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With the wide application of visual sensors and development of digital image processing technology, image copy-move forgery detection (CMFD) has become more and more prevalent. Copy-move forgery is copying one or several areas of an image and pasting them into another part of the same image, and CMFD is an efficient means to expose this. There are improper uses of forged images in industry, the military, and daily life. In this paper, we present an efficient end-to-end deep learning approach for CMFD, using a span-partial structure and attention mechanism (SPA-Net). The SPA-Net extracts feature roughly using a pre-processing module and finely extracts deep feature maps using the span-partial structure and attention mechanism as a SPA-net feature extractor module. The span-partial structure is designed to reduce the redundant feature information, while the attention mechanism in the span-partial structure has the advantage of focusing on the tamper region and suppressing the original semantic information. To explore the correlation between high-dimension feature points, a deep feature matching module assists SPA-Net to locate the copy-move areas by computing the similarity of the feature map. A feature upsampling module is employed to upsample the features to their original size and produce a copy-move mask. Furthermore, the training strategy of SPA-Net without pretrained weights has a balance between copy-move and semantic features, and then the module can capture more features of copy-move forgery areas and reduce the confusion from semantic objects. In the experiment, we do not use pretrained weights or models from existing networks such as VGG16, which would bring the limitation of the network paying more attention to objects other than copy-move areas.To deal with this problem, we generated a SPANet-CMFD dataset by applying various processes to the benchmark images from SUN and COCO datasets, and we used existing copy-move forgery datasets, CMH, MICC-F220, MICC-F600, GRIP, Coverage, and parts of USCISI-CMFD, together with our generated SPANet-CMFD dataset, as the training set to train our model. In addition, the SPANet-CMFD dataset could play a big part in forgery detection, such as deepfakes. We employed the CASIA and CoMoFoD datasets as testing datasets to verify the performance of our proposed method. The Precision, Recall, and F1 are calculated to evaluate the CMFD results. Comparison results showed that our model achieved a satisfactory performance on both testing datasets and performed better than the existing methods. [ABSTRACT FROM AUTHOR]

Since individual neural networks have limited deep expressiveness and effectiveness, many learning frameworks face difficulties in the availability and balance of sample selection. As a result, in change detection, it is difficult to upgrade the hit rate of a high-performance model on both positive and negative pixels. Therefore, supposing that the sacrificed components coincide perfectly with the important evaluation objectives, such as positives, it would lose more than gain. To address this issue, in this paper, we propose a multi-visual collaborative deep network (MV-CDN) served by three collaborative network members that consists of three subdivision approaches, the CDN with one collaborator (CDN-C), CDN with two collaborators (CDN-2C), and CDN with three collaborators (CDN-3C). The purpose of the collaborator is to re-evaluate the feature elements in the network transmission, and thus to translate the group-thinking into a more robust field of vision. We use three sets of public double-temporal hyperspectral images taken by the AVIRIS and HYPERION sensors to show the feasibility of the proposed schema. The comparison results have confirmed that our proposed schema outperforms the existing state-of-the-art algorithms on the three tested datasets, which demonstrates the broad adaptability and progressiveness of the proposal. [ABSTRACT FROM AUTHOR]

The mainstream image matching method for recovering the motion of the camera is based on local feature matching, which faces the challenges of rotation, illumination, and the presence of dynamic objects. In addition, local feature matching relies on the distance between descriptors, which easily leads to lots of mismatches. In this paper, we propose a new robust image matching method for camera pose estimation, called IM_CPE. It is a novel descriptor matching method combined with 3-D point clouds for image matching. Specifically, we propose to extract feature points based on a pair of matched point cloud planes, which are generated and segmented based on depth images. Then, the feature points are matched based on the distance between their corresponding 3-D points on the point cloud planes and the distance between their descriptors. Moreover, the robustness of the matching can be guaranteed by the centroid distance of the matched point cloud planes. We evaluate the performance of IM_CPE using four well-known key point extraction algorithms, namely Scale-Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF), Features from Accelerated Segment Test (FAST), and Oriented FAST and Rotated Brief (ORB), with four sequences from the TUM RGBD dataset. According to the experimental results, compared to the original SIFT, SURF, FAST, and ORB algorithms, the NN_mAP performance of the four key point algorithms has been improved by 11.25%, 13.98%, 16.63%, and 10.53% on average, respectively, and the M.Score has also been improved by 25.15%, 23.05%, 22.28%, and 11.05% on average, respectively. The results show that the IM_CPE can be combined with the existing key points extraction algorithms and the IM_CPE can significantly improve the performance of these key points algorithms. [ABSTRACT FROM AUTHOR]

Summary: Erwinia amylovora, the causative agent of fire blight, uses flagella‐based motilities to translocate to host plant natural openings; however, little is known about how this bacterium migrates systemically in the apoplast. Here, we reveal a novel surface motility mechanism, defined as sliding, in E. amylovora. Deletion of flagella assembly genes did not affect this movement, whereas deletion of biosynthesis genes for the exopolysaccharides (EPSs) amylovoran and levan resulted in non‐sliding phenotypes. Since EPS production generates osmotic pressure that potentially powers sliding, we validated this mechanism by demonstrating that water potential positively contributes to sliding. In addition, no sliding was observed when the water potential of the surface was lower than −0.5 MPa. Sliding is a passive motility mechanism. We further show that the force of gravity plays a critical role in directing E. amylovora sliding on unconfined surfaces but has a negligible effect when cells are sliding in confined microcapillaries, in which EPS‐dependent osmotic pressure acts as the main force. Although amylovoran and levan are both required for sliding, we demonstrate that they exhibit different roles in bacterial communities. In summary, our study provides fundamental knowledge for a better understanding of mechanisms that drive bacterial sliding motility. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) could be affected by the regression level of learning frameworks and challenging changes caused by external factors; their deep expressiveness is greatly restricted. Inspired by the fine-tuned DNNs with sensitivity disparity to the pixels of two states, in this paper, we propose a novel change detection scheme served by sensitivity disparity networks (CD-SDN). The CD-SDN is proposed for detecting changes in bi-temporal hyper-spectral images captured by the AVIRIS sensor and HYPERION sensor over time. In the CD-SDN, two deep learning frameworks, unchanged sensitivity network (USNet) and changed sensitivity network (CSNet), are utilized as the dominant part for the generation of binary argument map (BAM) and high assurance map (HAM). Then two approaches, arithmetic mean and argument learning, are employed to re-estimate the changes of BAM. Finally, the detected results are merged with HAM and obtain the final detected binary change maps (BCMs). Experiments are performed on three real-world hyperspectral image datasets, and the results indicate the good universality and adaptability of the proposed scheme, as well as its superiority over other existing state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This article proposes an adaptive scheme for image tampered region localization and content recovery. To generate the watermark information comprised of the authentication data and recovery data, we firstly propose the Adaptive Authentication Feature Calculation algorithm to obtain the authentication data, which includes the information of block location and block feature. The DWT-based Block Feature Calculation method is then proposed to calculate the block feature, and the quantization method is employed to calculate the block location. The recovery data is composed of self-recovery bits and mapped-recovery bits. The self-recovery bits are obtained by the Set Partitioning in Hierarchical Trees encoding algorithm. For retrieving the damaged codes caused by tampering, we propose the Diagonal Mapping algorithm and apply it to the self-recovery bits, thus generating the mapped-recovery bits, to provide a guarantee of recovery data. Experimental results show the superior performance of the proposed scheme in terms of tamper detection and image recovery, by comparing with the state-of-the-art works. The results demonstrate that the proposed method shows efficiency in the adaptiveness, well localization, strong capability for image recovery, and the effectiveness of attack resistance. [ABSTRACT FROM AUTHOR]

In this paper, we construct CAMU-Net, an image forgery detection method, to obtain evidence of copy-move forgery areas in images. In CAMU-Net, the hierarchical feature extraction stage (HFE_Stage) is used to extract multi-scale key feature maps. Next, a hierarchical feature matching stage (HFM_Stage) based on self-correlation combined with a multi-scale structure is designed to predict copy-move forgery areas with different scales of information. To optimize the matching results, we design a coordinate attention-based resource allocation stage (CARA_Stage), which uses a location and channel attention mechanism to assign more weight to copy-move areas. In this way, useful information can be strengthened while irrelevant information is suppressed. To effectively use the multi-scale prediction results in the multi-scale feature fusion-based up-sampling stage (MFFU_Stage), we integrate the high-level and low-level information into one information flow. By combining the global feature information of the deep layers and the location details of the shallow layers, the performance of CMFD can be improved. To demonstrate the validity of our model, we compare it with a variety of traditional methods and deep learning methods. The results show that our performance is outstanding. In particular, on the COVERAGE dataset, our AUC is 87.3%, which is 2.4% higher than the second place. In addition, we design a variety of baseline methods to perform several ablation experiments to demonstrate the validity of the modules in this model. [ABSTRACT FROM AUTHOR]

