Long-Range Feature Propagating for Natural Image Matting

Natural image matting estimates the alpha values of unknown regions in the trimap. Recently, deep learning based methods propagate the alpha values from the known regions to unknown regions according to the similarity between them. However, we find that more than 50\% pixels in the unknown regions cannot be correlated to pixels in known regions due to the limitation of small effective reception fields of common convolutional neural networks, which leads to inaccurate estimation when the pixels in the unknown regions cannot be inferred only with pixels in the reception fields. To solve this problem, we propose Long-Range Feature Propagating Network (LFPNet), which learns the long-range context features outside the reception fields for alpha matte estimation. Specifically, we first design the propagating module which extracts the context features from the downsampled image. Then, we present Center-Surround Pyramid Pooling (CSPP) that explicitly propagates the context features from the surrounding context image patch to the inner center image patch. Finally, we use the matting module which takes the image, trimap and context features to estimate the alpha matte. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods on the AlphaMatting and Adobe Image Matting datasets

Explicit Visual Prompts for Visual Object Tracking

How to effectively exploit spatio-temporal information is crucial to capture target appearance changes in visual tracking. However, most deep learning-based trackers mainly focus on designing a complicated appearance model or template updating strategy, while lacking the exploitation of context between consecutive frames and thus entailing the \textit{when-and-how-to-update} dilemma. To address these issues, we propose a novel explicit visual prompts framework for visual tracking, dubbed \textbf{EVPTrack}. Specifically, we utilize spatio-temporal tokens to propagate information between consecutive frames without focusing on updating templates. As a result, we cannot only alleviate the challenge of \textit{when-to-update}, but also avoid the hyper-parameters associated with updating strategies. Then, we utilize the spatio-temporal tokens to generate explicit visual prompts that facilitate inference in the current frame. The prompts are fed into a transformer encoder together with the image tokens without additional processing. Consequently, the efficiency of our model is improved by avoiding \textit{how-to-update}. In addition, we consider multi-scale information as explicit visual prompts, providing multiscale template features to enhance the EVPTrack's ability to handle target scale changes. Extensive experimental results on six benchmarks (i.e., LaSOT, LaSOT\rm $_{ext}$, GOT-10k, UAV123, TrackingNet, and TNL2K.) validate that our EVPTrack can achieve competitive performance at a real-time speed by effectively exploiting both spatio-temporal and multi-scale information. Code and models are available at https://github.com/GXNU-ZhongLab/EVPTrack

Rethinking Context Aggregation in Natural Image Matting

For natural image matting, context information plays a crucial role in estimating alpha mattes especially when it is challenging to distinguish foreground from its background. Exiting deep learning-based methods exploit specifically designed context aggregation modules to refine encoder features. However, the effectiveness of these modules has not been thoroughly explored. In this paper, we conduct extensive experiments to reveal that the context aggregation modules are actually not as effective as expected. We also demonstrate that when learned on large image patches, basic encoder-decoder networks with a larger receptive field can effectively aggregate context to achieve better performance.Upon the above findings, we propose a simple yet effective matting network, named AEMatter, which enlarges the receptive field by incorporating an appearance-enhanced axis-wise learning block into the encoder and adopting a hybrid-transformer decoder. Experimental results on four datasets demonstrate that our AEMatter significantly outperforms state-of-the-art matting methods (e.g., on the Adobe Composition-1K dataset, \textbf{25\%} and \textbf{40\%} reduction in terms of SAD and MSE, respectively, compared against MatteFormer). The code and model are available at \url{https://github.com/QLYoo/AEMatter}

Sirt7 protects against vascular calcification via modulation of reactive oxygen species and senescence of vascular smooth muscle cells

Vascular calcification is frequently seen in patients with chronic kidney disease (CKD), and significantly increases cardiovascular mortality and morbidity. Sirt7, a NAD+-dependent histone deacetylases, plays a crucial role in cardiovascular disease. However, the role of Sirt7 in vascular calcification remains largely unknown. Using in vitro and in vivo models of vascular calcification, this study showed that Sirt7 expression was significantly reduced in calcified arteries from mice administered with high dose of vitamin D3 (vD3). We found that knockdown or inhibition of Sirt7 promoted vascular smooth muscle cell (VSMC), aortic ring and vascular calcification in mice, whereas overexpression of Sirt7 had opposite effects. Intriguingly, this protective effect of Sirt7 on vascular calcification is dependent on its deacetylase activity. Unexpectedly, Sirt7 did not alter the osteogenic transition of VSMCs. However, our RNA-seq and subsequent studies demonstrated that knockdown of Sirt7 in VSMCs resulted in increased intracellular reactive oxygen species (ROS) accumulation, and induced an Nrf-2 mediated oxidative stress response. Treatment with the ROS inhibitor N-acetylcysteine (NAC) significantly attenuated the inhibitory effect of Sirt7 on VSMC calcification. Furthermore, we found that knockdown of Sirt7 delayed cell cycle progression and accelerated cellular senescence of VSMCs. Taken together, our results indicate that Sirt7 regulates vascular calcification at least in part through modulation of ROS and cellular senescence of VSMCs. Sirt7 may be a potential therapeutic target for vascular calcification.</p

