Causality-based Cross-Modal Representation Learning for Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) has gained significant research interest in recent years due to its potential applications in real-world scenarios. However, existing VLN methods struggle with the issue of spurious associations, resulting in poor generalization with a significant performance gap between seen and unseen environments. In this paper, we tackle this challenge by proposing a unified framework CausalVLN based on the causal learning paradigm to train a robust navigator capable of learning unbiased feature representations. Specifically, we establish reasonable assumptions about confounders for vision and language in VLN using the structured causal model (SCM). Building upon this, we propose an iterative backdoor-based representation learning (IBRL) method that allows for the adaptive and effective intervention on confounders. Furthermore, we introduce the visual and linguistic backdoor causal encoders to enable unbiased feature expression for multi-modalities during training and validation, enhancing the agent's capability to generalize across different environments. Experiments on three VLN datasets (R2R, RxR, and REVERIE) showcase the superiority of our proposed method over previous state-of-the-art approaches. Moreover, detailed visualization analysis demonstrates the effectiveness of CausalVLN in significantly narrowing down the performance gap between seen and unseen environments, underscoring its strong generalization capability.Comment: 16 page

Unbiased Directed Object Attention Graph for Object Navigation

Object navigation tasks require agents to locate specific objects in unknown environments based on visual information. Previously, graph convolutions were used to implicitly explore the relationships between objects. However, due to differences in visibility among objects, it is easy to generate biases in object attention. Thus, in this paper, we propose a directed object attention (DOA) graph to guide the agent in explicitly learning the attention relationships between objects, thereby reducing the object attention bias. In particular, we use the DOA graph to perform unbiased adaptive object attention (UAOA) on the object features and unbiased adaptive image attention (UAIA) on the raw images, respectively. To distinguish features in different branches, a concise adaptive branch energy distribution (ABED) method is proposed. We assess our methods on the AI2-Thor dataset. Compared with the state-of-the-art (SOTA) method, our method reports 7.4%, 8.1% and 17.6% increase in success rate (SR), success weighted by path length (SPL) and success weighted by action efficiency (SAE), respectively.Comment: 13 pages, ready to ACM Mutimedia, under revie

Comparative study of lectin domains in model species : new insights into evolutionary dynamics

Lectins are present throughout the plant kingdom and are reported to be involved in diverse biological processes. In this study, we provide a comparative analysis of the lectin families from model species in a phylogenetic framework. The analysis focuses on the different plant lectin domains identified in five representative core angiosperm genomes (Arabidopsisthaliana, Glycine max, Cucumis sativus, Oryza sativa ssp. japonica and Oryza sativa ssp. indica). The genomes were screened for genes encoding lectin domains using a combination of Basic Local Alignment Search Tool (BLAST), hidden Markov models, and InterProScan analysis. Additionally, phylogenetic relationships were investigated by constructing maximum likelihood phylogenetic trees. The results demonstrate that the majority of the lectin families are present in each of the species under study. Domain organization analysis showed that most identified proteins are multi-domain proteins, owing to the modular rearrangement of protein domains during evolution. Most of these multi-domain proteins are widespread, while others display a lineage-specific distribution. Furthermore, the phylogenetic analyses reveal that some lectin families evolved to be similar to the phylogeny of the plant species, while others share a closer evolutionary history based on the corresponding protein domain architecture. Our results yield insights into the evolutionary relationships and functional divergence of plant lectins