Multiple correlated traits/phenotypes are often collected in genetic association studies and they may share a common genetic mechanism. Joint analysis of correlated phenotypes has well‐known advantages over one‐at‐a‐time analysis including gain in power and better understanding of genetic etiology. However, when the phenotypes are of discordant types such as binary and continuous, the joint modeling is more challenging. Another research area of current interest is discovery of rare genetic variants. Currently there is no method available for detecting association of rare (or common) haplotypes with multiple discordant phenotypes jointly. Our goal is to fill this gap specifically for two discordant phenotypes. We consider a rare haplotype association method for a binary phenotype, logistic Bayesian LASSO (univariate LBL) and its extension for two correlated binary phenotypes (bivariate LBL‐2B). Under this framework, we propose a haplotype association test with binary and continuous phenotypes jointly (bivariate LBL‐BC). Specifically, we use a latent variable to induce correlation between the two phenotypes. We carry out extensive simulations to investigate bivariate LBL‐BC and compare it with univariate LBL and bivariate LBL‐2B. In most settings, bivariate LBL‐BC performs the best. In only two situations, bivariate LBL‐BC has similar performance—when the two phenotypes are (1) weakly or not correlated and the target haplotype affects the binary phenotype only and (2) strongly positively correlated and the target haplotype affects both phenotypes in positive direction. Finally, we apply the method to a data set on lung cancer and nicotine dependence and detect several haplotypes including a rare one. [ABSTRACT FROM AUTHOR]

Pericytes facilitate vessel maturation and endothelial barrier functions closely related with the pathogenesis of organ damage from cardiovascular and cerebrovascular diseases such as hypertension. We used a computational-based strategy to first screen for differentially expressed genes and lncRNAs and characterized associations between lncRNAs of microvascular pericytes and hypertension. In total, 22 lncRNAs were upregulated and 46 were downregulated in the rats afflicted with spontaneous hypertension. Expression profiles for lncRNAs were significantly altered in the hypertension afflicted tissue samples and the transcripts have good potential for use as molecular targets to inhibit the progression of hypertension. [ABSTRACT FROM AUTHOR]

This paper proposes an audio multi-watermarking scheme based on Gram–Schmidt orthogonalization. A random signal generator is applied to the host audio signal to generate the target segment for the local watermarking, thus improving the imperceptibility compared with the conventional global watermarking. The discrete cosine transform low-frequency coefficients of high stability are selected as watermark embedder, to ensure the robustness of this scheme. Consequently, the Gram–Schmidt orthogonalization process is employed to generate a set of orthogonal vectors, into which the spread transform dither modulation is applied to, respectively, embed the multiple watermark messages simultaneously. The orthogonality ensures that multiple watermark messages can be independently extracted without compromising the robustness against attacks. By embedding the multiple watermark messages into the same segment of the host audio signal, the watermark embedding capacity can be greatly enhanced while preserving the imperceptibility and robustness. A variety of experiments are conducted, and the results indicate the good performance of the proposed scheme. The proposed scheme has demonstrated superior performance gains over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A local color image watermarking scheme using adaptive segmentation-based feature extraction (ASFE) and singular value decomposition-based spread transform dither modulation (S-STDM) is proposed in this paper. The proposed ASFE can adaptively extract feature regions from blocks segmented by the simple linear iterative clustering. In addition, the stationary wavelet transform is employed to decompose each of the extracted feature regions because of its shift invariance. Consequently, the novel S-STDM watermarking method is proposed, where we employ singular value decomposition to calculate the approximation coefficients and then select the generated diagonal elements from the decomposed diagonal matrix for watermark message embedding. The experimental results show that the proposed scheme is superior to the existing schemes under various attacks. [ABSTRACT FROM AUTHOR]

Despite the significant progress made in object detection algorithms, their potential to operate effectively under the low-light environment remains to be fully explored. Recent methods realize dark object detection on the entire representation of dark images; however, they do not further consider the potential entanglement between dark disturbance and discriminative information in dark images, and thus, the learned representation may be sub-optimal. Towards this issue, we propose supervised contrastive detection (SCDet), a novel unified framework to learn the potential composition of dark images, and decouple the discriminative component for facilitating dark object detection. Specifically, we introduce the dense decoupling contrastive (DDC) pretext task to investigate the feature consistency based on a dark transformation, allowing the learned representation to be independent of the potential entanglement to realize decoupling. Moreover, to further drive the decoupled representation to be discriminative instead of a collapse solution for dark object detection, we incorporate the supervision detection task as an extra optimization objective, resulting in the joint optimization pattern. The two tasks are complementary to each other: the DDC task regularizes the detection to learn more decoupling-friendly representation, while the supervision detection task guides the discriminative representation decoupling. As a result, the SCDet achieves dark object detection by decoding the decoupled discriminative representation of dark images. Extensive experiments on four datasets demonstrate the effectiveness of our method in both synthetic and real-world scenarios. Code is available at https://github.com/TxLin7/SCDet. [ABSTRACT FROM AUTHOR]

• This paper presents a novel frame for wheezing detection by highlighting the wheezing features for machine learning-based classifiers. • This paper proposes the Adaptive Multi-Level In-Exhale Segmentation (AMIE_SEG) to automatically and precisely segment the respiratory sounds into inspiratory and expiratory phases, thus to enhance the features of wheezing for classification. • This paper proposes the Enhanced Generalized S-Transform (EGST)to highlight the wheezing features, thus to improve the reliability of wheezing detection. • Three machine learning-based classifiers, Support Vector Machine (SVM), Extreme Learning Machine (ELM) and K-Nearest Neighbor (KNN), are employed to evaluate the novelty and superiority of the proposed method. Wheezing is a common symptom of patients caused by asthma and chronic obstructive pulmonary diseases. Wheezing detection identifies wheezing lung sounds and helps physicians in diagnosis, monitoring, and treatment of pulmonary diseases. Different from the traditional way to detect wheezing sounds using digital image process methods, automatic wheezing detection uses computerized tools or algorithms to objectively and accurately assess and evaluate lung sounds. We propose an innovative machine learning-based approach for wheezing detection. The phases of the respiratory sounds are separated automatically and the wheezing features are extracted accordingly to improve the classification accuracy. To enhance the features of wheezing for classification, the Adaptive Multi-Level In-Exhale Segmentation (AMIE_SEG) is proposed to automatically and precisely segment the respiratory sounds into inspiratory and expiratory phases. Furthermore, the Enhanced Generalized S-Transform (EGST) is proposed to extract the wheezing features. The highlighted features of wheezing improve the accuracy of wheezing detection with machine learning-based classifiers. To evaluate the novelty and superiority of the proposed AMIE_SEG and EGST for wheezing detection, we employ three machine learning-based classifiers, Support Vector Machine (SVM), Extreme Learning Machine (ELM) and K-Nearest Neighbor (KNN), with public datasets at segment level and record level respectively. According to the experimental results, the proposed method performs the best using the KNN classifier at segment level, with the measured accuracy, sensitivity, specificity as 98.62%, 95.9% and 99.3% in average respectively. On the other aspect, at record level, the three classifiers perform excellent, with the accuracy, sensitivity, specificity up to 99.52%, 100% and 99.27% respectively. We validate the method with public respiratory sounds dataset. The comparison results indicate the very good performance of the proposed methods for long-term wheezing monitoring and telemedicine. [ABSTRACT FROM AUTHOR]

Summary: Dickeya dadantii is a plant pathogen that causes soft rot disease on vegetable and potato crops. To successfully cause infection, this pathogen needs to coordinately modulate the expression of genes encoding several virulence determinants, including plant cell wall degrading enzymes (PCWDEs), type III secretion system (T3SS) and flagellar motility. Here, we uncover a novel feed‐forward signalling circuit for controlling virulence. Global RNA chaperone Hfq interacts with an Hfq‐dependent sRNA ArcZ and represses the translation of pecT, encoding a LysR‐type transcriptional regulator. We demonstrate that the ability of ArcZ to be processed to a 50 nt 3′‐ end fragment is essential for its regulation of pecT. PecT down‐regulates PCWDE and the T3SS by repressing the expression of a global post‐transcriptional regulator‐ (RsmA‐) associated sRNA encoding gene rsmB. In addition, we show that the protein levels of two cyclic di‐GMP (c‐di‐GMP) diguanylate cyclases (DGCs), GcpA and GcpL, are repressed by Hfq. Further studies show that both DGCs are essential for the Hfq‐mediated post‐transcriptional regulation on RsmB. Overall, our report provides new insights into the interplays between ubiquitous signalling transduction systems that were most studied independently and sheds light on multitiered regulatory mechanisms for a precise disease regulation in bacteria. [ABSTRACT FROM AUTHOR]

