Identifying influential nodes in brain networks via self-supervised graph-transformer.

Background: Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes such as the regions of high centrality or rich-club organization. However, this approach relies heavily on prior knowledge from graph theory, which may overlook the intrinsic characteristics of the brain network, especially when its architecture is not fully understood.

Substrate interactions guide cyclase engineering and lasso peptide diversification.

Lasso peptides are a diverse class of naturally occurring, highly stable molecules kinetically trapped in a distinctive [1]rotaxane conformation. How the ATP-dependent lasso cyclase constrains a relatively unstructured substrate peptide into a low entropy product has remained a mystery owing to poor enzyme stability and activity in vitro. In this study, we combined substrate tolerance data with structural predictions, bioinformatic analysis, molecular dynamics simulations and mutational scanning to construct a model for the three-dimensional orientation of the substrate peptide in the lasso cyclase active site.

Favored Amorphous LiSi Process with Restrained Volume Change Enabling Long Cycling Quasi-Solid-State SiO anode.

Silicon-based anodes are becoming promising materials due to their high specific capacity. However, the intrinsically large volume change brought about by the alloying reaction results in the crushing of the active particles and destruction of the electrode structure, which severely limits its practical application. Various structured and modified silica-based anodes exhibit improved cycling stability and the demonstrated ability to mitigate their volume changes through interfacial and binder strategies.

Solvation Effect: The Cornerstone of High-Performance Battery Design for Commercialization-Driven Sodium Batteries.

Sodium batteries (SBs) emerge as a potential candidate for large-scale energy storage and have become a hot topic in the past few decades. In the previous researches on electrolyte, designing electrolytes with the solvation theory has been the most promising direction is to improve the electrochemical performance of batteries through solvation theory. In general, the four essential factors for the commercial application of SBs, which are cost, low temperature performance, fast charge performance and safety.

Differentiating brain states via multi-clip random fragment strategy-based interactive bidirectional recurrent neural network.

EEG is widely adopted to study the brain and brain computer interface (BCI) for its non-invasiveness and low costs. Specifically EEG can be applied to differentiate brain states, which is important for better understanding the working mechanisms of the brain. Recurrent neural network (RNN)-based learning strategy has been widely utilized to differentiate brain states, because its optimization architectures improve the classification performance for differentiating brain states at the group level.

An explainable deep learning framework for characterizing and interpreting human brain states.

Deep learning approaches have been widely adopted in the medical image analysis field. However, a most of existing deep learning approaches focus on achieving promising performances such as classification, detection, and segmentation, and much less effort is devoted to the explanation of the designed models. Similarly, in the brain imaging field, many deep learning approaches have been designed and applied to characterize and predict human brain states.

Differentiate preterm and term infant brains and characterize the corresponding biomarkers DICCCOL-based multi-modality graph neural networks.

Preterm birth is a worldwide problem that affects infants throughout their lives significantly. Therefore, differentiating brain disorders, and further identifying and characterizing the corresponding biomarkers are key issues to investigate the effects of preterm birth, which facilitates the interventions for neuroprotection and improves outcomes of prematurity. Until now, many efforts have been made to study the effects of preterm birth; however, most of the studies merely focus on either functional or structural perspective.

A hybrid learning framework for fine-grained interpretation of brain spatiotemporal patterns during naturalistic functional magnetic resonance imaging.

Naturalistic stimuli, including movie, music, and speech, have been increasingly applied in the research of neuroimaging. Relative to a resting-state or single-task state, naturalistic stimuli can evoke more intense brain activities and have been proved to possess higher test-retest reliability, suggesting greater potential to study adaptive human brain function. In the current research, naturalistic functional magnetic resonance imaging (N-fMRI) has been a powerful tool to record brain states under naturalistic stimuli, and many efforts have been devoted to study the high-level semantic features from spatial or temporal representations N-fMRI.

Joint Analysis of Functional and Structural Connectomes Between Preterm and Term Infant Brains via Canonical Correlation Analysis With Locality Preserving Projection.

