Circular RNAs are non-coding RNAs, and are enriched in the CNS. Dorsal horn neurons of the spinal cord contribute to pain-like hypersensitivity after nerve injury in rodents. Here we show that spinal ...nerve ligation is associated with an increase in expression of circAnks1a in dorsal horn neurons, in both the cytoplasm and the nucleus. Downregulation of circAnks1a by siRNA attenuates pain-like behaviour induced by nerve injury. In the cytoplasm, we show that circAnks1a promotes the interaction between transcription factor YBX1 and transportin-1, thus facilitating the nucleus translocation of YBX1. In the nucleus, circAnks1a binds directly to the Vegfb promoter, increases YBX1 recruitment to the Vegfb promoter, thereby facilitating transcription. Furthermore, cytoplasmic circAnks1a acts as a miRNA sponge in miR-324-3p-mediated posttranscriptional regulation of VEGFB expression. The upregulation of VEGFB contributes to increased excitability of dorsal horn neurons and pain behaviour induced by nerve injury. We propose that circAnks1a and VEGFB are regulators of neuropathic pain.
As an interesting and emerging topic, cosaliency detection aims at simultaneously extracting common salient objects in multiple related images. It differs from the conventional saliency detection ...paradigm in which saliency detection for each image is determined one by one independently without taking advantage of the homogeneity in the data pool of multiple related images. In this paper, we propose a novel cosaliency detection approach using deep learning models. Two new concepts, called intrasaliency prior transfer and deep intersaliency mining, are introduced and explored in the proposed work. For the intrasaliency prior transfer, we build a stacked denoising autoencoder (SDAE) to learn the saliency prior knowledge from auxiliary annotated data sets and then transfer the learned knowledge to estimate the intrasaliency for each image in cosaliency data sets. For the deep intersaliency mining, we formulate it by using the deep reconstruction residual obtained in the highest hidden layer of a self-trained SDAE. The obtained deep intersaliency can extract more intrinsic and general hidden patterns to discover the homogeneity of cosalient objects in terms of some higher level concepts. Finally, the cosaliency maps are generated by weighted integration of the proposed intrasaliency prior, deep intersaliency, and traditional shallow intersaliency. Comprehensive experiments over diverse publicly available benchmark data sets demonstrate consistent performance gains of the proposed method over the state-of-the-art cosaliency detection methods.
Conventional zero-shot learning approaches often suffer from severe performance degradation in the generalized zero-shot learning (GZSL) scenario, i.e., to recognize test images that are from both ...seen and unseen classes. This paper studies the Class-level Over-fitting (CO) and empirically shows its effects to GZSL. We then address ZSL as a triple verification problem and propose a unified optimization of regression and compatibility functions, i.e., two main streams of existing ZSL approaches. The complementary losses mutually regularizes the same model to mitigate the CO problem. Furthermore, we implement a deep extension paradigm to linear models and significantly outperform state-of-the-art methods in both GZSL and ZSL scenarios on the four standard benchmarks.
Abstract
We investigated the role of nuclear factor erythroid 2–related factor 2 (Nrf2) in renin-angiotensin system (RAS) gene expression in renal proximal tubule cells (RPTCs) and in the development ...of systemic hypertension and kidney injury in diabetic Akita mice. We used adult male Akita Nrf2 knockout mice and Akita mice treated with trigonelline (an Nrf2 inhibitor) or oltipraz (an Nrf2 activator). We also examined rat immortalized RPTCs (IRPTCs) stably transfected with control plasmids or plasmids containing rat angiotensinogen (Agt), angiotensin-converting enzyme (ACE), angiotensin-converting enzyme-2 (Ace2), or angiotensin 1-7 (Ang 1-7) receptor (MasR) gene promoters. Genetic deletion of Nrf2 or pharmacological inhibition of Nrf2 in Akita mice attenuated hypertension, renal injury, tubulointerstitial fibrosis, and the urinary albumin/creatinine ratio. Furthermore, loss of Nrf2 upregulated RPTC Ace2 and MasR expression, increased urinary Ang 1-7 levels, and downregulated expression of Agt, ACE, and profibrotic genes in Akita mice. In cultured IRPTCs, Nrf2 small interfering RNA transfection or trigonelline treatment prevented high glucose stimulation of Nrf2 nuclear translocation, Agt, and ACE transcription with augmentation of Ace2 and MasR transcription, which was reversed by oltipraz. These data identify a mechanism, Nrf2-mediated stimulation of intrarenal RAS gene expression, by which chronic hyperglycemia induces hypertension and renal injury in diabetes.
