Retraction: The lncRNA XIST/miR-29b-3p/COL3A1 axis regulates central carbon metabolism in head and neck squamous cell carcinoma and is associated with poor tumor prognosis.

[This retracts the article DOI: 10.21037/atm-23-30.].

In situ study of structural changes: Exploring the mechanism of protein corona transition from soft to hard.

Hypothesis: The process of protein corona changes has been widely believed to follow the Vroman effect, while protein structural change during the process is rarely reported, due to the lack of analytical methods. In-situ interpretation for protein structural change is critical to processes such as the recognition and transport of nanomaterials.

Experiments: Molecular dynamics (MD) simulation was used to predict the deflection and twist of the protein tertiary structure.

The lncRNA XIST/miR-29b-3p/COL3A1 axis regulates central carbon metabolism in head and neck squamous cell carcinoma and is associated with poor tumor prognosis.

Background: Recent evidence shows that COL3A1 promotes the progression of many types of cancer. The purpose of our study is to explore the correlation between COL3A1 and the prognosis of patients with head and neck squamous cell carcinoma (HNSCC) and its potential mechanism.

Methods: We initially screened the differentially expressed gene COL3A1 in The Cancer Genome Atlas (TCGA) database, and the association between the expression level of COL3A1, prognosis, and the clinical parameters of HNSCC patients was verified.

Soft integration of a neural cells network and bionic interfaces.

Both glial cells and neurons can be considered basic computational units in neural networks, and the brain-computer interface (BCI) can play a role in awakening the latency portion and being sensitive to positive feedback through learning. However, high-quality information gained from BCI requires invasive approaches such as microelectrodes implanted under the endocranium. As a hard foreign object in the aqueous microenvironment, the soft cerebral cortex's chronic inflammation state and scar tissue appear subsequently.

VDL-Surrogate: A View-Dependent Latent-based Model for Parameter Space Exploration of Ensemble Simulations.

We propose VDL-Surrogate, a view-dependent neural-network-latent-based surrogate model for parameter space exploration of ensemble simulations that allows high-resolution visualizations and user-specified visual mappings. Surrogate-enabled parameter space exploration allows domain scientists to preview simulation results without having to run a large number of computationally costly simulations. Limited by computational resources, however, existing surrogate models may not produce previews with sufficient resolution for visualization and analysis.

GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations.

We propose GNN-Surrogate, a graph neural network-based surrogate model to explore the parameter space of ocean climate simulations. Parameter space exploration is important for domain scientists to understand the influence of input parameters (e.g.