A Marine Anthraquinone SZ-685C Overrides Adriamycin-Resistance in Breast Cancer Cells through Suppressing Akt Signaling

Breast cancer remains a major health problem worldwide. While chemotherapy represents an important therapeutic modality against breast cancer, limitations in the clinical use of chemotherapy remain formidable because of chemoresistance. The HER2/PI-3K/Akt pathway has been demonstrated to play a causal role in conferring a broad chemoresistance in breast cancer cells and thus justified to be a target for enhancing the effects of anti-breast cancer chemotherapies, such as adriamycin (ADR). Agents that can either enhance the effects of chemotherapeutics or overcome chemoresistance are urgently needed for the treatment of breast cancer. In this context, SZ-685C, an agent that has been previously shown, as such, to suppress Akt signaling, is expected to increase the efficacy of chemotherapy. Our current study investigated whether SZ-685C can override chemoresistance through inhibiting Akt signaling in human breast cancer cells. ADR-resistant cells derived from human breast cancer cell lines MCF-7, MCF-7/ADR and MCF-7/Akt, were used as models to test the effects of SZ-685C. We found that SZ-685C suppressed the Akt pathway and induced apoptosis in MCF-7/ADR and MCF-7/Akt cells that are resistant to ADR treatment, leading to antitumor effects both in vitro and in vivo. Our data suggest that use of SZ-685C might represent a potentially promising approach to the treatment of ADR-resistant breast cancer

Supplementary dataset for immune responses in hepatopancreas of Fenneropenaeus penicillatus under White Spot Syndrome Virus infection

Supplementary dataset for immune responses in hepatopancreas of Fenneropenaeus penicillatus under White Spot Syndrome Virus infection</p

On Pairwise Stability and Anti-Competitiveness of Cross-Holdings in Oligopoly

This paper considers a model of endogenous bilateral cross-holdings. A notion of pairwise stability is applied to analyze firms' incentives for cross-holdings. Under certain conditions and Cournot competition on the output market, it is shown that monopoly is the only outcome of pairwise stable cross-holdings when there are two firms; a wide range of outcomes is possible when there are three firms, including as special cases the triopoly and the duopoly Cournot equilibria without any cross-holding; and the Cournot equilibrium is an outcome of pairwise stable cross-holdings when there are four or more firms. Competitive implications of the results are also briefly discussed

The first complete mitochondrial genome of Actinopyga from Actinopyga echinites (Aspidochirotida: Holothuriidae)

The deep-water redfish, Actinopyga echinites, is an ecologically and economically important holothuroid in China due to its valuable nutrition and pharmacological compounds. However, the taxonomy and phylogeny of the Actinopyga have been debated and misidentifications have been reported recently. Moreover, there remain considerable doubts about cryptic species complex within Actinopyga. In this study, we report the first complete mitochondrial genome of Actinopyga from A. echinites. The mitogenome has 15,619 base pairs (62.9% A + T content) and made up of a total of 37 genes (13 protein-coding, 22 transfer RNAs, and 2 ribosomal RNAs), and a putative control region. This study was the first available complete mitogenome of Actinopyga and will provide useful genetic information for future phylogenetic and taxonomic classification of Holothuriidae

The first complete mitochondrial genome of Bursidae from Bufonaria rana (Caenogastropoda: Tonnoidea)

The common frogsnail Bufonaria rana, is an ecologically and economically important Tonnoideans in China due to valuable nutrition and pharmacological compounds. However, the taxonomy and phylogeny of the Bursidae have been debated and synonyms among Bursidae species have been reported recently. In this study, we report the first complete mitochondrial genome of Bursidae from B. rana. The mitogenome has 15,510 base pairs (69.0% A + T content) and made up of total of 37 genes (13 protein-coding, 22 transfer RNAs and 2 ribosomal RNAs), and a putative control region. This study was the first available complete mitogenomes of Bursidae and will provide useful genetic information for future phylogenetic and taxonomic classification of Tonnoideans