FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation

Over the past few years, we have witnessed the success of deep learning in image recognition thanks to the availability of large-scale human-annotated datasets such as PASCAL VOC, ImageNet, and COCO. Although these datasets have covered a wide range of object categories, there are still a significant number of objects that are not included. Can we perform the same task without a lot of human annotations? In this paper, we are interested in few-shot object segmentation where the number of annotated training examples are limited to 5 only. To evaluate and validate the performance of our approach, we have built a few-shot segmentation dataset, FSS-1000, which consists of 1000 object classes with pixelwise annotation of ground-truth segmentation. Unique in FSS-1000, our dataset contains significant number of objects that have never been seen or annotated in previous datasets, such as tiny daily objects, merchandise, cartoon characters, logos, etc. We build our baseline model using standard backbone networks such as VGG-16, ResNet-101, and Inception. To our surprise, we found that training our model from scratch using FSS-1000 achieves comparable and even better results than training with weights pre-trained by ImageNet which is more than 100 times larger than FSS-1000. Both our approach and dataset are simple, effective, and easily extensible to learn segmentation of new object classes given very few annotated training examples. Dataset is available at https://github.com/HKUSTCV/FSS-1000

APPAS: A Privacy-Preserving Authentication Scheme Based on Pseudonym Ring in VSNs

Vehicular social networks (VSNs) provide a variety of services for users based on social relationships through vehicular ad hoc networks (VANETs). During the communication in VSNs, vehicles are at risk of exposure to privacy information. Consequently, how to guarantee the security and privacy of vehicles is a critical issue. Ring signature is an effective mechanism to achieve anonymous authentication and communication. However, how to establish rings and how to select ring members become open problems. In this paper, a privacy-preserving scheme based on the pseudonym ring in VSNs is proposed. Hierarchical network architecture and trust model are established. A series of authentication protocols are then elaborated. According to the security and performance analysis, the proposed scheme is more robust and efficient compared with the typical ones

Biomimetic nanotherapies: red blood cell based core-shell structured nanocomplexes for atherosclerosis management

Cardiovascular disease is the leading cause of mortality worldwide. Atherosclerosis, one of the most common forms of the disease, is characterized by a gradual formation of atherosclerotic plaque, hardening, and narrowing of the arteries. Nanomaterials can serve as powerful delivery platforms for atherosclerosis treatment. However, their therapeutic efficacy is substantially limited in vivo due to nonspecific clearance by the mononuclear phagocytic system. In order to address this limitation, rapamycin (RAP)‐loaded poly(lactic‐co‐glycolic acid) (PLGA) nanoparticles are cloaked with the cell membrane of red blood cells (RBCs), creating superior nanocomplexes with a highly complex functionalized bio‐interface. The resulting biomimetic nanocomplexes exhibit a well‐defined “core–shell” structure with favorable hydrodynamic size and negative surface charge. More importantly, the biomimetic nature of the RBC interface results in less macrophage‐mediated phagocytosis in the blood and enhanced accumulation of nanoparticles in the established atherosclerotic plaques, thereby achieving targeted drug release. The biomimetic nanocomplexes significantly attenuate the progression of atherosclerosis. Additionally, the biomimetic nanotherapy approach also displays favorable safety properties. Overall, this study demonstrates the therapeutic advantages of biomimetic nanotherapy for atherosclerosis treatment, which holds considerable promise as a new generation of drug delivery system for safe and efficient management of atherosclerosis

Macrophage membrane functionalized biomimetic nanoparticles for targeted anti-atherosclerosis applications

Atherosclerosis (AS), the underlying cause of most cardiovascular events, is one of the most common causes of human morbidity and mortality worldwide due to the lack of an efficient strategy for targeted therapy. In this work, we aimed to develop an ideal biomimetic nanoparticle for targeted AS therapy. Methods: Based on macrophage “homing” into atherosclerotic lesions and cell membrane coating nanotechnology, biomimetic nanoparticles (MM/RAPNPs) were fabricated with a macrophage membrane (MM) coating on the surface of rapamycin-loaded poly (lactic-co-glycolic acid) copolymer (PLGA) nanoparticles (RAPNPs). Subsequently, the physical properties of the MM/RAPNPs were characterized. The biocompatibility and biological functions of MM/RAPNPs were determined in vitro. Finally, in AS mouse models, the targeting characteristics, therapeutic efficacy and safety of the MM/RAPNPs were examined. Results: The advanced MM/RAPNPs demonstrated good biocompatibility. Due to the MM coating, the nanoparticles effectively inhibited the phagocytosis by macrophages and targeted activated endothelial cells in vitro. In addition, MM-coated nanoparticles effectively targeted and accumulated in atherosclerotic lesions in vivo. After a 4-week treatment program, MM/RAPNPs were shown to significantly delay the progression of AS. Furthermore, MM/RAPNPs displayed favorable safety performance after long-term administration. Conclusion: These results demonstrate that MM/RAPNPs could efficiently and safely inhibit the progression of AS. These biomimetic nanoparticles may be potential drug delivery systems for safe and effective anti-AS applications

