen-benzyl-resistant E. crus-galli biotype with a resistance index (RI) of 11.89 using screen house herbicide experiments. To understand the phytotoxicity mechanisms of florpyrauxifen-benzyl, we used transcriptomics technologies to compare the gene expression profiles of florpyrauxifen-benzyl treatment on phytohormone transduction between florpyrauxifen-benzyl-resistant and -susceptible barnyard grasses (Echinochloa crus-galli (L.) P. Beauv). A total of 1810 DEGs were identified in the S comparison setting (FTS vs. UTS), and 915 DEGs were identified in the R comparison setting (FTR vs. UTR); 464 genes overlapped between the two comparison groups. Approximately sixty-nine hormone-related DEGs were detected after treatment with florpyrauxifen-benzyl in both R and S biotypes. At 24 h after florpyrauxifen-benzyl treatment, compared with the R biotype, the S biotype showed a stronger auxin response and higher expression of related genes involved in ethylene and abscisic acid biosynthesis and signal transduction. In addition, a brassinolide receptor gene was upregulated after florpyrauxifen-benzyl treatment and had higher expression in the S biotype than in the R biotype. This study is the first transcriptome analysis of the differential effects of florpyrauxifen-benzyl treatment between florpyrauxifen-benzyl-resistant and -susceptible E. crus-galli. It reflects the difference in phytohormone biosynthesis and signal transduction between R and S barnyard grasses in response to florpyrauxifen-benzyl treatment and will be helpful for understanding the phytotoxicity mechanisms of florpyrauxifen-benzyl. [ABSTRACT FROM AUTHOR]

Anomaly detection aims at locating the spectral different objects of a specific scene without any prior information, and has gained increasing attention. By decomposing the input hyperspectral image (HSI) into a background tensor and an anomaly tensor, the tensor approximation is an efficient tool for detecting the anomalies. Low rankness is usually utilized as the regularizer during the background reconstruction process. Different from most existing hyperspectral anomaly detection methods which compute the truncated nuclear norm of the third folding of the original HSI, a novel weighted‐sparsity‐regularized tensor linear representation (WsrTLR) method is proposed for hyperspectral anomaly detection in this paper. Tensor linear representation is utilized to formulate the background HSI by a three‐dimensional (3D) representation base and the corresponding 3D representation coefficient. Low rankness is applied to constrict the representation coefficient, an operation which avoids destroying the multi‐way structure and losing information during the matrixing process, and ensures a satisfactory detection accuracy. Meanwhile, by incorporating the weighted‐sparsity‐regularized tensor linear representation to reconstruct the background tensor, the anomalies can be easily detected by eliminating the background tensor from the original scene. In addition, to avoid negative influence caused by the redundant bands and noisy bands in the representation process, informative bands have been first selected via an optimal neighborhood reconstruction strategy. Experimental results and data analysis on four real hyperspectral datasets, which contain anomalies with different sizes, have demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel end-to-end deep neural network for the problem of video super-resolution. In contrast to most previous methods where frames need to wrap for temporal alignment based on the estimated optical flow, we propose short-temporal and bidirectional long-temporal blocks to exploit the spatial-temporal dependencies existing in inter-frames. It can effectively model the sudden and smooth varying motions of videos and overcome the limitations of explicit motion estimation. In addition, by introducing dense feature concatenation, it provides an effective way to combine the low-level and high-level features for boosting the reconstruction of mid/high-frequency information as shown in our analysis and experiment. Furthermore, we present a region-level non-local feature enhancing structure, which captures the spatial-temporal correlations of any two positions and makes use of long-distance relevant information. Extensive evaluations and comparisons with the current state-of-the-art approaches demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Lipocalin is a large family with complex functions including retinol-binding protein (RBP), crustacyanin (CRCN), apolipoprotein D, etc. In shrimps, it is well known that CRCN is related to body color. Recently, retinoic acid/retinol-binding protein was found in shrimp. However, little is known about the function of RBP and relationships among the gene members of lipocalin in shrimps. Based on the transcriptome sequences responding to starvation stress, three genes of the lipocalin–retinol-binding protein-like gene family (lipocalin-1, lipocalin-2, and lipocalin-3) were cloned by RACE from the ridgetail white prawn, Exopalaemon carinicauda. Homology analysis showed that these three genes had high similarity with the known insect apolipoprotein D gene and vertebrate retinol-binding protein gene, and they are of the same type in terms of evolution. Fluorescence quantitative PCR showed that the above three genes were mainly expressed in the ventral nerve cord of E. carinicauda. The expression characteristics of the three genes at different developmental stages showed that they were more highly expressed at the larval stage, which suggests that they might be related to embryonic and larval development. The RNA interference tests showed that after silencing lipocalin-1 and lipocalin-3, the body color of individual shrimps turned slightly red and the blue pigment in the epidermis largely disappeared, but no significant change took place in the appearance of individuals after silencing lipocalin-2. In addition, on the 6th and 16th days of interference, dead shrimps appeared in the lipocalin-1 and lipocalin-3 interference groups. The dead shrimps had hard crusts and remained in a molting posture. Totally, this study showed that the retinol-binding protein-like gene obtained in this study had certain biological functions in the growth and development and body color formation as CRCN; in addition, it also plays a role in nerve system and molting of E. carinicauda. [ABSTRACT FROM AUTHOR]