Preterm is a worldwide problem that affects infants' lives significantly. Moreover, the early impairment is more than limited to isolated brain regions but also to global and profound negative outcomes later, such as cognitive disorder. Therefore, seeking the differences of brain connectome between preterm and term infant brains is a vital step for understanding the developmental impairment caused by preterm.

Association between sex hormones regulation-related SNP rs12233719 and lung cancer risk among never-smoking Chinese women.

Background: The mechanism of rapidly increased non-small cell lung cancer (NSCLC) among never-smoking Chinese women has not been elucidated. Ovarian sex steroid hormones have been suggested to counteract lung cancer development, and sex hormone-binding globulin (SHBG) is essential in sex hormones regulation. This study aims to exploring single nucleotide polymorphisms (SNPs) in genomic regions associated with SHBG concentrations that contributed to never-smoking female NSCLC.

SNP rs17079281 decreases lung cancer risk through creating an YY1-binding site to suppress DCBLD1 expression.

Genome-wide association studies (GWAS) have identified numerous genetic variants that are associated with lung cancer risk, but the biological mechanisms underlying these associations remain largely unknown. Here we investigated the functional relevance of a genetic region in 6q22.2 which was identified to be associated with lung cancer risk in our previous GWAS.

Silica nanoparticles enhance germ cell apoptosis by inducing reactive oxygen species (ROS) formation in Caenorhabditis elegans.

Silica nanoparticles (SiO NPs) are widely used in daily life and can enter organisms through several pathways, often causing unpredictable toxicity. Although SiO NPs are known to cause damage to the respiratory system, little is known about their oral toxicity, and their potential harm to the reproductive system is unclear. In this study, we used a Caenorhabditis elegans model to clarify SiO NPs oral toxicity in vivo and explore their effect on the reproductive system.

MicroRNA Biomarker for Detecting the Risk of Lung Cancer.

Background: Lung cancer is one of the leading diagnosed cancers worldwide, and microRNAs could be used as biomarkers to diagnose lung cancer. has been demonstrated to affect the prognosis of NSCLC (non-small-cell lung cancer) in a previous study. However, the diagnostic value of in lung cancer has not been investigated.

SNPs in LncRNA genes are associated with non-small cell lung cancer in a Chinese population.

Background: It has indicated that single nuclear polymorphisms (SNPs) in the regions encoding non-coding transcripts are associated with lung cancer susceptibility. In a previous microarray study, we identified 13 differentially expressed long non-coding RNAs (lncRNAs) in non-small cell lung cancer (NSCLC) and associations of SNPs in these lncRNA genes with lung cancer were unknown. We conducted a case-control study to address this issue.

LncRNA LCPAT1 is involved in DNA damage induced by CSE.

Cigarette smoking plays an important role in the process of lung cancer, during which DNA damage is proved to be involved. Non-coding RNAs are found to be involved in the DNA damage and repair processes induced by cigarette smoke. In the present study, we investigated the role of lncRNA LCPAT1 in DNA damage caused by CSE in Beas-2B cells.

LncRNA LCPAT1 Mediates Smoking/ Particulate Matter 2.5-Induced Cell Autophagy and Epithelial-Mesenchymal Transition in Lung Cancer Cells via RCC2.

Background/aims: Ecological studies have shown that air pollution and prevalence of cigarette smoking are positively correlated. Evidence also suggests a synergistic effect of cigarette smoking and PM2.5 exposure (Environmental Particulate Matter ≤ 2.

Effect of Calcipotriol on IFN-γ-Induced Keratin 17 Expression in Immortalized Human Epidermal Keratinocyte Cells.

BACKGROUND Calcipotriol ointment has been demonstrated to be a very safe and effective topical drug for psoriasis. This study aims to investigate the effect of calcipotriol on IFN-γ-induced keratin 17 (K17) expression in a human keratinocyte cell line (HaCaT), which is a widely accepted as a mimic in vitro model for psoriasis. MATERIAL AND METHODS We used Western blot, immunofluorescence staining, and luciferase reporter system assays to evaluate the expression of K17 and the possible underlying mechanisms.