82 research outputs found
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
Improving Fairness of Graph Neural Networks: A Graph Counterfactual Perspective
Graph neural networks have shown great ability in representation (GNNs)
learning on graphs, facilitating various tasks. Despite their great performance
in modeling graphs, recent works show that GNNs tend to inherit and amplify the
bias from training data, causing concerns of the adoption of GNNs in high-stake
scenarios. Hence, many efforts have been taken for fairness-aware GNNs.
However, most existing fair GNNs learn fair node representations by adopting
statistical fairness notions, which may fail to alleviate bias in the presence
of statistical anomalies. Motivated by causal theory, there are several
attempts utilizing graph counterfactual fairness to mitigate root causes of
unfairness. However, these methods suffer from non-realistic counterfactuals
obtained by perturbation or generation. In this paper, we take a causal view on
fair graph learning problem. Guided by the casual analysis, we propose a novel
framework CAF, which can select counterfactuals from training data to avoid
non-realistic counterfactuals and adopt selected counterfactuals to learn fair
node representations for node classification task. Extensive experiments on
synthetic and real-world datasets show the effectiveness of CAF
Improvement of the Magnetic Properties of Nanocrystalline Nd 12.3
Nd12.3−xDyxFe81.7Zr0.8Nb0.8Cu0.4B6.0  (x=0–2.5) ribbons have been prepared by melt-spun at 30 m/s and subsequent annealing. The influence of addition of Dy on the crystallization behavior, magnetic properties, and microstructure were investigated. Differential scanning calorimeter (DSC) and X-ray diffraction (XRD) revealed a single-phase material. Microstructure studies using transmission electron microscopy (TEM) had shown a significant microstructure refinement with Dy addition. Wohlfarth’s analysis showed that the exchange coupling interactions increased first with Dy content x increasing, reached the maximum value at x=0.5, and then slightly decreased with x further increasing. Optimal magnetic properties with Jr=1.09 T, Hci=1048 kA/m, and BHmax=169.5 kJ/m3 are achieved by annealing the melt-spun ribbons with x=0.5 at% at 700°C for 10 min
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Graph Neural Networks (GNNs) have made rapid developments in the recent
years. Due to their great ability in modeling graph-structured data, GNNs are
vastly used in various applications, including high-stakes scenarios such as
financial analysis, traffic predictions, and drug discovery. Despite their
great potential in benefiting humans in the real world, recent study shows that
GNNs can leak private information, are vulnerable to adversarial attacks, can
inherit and magnify societal bias from training data and lack interpretability,
which have risk of causing unintentional harm to the users and society. For
example, existing works demonstrate that attackers can fool the GNNs to give
the outcome they desire with unnoticeable perturbation on training graph. GNNs
trained on social networks may embed the discrimination in their decision
process, strengthening the undesirable societal bias. Consequently, trustworthy
GNNs in various aspects are emerging to prevent the harm from GNN models and
increase the users' trust in GNNs. In this paper, we give a comprehensive
survey of GNNs in the computational aspects of privacy, robustness, fairness,
and explainability. For each aspect, we give the taxonomy of the related
methods and formulate the general frameworks for the multiple categories of
trustworthy GNNs. We also discuss the future research directions of each aspect
and connections between these aspects to help achieve trustworthiness
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
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
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