Introduction: Irritable bowel syndrome with diarrhea (IBS-D) is frequently accompanied by depression and anxiety, resulting in a reduced quality of life and increased medical expenditures. Although psychological factors are known to play an important role in the genesis and development of IBS-D, an understanding of the central neural control of intestinal dysfunction remains elusive. Melanin-concentrating hormone (MCH) is a gut-brain peptide involved in regulating feeding, sleep-wake rhythms, and emotional states. Methods: This study investigated the regulation of the MCHergic neural circuit from the lateral hypothalamic area (LHA) to the dorsal raphe nucleus (DRN) on anxiety- and depression-like behaviors, intestinal motility, and visceral hypersensitivity in a mice model of IBS-D. The models of IBS-D were prepared by inducing chronic unpredictable mild stress. Results: Chemogenetic activation of the MCH neurons in the LHA could excite serotonin (5-HT) neurons in the DRN and induce anxiety- and depression-like behaviors and IBS-D-like symptoms, which could be recovered by microinjection of the MCH receptor antagonist SNAP94847 into the DRN. The mice model of IBS-D showed a reduction of 5-HT and brain-derived neurotrophic factor (BDNF) expression in the DRN, while an elevation of 5-HT and BDNF was observed in the colon through immunofluorescent staining, ELISA, and Western blot analysis. SNAP94847 treatment in the DRN alleviated anxiety- and depression-like behaviors, improved intestinal motility, and alleviated visceral hypersensitivity responses by normalizing the 5-HT and BDNF expression in the DRN and colon. Conclusion: This study suggests that the activation of MCH neurons in the LHA may induce IBS-D symptoms via the DRN and that the MCH receptor antagonist could potentially have therapeutic effects. [ABSTRACT FROM AUTHOR]

The traditional imaging approach with circular aperture lenses lose the high frequency part of the scene because of limited cut-off frequency of the aperture, which could not be recovered only with the post-processing method. Our analysis of the frequency shows that different apertures have different frequency retention, and a single aperture can not preserve more high frequency information, which brings on unsuccessful reconstruction of High-Resolution(HR) images. So a HR imaging method based on frequency-complementary compressive coded aperture set is proposed. We derive a criterion for evaluating compressive coded aperture set with respect to the spectral complementarity maximization and the precision of restoration. This criterion is optimized with a genetic algorithm to get a set of coded aperture which can preserve more high frequency part together when compressive coded sampling. This property can obtain more details that are missed in each individual coded sensing image but preserved mutually among all coded sensing images. In the reconstruction stage, we utilize the non-local similarity sparse prior and frequency complementarity among multi-images to build a reconstruction model for the HR image recovery. Finally, by combining compressive coded sampling with sensing reconstruction, we construct a coded sampling-reconstruction camera system. Extensive experiments with the sampling-reconstruction system on the simulations as well as the real-world images demonstrate that the proposed method can recover more details and get better results compared with other coded apertures and super-resolution methods. [ABSTRACT FROM AUTHOR]