It has been previously reported that the blockade of interleukin-7 receptor (IL-7R) promotes functional recovery following spinal cord injury (SCI), however, the direct function and molecular mechanism of IL-7 involved in this pathogenic process are unclear. Here, we report that, contrary to IL-7R blockade, the intraspinal administration of IL-7 limits functional recovery following SCI. In addition, IL-7 treatment promotes neuronal apoptosis in spinal cord lesions, which may be attributed to exacerbated focal inflammatory response, as shown by increased accumulation of activated microglia/macrophage and production of proinflammatory mediators. Moreover, IL-7 treatment activates JAK/STAT5 pathway following SCI. At last, more importantly, the pharmacological inhibition of STAT5 abrogates the effects of IL-7 treatment on functional recovery, neuronal apoptosis and focal inflammatory response, suggesting that the effects of IL-7 treatment following SCI are dependent on activating the JAK/STAT5 pathway. Overall, this study reveals the JAK/STAT5 pathway-dependent detrimental role of IL-7 following SCI, and also implies that targeting the IL-7/JAK/STAT5 axis may represent a potential therapeutic approach for SCI treatment. • Intraspinal administration of IL-7 limits functional recovery following SCI. • Intraspinal administration of IL-7 promotes focal inflammatory response. • Intraspinal administration of IL-7 promotes neuronal apoptosis in spinal cord lesions. • Intraspinal administration of IL-7 activates JAK/STAT5 pathway following SCI. [ABSTRACT FROM AUTHOR]

This work was aimed to study skin blood perfusion, vasomotion and vascular responses of the Wistar-Kyoto rats (WKY) and spontaneously hypertensive rats (SHR) in different stages of age using spectral. Laser-Doppler flowmetry (LDF) was used to examine the ears and limbs of WKY (12 and 48 weeks old) and SHR (12 and 48 weeks old). The skin blood flow oscillations (SBFOs) were studied by wavelet spectral analysis of LDF tracings. Then, we observed that old groups showed decreased perfusion and SBFO in the ears of both SHR and WKY. The SHR showed obviously lower postocclusive reactive hypera (PORH) ratio at the same age. A decreased peak-time occurred in the SHR of old age group. After PORH test, a statistically significant increase was observed within all subintervals in the absolute amplitude of 12-week WKY and only within IV and III subintervals in the absolute amplitude of 12-week SHR. But, the absolute amplitude of 48-week WKY and SHR showed no statistically significant increase within all subintervals. Results indicated that local regulating function of peripheral vascular was impaired in rat with hypertension and aging. Abbreviations LDF: Laser-Doppler flowmetry; SBF: Skin blood flow; SBFO: Skin blood flow oscillation; PORH: Postocclusive reactive hyperemia; SHR: Spontaneously hypertensive rats; WKY: Wistar-Kyoto rats; LDF: Laser-Doppler flowmetry; LDI: Laser Doppler Imaging; BP: Blood pressure. [ABSTRACT FROM AUTHOR]

Abstract Background Spinal cord injury (SCI) disturbs the autonomic nervous system and induces dysfunction or failure of multiple organs. The systemic microcirculation disturbance that contributes to the complications associated with SCI remains to be clarified. Methods We used male mice (29–32 g) and modified weight-drop injury at T10 to evaluate the systemic microcirculation dysfunction during the first 2 weeks after SCI. We determined permeability and microvascular blood flow in several organs and evaluated their vasomotor function. We also measured circulating endothelial cells (CECs), circulating endothelial progenitor cells (CEPCs), circulating pericyte progenitor cells (CPPCs), and serum proinflammatory cytokines. Results The endothelial permeability of almost all organs increased after SCI. Microvascular blood flow decreased in the bladder and kidney and increased in the spleen and was accompanied by endothelial vasomotor dysfunction. SCI also induced an increase in CECs, CEPCs, and CPPCs in peripheral blood. Finally, we confirmed changes in a systemic cytokine profile (interleukin [IL]-3, IL-6, IL-10, IL-13, granulocyte colony-stimulating factor, and regulated on activation normal T cell expressed and secreted) after SCI. Conclusions These data indicate that a systemic microcirculation disturbance occurs after SCI. This information may play a key role in the development of effective therapeutic strategies for SCI. [ABSTRACT FROM AUTHOR]

This paper proposes a scheme for localization and restoration of image tampered regions using block labelling and adaptive significance. To generate the watermark information which includes authentication data and recovery data, we propose a block coordinate labelling method, which extracts the exact coordinate position information of each block, while the recovery data is composed of Block Adaptive Significances (BAS) and bitmaps, which are composed of high and low adaptive significance. To detect the tampered area more effectively, we propose a dual detection approach that combines the block-based labeling (BBL) and pixel-based labeling (PBL). We embed the authentication data into each pixel in the block sequentially and embed the position coordinate information of the block into the whole image in ascending order. The PBL approach can be used to rapidly complete tamper detection when the requirements for PBL are satisfied, whereas the BBL is used to increase the possibility of successfully detecting tampering if the conditions are not satisfied. Furthermore, we propose a block-level partially symmetric mapping and apply it to self-recovery bits in block units, thereby reducing the possibility of recovery bits being lost. The experimental results show that in our scheme, the average precision reaches 86.70%, which is 4% higher than the existing results, and the average F1score reaches 92.02%, which is 2% higher than the existing results. [ABSTRACT FROM AUTHOR]

Objective. Due to non-invasive imaging and the multimodality of magnetic resonance imaging (MRI) images, MRI-based multi-modal brain tumor segmentation (MBTS) studies have attracted more and more attention in recent years. With the great success of convolutional neural networks in various computer vision tasks, lots of MBTS models have been proposed to address the technical challenges of MBTS. However, the problem of limited data collection usually exists in MBTS tasks, making existing studies typically have difficulty in fully exploring the multi-modal MRI images to mine complementary information among different modalities. Approach. We propose a novel quaternion mutual learning strategy (QMLS), which consists of a voxel-wise lesion knowledge mutual learning mechanism (VLKML mechanism) and a quaternion multi-modal feature learning module (QMFL module). Specifically, the VLKML mechanism allows the networks to converge to a robust minimum so that aggressive data augmentation techniques can be applied to expand the limited data fully. In particular, the quaternion-valued QMFL module treats different modalities as components of quaternions to sufficiently learn complementary information among different modalities on the hypercomplex domain while significantly reducing the number of parameters by about 75%. Main results. Extensive experiments on the dataset BraTS 2020 and BraTS 2019 indicate that QMLS achieves superior results to current popular methods with less computational cost. Significance. We propose a novel algorithm for brain tumor segmentation task that achieves better performance with fewer parameters, which helps the clinical application of automatic brain tumor segmentation. [ABSTRACT FROM AUTHOR]

• Embedding multiple watermarks method based on inter-block correlation is proposed. • The texture complexity method is designed to adaptively select adjacent blocks. • Multiple watermarks are embedding in the same region of the image in parallel. • Two methods of adjacent block selection are proposed for image adaptive selection. • The watermark is embedded alternately in the middle frequency and low frequency. People pay more and more attention to copyright protection and digital watermarking technology is a reliable method for copyright identification. In this paper, we propose the parallel multiple watermarking method using adaptive inter-block correlation. Considering the image texture characteristics, the texture complexity method is designed, based on which, the Circular-shaped Adjacent Block Selection and the Arch-shaped Adjacent Block Selection will be adaptively selected for adjacent block selection. The multiple watermarks are alternately embedded into the low and middle frequency band in DCT domain of the image blocks to good imperceptibility and high robustness. By adjusting the difference of projection of embedding coefficients and reference coefficients on the spreading vector, the multiple watermarks can be embedded, so the embedding capacity increases. In addition, we improve the multiple watermarks embedding based on orthogonal spreading vectors so that it can be adapted to embedding multiple watermarks in parallel based on inter-block correlation, saving embedding and extraction time. We theoretically analyze the rationality of parallel orthogonal embedding of multiple watermarks and experimentally verify the high efficiency of parallelism. Cover images of different texture complexity are included in the test dataset, and various attacks have been simulated in the experiments. Experimental results on imperceptibility, robustness, time cost and watermark capacity show the satisfied performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

