Scalable Video Object Segmentation With Identification Mechanism.

This paper delves into the challenges of achieving scalable and effective multi-object modeling for semi-supervised Video Object Segmentation (VOS). Previous VOS methods decode features with a single positive object, limiting the learning of multi-object representation as they must match and segment each target separately under multi-object scenarios. Additionally, earlier techniques catered to specific application objectives and lacked the flexibility to fulfill different speed-accuracy requirements. To address these problems, we present two innovative approaches, Associating Objects with Transformers (AOT) and Associating Objects with Scalable Transformers (AOST). In pursuing effective multi-object modeling, AOT introduces the IDentification (ID) mechanism to allocate each object a unique identity. This approach enables the network to model the associations among all objects simultaneously, thus facilitating the tracking and segmentation of objects in a single network pass. To address the challenge of inflexible deployment, AOST further integrates scalable long short-term transformers that incorporate scalable supervision and layer-wise ID-based attention. This enables online architecture scalability in VOS for the first time and overcomes ID embeddings' representation limitations. Given the absence of a benchmark for VOS involving densely multi-object annotations, we propose a challenging Video Object Segmentation in the Wild (VOSW) benchmark to validate our approaches. We evaluated various AOT and AOST variants using extensive experiments across VOSW and five commonly used VOS benchmarks, including YouTube-VOS 2018 & 2019 Val, DAVIS-2017 Val & Test, and DAVIS-2016. Our approaches surpass the state-of-the-art competitors and display exceptional efficiency and scalability consistently across all six benchmarks. Moreover, we notably achieved the 1st position in the 3 rd Large-scale Video Object Segmentation Challenge. Project page: https://github.com/yoxu515/aot-benchmark.

Histone derived antimicrobial peptides identified from Mytilus coruscus serum by peptidomics.

Histones and their N-terminal or C-terminal derived peptides have been studied in vertebrates and presented as potential antimicrobial agents playing important roles in the innate immune defenses. Although histones and their derived peptides had been reported as components of innate immunity in invertebrates, the knowledge about the histone derived antimicrobial peptides (HDAPs) in invertebrates are still limited. Using a peptidomic technique, a set of peptide fragments derived from the histones was identified in this study from the serum of microbes challenged Mytilus coruscus. Among the 85 identified histone-derived-peptides with high confidence, 5 HDAPs were chemically synthesized and the antimicrobial activities were verified, showing strong growth inhibition against Gram-positive bacteria, Gram-negative bacteria, and fungus. The gene expression level of the precursor histones matched by representative HDAPs were further tested using q-PCR, and the results showed a significant upregulation of the histone gene expression levels in hemocytes, gill, and mantle of the mussel after immune stress. In addition, three identified HDAPs were selected for preparation of specific antibodies, and the corresponding histones and their derived C-terminal fragments were detected by Western blotting in the blood cell and serum of immune challenged mussel, respectively, indicating the existence of HDAPs in M. coruscus. Our findings revealed the immune function of histones in Mytilus, and confirmed the existence of HDAPs in the mussel. The identified Mytilus HDAPs represent a new source of immune effector with antimicrobial function in the innate immune system, and thus provide promising candidates for the treatment of microbial infections in aquaculture and medicine.

Collaborative Content-Dependent Modeling: A Return to the Roots of Salient Object Detection.

Salient object detection (SOD) aims to identify the most visually distinctive object(s) from each given image. Most recent progresses focus on either adding elaborative connections among different convolution blocks or introducing boundary-aware supervision to help achieve better segmentation, which is actually moving away from the essence of SOD, i.e., distinctiveness/salience. This paper goes back to the roots of SOD and investigates the principles of how to identify distinctive object(s) in a more effective and efficient way. Intuitively, the salience of one object should largely depend on its global context within the input image. Based on this, we devise a clean yet effective architecture for SOD, named Collaborative Content-Dependent Networks (CCD-Net). In detail, we propose a collaborative content-dependent head whose parameters are conditioned on the input image's global context information. Within the content-dependent head, a hand-crafted multi-scale (HMS) module and a self-induced (SI) module are carefully designed to collaboratively generate content-aware convolution kernels for prediction. Benefited from the content-dependent head, CCD-Net is capable of leveraging global context to detect distinctive object(s) while keeping a simple encoder-decoder design. Extensive experimental results demonstrate that our CCD-Net achieves state-of-the-art results on various benchmarks. Our architecture is simple and intuitive compared to previous solutions, resulting in competitive characteristics with respect to model complexity, operating efficiency, and segmentation accuracy.