For the purpose of studying the epigenetic characteristics of the mitochondrial genome of the ridgetail white prawn, Exopalaemon carinicauda (Holthuis, 1950) under Cd2+ and Cr6+ heavy metal stress, the mitochondrial DNA methylation of E. carinicauda was analysed by bisulfite sequencing PCR (BSP). Many methylation sites were found at the 3′ end sequence of COX3 and at the starting region sequence of ND3, while only a few methylation sites were found at the 3′ end sequence of ND5. The mitochondrial genome was inferred to regulate the energy metabolism through the methylation process. In addition, under Cd2+ stress, mitochondrial DNA methylation was more common, and found during all stress periods (3, 6, 12, 24, 48, 72 and 96 h), while under Cr6+ stress, mitochondrial DNA methylation was less common, mainly occurring after 48 hours of stress. The sensitivity of the mitochondrial genome response to Cd2+ stress was inferred to be greater than that to Cr6+. This study revealed for the first time that methylation occurs in the mitochondrial genome of E. carinicauda in response to heavy metal stress. [ABSTRACT FROM AUTHOR]

Abstract RANSAC is a popular robust model estimation algorithm in various computer vision applications. However, the speed of RANSAC declines dramatically as the inlier rate of the measurements decreases. In this paper, a novel Adaptively Ranked Sample Consensus(ARSAC) algorithm is presented to boost the speed and robustness of RANSAC. The algorithm adopts non-uniform sampling based on the ranked measurements to speed up the sampling process. Instead of a fixed measurement ranking, we design an adaptive scheme which updates the ranking of the measurements, to incorporate high quality measurements into sample at high priority. At the same time, a geometric constraint is proposed during sampling process to select measurements with scattered distribution in images, which could alleviate degenerate cases in epipolar geometry estimation. Experiments on both synthetic and real-world data demonstrate the superiority in efficiency and robustness of the proposed algorithm compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Multiple Object Tracking (MOT) methods based on per-pixel prediction and association have achieved remarkable progress recently. These approaches prefer to select points in central region of the bounding box as positive samples and other points as negative samples. Under severe occlusions, these sample allocation methods might lead to the central region being contaminated by occluding samples, resulting in significant degradation of both detection and association performance. To address this issue, we propose a novel Dynamic-Center-Point-based Multiple Object Tracking method (DCP-MOT), which aims to self-identify the visible center region for occluded objects. Compared with the previous bounding box center point, our Dynamic-Center-Point (DCP) can better represent the visible region of the occluded object. Specifically, we design an Iterative Refinement Branch to generate dynamic center points for each occluded object. It includes two parts: Center Probability Predictor and Center Generator. Initially, we utilize the Center Probability Predictor to derive an accurate probability map for the occluded object. Subsequently, we use Center Generator to quantify the probability map by introducing a Mutual Exclusive Potential Function, yielding a dynamic center point for the occluded object that is distinct from its occluding counterpart. Finally, by joining the bounding box center points for unoccluded objects and our dynamic center points for occluded objects, our JDE branch can achieve better tracking performance. Extensive experiments demonstrate our DCP-based MOT method surpasses the bounding box center based SOTA in metrics MOTA (+ 0. 7 , + 2. 1 , + 1. 3) and IDF1 (+ 0. 7 , + 0. 9 , + 1. 5) on the challenging MOT16, MOT17, MOT20 datasets. [ABSTRACT FROM AUTHOR]

Irritable bowel syndrome (IBS), which is characterized by chronic abdominal pain, has a high global prevalence. The anterior cingulate cortex (ACC), which is a pivotal region involved in pain processing, should be further investigated regarding its role in the regulation of visceral sensitivity and mental disorders. A C57BL/6J mouse model for IBS was established using chronic acute combining stress (CACS). IBS‐like symptoms were assessed using behavioral tests, intestinal motility measurements, and abdominal withdrawal reflex scores. Fluoro‐Gold retrograde tracing and immunohistochemistry techniques were employed to investigate the projection of ACC gamma‐aminobutyric acid‐producing (GABAergic) neurons to the lateral hypothalamus area (LHA). Chemogenetic approaches enabled the selective activation or inhibition of the ACC–LHA GABAergic pathway. Enzyme‐linked immunosorbent assay (ELISA) and western blot analyses were conducted to determine the expression of histamine, 5‐hydroxytryptamine (5‐HT), and transient receptor potential vanilloid 4 (TRPV4). Our findings suggest that CACS induced IBS‐like symptoms in mice. The GABA type A receptors (GABAAR) within LHA played a regulatory role in modulating IBS‐like symptoms. The chemogenetic activation of ACC–LHA GABAergic neurons elicited anxiety‐like behaviors, intestinal dysfunction, and visceral hypersensitivity in normal mice; however, these effects were effectively reversed by the administration of the GABAAR antagonist Bicuculline. Conversely, the chemogenetic inhibition of ACC–LHA GABAergic neurons alleviated anxiety‐like behaviors, intestinal dysfunction, and visceral hypersensitivity in the mouse model for IBS. These results highlight the crucial involvement of the ACC–LHA GABAergic pathway in modulating anxiety‐like behaviors, intestinal motility alterations, and visceral hypersensitivity, suggesting a potential therapeutic strategy for alleviating IBS‐like symptoms. [ABSTRACT FROM AUTHOR]