• Embedding multiple watermarks method based on inter-block correlation is proposed. • The texture complexity method is designed to adaptively select adjacent blocks. • Multiple watermarks are embedding in the same region of the image in parallel. • Two methods of adjacent block selection are proposed for image adaptive selection. • The watermark is embedded alternately in the middle frequency and low frequency. People pay more and more attention to copyright protection and digital watermarking technology is a reliable method for copyright identification. In this paper, we propose the parallel multiple watermarking method using adaptive inter-block correlation. Considering the image texture characteristics, the texture complexity method is designed, based on which, the Circular-shaped Adjacent Block Selection and the Arch-shaped Adjacent Block Selection will be adaptively selected for adjacent block selection. The multiple watermarks are alternately embedded into the low and middle frequency band in DCT domain of the image blocks to good imperceptibility and high robustness. By adjusting the difference of projection of embedding coefficients and reference coefficients on the spreading vector, the multiple watermarks can be embedded, so the embedding capacity increases. In addition, we improve the multiple watermarks embedding based on orthogonal spreading vectors so that it can be adapted to embedding multiple watermarks in parallel based on inter-block correlation, saving embedding and extraction time. We theoretically analyze the rationality of parallel orthogonal embedding of multiple watermarks and experimentally verify the high efficiency of parallelism. Cover images of different texture complexity are included in the test dataset, and various attacks have been simulated in the experiments. Experimental results on imperceptibility, robustness, time cost and watermark capacity show the satisfied performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

This study was conducted to examine the systemic metabolic strategies of grass carp to maintain lipid homeostasis when fed with low- or high-fat diets. The isonitrogenous diets with different fat levels (9.3, 48.7 and 107.9 g kg − 1 ) were fed to grass carp for 8 weeks. After the feeding trial, the growth rate and feed intake of grass carp fed with low-lipid (LL) diet or high-lipid (HL) diet were lower than fish fed with medium-lipid (ML) diet. Serum triglyceride (TG) and total body fat contents were significantly increased in grass carp with increasing lipid intake. Gene expression data indicated that fish increased hepatic gk and pk expressions to elevate glycolysis, and enhanced acc and fas expressions to accelerate biosynthesis of fatty acid (FA) to adapt to low lipid intake. Meanwhile, fish fed with LL diet decreased hepatic cpt1 expression to depress lipolysis, leading to low contents of serum TG and body fat. In contrast, excess lipid intake increased g6pase , pepck and pparα expressions to stimulate gluconeogenesis and β-oxidative, while decreased acc and fas expressions to reduce lipid synthesis in fish liver. Moreover, increased β-oxidative-induced FA or gluconeogenesis-induced serum glucose might induce the appetite suppression by high dietary fat through modulation of leptin expression. This study could be a reference in the systemic adaptation of lipid metabolism responding to dietary fat in fish. Statement of relevance Systemic adaptation of lipid metabolism responding to dietary lipid. [ABSTRACT FROM AUTHOR]

Most fish species possess duplicate leptin genes ( LEP ). Mandarin fish ( Siniperca chuatsi ) leptin A gene ( sLEP-A ) have been cloned in the previous study. In the present study, we cloned and characterized leptin B gene ( sLEP-B ) in mandarin fish, including a 471 bp open reading frame (ORF) encoding a 158-amino acid protein. The three-dimensional (3D) structural model of sLEP-B protein showed a highly conserved of tertiary structure similar to that of other vertebrates. Genomic sequencing results indicated that sLEP-B possessed only one intron. This is the first report of the loss of an intron in LEP-B in Perciformes. The different distribution patterns of sLEPs suggest different physiological roles of these two genes. The presence of HNF3β, a liver-enriched transcription factor, only in sLEP-A indicated abundant expression and metabolic function of sLEP-A in the liver. In an in vivo experiment, the expressions of brain sLEP-A and sLEP-B were observed to increase after a meal. During the short-term fasting, the expressions of sLEPs in mandarin fish brain were decreased significantly. A persistent and significant increase in hepatic sLEP-A expression supported a relationship between leptin and food intake in mandarin fish. These results suggest that sLEP-A plays an important role in the regulation of energy homeostasis in this carnivorous fish, and sLEP-B is probably a specialized gene responsible for the central nervous system (CNS) control of energy regulation. [ABSTRACT FROM AUTHOR]

Dickeya dadantii is a globally dispersed phytopathogen which causes diseases on a wide range of host plants. This pathogen utilizes the type III secretion system (T3SS) to suppress host defense responses, and secretes pectate lyase (Pel) to degrade the plant cell wall. Although the regulatory small RNA (sRNA) RsmB, cyclic diguanylate monophosphate (c-di-GMP) and flagellar regulator have been reported to affect the regulation of these two virulence factors or multiple cell behaviours such as motility and biofilm formation, the linkage between these regulatory components that coordinate the cell behaviours remain unclear. Here, we revealed a sophisticated regulatory network that connects the sRNA, c-di-GMP signalling and flagellar master regulator FlhDC. We propose multi-tiered regulatory mechanisms that link the FlhDC to the T3SS through three distinct pathways including the FlhDC-FliAYcgR3937 pathway; the FlhDC-EcpC-RpoN-HrpL pathway; and the FlhDC-rsmB-RsmA-HrpL pathway. Among these, EcpC is the most dominant factor for FlhDC to positively regulate T3SS expression. [ABSTRACT FROM AUTHOR]

Ghrelin and obestatin are two gastrointestinal peptides obtained by post-translational processing of a common precursor, preproghrelin. The effect of obestatin on food intake is still controversial. The aim of the present study was to investigate the effects of ghrelin and obestatin on food intake in grass carp, Ctenopharyngodon idellus . Fish received intraperitoneal (IP) injection of saline, ghrelin (100 ng g − 1 BW), obestatin-like (25 ng g − 1 BW) and ghrelin in combination with obestatin-like. Ghrelin stimulation of food intake varied considerably among individual fish with 70.8% eliciting a robust response. In these high-responders, food intake was significantly increased by IP ghrelin within 2 h. Co-administration of ghrelin and obestatin-like resulted in a decrease in food intake, indicating that obestatin was able to antagonize the effect of ghrelin. However, IP obestatin-like alone could not regulate food intake in grass carp. RT-PCR analysis demonstrated that IP ghrelin peptide led to a significant increase in mRNA abundance of NPY, Y8a and Y8b genes compared to saline injected fish, while in combination with obestatin-like peptide decreased ghrelin-induced gene expressions of these three genes. IP sole obestatin-like peptide did not modify the expression levels of NPY, Y8a, Y8b, CART and POMC compared to the control group. Therefore, IP administration of obestatin-like peptide, partially blocking the ghrelin-induced appetite, investigated the possible involvement of obestatin as a mediator of the ghrelin stimulatory action on food intake, at least in “high-responders” grass carp. [ABSTRACT FROM AUTHOR]

Abstract: Plant protein sources have been identified to have the greatest potential to replace fish meal protein in aquafeeds. However, plant ingredients contain significant quantities of carbohydrates, and the ability of fish to utilize dietary carbohydrates as energy sources for growth is limited. Included in the carbohydrate group, cellulose is not tolerated by most fish. In the present study, the grass carp, a typical herbivorous fish, fed with duckweed was selected to study the effect of exogenous cellulase on the growth. The results of 2-month feeding experiment showed that the cellulase promoted the growth of grass carp. In addition, the cellulase increased various digestive enzyme activities, such as cellulase, amylase and protease but not the lipase activity. Meanwhile, the polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE) analysis indicated that the intestinal microbiota of fish fed with the supplemental cellulase changed in bacteria species and density. Band patterns derived from control and cellulase samples showed a low degree of similarity when analyzed by cluster analysis. Some bands were unique to control samples, whereas other bands were obtained only with samples of the cellulase group. The 16S rRNA gene sequencing identified that Proteobacteria and Firmicutes were the two dominant groups, and the emergence of certain bacterial strains including Bacilli and Sphingomonas may contribute to the digestion of cellulose. The former researches and this paper results suggest that the endogenous cellulase is far from sufficient to fully digest the ingested fiber, so cellulase should be developed as a kind of aquatic additive. [Copyright &y& Elsevier]

