RGB-Event Fusion for Moving Object Detection in Autonomous Driving

Moving Object Detection (MOD) is a critical vision task for successfully achieving safe autonomous driving. Despite plausible results of deep learning methods, most existing approaches are only frame-based and may fail to reach reasonable performance when dealing with dynamic traffic participants. Recent advances in sensor technologies, especially the Event camera, can naturally complement the conventional camera approach to better model moving objects. However, event-based works often adopt a pre-defined time window for event representation, and simply integrate it to estimate image intensities from events, neglecting much of the rich temporal information from the available asynchronous events. Therefore, from a new perspective, we propose RENet, a novel RGB-Event fusion Network, that jointly exploits the two complementary modalities to achieve more robust MOD under challenging scenarios for autonomous driving. Specifically, we first design a temporal multi-scale aggregation module to fully leverage event frames from both the RGB exposure time and larger intervals. Then we introduce a bi-directional fusion module to attentively calibrate and fuse multi-modal features. To evaluate the performance of our network, we carefully select and annotate a sub-MOD dataset from the commonly used DSEC dataset. Extensive experiments demonstrate that our proposed method performs significantly better than the state-of-the-art RGB-Event fusion alternatives

Event-Free Moving Object Segmentation from Moving Ego Vehicle

Moving object segmentation (MOS) in dynamic scenes is challenging for autonomous driving, especially for sequences obtained from moving ego vehicles. Most state-of-the-art methods leverage motion cues obtained from optical flow maps. However, since these methods are often based on optical flows that are pre-computed from successive RGB frames, this neglects the temporal consideration of events occurring within inter-frame and limits the practicality of these methods in real-life situations. To address these limitations, we propose to exploit event cameras for better video understanding, which provide rich motion cues without relying on optical flow. To foster research in this area, we first introduce a novel large-scale dataset called DSEC-MOS for moving object segmentation from moving ego vehicles. Subsequently, we devise EmoFormer, a novel network able to exploit the event data. For this purpose, we fuse the event prior with spatial semantic maps to distinguish moving objects from the static background, adding another level of dense supervision around our object of interest - moving ones. Our proposed network relies only on event data for training but does not require event input during inference, making it directly comparable to frame-only methods in terms of efficiency and more widely usable in many application cases. An exhaustive comparison with 8 state-of-the-art video object segmentation methods highlights a significant performance improvement of our method over all other methods. Project Page: https://github.com/ZZY-Zhou/DSEC-MOS

Détection d'objets en mouvement dans un milieu urbain par fusion de données RVB et événementielles

-La détection d'objets en mouvement (DOM) est une tâche essentielle pour parvenir à une conduite autonome sûre. En vision par ordinateur, les méthodes d'apprentissage profond donnent des résultats intéressants mais elles sont généralement basées sur des images classiques et souffrent par conséquent des limites de ce type de capteur dans des scènes hautement dynamiques. Les avancées technologiques récentes dans le développement de capteurs bio-inspirés, en particulier les caméras événementielles, peuvent naturellement compléter les données issues des caméras conventionnelles afin de mieux modéliser les objets en mouvement. Nous proposons dans cet article, un nouveau réseau de fusion de données multimodales RVB/événements, appelé RENet (RGB-Event fusion Network), qui exploite conjointement ces deux modalités complémentaires pour obtenir une DOM plus robuste dans des scénarios difficiles de conduite autonome

Identification of miRNAs and Their Response to Cold Stress in <i>Astragalus Membranaceus</i>