At present, most mainstream algorithms for single image super-resolution (SISR) assume the image degradation process as an ideal degradation process (e.g. bicubic downscaling), which violates the actual degeneration conditions. In real-world image capturing, objects often move in a dynamic environment, and camera shake also often occurs, which results in serious blurs. Our work focuses on the task of image super-resolution with heavy motion blur, for which we adopt a network with two branches: one branch for image deblurring and the other one for super-resolution. Since the features obtained by the deblurring are rich in details, we apply their features as supplementary information to the super-resolution branch. Based on the adopted dual-branch framework, our major technical novelties lie in two novel modules: Multi-Scale Feature Fusion (MSFF1) module which fuses features of different scale from the deblurring branch to get local and global information, and Multi-Stage Feature Fusion (MSFF2) module which further filters useful information with attention. We evaluate the proposed method under various blur scenarios on the benchmark datasets, demonstrating competitive performance against existing methods. [ABSTRACT FROM AUTHOR]

An accurate blur kernel is key to blind image deblurring and kernel estimation heavily relies on strong edges in the observed image 1 , 2, 3]. Previous methods [4] [5] leveraging image gradient prior with i.i.d statistics can hardly restrict strong edges in a noisy-blurred image, since both noise and strong edges are presented as strong gradients. In this paper, we propose a dynamic structure prior (DSP) to distinguish strong edges from noise by a non local total variation regularization. Our method measures accumulated intensity change of each pixel along all possible directions within its neighborhood. In this case, the structural contents with less change in certain directions, i.e.strong edges, will be accumulated lower than non-structural contents such as noise. By contrast, the previous image gradient priors only measure the changes in horizontal and vertical directions, which is not capable to present the directional characteristic of image edges. Moreover, in our method, the prior changes the weight dynamically with regard to local structures to impose fewer penalties on pixels with large changes. As a result, strong edges are preserved while fine textures are suppressed by large penalties. At last, a cost function containing the dynamic structure prior and L 0 gradient prior that sharpens the preserved edges is proposed to estimate blur kernels from noisy-blurred images. Comprehensive experimental results show that our method outperforms previous methods in both commonly used datasets with various noise levels and real world images, from monochrome image to color image. [ABSTRACT FROM AUTHOR]

On the basis of transcriptome sequences, the fatty acid-binding protein (FABP) gene of Exopalaemon carinicauda was obtained by rapid amplification of cDNA ends (RACE) and named FABP. The full length of the FABP gene is 878 bp, with the open reading frame being 399 bp and the non-coding regions of the 5′ end and the 3′ end being 94 bp and 385 bp, respectively. The gene encodes a protein composed of 132 amino acids. A homology analysis showed that the FABP gene has high similarity with fatty acid-binding protein and retinoic acid-binding protein genes. Real-time PCR showed that the FABP gene was expressed mainly in subepidermal adipose tissue, and secondarily in intestine and heart. The expression characteristics at different developmental stages showed that the expression of the gene was low at the larval stage (0–25 d) and reached the highest level after 30 days. RNA interference results showed no significant difference in the appearance of E. carinicauda and no individual death after this gene was silenced. Compared with the control group, except C20:2n-6 which increased, the contents of the other 12 fatty acids in shrimps in the RNA interference group decreased significantly, and the total amount of fatty acids also decreased significantly, which suggests that the FABP gene is closely related to changes in fatty acid content in shrimp. [ABSTRACT FROM AUTHOR]