Abstract: The R/L type pyruvate kinase (PKLR) gene was cloned from liver of grass carp (Ctenopharyngodon idella) by degenerate oligonucleotide primed PCR, including a complete open reading frame (ORF) of 1617bp. Homological protein analysis showed that the PKLR in grass carp shared a high degree of sequence identity with other known PK sequences. In addition, tissue distribution analysis showed that the highest expression of the PKLR was detected in liver. Then triplicate groups of grass carp (9 tanks) were fed twice daily to satiation with 91.8, 192.9, and 402.6gkg−1 carbohydrate diets for 8weeks, respectively. Dietary 402.6gkg−1 carbohydrate decreased growth performance, feed intake and feed utilization. With increasing dietary carbohydrates, the hepatic PK activity and mRNA expression of grass carp were up-regulated significantly. The results indicated that the PKLR play a modulation role in the adaptation of hepatic glycolysis to dietary high carbohydrates in grass carp. [Copyright &y& Elsevier]

Melatonin is a key factor in the coordination of circadian rhythms and seasonal reproduction. Melatonin and its metabolites directly scavenge free radicals, increase the expression of antioxidant enzymes, and play a role in the anti-inflammatory phase of defense responses. At present, there are no direct data available as to melatonin's possible influence on endothelial cell monolayer permeability, which is a major biological process responsible for vascular diseases. The aim of this study was to investigate the effect of melatonin on IL-1β-induced human umbilical vein endothelial cells (HUVECs) monolayer permeability and then to test the involvement of small GTPase Rac in the melatonin-induced endothelial barrier-protective effects as well as cell contact reorganization. It was found that IL-1β treatment increased the permeability of HUVECs monolayer, disrupted adherens junctions, and down-regulated the expression of VE-cadherin which is the main functional protein of adherens junctions. Melatonin, however, decreased dextran permeability and increased intercellular adherens junction areas reflecting an endothelial cell barrier-protective response. Furthermore, melatonin dramatically improved IL-1β-induced Rac inactivation. Our results show that the barrier-protective effects of melatonin on endothelial cells are mediated by Rac activation and leads to enhancement of adherens junctions. [ABSTRACT FROM AUTHOR]

Numerous alteration detection methods are designed based on image transformation algorithms and divergence of bi-temporal images. In the process of feature transformation, pseudo variant information caused by complex external factors will be highlighted. As a result, the error of divergence between the two images will be further enhanced. In this paper, we propose to fuse the variability of Deep Neural Networks' (DNNs) structure flexibly with various detection algorithms for bi-temporal multispectral/hyperspectral imagery alteration detection. Specifically, the novel Dual-path Partial Recurrent Networks (D-PRNs) was proposed to project more accurate and effective deep features. The Unsupervised Slow Feature Analysis (USFA), Iteratively Reweighted Multivariate Alteration Detection (IRMAD), and Principal Component Analysis (PCA) were then utilized, respectively, with the proposed D-PRNs, to generate two groups of transformed features corresponding to the bi-temporal remote sensing images. We next employed the Chi-square distance to compute the divergence between two groups of transformed features and, thus, obtain the Alteration Intensity Map. Finally, threshold algorithms K-means and Otsu were, respectively, applied to transform the Alteration Intensity Map into Binary Alteration Map. Experiments were conducted on two bi-temporal remote sensing image datasets, and the testing results proved that the proposed alteration detection model using D-PRNs outperformed the state-of-the-art alteration detection model. [ABSTRACT FROM AUTHOR]

Few-shot semantic segmentation aims to learn a generalized model for unseen-class segmentation with just a few densely annotated samples. Most current metric-based prototype learning models utilize prototypes to assist in query sample segmentation by directly utilizing support samples through Masked Average Pooling. However, these methods frequently fail to consider the semantic ambiguity of prototypes, the limitations in performance when dealing with extreme variations in objects, and the semantic similarities between different classes. In this paper, we introduce a novel network architecture named Prototype-guided Salient Attention Network (PSANet). Specifically, we employ prototype-guided attention to learn salient regions, allocating different attention weights to features at different spatial locations of the target to enhance the significance of salient regions within the prototype. In order to mitigate the impact of external distractor categories on the prototype, our proposed contrastive loss has the capability to acquire a more discriminative prototype to promote inter-class feature separation and intra-class feature compactness. Moreover, we suggest implementing a refinement operation for the multi-scale module in order to enhance the ability to capture complete contextual information regarding features at various scales. The effectiveness of our strategy is demonstrated by extensive tests performed on the PASCAL-5i\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\mathrm{PASCAL-5}^{i}$$\end{document} and COCO-20i\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\mathrm{COCO-20}^{i}$$\end{document} datasets, despite its inherent simplicity. Our code is available at https://github.com/woaixuexixuexi/PSANet. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Gauss–Jordan elimination-based image tampering detection and self-recovery scheme, aiming at dealing with the problem of malicious tampering on digital images. To deal with the copy–move tampering which is challenging because the tampered region may contain the watermark information, we propose the Improved Check Bits Generation algorithm during watermark generation, to generate the check bits for tampering detection. Meanwhile, the recovery bits are reconstructed according to the fundamental of Gauss–Jordan Elimination, for purpose of image contents self-recovery. To improve the accuracy of detection and the quality of recovered images, we propose the Morphological Processing-Based Enhancement method and the Edge Extension preprocessing respectively during and after the tampering detection Finally, the Gauss–JordanElimination-Based Self-Recovery method is proposed to recover the damaged content mathematically on basis of the detected results. By employing the unchanged recovery bits which are embedded in the non-tampered region, the failure in recovery caused by the damaged recovery bits can be completely avoided. A large number of experiments have been conducted to show the very good performance of the proposed scheme. The precision , recall , and F1 score are calculated for evaluation of tampering detection, while the PSNR values are calculated for evaluation of image recovery. The comparisons with the state-of-the-art methods show that the proposed scheme shows the superiorities in terms of imperceptibility, security and recovery capability. The experimental result indicates the average PSNR of recovered image is 44.415dB. • A novel Gauss–Jordan elimination-based scheme is proposed for image tamper detection. • The improved check bits generation algorithm is proposed to generate the check bits. • The morphological processing-based enhancement algorithm is proposed. • The edge extension preprocessing is proposed to improve accuracy of recovered region. • The Gauss–Jordan Elimination-Based Self-Recovery method is proposed for recovering. [ABSTRACT FROM AUTHOR]

Weakly Supervised Semantic Segmentation (WSSS) has become increasingly popular for achieving remarkable segmentation with only image-level labels. Current WSSS approaches extract Class Activation Mapping (CAM) from classification models to produce pseudo-masks for segmentation supervision. However, due to the gap between image-level supervised classification loss and pixel-level CAM generation tasks, the model tends to activate discriminative regions at the image level rather than pursuing pixel-level classification results. Moreover, insufficient supervision leads to unrestricted attention diffusion in the model, further introducing inter-class recognition noise. In this paper, we introduce a framework that employs Saliency Perception and Uncertainty, which includes a Saliency Perception Module (SPM) with Pixel-wise Transfer Loss (SP-PT), and an Uncertainty-guided Noise Suppression method. Specifically, within the SPM, we employ a hybrid attention mechanism to expand the receptive field of the module and enhance its ability to perceive salient object features. Meanwhile, a Pixel-wise Transfer Loss is designed to guide the attention diffusion of the classification model to non-discriminative regions at the pixel-level, thereby mitigating the bias of the model. To further enhance the robustness of CAM for obtaining more accurate pseudo-masks, we propose a noise suppression method based on uncertainty estimation, which applies a confidence matrix to the loss function to suppress the propagation of erroneous information and correct it, thus making the model more robust to noise. We conducted experiments on the PASCAL VOC 2012 and MS COCO 2014, and the experimental results demonstrate the effectiveness of our proposed framework. Code is available at https://github.com/pur-suit/SPU. [ABSTRACT FROM AUTHOR]

Fire blight, caused by the bacterial phytopathogen Erwinia amylovora, is an economically important and mechanistically complex disease that affects apple and pear production in most geographic production hubs worldwide. We compile, assess, and present a genetic outlook on the progression of an E. amylovora infection in the host. We discuss the key aspects of type III secretion–mediated infection and systemic movement, biofilm formation in xylem, and pathogen dispersal via ooze droplets, a concentrated suspension of bacteria and exopolysaccharide components. We present an overall outlook on the genetic elements contributing to E. amylovora pathogenesis, including an exploration of the impact of floral microbiomes on E. amylovora colonization, and summarize the current knowledge of host responses to an incursion and how this response stimulates further infection and systemic spread. We hope to facilitate the identification of new, unexplored areas of research in this pathosystem that can help identify evolutionarily susceptible genetic targets to ultimately aid in the design of sustainable strategies for fire blight disease mitigation. [ABSTRACT FROM AUTHOR]

