Advances and Mechanisms of RNA-Ligand Interaction Predictions.

The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA's specific recognition of other molecules. With advancements in biotechnology, RNA-ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions.

Thermodynamic and Kinetic Analysis of TiN Precipitation in Nickel-Based Superalloys During Solidification.

Large TiN inclusions in nickel-based superalloys promote micropore formation, compromising the mechanical properties of the alloys. However, current research lacks a comprehensive coupled model that considers both solute element microsegregation and TiN precipitation specifically in nickel-based superalloys. This study investigated TiN precipitation during the solidification of IN718 alloys through a combined thermodynamic and kinetic approach.

AI-integrated network for RNA complex structure and dynamic prediction.

RNA complexes are essential components in many cellular processes. The functions of these complexes are linked to their tertiary structures, which are shaped by detailed interface information, such as binding sites, interface contact, and dynamic conformational changes. Network-based approaches have been widely used to analyze RNA complex structures.

Advances and Challenges in Scoring Functions for RNA-Protein Complex Structure Prediction.

RNA-protein complexes play a crucial role in cellular functions, providing insights into cellular mechanisms and potential therapeutic targets. However, experimental determination of these complex structures is often time-consuming and resource-intensive, and it rarely yields high-resolution data. Many computational approaches have been developed to predict RNA-protein complex structures in recent years.

High-Precision Instance Segmentation Detection of Micrometer-Scale Primary Carbonitrides in Nickel-Based Superalloys for Industrial Applications.

In industrial production, identification and characterization of micron-sized second phases, such as carbonitrides in alloys, hold significant importance for optimizing alloy compositions and processes. However, conventional methods based on threshold segmentation suffer from drawbacks, including low accuracy, inefficiency, and subjectivity. Addressing these limitations, this study introduced a carbonitride instance segmentation model tailored for various nickel-based superalloys.

Predicting Small Molecule Binding Nucleotides in RNA Structures Using RNA Surface Topography.

RNA small molecule interactions play a crucial role in drug discovery and inhibitor design. Identifying RNA small molecule binding nucleotides is essential and requires methods that exhibit a high predictive ability to facilitate drug discovery and inhibitor design. Existing methods can predict the binding nucleotides of simple RNA structures, but it is hard to predict binding nucleotides in complex RNA structures with junctions.

Evaluation of DNA-protein complex structures using the deep learning method.

Biological processes such as transcription, repair, and regulation require interactions between DNA and proteins. To unravel their functions, it is imperative to determine the high-resolution structures of DNA-protein complexes. However, experimental methods for this purpose are costly and technically demanding.

RPflex: A Coarse-Grained Network Model for RNA Pocket Flexibility Study.

RNA regulates various biological processes, such as gene regulation, RNA splicing, and intracellular signal transduction. RNA's conformational dynamics play crucial roles in performing its diverse functions. Thus, it is essential to explore the flexibility characteristics of RNA, especially pocket flexibility.

Evaluating native-like structures of RNA-protein complexes through the deep learning method.

RNA-protein complexes underlie numerous cellular processes, including basic translation and gene regulation. The high-resolution structure determination of the RNA-protein complexes is essential for elucidating their functions. Therefore, computational methods capable of identifying the native-like RNA-protein structures are needed.

Perceived stress and mobile phone addiction among college students: The roles of self-control and security.

Objective: According to the General Strain Theory, stress can lead to a range of problem behaviors. In the current study, we focused on the association between perceived stress and mobile phone addiction. We hypothesized that this association is mediated by low self-control and that the first path of the mediation is moderated by security.

Depression and Loneliness as Mediators Between Social Support and Mobile Phone Addiction.

Background: Mobile phone addiction among adolescents has attracted a lot of attention in recent years. Previous researches revealed a significant relation between low social support and addiction. This study aim to investigated the association between social support and mobile phone addiction, and the mediating effects of depression and loneliness.

DLSSAffinity: protein-ligand binding affinity prediction a deep learning model.

Evaluating the protein-ligand binding affinity is a substantial part of the computer-aided drug discovery process. Most of the proposed computational methods predict protein-ligand binding affinity using either limited full-length protein 3D structures or simple full-length protein sequences as the input features. Thus, protein-ligand binding affinity prediction remains a fundamental challenge in drug discovery.

The TAR binding dynamics and its implication in Tat degradation mechanism.

Human immunodeficiency virus (HIV) is a retrovirus that progressively attacks the human immune system. It is known that the HIV viral protein Tat recruits the host elongation factor, positive transcription elongation factor b (P-TEFb), onto the nascent HIV viral transactivation response element (TAR) RNA to overcome the elongation pause for active transcription of the entire viral genome. Interestingly, there exists an amplifying feedback loop between Tat and TAR-a reduction in Tat increases the elongation pause, resulting in more TAR RNA fragments instead of the entire viral genome transcript, and the TAR fragments as a scaffold for PRC2 complex in turn promote Tat ubiquitination and degradation.