One of the most challenging problems in reconstructing a high dynamic range (HDR) image from multiple low dynamic range (LDR) inputs is the ghosting artifacts caused by the object motion across different inputs. When the object motion is slight, most existing methods can well suppress the ghosting artifacts through aligning LDR inputs based on optical flow or detecting anomalies among them. However, they often fail to produce satisfactory results in practice, since the real object motion can be very large. In this study, we present a novel deep framework, termed NHDRRnet, which adopts an alternative direction and attempts to remove ghosting artifacts by exploiting the non-local correlation in inputs. In NHDRRnet, we first adopt an Unet architecture to fuse all inputs and map the fusion results into a low-dimensional deep feature space. Then, we feed the resultant features into a novel global non-local module which reconstructs each pixel by weighted averaging all the other pixels using the weights determined by their correspondences. By doing this, the proposed NHDRRnet is able to adaptively select the useful information (e.g., which are not corrupted by large motions or adverse lighting conditions) in the whole deep feature space to accurately reconstruct each pixel. In addition, we also incorporate a triple-pass residual module to capture more powerful local features, which proves to be effective in further boosting the performance. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed NDHRnet in terms of suppressing the ghosting artifacts in HDR reconstruction, especially when the objects have large motions. [ABSTRACT FROM AUTHOR]

• A simulated exposure images mechanism generates more input data. • Four weighted maps increase the visualization of the degraded regions. • Color corrected of the simulated exposure images. • Gradient enhanced of the simulated exposure images. Image visuality enhancement aims at increasing visual quality of a given image to convey more useful information. The key for visuality enhancement is to comprehensively exploit the details of the image scene. However, one (or several) observed image only provides partial information of the scene. To address this problem, we present a novel multi-exposure fusion based visuality enhancement method in this study. Firstly, we propose a simulated exposure model to synthesize the results of the observed image under different exposure conditions. Then, white balancing and image gradient are separately performed on those simulated exposure results. By doing these, scene details in different exposure conditions as well as image feature spaces (i.e., color and gradient spaces) can be well exploited. To appropriately take advantage of those resultant details, we adopt the Laplacian pyramid based linear fusion framework to integrate them into the enhanced image in a multi-scale way. Different from conventional fusion methods, we develop a more powerful weight map for fusion, which is able to simultaneously highlight pixels with good exposedness, contrast, saturation as well as Gamut. Experimental results demonstrate that the proposed method can effectively enhance the visuality of the observed image. Compared with existing methods, the proposed method well highlights the fine details as well as avoiding halo artifacts. [ABSTRACT FROM AUTHOR]

Blind image deblurring aims to recover the sharp image from a blurred observation, which is an ill-posed inverse problem. Proper image priors for the unknown variables ( i.e. latent sharp image and blur kernel) are crucial. Abundant previous methods have shown the effectiveness of the sparsity-based priors on both image gradients and the blur kernel. The correlation among the elements of the sparse variables is paid less attention, however. In this paper, we propose to handle the blind image deblurring problem by promoting group sparsity. The proposed group sparsity priors are based on the fact that the nonzero elements of natural image gradients and blur kernels tend to cluster in structured group pattern. Based on the proposed priors, we introduce proper algorithms to iteratively update latent image gradients and blur kernel, respectively. The proposed algorithms preserve the salient structures and smooth the minor components in image gradients and restrict the blur kernel in a domain of dynamic group sparse vector. To illustrate the reliability of the proposed algorithm, we conduct experiments to analyze the properties of the regularizers and the convergence property of the proposed algorithm. Experiments with both quantitative and visual comparison further prove the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

High dynamic range (HDR) imaging technology is becoming increasingly popular in various applications. A common approach to get an HDR image is the multiple exposed images fusion. However, the phenomenon of ghosting artifacts is brought in for the scene with non-static objects. This paper proposes a ghost-free HDR image synthesis algorithm that utilizes a sparse representation framework. Based on the dependency among adjacent low dynamic range (LDR) images and the sparsity of the moving object that leads to the ghost artifacts, we formulate the problem into two steps: moving object detection and ghost free HDR generation. In the moving object detection step, we formulate the problem as sparse representation due to the sparsity and instantaneous of the moving objects. In the HDR generation step, joint weighting is proposed to generate a ghost-free HDR image from the reference image. Experiments show that the proposed algorithm outperforms the state-of-the-art methods favorably on the textures and colors. [ABSTRACT FROM AUTHOR]