New insights into Nrf2 stimulation of intrarenal RAS activation and hypertension development in diabetes.
SUMMARY
Gametophytic self‐incompatibility (GSI) has been widely studied in flowering plants, but studies of the mechanisms underlying pollen tube growth arrest by self S‐RNase in GSI species are ...limited. In the present study, two leucine‐rich repeat extensin genes in pear (Pyrus bretschneideri), PbLRXA2.1 and PbLRXA2.2, were identified based on transcriptome and quantitative real‐time PCR analyses. The expression levels of these two LRX genes were significantly higher in the pollen grains and pollen tubes of the self‐compatible cultivar ‘Jinzhui’ (harboring a spontaneous bud mutation) than in those of the self‐incompatible cultivar ‘Yali’. Both PbLRXA2.1 and PbLRXA2.2 stimulated pollen tube growth and attenuated the inhibitory effects of self S‐RNase on pollen tube growth by stabilizing the actin cytoskeleton and enhancing cell wall integrity. These results indicate that abnormal expression of PbLRXA2.1 and PbLRXA2.2 is involved in the loss of self‐incompatibility in ‘Jinzhui’. The PbLRXA2.1 and PbLRXA2.2 promoters were directly bound by the ABRE‐binding factor PbABF.D.2. Knockdown of PbABF.D.2 decreased PbLRXA2.1 and PbLRXA2.2 expression and inhibited pollen tube growth. Notably, the expression of PbLRXA2.1, PbLRXA2.2, and PbABF.D.2 was repressed by self S‐RNase, suggesting that self S‐RNase can arrest pollen tube growth by restricting the PbABF.D.2–PbLRXA2.1/PbLRXA2.2 signal cascade. These results provide novel insight into pollen tube growth arrest by self S‐RNase.
Significance Statement
PbLRXA2.1 and PbLRX.A2.2 mediate pollen tube growth and attenuate the inhibitory effects of self S‐RNase on pollen tube growth by stabilizing the actin cytoskeleton and enhancing cell wall integrity. Self S‐RNase can arrest pollen tube growth by restricting the PbABF.D.2–PbLRXA2.1/PbLRXA2.2 signal cascade.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance ...in the projected Hamming space. Recently, deep learning-based methods have become more popular, and outperform traditional non-deep methods. However, without label information, most state-of-the-art unsupervised deep hashing (DH) algorithms suffer from severe performance degradation for unsupervised scenarios. One of the main reasons is that the ad-hoc encoding process cannot properly capture the visual feature distribution. In this paper, we propose a novel unsupervised framework that has two main contributions: 1) we convert the unsupervised DH model into supervised by discovering pseudo labels; 2) the framework unifies likelihood maximization, mutual information maximization, and quantization error minimization so that the pseudo labels can maximumly preserve the distribution of visual features. Extensive experiments on three popular data sets demonstrate the advantages of the proposed method, which leads to significant performance improvement over the state-of-the-art unsupervised hashing algorithms.