Free metal particle can affect surface charge accumulation on epoxy insulator installed in gas-insulated transmission line (GIL) or gas-insulated switchgear (GIS), which results in reduction of surface flashover voltage thus threatening the safe operation of the power equipment. This article reports on the charge accumulation behavior on a real size epoxy insulator with bouncing metal particle under DC voltage. A coaxial electrode system was designed for the test under −30 kV, a metal particle-launching system was employed to inject the bouncing linear particle to the insulator surface. The charge distribution was measured by a Kelvin-type probe, and the influence of the particle on the charge accumulation was analyzed with the help of electric field simulation. The results showed that there were mainly two moving patterns of the bouncing particle: the charges were accumulated with speckle shape and the charged particle that hit the edge of electrode had more remarkable influence on the charge accumulation than other particles. It is suggested that the charge distribution feature is dependent upon the collision point and the trajectory of the bouncing particle. [ABSTRACT FROM AUTHOR]

As the most commonly used attack strategy by Botnets, the Domain Generation Algorithm (DGA) has strong invisibility and variability. Using deep learning models to detect different families of DGA domain names can improve the network defense ability against hackers. However, this task faces an extremely imbalanced sample size among different DGA categories, which leads to low classification accuracy for small sample categories and even classification failure for some categories. To address this issue, we introduce the long-tailed concept and augment the data of small sample categories by transferring pre-trained knowledge. Firstly, we propose the Data Balanced Review Method (DBRM) to reduce the sample size difference between the categories, thus a relatively balanced dataset for transfer learning is generated. Secondly, we propose the Knowledge Transfer Model (KTM) to enhance the knowledge of the small sample categories. KTM uses a multi-stage transfer to transfer weights from the big sample categories to the small sample categories. Furthermore, we propose the Knowledge Distillation Transfer Model (KDTM) to relieve the catastrophic forgetting problem caused by transfer learning, which adds knowledge distillation loss based on the KTM. The experimental results show that KDTM can significantly improve the classification performance of all categories, especially the small sample categories. It can achieve a state-of-the-art macro average F1 score of 84.5%. The robustness of the KDTM model is verified using three DGA datasets that follow the Pareto distributions. [ABSTRACT FROM AUTHOR]

Most current style transfer models are designed as encoder–decoder structures. Some encoding operations, such as downsampling and pooling, cause a loss of image details. If the encoder and decoder are not compatible, it can also introduce distortion. Reversible neural networks have demonstrated their superior power in lossless projection. However, since the inputs and outputs of neural flows are holistic features, merely the high-level features can be utilized for image generation through reverse inference. These high-level features emphasize the image style more, leading to the generated results easily losing content details and producing abstract colors. To address the above issues, we propose LSTFlow, the first progressive reversible neural network capable of feature decomposition. First, LSTFlow incorporates our proposed reversible Learnable Spectrum Transform (LST), which can dynamically decompose the feature into feature spectrum and recover them losslessly. LSTFlow can retain more details by enabling multi-level features to be fused in backward inference. Second, we propose a Progressive Flow Stylization Strategy (PFSS) to balance the model's emphasis between content and style and enhance the color perception. Forward inference based PFSS is carried out progressively, while the backward inference focuses on progressive generation. To demonstrate the effectiveness of our proposed method, we conducted comparative experiments with seven other state-of-the-art algorithms. The stylized effects are evaluated in terms of visual effects and quantitative indicators. The experiments show that the lightest LSTFlow performs the best in SSIM, Color Entropy, Color Uniformity and FID indicators and outperforms state-of-the-art methods. • Traditional flow-based models tend to lose details and introduce distortion in style transfer task. • A novel Learnable Spectrum Transform and a novel Progressive Flow Stylization Strategy. • Reversible neural networks can retain more details by fusing multi-layer features during backward inference. • The novel stylization strategy endows lightweight model with powerful color perception. • First reversible neural network based on a progressive structure with feature spectrum decomposition. [ABSTRACT FROM AUTHOR]

As typical herbivorous fish, grass carp possesses greater capability to utilize carbohydrate than carnivorous and omnivorous fish. However, mechanism of carbohydrate utilization in herbivorous fish was unclear. This study is intended to investigate response of grass carp to insufficient or excessive dietary carbohydrate. Grass carp accepted isonitrogenous diets with different carbohydrate levels (15.90, 21.81 and 45.45 g 100 g −1 ) were cultured. After 8 weeks, weight gain and feed intake were highest in fish fed with medium-carbohydrate diet compared with others. Gene expression data indicated that expressions of glucose-6-phosphatase (G6Pase) were down-regulated and gene expressions of glycogen synthase (GS) were up-regulated in fish accepted lower-carbohydrate diet. In high-carbohydrate group, glucose catabolism was increased by enhanced expressions of glucokinase genes (GK). Meanwhile, fatty acid synthase (FAS) and acetyl coenzyme-A carboxylase (ACC1) genes were significantly up-regulated and lipoprotein lipase and carnitine palmitoyltransferase 1 (CPT1) genes expressions were down-regulate, inducing more lipid deposition. Subsequently, serum cholesterol and total lipid contents were significantly increased, and then induced appetite suppression through up-regulation of leptin and anorexigenic genes expressions. Furthermore, depressed food intake caused lower growth rate in grass carp with excessive carbohydrate intake. [ABSTRACT FROM AUTHOR]

Plant diseases caused by bacterial pathogens place major constraints on crop production and cause significant annual losses on a global scale. The attainment of consistent effective management of these diseases can be extremely difficult, and management potential is often affected by grower reliance on highly disease-susceptible cultivars because of consumer preferences, and by environmental conditions favouring pathogen development. New and emerging bacterial disease problems (e.g. zebra chip of potato) and established problems in new geographical regions (e.g. bacterial canker of kiwifruit in New Zealand) grab the headlines, but the list of bacterial disease problems with few effective management options is long. The ever-increasing global human population requires the continued stable production of a safe food supply with greater yields because of the shrinking areas of arable land. One major facet in the maintenance of the sustainability of crop production systems with predictable yields involves the identification and deployment of sustainable disease management solutions for bacterial diseases. In addition, the identification of novel management tactics has also come to the fore because of the increasing evolution of resistance to existing bactericides. A number of central research foci, involving basic research to identify critical pathogen targets for control, novel methodologies and methods of delivery, are emerging that will provide a strong basis for bacterial disease management into the future. Near-term solutions are desperately needed. Are there replacement materials for existing bactericides that can provide effective disease management under field conditions? Experience should inform the future. With prior knowledge of bactericide resistance issues evolving in pathogens, how will this affect the deployment of newer compounds and biological controls? Knowledge is critical. A comprehensive understanding of bacterial pathosystems is required to not only identify optimal targets in the pathogens, but also optimal seasonal timings for deployment. Host resistance to effectors must be exploited, carefully and correctly. Are there other candidate genes that could be targeted in transgenic approaches? How can new technologies (CRISPR, TALEN, etc.) be most effectively used to add sustainable disease resistance to existing commercially desirable plant cultivars? We need an insider’s perspective on the management of systemic pathogens. In addition to host resistance or reduced sensitivity, are there other methods that can be used to target these pathogen groups? Biological systems are variable. Can biological control strategies be improved for bacterial disease management and be made more predictable in function? The answers to the research foci outlined above are not all available, as will become apparent in this article, but we are heading in the right direction. In this article, we summarize the contributions from past experiences in bacterial disease management, and also describe how advances in bacterial genetics, genomics and host–pathogen interactions are informing novel strategies in virulence inhibition and in host resistance. We also outline potential innovations that could be exploited as the pressures to maximize a safe and productive food supply continue to become more numerous and more complex. [ABSTRACT FROM AUTHOR]