• Proposed Semantic-aware Spatial Feature Modulation (SSFM) scheme to enforce category-specific depth distributions. • Proposed a semantic-guided ranking loss (SRL) to constrain depth to have consistent borders with the segmentations predictions. • Proposed the robust sampling strategy and prediction uncertainty weighting to alleviate semantic noise for depth estimation. • The proposed method outperforms the state-of-the-art methods by significant margins. Self-supervised monocular depth estimation has shown great success in learning depth using only images for supervision. In this paper, we propose to enhance self-supervised depth estimation with semantics and propose a novel learning scheme, which incorporates both implicit and explicit semantic guidances. Specifically, we propose to relate depth distributions to the semantic category information by proposing a Semantic-aware Spatial Feature Modulation (SSFM) scheme, which implicitly modulates the semantic and depth features in a joint learning framework. The modulation parameters are generated from semantic labels to acquire category-level guidance. Meanwhile, a semantic-guided ranking loss is proposed to explicitly constrain the estimated depth borders using the corresponding segmentation labels. To avoid the impact brought by erroneous segmentation labels, both robust sampling strategy and prediction uncertainty weighting are proposed for the ranking loss. Extensive experimental results show that our method produces high-quality depth maps with semantically consistent depth distributions and accurate depth edges, outperforming the state-of-the-art methods by significant margins. [ABSTRACT FROM AUTHOR]

• Proposed a Position-Aware Transformer (PAT) architecture that leverages the Adaptive Multi-Scale Position Encoding (AMSPE) to encode both global and local position information for better point feature representations. • Proposed a 3D Correlation-Guided Enhancement (CGE) network to alleviate the ambiguous or noisy representations in the 2D feature projection. • The proposed method outperforms the state-of-the-art methods in both quantitative and qualitative comparisons. Convolutional Occupancy Networks (ConvONet) have gained popularity in object-level and scene-level reconstruction. However, how to better represent the 3D features for ConvONet remains an open question. In this paper, we propose to improve the representation for ConvONet by enhancing both 3D positional information and 3D-2D correlations. Considering that position information acts as the fundamental component of a 3D shape, we propose a Position-Aware Transformer (PAT) architecture that incorporates the Adaptive Multi-Scale Position Encoding (AMSPE) into the self-attention computation. By leveraging both global and local position aggregations in a multi-level manner, AMSPE enables better representations of both coarse and fine structures of the 3D shape. Meanwhile, since projecting 3D features to 2D planes for convolution inevitably introduces ambiguous or noisy representations, we propose a 3D Correlation-Guided Enhancement (CGE) network to bridge the gap between 3D and 2D shape representations. Specifically, we leverage the projected 3D correlations from PAT as the structural guidance, then compute the 3D Correlation-Guided Attentions (CGAs) to enhance the most representative features in the 2D space. In this way, the proposed architecture preserves the most informative structural representations while alleviating the impact of the mis-projected and noisy features. Experiments on ShapeNet and indoor scene dataset demonstrate the superiority of our method. Both quantitative and qualitative experiments show that our method achieves state-of-the-art performance for implicit-based 3D reconstruction. [ABSTRACT FROM AUTHOR]

As a valuable biomarker for various tumor, sensitive and reliable quantitative determination of microRNA (miRNA) is crucial for both disease diagnosis and cancer treatment. Herein, we depict a novel simple and sensitive miRNA detection approach by exploiting an elegantly designed target recognition initiated self-dissociation based DNA nanomachine. In this nanomachine, target recognition assists the formation of active DNAzyme secondary conformation, and the active DNAzyme generates a nicking site in H probe, initiating the self-assembly of H probe. With the reflexed sequences as primer, dual signal recycles are formed under the cooperation of DNA polymerase and Nb.BbvCI. Eventually, the method exhibits a high sensitivity with the limit of detection as low as 12 fM. In addition, the method is also demonstrated with a high selectivity that can distinguish one mismatched base pair. We believe the established approach can be a robust tool for the early-diagnosis of a variety of cancers and also in anticancer drug development. [Display omitted] • We depict here a novel simple and sensitive miRNA detection approach. • The method was established on the basis of an elegantly designed switchable nanomachine. • Chain extension based dual signal recycles endows the method a high sensitivity. [ABSTRACT FROM AUTHOR]