Human action recognition is an important yet challenging task. This paper presents a low-cost descriptor called 3D histograms of texture (3DHoTs) to extract discriminant features from a sequence of ...depth maps. 3DHoTs are derived from projecting depth frames onto three orthogonal Cartesian planes, i.e., the frontal, side, and top planes, and thus compactly characterize the salient information of a specific action, on which texture features are calculated to represent the action. Besides this fast feature descriptor, a new multi-class boosting classifier (MBC) is also proposed to efficiently exploit different kinds of features in a unified framework for action classification. Compared with the existing boosting frameworks, we add a new multi-class constraint into the objective function, which helps to maintain a better margin distribution by maximizing the mean of margin, whereas still minimizing the variance of margin. Experiments on the MSRAction3D, MSRGesture3D, MSRActivity3D, and UTD-MHAD data sets demonstrate that the proposed system combining 3DHoTs and MBC is superior to the state of the art.
•A Deep Transductive Network is proposed to solve the domain shift problem of ZSL.•KL Divergence is exploited as the soft assignment for unlabeled unseen data with an auxiliary target ...distribution.•New experimental paradigm is demonstrated to solve the meaningless assumption for transductive GZSL.•Extensive experiments show the superiority of the proposed algorithm.
Zero Shot Learning (ZSL) aims to learn projective functions on labeled seen data and transfer the learned functions to unseen classes by discovering their relationship with semantic embeddings. However, the mapping process often suffers from the domain shift problem caused by only using the labeled seen data. In this paper, we propose a novel explainable Deep Transductive Network (DTN) for the task of Generalized ZSL (GZSL) by training on both labeled seen data and unlabeled unseen data, with subsequent testing on both seen classes and unseen classes. The proposed network exploits a KL Divergence constraint to iteratively refine the probability of classifying unlabeled instances by learning from their high confidence assignments with the assistance of an auxiliary target distribution. Besides, to avoid the meaningless ascription assumption of unseen data on GZSL, we also propose an experimental paradigm by splitting the unseen data into two equivalent parts for training and testing respectively. Extensive experiments and detailed analysis demonstrate that our DTN can efficiently handle the problems and achieve the state-of-the-art performance on four popular datasets.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
High-level image representations have drawn increasing attention in visual recognition, e.g., scene classification, since the invention of the object bank. The object bank represents an image as a ...response map of a large number of pretrained object detectors and has achieved superior performance for visual recognition. In this paper, based on the object bank representation, we propose the object-to-class (O2C) distances to model scene images. In particular, four variants of O2C distances are presented, and with the O2C distances, we can represent the images using the object bank by lower-dimensional but more discriminative spaces, called distance spaces, which are spanned by the O2C distances. Due to the explicit computation of O2C distances based on the object bank, the obtained representations can possess more semantic meanings. To combine the discriminant ability of the O2C distances to all scene classes, we further propose to kernalize the distance representation for the final classification. We have conducted extensive experiments on four benchmark data sets, UIUC-Sports, Scene-15, MIT Indoor, and Caltech-101, which demonstrate that the proposed approaches can significantly improve the original object bank approach and achieve the state-of-the-art performance.
Ethylene response factor (ERF) has been widely studied in regulating fruit ripening in tomato, apple, banana and kiwifruit, but little is known in pear. In this study 1‐methylcyclopropene (1‐MCP) ...treatment, an inhibitor of ethylene perception, was conducted at approximately 30 days before harvest to delay fruit ripening in a climacteric white pear cultivar Yali. Transcriptome libraries were constructed and sequenced in pre‐ripening, ripening, and 1‐MCP treated fruits. Data analysis showed that 73 candidate genes related to fruit ripening were induced by 1‐MCP, among which two were positively related, namely 1‐aminocyclopropane‐1‐carboxyla oxidase and an ERF gene (designated as ACO54 and ERF24). Transient transformations in pear fruit revealed that over‐expression of ACO54 enhance transcription level of ERF24 and most ripening‐related genes. Meanwhile, over‐expression of ERF24 raises expression level of ACO54 and partially ripening‐related genes. Moreover, dual‐luciferase and yeast‐one‐hybrid assays unravel an interaction between ERF24 and the ACO54 promoter. Therefore, the ERF24 could directly regulate ACO54 expression by binding to its promoter. These results suggested that the first identified ERF24 is involved in regulating fruit ripening in Chinese white pear.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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