Astragalus membranaceus is an important medicinal plant widely cultivated in East Asia. MicroRNAs (miRNAs) are endogenous regulatory molecules that play essential roles in plant growth, development, and the response to environmental stresses. Cold is one of the key environmental factors affecting the yield and quality of A. membranaceus, and miRNAs may mediate the gene regulation network under cold stress in A. membranaceus. To identify miRNAs and reveal their functions in cold stress response in A. membranaceus, small RNA sequencing was conducted followed by bioinformatics analysis, and quantitative real time PCR (qRT-PCR) analysis was performed to profile the expression of miRNAs under cold stress. A total of 168 conserved miRNAs belonging to 34 families and 14 putative non-conserved miRNAs were identified. Many miRNA targets were predicted and these targets were involved in diversified regulatory and metabolic pathways. By using qRT-PCR, 27 miRNAs were found to be responsive to cold stress, including 4 cold stress-induced and 17 cold-repressed conserved miRNAs, and 6 cold-induced non-conserved miRNAs. These cold-responsive miRNAs probably mediate the response to cold stress by regulating development, hormone signaling, defense, redox homeostasis, and secondary metabolism in A. membranaceus. These cold-corresponsive miRNAs may be used as the candidate genes in further molecular breeding for improving cold tolerance of A. membranaceus

Anti-metastatic effects of AGS-30 on breast cancer through the inhibition of M2-like macrophage polarization

AGS-30, a new andrographolide derivative, showed significant anticancer and anti-angiogenic characteristics. However, its role in controlling macrophage polarization and tumor immune response is unknown. Thus, the main goals of this study are to investigate how AGS-30 regulates macrophage polarization and how it suppresses breast cancer metastasis. AGS-30 inhibited IL-4 and IL-13-induced RAW 264.7 and THP-1 macrophages into M2-like phenotype. However, AGS-30 did not affect the LPS and IFN-γ-induced polarization of M1-like macrophages. AGS-30 reduced the mRNA expressions of CD206, Arg-1, Fizz-1, Ym-1, VEGF, IL-10, MMP2, and MMP9 in M2-like macrophages in a concentration-dependent manner. In contrast, andrographolide treatment at 5 μM did not affect M1-like and M2-like macrophage polarization. The conditioned medium from M2-like macrophages increased 4T1 breast cancer cell migration and invasion, whereas AGS-30 inhibited these effects. In the 4T1 breast tumor xenograft mice, the tumor volume and weight were reduced without affecting body weight after receiving AGS-30. AGS-30 treatment also reduced lung and liver metastasis, with reduced STAT6, CD31, VEGF, and Ki67 protein expressions. Moreover, the tumors had considerably fewer M2-like macrophages and Arg-1 expression, but the proportion of M1-like macrophages and iNOS expression increased after AGS-30 treatment. Same results were found in the tail vein metastasis model. In conclusion, this study shows that AGS-30 inhibits breast cancer growth and metastasis, probably through inhibiting M2-like macrophage polarization. Our findings suggest that AGS-30 may be a potential immunotherapeutic alternative for metastatic breast cancer

Endothelial Cell‐Derived Lactate Triggers Bone Mesenchymal Stem Cell Histone Lactylation to Attenuate Osteoporosis

Abstract Blood vessels play a role in osteogenesis and osteoporosis; however, the role of vascular metabolism in these processes remains unclear. The present study finds that ovariectomized mice exhibit reduced blood vessel density in the bone and reduced expression of the endothelial glycolytic regulator pyruvate kinase M2 (PKM2). Endothelial cell (EC)‐specific deletion of Pkm2 impairs osteogenesis and worsens osteoporosis in mice. This is attributed to the impaired ability of bone mesenchymal stem cells (BMSCs) to differentiate into osteoblasts. Mechanistically, EC‐specific deletion of Pkm2 reduces serum lactate levels secreted by ECs, which affect histone lactylation in BMSCs. Using joint CUT&Tag and RNA sequencing analyses, collagen type I alpha 2 chain (COL1A2), cartilage oligomeric matrix protein (COMP), ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), and transcription factor 7 like 2 (TCF7L2) as osteogenic genes regulated by histone H3K18la lactylation are identified. PKM2 overexpression in ECs, lactate addition, and exercise restore the phenotype of endothelial PKM2‐deficient mice. Furthermore, serum metabolomics indicate that patients with osteoporosis have relatively low lactate levels. Additionally, histone lactylation and related osteogenic genes of BMSCs are downregulated in patients with osteoporosis. In conclusion, glycolysis in ECs fuels BMSC differentiation into osteoblasts through histone lactylation, and exercise partially ameliorates osteoporosis by increasing serum lactate levels