Background: As observed in recent genetic studies, PITX2 is one of the most popular genes with atrial fibrillation; single nucleotide polymorphism (rs2200733) at chromosome 4q25 (near PITX2) is found to be strongly associated with atrial fibrillation, but it has a difference among Chinese Han population. The basic aim of conducting this study is to find the correlation between PITX2 gene polymorphism and the risk of atrial fibrillation and to identify the possibility for early diagnosis of silent atrial fibrillation and high-risk atrial fibrillation. Methods: The study included 98 cases of atrial fibrillation patients and 88 non-atrial fibrillation patients in Affiliated Hospital of Yangzhou University were enrolled in a casecontrol study. The single nucleotide polymorphism of rs2200733 at 4q25 near PITX2 was genotyped by polymerase chain reaction-restriction fragment length polymorphism analysis. Results: A total of 98 patients with atrial fibrillation were genotyped, and the following frequencies were included in genotype percentages (44.9%, 50%, and 5.1%) while distribution of significant single nucleotide polymorphism rs2200733 consisted (29.55%, 53.41%, and 17.05%) which showed (X2 = 9.159, P = .01). There was no significant difference in TC genotype frequency (P = .642), frequency of T allele (X2 = 7.447, P = .006), and T allele was 1.806 times that of the control group (odds ratio = 1.806, 95% CI = 1.179-2.766, P = .006). According to logistic regression analysis, following results were concluded for TC genotype (odds ratio = 3.128, 95% CI = 1.053-9.287, P = .04), or TT genotype (odds ratio = 5.077, 95% CI = 1.653-15.595, P = .005) increased the risk of atrial fibrillation. Conclusions: The genotype and allele frequency distribution of rs2200733 (T/C) near PITX2 is different in the atrial fibrillation group and the control group. The T allele is a risk factor for atrial fibrillation. Compared with the CC genotype, the TT genotype increased the risk of atrial fibrillation. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNNs) have developed rapidly in data privacy protection applications such as medical treatment and finance. However, DNNs require high-speed and high-memory computers in terms of computation, otherwise training can be very lengthy. Furthermore, DNNs are often not available in resource-constrained mobile devices. Therefore, training and executing DNNs are increasingly using cloud computing. In the paper, the Power Normalized Cepstrum-based Robust Feature Detector (PNC-RFD), with deep learning in the cloud computing, is proposed for data privacy protection. The proposed PNC-RFD extracts a specified number of signal segments of high robustness used to embed and extract various data. For the sake of embedding and extracting the data, a method of information hiding employing Dual-Tree Complex Wavelet Packet Transform (DT CWPT) is therefore presented. The presented scheme simultaneously embeds multiple data into coefficients of the DT CWPT of signal segments. By embedding the data into the orthogonal spaces, the proposed method ensures the independent extraction of the multiple data. In line with the performance analysis, the superiority of the presented scheme is elaborated through making the comparison with the current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Group-buying is an intriguing e-commerce model that aims to recruit more participants to generate sufficient orders to establish a low-price foundation. It appears to be a good model for both sellers and buyers; however, most online group-buying websites have failed. Many academics believe that reputation-sharing across multiple platforms is the way to solve the industry's dilemma. RS-Chain is a blockchain-based decentralized reputation-sharing framework, has been proposed in this paper. It is a hybrid blockchain comprising multiple public chains that store transaction ledgers and a consortium chain that stores reputation ledgers. It employs a trusted execution environment as a data verification component. We introduce the framework and describe the reputation operations. For the public chain, we propose proof of reputation (PoR), a new consensus protocol. The comparison experiment demonstrates that the PoR protocol outperforms the proof of work protocol in terms of fairness, orphan blocks, throughput, and latency. RS-Chain is the first reputation-sharing framework based on blockchain technology in the group-buying industry. It could be applied to similar reputation-sensitive industries. [ABSTRACT FROM AUTHOR]

Insulin receptors have been demonstrated to be involved in embryogenesis, food intake regulation and glucose metabolism in several fish, while more researchis needed for further understanding. In this study, the complete coding sequence (CDS) of insulin receptor a (insra) gene and insulin receptor b (insrb) gene in grass carp were obtained, the CDS were 4068 bp and 4514 bp in length, encoding 1355 aa protein and 1351 aa protein. Both of insra and insrb in grass carp showed high amino acid identities with other fish. Insra and insrb genes were widely expressed in all tested tissues with an overlapping but distinct expressions. The high levels of insra mRNA were distributed in hindgut and heart tissues. The insrb gene showed the highest expression levels in liver and hindgut. We also proved that two forms of grass carp insulin receptors participate in the regulation of blood glucose and might act differently. Phylogenetic analysis confirmed that different isoforms of fish insulin receptors are derived from two distinct genes, which was inconsistent with the generation of mammalian insulin receptors. Synteny analyses of insulin receptor genes showed that genes surrounding the insulin receptor genes were conserved in fish. Arhgef18, PEX11G, humanC19orf45 genes were highly conserved among mammal species. However, no conserved synteny was observed among fish, mammals, avians and amphibians. [ABSTRACT FROM AUTHOR]

Previous 3D encoder-decoder segmentation architectures struggled with fine-grained feature decomposition, resulting in unclear feature hierarchies when fused across layers. Furthermore, the blurred nature of contour boundaries in medical imaging limits the focus on high-frequency contour features. To address these challenges, we propose a Multi-oriented Hierarchical Extraction and Dual-frequency Decoupling Network (HEDN), which consists of three modules: Encoder-Decoder Module (E-DM), Multi-oriented Hierarchical Extraction Module (Multi-HEM), and Dual-frequency Decoupling Module (Dual-DM). The E-DM performs the basic encoding and decoding tasks, while Multi-HEM decomposes and fuses spatial and slice-level features in 3D, enriching the feature hierarchy by weighting them through 3D fusion. Dual-DM separates high-frequency features from the reconstructed network using self-supervision. Finally, the self-supervised high-frequency features separated by Dual-DM are inserted into the process following Multi-HEM, enhancing interactions and complementarities between contour features and hierarchical features, thereby mutually reinforcing both aspects. On the Synapse dataset, HEDN outperforms existing methods, boosting Dice Similarity Score (DSC) by 1.38% and decreasing 95% Hausdorff Distance (HD95) by 1.03 mm. Likewise, on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset, HEDN achieves  0.5% performance gains across all categories.Graphical abstract: Previous 3D encoder-decoder segmentation architectures struggled with fine-grained feature decomposition, resulting in unclear feature hierarchies when fused across layers. Furthermore, the blurred nature of contour boundaries in medical imaging limits the focus on high-frequency contour features. To address these challenges, we propose a Multi-oriented Hierarchical Extraction and Dual-frequency Decoupling Network (HEDN), which consists of three modules: Encoder-Decoder Module (E-DM), Multi-oriented Hierarchical Extraction Module (Multi-HEM), and Dual-frequency Decoupling Module (Dual-DM). The E-DM performs the basic encoding and decoding tasks, while Multi-HEM decomposes and fuses spatial and slice-level features in 3D, enriching the feature hierarchy by weighting them through 3D fusion. Dual-DM separates high-frequency features from the reconstructed network using self-supervision. Finally, the self-supervised high-frequency features separated by Dual-DM are inserted into the process following Multi-HEM, enhancing interactions and complementarities between contour features and hierarchical features, thereby mutually reinforcing both aspects. On the Synapse dataset, HEDN outperforms existing methods, boosting Dice Similarity Score (DSC) by 1.38% and decreasing 95% Hausdorff Distance (HD95) by 1.03 mm. Likewise, on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset, HEDN achieves  0.5% performance gains across all categories. [ABSTRACT FROM AUTHOR]

Reversible Adversarial Example (RAE) has been widely researched for its ability to ensure authorized access while preventing unauthorized recognition. Existing RAE schemes focus on Reversible Data Hiding techniques and white-box attacks. However, white-box attacks might be impractical due to the unknown parameters of the target model. Besides, these methods suffer massive loss during the embedding of perturbations, impacting the RAE's quality. In this paper, we propose I-RAE scheme to generate black-box RAE with minimal loss based on Invertible Neural Network (INN). Specifically, Black-box Attack Flow (BAFlow) is introduced to generate perturbations on a Gaussian distribution that are more easily embeddable. Furthermore, to enhance the embedding capability of RAE, we innovatively treat the embedding of perturbation as an image hiding and propose Perturbation Hiding Network (PHN) to reversibly hide the entire perturbation into the adversarial example. We also implement wavelet high-frequency hiding to reduce the degradation in the visual quality of RAE. Experimental results on the ImageNet and CIFAR-10 datasets demonstrate that I-RAE achieves state-of-the-art black-box attack ability and visual quality. • The proposed I-RAE scheme focuses on black-box Reversible Adversarial Example (RAE) generation. • The proposed I-RAE applies invertible neural network for generation. • The proposed I-RAE reduces the embedding loss during RAE generation. • The proposed I-RAE outperforms other RAE schemes. [ABSTRACT FROM AUTHOR]