• We explore to improve generalize ability of blind IQA models with limited training data • We propose to explicitly learn a distortion manifold • A novel model proposed to extract both low level distortion patterns and high level semantic information • An effective training framework including a masked labeling strategy and a gradual weighting curriculum Though current no-reference image quality assessment (NR-IQA) approaches have achieved impressive performance gain thanks to deep learning techniques, it is claimed that the risk of over-fitting exists. To improve model generalization ability, most of the current researches incorporate mass data to train or tune the data-driven models. However, the process of image data collection and quality label annotation is quite time-consuming and labour-intensive. Therefore, in this paper, we explore an alternative solution to promote model generalizability but with relatively small fractions of training data. Compared with previous approaches which make effort to approximate the whole complex image distribution, we propose to explicitly learn an image distortion manifold first, which lies in a much lower dimension space and also representative in capturing general degradation patterns. We then project the images to their perceived quality from the learned manifold to obtain quality predictions. Since the manifold embeds general distortion features despite of varying image contents, it can be learned with relatively small amount of samples. In order to learn the manifold and quality projection, we introduce a two-branched network to learn both low level distortions and high level semantics. We also propose a simple but efficient training framework, composing of a masked labelling strategy and a gradual weighting curriculum to fulfill the task. Thanks to the learned distortion manifold, the proposed model achieves superior generalizability compared with previous models. Extensive experiments demonstrate its effectiveness in terms of training with limited data, testing on large scale images, and with unseen types of distorted images. [ABSTRACT FROM AUTHOR]

• a novel network integrated image context and the corresponding gradient information. • contextual dilated network to capture contextual information. • designed a new fusion block. • built a new real-world HDR dataset. Obtaining a high dynamic range (HDR) image from multiple low dynamic range images with different exposures is an important step in various computer vision tasks. One of the ongoing challenges in the field is to generate HDR images without ghosting artifacts. Motivated by an observation that such artifacts are particularly noticeable in the gradient domain, in this paper, we propose an HDR imaging approach that aggregates the information from multiple LDR images with guidance from image gradient domain. The proposed method generates artifact-free images by integrating the image gradient information and the image context information in the pixel domain. The context information in a large area helps to reconstruct the contents contaminated by saturation and misalignments. Specifically, an additional gradient stream and the supervision in the gradient domain are applied to incorporate the gradient information in HDR imaging. To use the context information captured from a large area while preserving spatial resolution, we adopt dilated convolutions to extract multi-scale features with rich context information. Moreover, we build a new dataset containing 40 groups of real-world images from diverse scenes with ground truth to validate the proposed model. The samples in the proposed dataset include more challenging moving objects inducing misalignments. Extensive experimental results demonstrate that our proposed model outperforms previous methods on different datasets in terms of both quantitative measure and visual perception quality. [ABSTRACT FROM AUTHOR]

• A two-branch component divided guidance model is proposed for non-uniform deblurring. • An attention module adaptively divides the blur into large and small blurry regions. • An orientation-based fusion module to merge the two branches' de-blurred features. • Considering the guidance delivers a remarkable performance. • The parameters and computation of the model are smaller than other methods. Blind image deblurring is a fundamental and challenging computer vision problem, which aims to recover both the blur kernel and the latent sharp image from only a blurry observation. Despite the superiority of deep learning methods in image deblurring have displayed, there still exists a major challenge with various non-uniform motion blur. Previous methods simply take all the image features as the input to the decoder, which handles different degrees (e.g. large blur, small blur) simultaneously, leading to challenges for sharp image generation. To tackle the above problems, we present a deep two-branch network to deal with blurry images via a component divided module, which divides an image into two components based on the representation of blurry degree. Specifically, two component attentive blocks are employed to learn attention maps to exploit useful deblurring feature representations on both large and small blurry regions. Then, the blur-aware features are fed into two-branch reconstruction decoders respectively. In addition, a new feature fusion mechanism, orientation-based feature fusion, is proposed to merge sharp features of the two branches. Both qualitative and quantitative experimental results show that our method performs favorably against the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]