Co-Learning Meets Stitch-Up for Noisy Multi-Label Visual Recognition.

In real-world scenarios, collected and annotated data often exhibit the characteristics of multiple classes and long-tailed distribution. Additionally, label noise is inevitable in large-scale annotations and hinders the applications of learning-based models. Although many deep learning based methods have been proposed for handling long-tailed multi-label recognition or label noise respectively, learning with noisy labels in long-tailed multi-label visual data has not been well-studied because of the complexity of long-tailed distribution entangled with multi-label correlation. To tackle such a critical yet thorny problem, this paper focuses on reducing noise based on some inherent properties of multi-label classification and long-tailed learning under noisy cases. In detail, we propose a Stitch-Up augmentation to synthesize a cleaner sample, which directly reduces multi-label noise by stitching up multiple noisy training samples. Equipped with Stitch-Up, a Heterogeneous Co-Learning framework is further designed to leverage the inconsistency between long-tailed and balanced distributions, yielding cleaner labels for more robust representation learning with noisy long-tailed data. To validate our method, we build two challenging benchmarks, named VOC-MLT-Noise and COCO-MLT-Noise, respectively. Extensive experiments are conducted to demonstrate the effectiveness of our proposed method. Compared to a variety of baselines, our method achieves superior results.

A novel resilience analysis methodology for airport networks system from the perspective of different epidemic prevention and control policy responses.

As the COVID-19 pandemic fades, the aviation industry is entering a fast recovery period. To analyze airport networks' post-pandemic resilience during the recovery process, this paper proposes a Comprehensive Resilience Assessment (CRA) model approach using the airport networks of China, Europe, and the U.S.A as case studies. The impact of COVID-19 on the networks is analyzed after populating the models of these networks with real air traffic data. The results suggest that the pandemic has caused damage to all three networks, although the damages to the network structures of Europe and the U.S.A are more severe than the damage in China. The analysis suggests that China, as the airport network with less network performance change, has a more stable level of resilience. The analysis also shows that the different levels of stringency policy in prevention and control measures during the epidemic directly affected the recovery rate of the network. This paper provides new insights into the impact of the pandemic on airport network resilience.

Myticofensin, a novel antimicrobial peptide family identified from Mytilus coruscus.

In this study, seven transcripts representing a novel antimicrobial peptide (AMP) family with structural features similar to those of arthropod defensins were identified from Mytilus coruscus. These novel defensins from the Mytilus AMP family were named myticofensins. To explore the possible immune-related functions of these myticofensins, we examined their expression profiles in different tissues and larval stages, as well as in three immune-related tissues under the threat of different microbes. Our data revealed that the seven myticofensins had relatively high expression levels in immune-related tissues. Most myticofensins were undetectable, or had low expression levels, in different larval mussel stages. Additionally, in vivo microbial challenges significantly increased the expression levels of myticofensins in M. coruscus hemocytes, gills, and digestive glands, showing different immune response patterns under challenges from different microbes. Our data indicates that different myticofensins may have different immune functions in different tissues. Furthermore, peptide sequences corresponding to the beta-hairpin, alpha-helix, and N-terminal loop of myticofensin were synthesized and the antimicrobial activities of these peptide fragments were tested. Our data confirms the diversity of defensins in Mytilus and reports the complex regulation of these defensins in the mussel immune response to different microbes in immune-related tissues. The immune system of Mytilus has been studied for years as they are a species with strong environmental adaptations. Our data can be regarded as a step forward in the study of the adaptation of Mytilus spp. to an evolving microbial world.

Molecular characterization of peptidoglycan recognition proteins from Mytilus coruscus.

Mytilus shows great immune resistance to various bacteria from the living waters, indicating a complex immune recognition mechanism against various microbes. Peptidoglycan recognition proteins (PGRPs) play an important role in the defense against invading microbes via the recognition of the immunogenic substance peptidoglycan (PGN). Therefore, eight PGRPs were identified from the gill transcriptome of Mytilus coruscus. The sequence features, expression pattern in various organs and larval development stages, and microbes induced expression profiles of these Mytilus PGRPs were determined. Our data revealed the constitutive expression of PGRPs in various organs with relative higher expression level in immune-related organs. The expression of PGRPs is developmentally regulated, and most PGRPs are undetectable in larvae stages. The expression level of most PGRPs was significantly increased with in vivo microbial challenges, showing strong response to Gram-positive strain in gill and digestive gland, strong response to Gram-negative strain in hemocytes, and relative weaker response to fungus in the three tested organs. In addition, the function analysis of the representative recombinant expressed PGRP (rMcPGRP-2) confirmed the antimicrobial and agglutination activities, showing the immune-related importance of PGRP in Mytilus. Our work suggests that Mytilus PGRPs can act as pattern recognition receptors to recognize the invading microorganisms and the antimicrobial effectors during the innate immune response of Mytilus.

Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integration.

This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation. Unlike previous practices that focus on exploring the embedding learning of foreground object (s), we consider background should be equally treated. Thus, we propose a Collaborative video object segmentation by Foreground-Background Integration (CFBI) approach. CFBI separates the feature embedding into the foreground object region and its corresponding background region, implicitly promoting them to be more contrastive and improving the segmentation results accordingly. Moreover, CFBI performs both pixel-level matching processes and instance-level attention mechanisms between the reference and the predicted sequence, making CFBI robust to various object scales. Based on CFBI, we introduce a multi-scale matching structure and propose an Atrous Matching strategy, resulting in a more robust and efficient framework, CFBI+. We conduct extensive experiments on two popular benchmarks, i.e., DAVIS and YouTube-VOS. Without applying any simulated data for pre-training, our CFBI+ achieves the performance ( J& F) of 82.9 and 82.8 percent, outperforming all the other state-of-the-art methods. Code: https://github.com/z-x-yang/CFBI.

Quantitative method for resilience assessment framework of airport network during COVID-19.

The resilience and vulnerability of airport networks are significant challenges during the COVID-19 global pandemic. Previous studies considered node failure of networks under natural disasters and extreme weather. Herein, we propose a complex network methodology combined with data-driven to assess the resilience of airport networks toward global-scale disturbance using the Chinese airport network (CAN) and the European airport network (EAN) as a case study. The assessment framework includes vulnerability and resilience analyses from the network- and node-level perspectives. Subsequently, we apply the framework to analyze the airport networks in China and Europe. Specifically, real air traffic data for 232 airports in China and 82 airports in Europe are selected to form the CAN and EAN, respectively. The complex network analysis reveals that the CAN and the EAN are scale-free small-world networks, that are resilient to random attacks. However, the connectivity and vulnerability of the CAN are inferior to those of the EAN. In addition, we select the passenger throughput from the top-50 airports in China and Europe to perform a comparative analysis. By comparing the resilience evaluation of individual airports, we discovered that the factors of resilience assessment of an airport network for global disturbance considers the network metrics and the effect of government policy in actual operations. Additionally, this study also proves that a country's emergency response-ability towards the COVID-19 has a significantly affectes the recovery of its airport network.

Clinical significance of inflammatory cytokine and chemokine expression in hand, foot and mouth disease.

The present study examined the relationship between cytokine and chemokine expression and the clinical presentation of hand, foot and mouth disease (HFMD), which is currently unclear. The present study involved 28 patients with mild HFMD, 44 patients with severe HFMD and 26 healthy children. Venous blood was tested for cytokine [interleukin (IL)­4, IL­12, IL­18, tumor necrosis factor­α (TNF­α), interferon­Î³ (IFN­Î³)] and chemokine expression [IL­8, regulated on activation, normal T cell expressed and secreted (RANTES), monocyte chemoattractant protein­1 (MCP­1) and IFN-γ-inducible protein­10 (IP­10)]. Stool samples from the patients were tested for enterovirus 71 (EV71) RNA using reverse transcription-polymerase chain reaction. The results indicated that all cytokine/chemokine levels were increased in patients with severe HFMD compared with in patients with mild HFMD or control subjects. In addition, RANTES, MCP­1, IL­4, IL­12 and IL­18 levels were higher in mild HFMD patients than in the controls. In patients with severe HFMD, all expression levels (with the exception of IL­8 and IL­4) were increased in patients with encephalitis plus pulmonary edema compared with those with encephalitis alone. Furthermore, all levels (with the exception of IL­8) were increased in EV71­positive patients compared with EV71­negative patients. In mild HFMD, all levels (with the exception of IL­8 and IL­4) were increased in EV71­positive patients compared with EV71­negative patients. However, in severe HFMD, only RANTES, IP­10 and IFN­Î³ levels were increased in EV71­positive patients compared with EV71­negative patients. In the EV71­negative group, all levels were increased in severe HFMD compared with mild HFMD. In the EV71­positive group, all levels (with the exception of IL­8) were increased in severe HFMD compared with mild HFMD. These results indicated that cytokines and chemokines participate in HFMD pathogenesis, and may be useful to monitor disease progression and predict prognosis.