Amaranthin-Like Proteins with Aerolysin Domains in Plants

International audienceAmaranthin is a homodimeric lectin that was first discovered in the seeds of Amaranthus caudatus and serves as a model for the family of amaranthin-like lectins. Though these lectins have been purified and characterized only from plant species belonging to the Amaranthaceae, evidence accumulated in recent years suggests that sequences containing amaranthin domains are widely distributed in plants. In this study, 84 plant genomes have been screened to investigate the distribution of amaranthin domains. A total of 265 sequences with amaranthin domains were retrieved from 34 plant genomes. Within this group of amaranthin homologs, 22 different domain architectures can be distinguished. The most common domain combination consists of two amaranthin domains followed by a domain with sequence similarity to aerolysin. The latter protein belongs to the group of β-pore-forming toxins produced by bacteria such as Aeromonas sp. and exerts its toxicity by making transmembrane pores in the target membrane, as such facilitating bacterial invasion. In addition, amaranthin domains also occur in association with five other protein domains, including the fascin domain, the alpha/beta hydrolase domain, the TRAF-like domain, the B box type zinc finger domain and the Bet v1 domain. All 16 amaranthin-like proteins retrieved from the cucumber genome possess a similar domain architecture consisting of two amaranthin domains linked to one aerolysin domain. Based on phylogenetic differences, four sequences were selected for further investigation. Subcellular localization studies revealed that the amaranthin-like proteins from cucumber reside in the cytoplasm and/or the nucleus. Analyses using qPCR showed that the transcript levels for the amaranthin-like sequences are typically low and expression levels vary among tissues during the development of cucumber plants. Furthermore, the expression of amaranthin-like genes is enhanced after different abiotic stresses, suggesting that these amaranthin-like proteins play a role in the stress response. Finally, molecular modeling was performed to unravel the structure of amaranthin-like proteins and their carbohydrate-binding sites. This study provided valuable information on the distribution, phylogenetic relationships, and possible biological roles of amaranthin-like proteins in plants

Expression Analysis of Ribosome-inactivating Proteins From Cucumber

International audienceRibosome-inactivating proteins (RIPs) are a class of cytotoxic enzymes which possess highly specific rRNA N-glycosidase activity and are capable of catalytically inactivating prokaryotic or eukaryotic ribosomes. Due to their unique biological activities, RIPs have been considered to have great potential in medical and agricultural applications. The cucumber genome accommodates two genes encoding type 2 ribosome-inactivating proteins, further referred to as CumsaAB1 and CumsaAB2. Type 2 RIPs, represented by ricin, usually consist of two peptides linked by a disulfide bridge. A chain with N-glycosidase activity and B chain with carbohydrate-binding activity. In this study, the expression of the cucumber RIPs was analyzed. Sequence analysis showed that CumsaAB1 is synthesized with a signal peptide and subcellular localization studies further confirmed that the protein is expressed extracellularly, following the secretory pathway. Analyses of the transcript levels in various tissues during cucumber development showed that CumsaAB1 is present at extremely low levels in most tissues while the expression of CumsaAB2 is much higher, especially in leaves from plants at first-true-leaf stage and plants at the onset of flowering. Molecular modelling of the RIP sequences was performed to unravel the three-dimensional conformation of cucumber RIPs and their carbohydrate-binding sites. This study provided valuable information on the subcellular localization, the tissue-specific expression and the structure of RIPs from cucumber plants

Quantitative Proteomics Analysis of Berberine-Treated Colon Cancer Cells Reveals Potential Therapy Targets

Colon cancer is one of the most lethal malignancies worldwide. Berberine has been found to exert potential anti-colon cancer activity in vitro and in vivo, although the detailed regulatory mechanism is still unclear. This study aims to identify the underlying crucial proteins and regulatory networks associated with berberine treatment of colon cancer by using proteomics as well as publicly available transcriptomics and tissue array data. Proteome profiling of berberine-treated colon cancer cells demonstrated that among 5130 identified proteins, the expression of 865 and 675 proteins were changed in berberine-treated HCT116 and DLD1 cells, respectively. Moreover, 54 differently expressed proteins that overlapped in both cell lines were mainly involved in mitochondrial protein synthesis, calcium mobilization, and metabolism of fat-soluble vitamins. Finally, GTPase ERAL1 and mitochondrial ribosomal proteins including MRPL11, 15, 30, 37, 40, and 52 were identified as hub proteins of berberine-treated colon cancer cells. These proteins have higher transcriptional and translational levels in colon tumor samples than that of colon normal samples, and were significantly down-regulated in berberine-treated colon cancer cells. Genetic dependency analysis showed that silencing the gene expression of seven hub proteins could inhibit the proliferation of colon cancer cells. This study sheds a light for elucidating the berberine-related regulatory signaling pathways in colon cancer, and suggests that ERAL1 and several mitochondrial ribosomal proteins might be promising therapeutic targets for colon cancer