Indole contributes to tetracycline resistance via the outer membrane protein OmpN in Vibrio splendidus

As an interspecies and interkingdom signaling molecule, indole has recently received attention for its diverse effects on the physiology of both bacteria and hosts. In this study, indole increased the tetracycline resistance of Vibrio splendidus. The minimal inhibitory concentration of tetracycline was 10 mu g/mL, and the OD600 of V. splendidus decreased by 94.5% in the presence of 20 mu g/mL tetracycline; however, the OD600 of V. splendidus with a mixture of 20 mu g/mL tetracycline and 125 mu M indole was 10- or 4.5-fold higher than that with only 20 mu g/mL tetracycline at different time points. The percentage of cells resistant to 10 mu g/mL tetracycline was 600-fold higher in the culture with an OD600 of approximately 2.0 (higher level of indole) than that in the culture with an OD600 of 0.5, which also meant that the level of indole was correlated to the tetracycline resistance of V. splendidus. Furthermore, one differentially expressed protein, which was identified as the outer membrane porin OmpN using SDS-PAGE combined with MALDI-TOF/TOF MS, was upregulated. Consequently, the expression of the ompN gene in the presence of either tetracycline or indole and simultaneously in the presence of indole and tetracycline was upregulated by 1.8-, 2.54-, and 6.01-fold, respectively, compared to the control samples. The combined results demonstrated that indole enhanced the tetracycline resistance of V. splendidus, and this resistance was probably due to upregulation of the outer membrane porin OmpN

Link Prediction on Heterophilic Graphs via Disentangled Representation Learning

Link prediction is an important task that has wide applications in various domains. However, the majority of existing link prediction approaches assume the given graph follows homophily assumption, and designs similarity-based heuristics or representation learning approaches to predict links. However, many real-world graphs are heterophilic graphs, where the homophily assumption does not hold, which challenges existing link prediction methods. Generally, in heterophilic graphs, there are many latent factors causing the link formation, and two linked nodes tend to be similar in one or two factors but might be dissimilar in other factors, leading to low overall similarity. Thus, one way is to learn disentangled representation for each node with each vector capturing the latent representation of a node on one factor, which paves a way to model the link formation in heterophilic graphs, resulting in better node representation learning and link prediction performance. However, the work on this is rather limited. Therefore, in this paper, we study a novel problem of exploring disentangled representation learning for link prediction on heterophilic graphs. We propose a novel framework DisenLink which can learn disentangled representations by modeling the link formation and perform factor-aware message-passing to facilitate link prediction. Extensive experiments on 13 real-world datasets demonstrate the effectiveness of DisenLink for link prediction on both heterophilic and hemophiliac graphs. Our codes are available at https://github.com/sjz5202/DisenLin

Rethinking the Reference-based Distinctive Image Captioning

Distinctive Image Captioning (DIC) -- generating distinctive captions that describe the unique details of a target image -- has received considerable attention over the last few years. A recent DIC work proposes to generate distinctive captions by comparing the target image with a set of semantic-similar reference images, i.e., reference-based DIC (Ref-DIC). It aims to make the generated captions can tell apart the target and reference images. Unfortunately, reference images used by existing Ref-DIC works are easy to distinguish: these reference images only resemble the target image at scene-level and have few common objects, such that a Ref-DIC model can trivially generate distinctive captions even without considering the reference images. To ensure Ref-DIC models really perceive the unique objects (or attributes) in target images, we first propose two new Ref-DIC benchmarks. Specifically, we design a two-stage matching mechanism, which strictly controls the similarity between the target and reference images at object-/attribute- level (vs. scene-level). Secondly, to generate distinctive captions, we develop a strong Transformer-based Ref-DIC baseline, dubbed as TransDIC. It not only extracts visual features from the target image, but also encodes the differences between objects in the target and reference images. Finally, for more trustworthy benchmarking, we propose a new evaluation metric named DisCIDEr for Ref-DIC, which evaluates both the accuracy and distinctiveness of the generated captions. Experimental results demonstrate that our TransDIC can generate distinctive captions. Besides, it outperforms several state-of-the-art models on the two new benchmarks over different metrics.Comment: ACM MM 202

China is on the track tackling Enteromorpha spp forming green tide

Green tide management is supposed to be a long term fight rather than an episode during the 29th Olympic Games for China, since it has been gaining in scale and frequency during the past 3 decades in both marine and estuary environment all over the world. A number of rapid-responding studies including oceanographic comprehensive surveys along the coastline have been conducted during the bloom and post-bloom periods in 2008 by Chinese marine scientists. The preliminary results are as below: (1) phylogenetic analysis indicates that the bloom forming alga forms a clade with representatives of the green seaweed Enteromorpha linza, though, the alga has been identified as E. proliera by means of morphological; (2) the present data suggest that the bloom was originated from south of Yellow Sea, but not the severely affected area near Qingdao City; (3) pathways of reproduction for E. prolifera have approved to be multifarious, including sexual, asexual and vegetative propagation; (4) somatic cells may act as a propagule bank, which is supposed to be a very dangerous transmitting way for its marked movability, adaptability and viability; (5) pyrolysis of the alga showed that three stages appeared during the process, which are dehydration (18–20^o^C), main devolatilization (200–450^o^C) and residual decomposition (450–750^o^C), and activation energy of the alga was determined at 237.23 KJ•mol^-1^. Although the scarce knowlegde on E. prolifera not yet allow a fully understanding of the green tide, some of the results suggests possible directions in further green tide research and management