Fine-grained single-label classification tasks aim to distinguish highly similar categories but often overlook inter-category relationships. Hierarchical multi-granularity visual classification strives to categorize image labels at various hierarchy levels, offering optimize label selection for people. This paper addresses the hierarchical multi-granularity classification problem from two perspectives: (1) effective utilization of labels at different levels and (2) efficient learning to distinguish multi-granularity visual features. To tackle these issues, we propose a novel multi-granularity hypergraph-guided transformer learning framework (MHTL), seamlessly integrating swin transformers and hypergraph neural networks for handling visual classification tasks. Firstly, we employ swin transformer as an image hierarchical feature learning (IHFL) module to capture hierarchical features. Secondly, a feature reassemble (FR) module is applied to rearrange features at different hierarchy levels, creating a spectrum of features from coarse to fine-grained. Thirdly, we propose a feature relationship mining (FRM) module, to unveil the correlation between features at different granularity. Within this module, we introduce a learnable hypergraph modeling method to construct coarse to fine-grained hypergraph structures. Simultaneously, multi-granularity hypergraph neural networks are employed to explore grouping relationships across different granularities, thereby enhancing the learning of semantic feature representations. Finally, we adopt a multi-granularity classifier (MC) to predict hierarchical label probabilities. Experimental results demonstrate that MHTL outperforms other state-of-the-art classification methods across three multi-granularity datasets. The source code and models are released at https://github.com/JJJTF/MHTL. [ABSTRACT FROM AUTHOR]

Abstract: Neuropeptide Y (NPY) has been established as an important regulator of food intake in mammals. However, the involvement of NPY in the feeding behavior of fish has not yet been fully understood. The present study investigated the role of NPY in food intake in grass carp. The full-length cDNA sequence of gc-NPY was 797bp, and an ORF of 96 amino acids, including the 28-residue signal peptide and 36-residue mature peptide, which had high identities to the NPY of cyprinid fishes. Two receptors Y1 and Y5 which mediate the appetite-stimulating effects of NPY in mammals lost from some of the euteleost genomes. However, sequences of other NPY receptors Y8a and Y8b were obtained from the ESTs library of grass carp in our previous studies. NPY and both Y8 receptors were predominantly expressed in the brain. Cumulative food intake was significantly increased by intracerebroventricular administration of NPY during a 72-h observation period. For better understanding of NPY''s action in grass carp, the mRNA levels of appetite regulators, energy metabolism genes and key digestive enzymes were assessed. The results showed that the mRNA levels of Y8b were significantly increased, while the expressions of cholecystokinin (CCK), cocaine and amphetamine regulated transcript (CART), pituitary adenylate cyclase-activating peptide (PACAP), leptin, leptin receptor (leptinR) and leptin receptor overlapping transcript protein (LEPROT) were significantly decreased after NPY injection. In addition, the marked stimulation of insulin-like growth factor1 (IGF1), insulin-like growth factor 1 receptor (IGF1R), lipoprotein lipase (LPL), Stearoyl-CoA desaturase 1 (SCD1), amylase and trypsin mRNA levels in liver were caused by NPY administration, and the mRNA level of uncoupling protein 1(UCP 1) tended to decrease but not significantly. These results indicate that NPY acts as an orexigenic factor in the grass carp, and Y8b may involve in the feeding regulation. NPY not only affects food intake, but also has marked effects on energy metabolism in grass carp. Moreover, NPY seems to closely interact with other appetite regulators, and the present results provide novel insight into these complex interactions. Further understandings of appetite regulation in fish ultimately have the potential to yield methods for improving feed efficiency in aquaculture. [Copyright &y& Elsevier]

Plant protein is the main substitution protein for fishmeal, especially rapeseed meal, and it contains anti‐nutritional factors and toxicants such as tannin. Few studies have identified clear physiological or toxicological impacts of tannin on fish. Here, our experiment evaluated the physiological and toxicological effects of tannin on grass carp (Ctenopharyngodon idellus). Different concentrations of dietary tannin (1.5, 7.2, 19.9, 39.4, 83.2 and 162.0 g kg−1 diet) were tested. Dietary tannin (≥7.2 g kg−1 diet) inhibited feed intake, and the appetite‐promoting gene expression (Grehlin, NPY, Y8a and Y8b), meanwhile, increased the appetite‐inhibiting gene expression (CCK and CART). Moreover, tannin addition reduced feed utilization (dietary nutrient retention, fat depot, activity of protease, lipase and amylase) of grass carp (p <.05). Besides, high dietary tannin (≥39.4 g kg−1 diet) lowered immunity (serum superoxide dismutase and catalase activities and the contents of total protein, albumin, complement component C3 and C4) (p <.05) and damaged the hepatic structure of grass carp, leading to attenuated growth in grass carp. This suggests that grass carp could not tolerate dietary tannin of >19.9 g kg−1. The second trial designed to test the alleviation the negative effect of tannin by tannase. After adding tannase with a dose of 1000 or 1500 U kg−1 to the diets, the weight gain of grass carp fed those diets was significantly increased compared with those fed the tannin and rapeseed meal addition groups (p <.05). Also, when high‐level tannase was supplemented to the control diet, weight gain significantly improved. The decrease in feed intake by high dietary tannin and rapeseed meal was relieved through adding tannase in diet of grass carp, especially when tannase supplemented level reached 1000 U kg−1 diet (p <.05). This study indicates that dietary tannin (≥7.2 g kg−1 diet) negatively affects the growth of grass carp through changing feed intake, feed utilization, immunity and hepatic structure, and addition of tannase reached 1000 U kg−1 diet almost alleviate adverse effects of tannin and rapeseed meal (which included high contents of tannin) on grass carp. [ABSTRACT FROM AUTHOR]

Surface charge accumulation on epoxy insulator installed in gas-insulated transmission line (GIL) and gas-insulated switchgear (GIS) has been recognized as a crucial factor for electric field distortion that results in the reduction of surface flashover voltage. This article reports on the gaseous ionization dependence of the charge accumulation pattern on a real-size epoxy insulator with multiarc surface profile by using various gas atmospheres. A coaxial electrode system was designed to investigate the charging behavior under dc voltages of −10, −20, and −30 kV. The insulator and electrodes were placed in a metal-enclosed test chamber, which was filled with 0.1 MPa air or 0.4 MPa SF6/N2 mixture to estimate the influence of the gas atmosphere on the charge accumulation behavior by a Kelvin-type probe. The accumulation mechanism and the electric field distortion induced by the charges were demonstrated based on numerical simulation. The results indicated that positive charges were accumulated in the shape of speckle on insulator surface in air, whereas both positive and negative charges were accumulated in SF6/N2 mixture and positive charge speckles appeared in SF6/N2 mixture only when the voltage raised up to −30 kV. It is considered that the charge accumulation behavior is determined by the competition result among the bulk, the gas and the surface conductions. As compared with the case in air, gaseous ionization in SF6/N2 mixture is more difficult to occur, which leads to the obvious difference in the charge accumulation pattern. [ABSTRACT FROM AUTHOR]

Surface charge accumulation on epoxy insulator installed in gas insulated switchgear (GIS) or gas insulated transmission line (GIL) has been recognized as an important factor for electric field distortion that results in reduction of surface flashover voltage. This paper reports on surface charge accumulation behavior on a actual epoxy insulator for 100 kV GIL prototype with multi-arc surface profile. A coaxial electrode system is used to investigate the charging behavior under DC voltages with metal particles positioned at various positions of the surface so as to estimate its influence on the charge accumulation behavior using a Kelvin type probe. The results show that positive charges are accumulated mainly on the nonplanar region around the high voltage electrode in the shape of speckles, and the average charge distribution along the radial direction presented a double-peak feature. Moreover, the charge accumulation is enhanced by metal particles contacting to the grounded electrode. Using a numerical simulation method, it is suggested that the charge accumulation is mainly determined by gaseous ionization which is enhanced by the metal particle induced electric field distortion. [ABSTRACT FROM AUTHOR]

Mandarin fish is a typical carnivorous fish, it can accept artificial diet after domestication, which is significantly essential to optimize its artificial diet. Nevertheless, only few studies were conducted on artificial diet of mandarin fish. Therefore, an 8‐week feeding trial was conducted to evaluate the influence of feeding stimulants on growth performance, feed intake and appetite regulation of mandarin fish. In this trial, six diets were formulated, a basal diet contained 80% of Fish Meal without feeding stimulants, named as Control, 0.4% inosinic acid meal, 0.4% L‐Alanine meal, 3% Yeast meal, 3% a commercial squid extract meal and 0.4% betaine meal were added into the basal diet to make five experimental diets, which were named as IM, AM, YM, SVO and BM respectively. At the end of feeding trial, SVO group showed higher feed intake, up‐regulated mRNA expression of neuropeptide Y (NPY) and down‐regulated mRNA expression of pro‐opiomelanocorticoid (POMC) significantly as compared to control group, which suggested that squid extract improved feed acceptability and promoted appetite in mandarin fish. Similarly, compared to control group, SVO group showed low food conversion ratio, high weight gain and SGR, indicated that squid extract enhanced the growth performance. Our results suggested that the appropriate level of squid extract addition could contribute to optimize artificial diet in mandarin fish. [ABSTRACT FROM AUTHOR]