Two polycatenated metal–organic framework materials with different metal centers (Zn2+ for 1 and Cd2+ for 2) were synthesized, which underwent pronounced framework phase transition upon desolvation, ...and exhibited uncommon temperature-dependent stepwise adsorption and hysteretic desorption of CO2 around room temperature.
Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that aims to obtain a high-resolution output from one of its low-resolution versions. Recently, powerful deep ...learning algorithms have been applied to SISR and have achieved state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods and group them into two categories according to their contributions to two essential aspects of SISR: The exploration of efficient neural network architectures for SISR and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is first established, and several critical limitations of the baseline are summarized. Then, representative works on overcoming these limitations are presented based on their original content, as well as our critical exposition and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally, we conclude this review with some current challenges and future trends in SISR that leverage deep learning algorithms.
The suppression of types I and III interferon (IFN) responses by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) contributes to the pathogenesis of coronavirus disease 2019 (COVID‐19). ...The strategy used by SARS‐CoV‐2 to evade antiviral immunity needs further investigation. Here, we reported that SARS‐CoV‐2 ORF9b inhibited types I and III IFN production by targeting multiple molecules of innate antiviral signaling pathways. SARS‐CoV‐2 ORF9b impaired the induction of types I and III IFNs by Sendai virus and poly (I:C). SARS‐CoV‐2 ORF9b inhibited the activation of types I and III IFNs induced by the components of cytosolic dsRNA‐sensing pathways of RIG‐I/MDA5‐MAVS signaling, including RIG‐I, MDA‐5, MAVS, TBK1, and IKKε, rather than IRF3‐5D, which is the active form of IRF3. SARS‐CoV‐2 ORF9b also suppressed the induction of types I and III IFNs by TRIF and STING, which are the adaptor protein of the endosome RNA‐sensing pathway of TLR3‐TRIF signaling and the adaptor protein of the cytosolic DNA‐sensing pathway of cGAS–STING signaling, respectively. A mechanistic analysis revealed that the SARS‐CoV‐2 ORF9b protein interacted with RIG‐I, MDA‐5, MAVS, TRIF, STING, and TBK1 and impeded the phosphorylation and nuclear translocation of IRF3. In addition, SARS‐CoV‐2 ORF9b facilitated the replication of the vesicular stomatitis virus. Therefore, the results showed that SARS‐CoV‐2 ORF9b negatively regulates antiviral immunity and thus facilitates viral replication. This study contributes to our understanding of the molecular mechanism through which SARS‐CoV‐2 impairs antiviral immunity and provides an essential clue to the pathogenesis of COVID‐19.
MicroRNAs (miRNAs) are known to be involved in carcinogenesis and tumor progression in hepatocellular carcinoma (HCC). Recently, microRNA‐7 (miR‐7) has been proven to play a substantial role in ...glioblastoma and breast cancer, but its functions in the context of HCC remain unknown. Here, we demonstrate that miR‐7 inhibits HCC cell growth and metastasis invitro and in vivo. We first screened and identified a novel miR‐7 target, phosphoinositide 3‐kinase catalytic subunit delta (PIK3CD). Overexpression of miR‐7 would specifically and markedly down‐regulate its expression. miR‐7‐overexpressing subclones showed significant cell growth inhibition by G0/G1‐phase cell‐cycle arrest and significant impairment of cell migration in vitro. To identify the mechanisms, we investigated the phosphoinositide 3‐kinase (PI3K)/Akt pathway and found that Akt, mammalian target of rapamycin (mTOR), and p70S6K were down‐regulated, whereas 4EBP1 was up‐regulated in miR‐7‐overexpressing subclones. We also identified two novel, putative miR‐7 target genes, mTOR and p70S6K, which further suggests that miR‐7 may be a key regulator of the PI3K/Akt pathway. In xenograft animal experiments, we found that overexpressed miR‐7 effectively repressed tumor growth (3.5‐fold decrease in mean tumor volume; n = 5) and abolished extrahepatic migration from liver to lung in a nude mouse model of metastasis (n = 5). The number of visible nodules on the lung surface was reduced by 32‐fold. A correlation between miR‐7 and PIK3CD expression was also confirmed in clinical samples of HCC. Conclusion: These findings indicate that miR‐7 functions as a tumor suppressor and plays a substantial role in inhibiting the tumorigenesis and reversing the metastasis of HCC through the PI3K/Akt/mTOR‐signaling pathway in vitro and in vivo. By targeting PIK3CD, mTOR, and p70S6K, miR‐7 efficiently regulates the PI3K/Akt pathway. Given these results, miR‐7 may be a potential therapeutic or diagnostic/prognostic target for treating HCC. (HEPATOLOGY 2012;55:1852–1862)
Few-shot learning for fine-grained image classification has gained recent attention in computer vision. Among the approaches for few-shot learning, due to the simplicity and effectiveness, ...metric-based methods are favorably state-of-the-art on many tasks. Most of the metric-based methods assume a single similarity measure and thus obtain a single feature space. However, if samples can simultaneously be well classified via two distinct similarity measures, the samples within a class can distribute more compactly in a smaller feature space, producing more discriminative feature maps. Motivated by this, we propose a so-called Bi-Similarity Network ( BSNet ) that consists of a single embedding module and a bi-similarity module of two similarity measures. After the support images and the query images pass through the convolution-based embedding module, the bi-similarity module learns feature maps according to two similarity measures of diverse characteristics. In this way, the model is enabled to learn more discriminative and less similarity-biased features from few shots of fine-grained images, such that the model generalization ability can be significantly improved. Through extensive experiments by slightly modifying established metric/similarity based networks, we show that the proposed approach produces a substantial improvement on several fine-grained image benchmark datasets. Codes are available at: https://github.com/PRIS-CV/BSNet .