Rethinking Multi-Modal Alignment in Video Question Answering from Feature and Sample Perspectives

Reasoning about causal and temporal event relations in videos is a new destination of Video Question Answering (VideoQA).The major stumbling block to achieve this purpose is the semantic gap between language and video since they are at different levels of abstraction. Existing efforts mainly focus on designing sophisticated architectures while utilizing frame- or object-level visual representations. In this paper, we reconsider the multi-modal alignment problem in VideoQA from feature and sample perspectives to achieve better performance. From the view of feature,we break down the video into trajectories and first leverage trajectory feature in VideoQA to enhance the alignment between two modalities. Moreover, we adopt a heterogeneous graph architecture and design a hierarchical framework to align both trajectory-level and frame-level visual feature with language feature. In addition, we found that VideoQA models are largely dependent on language priors and always neglect visual-language interactions. Thus, two effective yet portable training augmentation strategies are designed to strengthen the cross-modal correspondence ability of our model from the view of sample. Extensive results show that our method outperforms all the state-of-the-art models on the challenging NExT-QA benchmark, which demonstrates the effectiveness of the proposed method

MTA3-SOX2 Module Regulates Cancer Stemness and Contributes to Clinical Outcomes of Tongue Carcinoma.

Cancer cell plasticity plays critical roles in both tumorigenesis and tumor progression. Metastasis-associated protein 3 (MTA3), a component of the nucleosome remodeling and histone deacetylase (NuRD) complex and multi-effect coregulator, can serve as a tumor suppressor in many cancer types. However, the role of MTA3 in tongue squamous cell cancer (TSCC) remains unclear although it is the most prevalent head and neck cancer and often with poor prognosis. By analyzing both published datasets and clinical specimens, we found that the level of MTA3 was lower in TSCC compared to normal tongue tissues. Data from gene set enrichment analysis (GSEA) also indicated that MTA3 was inversely correlated with cancer stemness. In addition, the levels of MTA3 in both samples from TSCC patients and TSCC cell lines were negatively correlated with SOX2, a key regulator of the plasticity of cancer stem cells (CSCs). We also found that SOX2 played an indispensable role in MTA3-mediated CSC repression. Using the mouse model mimicking human TSCC we demonstrated that the levels of MTA3 and SOX2 decreased and increased, respectively, during the process of tumorigenesis and progression. Finally, we showed that the patients in the MTA

Text-to-Song: Towards Controllable Music Generation Incorporating Vocals and Accompaniment

A song is a combination of singing voice and accompaniment. However, existing works focus on singing voice synthesis and music generation independently. Little attention was paid to explore song synthesis. In this work, we propose a novel task called text-to-song synthesis which incorporating both vocals and accompaniments generation. We develop Melodist, a two-stage text-to-song method that consists of singing voice synthesis (SVS) and vocal-to-accompaniment (V2A) synthesis. Melodist leverages tri-tower contrastive pretraining to learn more effective text representation for controllable V2A synthesis. A Chinese song dataset mined from a music website is built up to alleviate data scarcity for our research. The evaluation results on our dataset demonstrate that Melodist can synthesize songs with comparable quality and style consistency. Audio samples can be found in https://text2songMelodist.github.io/Sample/.Comment: ACL 2024 Mai

MTA3-SOX2 Module Regulates Cancer Stemness and Contributes to Clinical Outcomes of Tongue Carcinoma

Cancer cell plasticity plays critical roles in both tumorigenesis and tumor progression. Metastasis-associated protein 3 (MTA3), a component of the nucleosome remodeling and histone deacetylase (NuRD) complex and multi-effect coregulator, can serve as a tumor suppressor in many cancer types. However, the role of MTA3 in tongue squamous cell cancer (TSCC) remains unclear although it is the most prevalent head and neck cancer and often with poor prognosis. By analyzing both published datasets and clinical specimens, we found that the level of MTA3 was lower in TSCC compared to normal tongue tissues. Data from gene set enrichment analysis (GSEA) also indicated that MTA3 was inversely correlated with cancer stemness. In addition, the levels of MTA3 in both samples from TSCC patients and TSCC cell lines were negatively correlated with SOX2, a key regulator of the plasticity of cancer stem cells (CSCs). We also found that SOX2 played an indispensable role in MTA3-mediated CSC repression. Using the mouse model mimicking human TSCC we demonstrated that the levels of MTA3 and SOX2 decreased and increased, respectively, during the process of tumorigenesis and progression. Finally, we showed that the patients in the MTA