Targeted immunotherapy for HER2-low breast cancer with 17p loss

The clinical challenge for treating HER2 (human epidermal growth factor receptor 2)-low breast cancer is the paucity of actionable drug targets. HER2-targeted therapy often has poor clinical efficacy for this disease due to the low level of HER2 protein on the cancer cell surface. We analyzed breast cancer genomics in the search for potential drug targets. Heterozygous loss of chromosome 17p is one of the most frequent genomic events in breast cancer, and 17p loss involves a massive deletion of genes including the tumor suppressor TP53 Our analyses revealed that 17p loss leads to global gene expression changes and reduced tumor infiltration and cytotoxicity of T cells, resulting in immune evasion during breast tumor progression. The 17p deletion region also includes POLR2A, a gene encoding the catalytic subunit of RNA polymerase II that is essential for cell survival. Therefore, breast cancer cells with heterozygous loss of 17p are extremely sensitive to the inhibition of POLR2A via a specific small-molecule inhibitor, α-amanitin. Here, we demonstrate that α-amanitin-conjugated trastuzumab (T-Ama) potentiated the HER2-targeted therapy and exhibited superior efficacy in treating HER2-low breast cancer with 17p loss. Moreover, treatment with T-Ama induced immunogenic cell death in breast cancer cells and, thereby, delivered greater efficacy in combination with immune checkpoint blockade therapy in preclinical HER2-low breast cancer models. Collectively, 17p loss not only drives breast tumorigenesis but also confers therapeutic vulnerabilities that may be used to develop targeted precision immunotherapy

MAL2 drives immune evasion in breast cancer by suppressing tumor antigen presentation

Immune evasion is a pivotal event in tumor progression. To eliminate human cancer cells, current immune checkpoint therapy is set to boost CD8+ T cell-mediated cytotoxicity. However, this action is eventually dependent on the efficient recognition of tumor-specific antigens via T cell receptors. One primary mechanism by which tumor cells evade immune surveillance is to downregulate their antigen presentation. Little progress has been made toward harnessing potential therapeutic targets for enhancing antigen presentation on the tumor cell. Here, we identified MAL2 as a key player that determines the turnover of the antigen-loaded MHC-I complex and reduces the antigen presentation on tumor cells. MAL2 promotes the endocytosis of tumor antigens via direct interaction with the MHC-I complex and endosome-associated RAB proteins. In preclinical models, depletion of MAL2 in breast tumor cells profoundly enhanced the cytotoxicity of tumor-infiltrating CD8+ T cells and suppressed breast tumor growth, suggesting that MAL2 is a potential therapeutic target for breast cancer immunotherapy

Legal Judgment Prediction via Heterogeneous Graphs and Knowledge of Law Articles

Legal judgment prediction (LJP) is a crucial task in legal intelligence to predict charges, law articles and terms of penalties based on case fact description texts. Although existing methods perform well, they still have many shortcomings. First, the existing methods have significant limitations in understanding long documents, especially those based on RNNs and BERT. Secondly, the existing methods are not good at solving the problem of similar charges and do not fully and effectively integrate the information of law articles. To address the above problems, we propose a novel LJP method. Firstly, we improve the model’s comprehension of the whole document based on a graph neural network approach. Then, we design a graph attention network-based law article distinction extractor to distinguish similar law articles. Finally, we design a graph fusion method to fuse heterogeneous graphs of text and external knowledge (law article group distinction information). The experiments show that the method could effectively improve LJP performance. The experimental metrics are superior to the existing state of the art

Mask-Guided Local–Global Attentive Network for Change Detection in Remote Sensing Images

Change detection in remote sensing images is a challenging task due to object appearance diversity and the interference of complex backgrounds. Self-attention- and spatial-attention-based solutions face limitations, such as high memory consumption and an inadequate ability to capture long-range relations, leading to imprecise contextual information and restricted performance. To address these challenges, this article introduces a novel mask-guided local–global attentive network (MLA-Net). The MLA-Net incorporates a memory-efficient local–global attention module that leverages the benefits of both self-attention and spatial attention to accurately capture the local–global context. Through simultaneous exploitation of context within inter- and intrapatches and information refinement, the feature representation capability is significantly enhanced. In addition, we introduce a change mask to refine feature differences and eliminate interference from irrelevant changes caused by complex backgrounds. Accordingly, a mask loss is defined to guide the generation of the mask. Extensive experiments on the LEVIR-CD, WHU-CD, and CLCD datasets show that our MLA-Net performs better than state-of-the-art methods

The Patterns and Drivers of Bacterial and Fungal β-Diversity in a Typical Dryland Ecosystem of Northwest China

Dryland ecosystems cover more than 30% of the terrestrial area of China, while processes that shape the biogeographic patterns of bacterial and fungal β-diversity have rarely been evaluated synchronously. To compare the biogeographic patterns and its drivers of bacterial and fungal β-diversity, we collected 62 soil samples from a typical dryland region of northwest China. We assessed bacterial and fungal communities by sequencing bacterial 16S rRNA gene and fungal ITS data. Meanwhile, the β-diversity was decomposed into two components: species replacement (species turnover) and nestedness to further explore the bacterial and fungal β-diversity patterns and its causes. The results show that both bacterial and fungal β-diversity were derived almost entirely from species turnover rather than from species nestedness. Distance-decay relationships confirmed that the geographic patterns of bacterial and fungal β-diversity were significantly different. Environmental factors had the dominant influence on both the bacterial and fungal β-diversity and species turnover, however, the role of geographic distance varied across bacterial and fungal communities. Furthermore, both bacterial and fungal nestedness did not significantly respond to the environmental and geographic distance. Our findings suggest that the different response of bacterial and fungal species turnover to dispersal limitation and other, unknown processes may result in different biogeographic patterns of bacterial and fungal β-diversity in the drylands of northwest China. Together, we highlight that the drivers of β-diversity patterns vary between bacterial and fungal communities, and microbial β-diversity are driven by multiple factors in the drylands of northwest China