Spectral or spatial dictionary has been widely used in fusing low-spatial-resolution hyperspectral (LH) images and high-spatial-resolution multispectral (HM) images. However, only using spectral ...dictionary is insufficient for preserving spatial information, and vice versa. To address this problem, a new LH and HM image fusion method termed OTD using optimized twin dictionaries is proposed in this paper. The fusion problem of OTD is formulated analytically in the framework of sparse representation, as an optimization of twin spectral-spatial dictionaries and their corresponding sparse coefficients. More specifically, the spectral dictionary representing the generalized spectrums and its spectral sparse coefficients are optimized by utilizing the observed LH and HM images in the spectral domain; and the spatial dictionary representing the spatial information and its spatial sparse coefficients are optimized by modeling the rest of high-frequency information in the spatial domain. In addition, without non-negative constraints, the alternating direction methods of multipliers (ADMM) are employed to implement the above optimization process. Comparison results with the related state-of-the-art fusion methods on various datasets demonstrate that our proposed OTD method achieves a better fusion performance in both spatial and spectral domains.
•Propose a new metric, the adjusted IR, to better measure the class imbalance extent.•Study the effect of dimensionality on the classification performance of imbalanced data.•Demonstrate the ...effectiveness of the adjusted IR in both simulations and real-data experiments.
Class-imbalance extent metrics measure how imbalanced the data are. In pattern classification, it is usually expected that the higher the imbalance extent, the worse the classification performance, and thus an appropriate imbalance extent metric should show a negative correlation with the classification performance. Existing metrics, such as the popular imbalance ratio (IR), only consider the effect of the sample sizes of different classes. However, we note that the dimensionality of imbalanced data also affects the classification performance. Datasets with the same IR can present distinct classification performances when their dimensionalities are different, making IR suboptimal to reflect the imbalance extent for classification. We also observe that the classification performance becomes better with more discriminative features. Inspired by these observations, we propose a new imbalance extent metric, the adjusted IR, by adding a penalty term of the number of discriminative features that is effectively determined by the Pearson correlation test. The adjusted IR adaptively revises the IR when the number of discriminative features varies. The empirical studies demonstrate the effectiveness of the adjusted IR, in terms of its better negative correlation with the classification performance.
An iron oxychloride (FeOCl) catalyst was developed for oxidative degradation of persistent organic compounds in aqueous solutions. Exceptionally high activity for the production of hydroxyl radical ...(OH·) by H2O2 decomposition was achieved, being 2–4 orders of magnitudes greater than that over other Fe-based heterogeneous catalysts. The relationship of catalyst structure and performance has been established by using multitechniques, such as XRD, HRTEM, and EPR. The unique structural configuration of iron atoms and the reducible electronic properties of FeOCl are responsible for the excellent activity. This study paves the way toward the rational design of relevant catalysts for applications, such as wastewater treatment, soil remediation, and other emerging environmental problems.
In recent years, deep learning-based person re-identification (Re-ID) methods have made significant progress. However, the performance of these methods substantially decreases when dealing with ...occlusion, which is ubiquitous in realistic scenarios. In this article, we propose a novel semantic-aware occlusion-robust network (SORN) that effectively exploits the intrinsic relationship between the tasks of person Re-ID and semantic segmentation for occluded person Re-ID. Specifically, the SORN is composed of three branches, including a local branch, a global branch, and a semantic branch. In particular, the local branch extracts part-based local features, and the global branch leverages a novel spatial-patch contrastive loss (SPC) to extract occlusion-robust global features. Meanwhile, the semantic branch generates a foreground-background mask for a pedestrian image, which indicates the non-occluded areas of the human body. The three branches are jointly trained in a unified multi-task learning network. Finally, pedestrian matching is performed based on the local features extracted from the non-occluded areas and the global features extracted from the whole pedestrian image. Extensive experimental results on a large-scale occluded person Re-ID dataset (i.e., Occluded-DukeMTMC) and two partial person Re-ID datasets (i.e., Partial-REID and Partial-iLIDS) show the superiority of the proposed method compared with several state-of-the-art methods for occluded and partial person Re-ID. We also demonstrate the effectiveness of the proposed method on two general person Re-ID datasets (i.e., Market-1501 and DukeMTMC-reID).
Full-reference image quality assessment algorithms usually perform comparisons of features extracted from square patches. These patches do not have any visual meanings. On the contrary, a superpixel ...is a set of image pixels that share similar visual characteristics and is thus perceptually meaningful. Features from superpixels may improve the performance of image quality assessment. Inspired by this, we propose a new superpixel-based similarity index by extracting perceptually meaningful features and revising similarity measures. The proposed method evaluates image quality on the basis of three measurements, namely, superpixel luminance similarity, superpixel chrominance similarity, and pixel gradient similarity. The first two measurements assess the overall visual impression on local images. The third measurement quantifies structural variations. The impact of superpixel-based regional gradient consistency on image quality is also analyzed. Distorted images showing high regional gradient consistency with the corresponding reference images are visually appreciated. Therefore, the three measurements are further revised by incorporating the regional gradient consistency into their computations. A weighting function that indicates superpixel-based texture complexity is utilized in the pooling stage to obtain the final quality score. Experiments on several benchmark databases demonstrate that the proposed method is competitive with the state-of-the-art metrics.
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