STARFormer: A novel spatio-temporal aggregation reorganization transformer of FMRI for brain disorder diagnosis.

Many existing methods that use functional magnetic resonance imaging (fMRI) to classify brain disorders, such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), often overlook the integration of spatial and temporal dependencies of the blood oxygen level-dependent (BOLD) signals, which may lead to inaccurate or imprecise classification results. To solve this problem, we propose a spatio-temporal aggregation reorganization transformer (STARFormer) that effectively captures both spatial and temporal features of BOLD signals by incorporating three key modules. The region of interest (ROI) spatial structure analysis module uses eigenvector centrality (EC) to reorganize brain regions based on effective connectivity, highlighting critical spatial relationships relevant to the brain disorder.

EEG Emotion Copilot: Optimizing lightweight LLMs for emotional EEG interpretation with assisted medical record generation.

In the fields of affective computing (AC) and brain-computer interface (BCI), the analysis of physiological and behavioral signals to discern individual emotional states has emerged as a critical research frontier. While deep learning-based approaches have made notable strides in EEG emotion recognition, particularly in feature extraction and pattern recognition, significant challenges persist in achieving end-to-end emotion computation, including rapid processing, individual adaptation, and seamless user interaction. This paper presents the EEG Emotion Copilot, a system optimizing a lightweight large language model (LLM) with 0.

Active fault diagnosis for AUV over-actuated systems based on UIOs and control allocation.

The navigation performance of autonomous underwater vehicles (AUVs) relies mainly on the propulsion system, which makes the study of the safety and reliability of the propulsion system highly significant. This paper investigates an over-actuated AUV propulsion system with six degrees of freedom and presents an active fault diagnosis framework that combines unknown input observers (UIO) with control allocation. The simulation results show that the proposed integrated methodology successfully performs fault detection, magnitude estimation, and isolation for AUV over-actuated systems.

Application of an improved spherical omnidirectional loudspeaker array model in environmental noise pollution control.

Controlling environmental noise pollution presents a significant challenge for current noise control efforts, mainly when the noise reduction target is mobile. Traditional systems are often inadequate due to their fixed and limited coverage range, which struggles to meet the demands of a dynamic environment. In response, the paper proposes an innovative method for spatial target tracking and noise control based on spherical omnidirectional loudspeaker arrays.

MTX2 facilitates PKM2 tetramerization to promote cardiac glucose metabolism and protects the heart against ischemia/reperfusion injury.

Myocardial ischemia reperfusion (I/R) injury is a major cause of adverse outcomes following revascularization therapy. Although alterations in metabolic activities during reperfusion have been implicated, the molecular mechanisms underlying the pathogenesis of I/R injury remain elusive. Metaxin 2 (MTX2), initially identified as a core component of protein import complexes, has recently been characterized in diverse cellular functions.

Iron Quantification Using Spectral CT-Based Material Decomposition Technique for Noninvasive Hepatic Fibrosis Staging: An Experimental Study.

Background: Accurate staging is crucial for effective treatment of hepatic fibrosis (HF). This study evaluates whether spectral CT-derived liver iron concentration (LIC) quantification correlates with HF stages and validates its efficacy in diagnostic staging.

Methods: One hundred seventy-five New Zealand white rabbits were included in the prospective study (35 normal controls; 140 HF models).

Characteristics of atmospheric reduced-sulfur compounds at a suburban site of Shanghai.

Atmospheric dimethyl sulfide (DMS, CHSCH) and methanethiol (MeSH, CHSH) have been widely studied and recognized to significantly constrain the atmospheric sulfur budget. Nevertheless, while the role of DMS and MeSH remains largely uncertain in inland regions, learning about dimethyl disulfide (DMDS, CHSSCH) is also limited. In this study, we measured atmospheric DMS, MeSH and DMDS in winter, from 19 December 2022 to 30 January 2023, and spring, from 24 April to 2 June 2023 with a Vocus proton-transfer-reaction time-of-flight mass spectrometer (Vocus PTR-TOF) at the Dianshan Lake (DSL) Air Quality Monitoring Supersite in a suburban area of Shanghai, China.

An anti-FAP-scFv-functionalized exosome-carrying hydrogel delivers mRNA to fibrotic nucleus pulposus cells to alleviate intervertebral disc degeneration by regulating FOXO3.

Nucleus pulposus (NP) fibrosis is a contributing factor to intervertebral disc degeneration (IVDD), which lacks an effective treatment. This study focuses on elucidating the role and mechanisms of the TGF-β signaling repressor in NP fibrosis and exploring its therapeutic potential. Single-cell RNA sequencing (scRNA-seq) was used to investigate fibrotic nucleus pulposus cell (NPC) subsets and assess TGF-β signaling activation.

A drug screening platform for protein expression levels in neurological disorders.

Neurological and psychiatric diseases and disorders affect more than half of the population. Many of these diseases are caused by the malfunctioning of protein synthesis, where too little or too much production of a protein harms a cell and its functions within the brain. We developed a drug screening platform to identify compounds that target the primary cause of these diseases, namely protein expression amounts.

MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction.

Graph deep learning (GDL) has demonstrated impressive performance in predicting population-based brain disorders (BDs) through the integration of both imaging and non-imaging data. However, the effectiveness of GDL-based methods heavily depends on the quality of modeling multi-modal population graphs and tends to degrade as the graph scale increases. Moreover, these methods often limit interactions between imaging and non-imaging data to node-edge interactions within the graph, overlooking complex inter-modal correlations and resulting in suboptimal outcomes.

The multi-frequency decomposition entropy learning for nonlinear fMRI data analysis.

Functional magnetic resonance imaging (fMRI) have been widely adopted to explore the underlying neural mechanisms between psychiatric disorders which share common neurobiology and clinical manifestations. However, the existing studies mainly focus on linear relationships and ignore nonlinear contributions. To address the above issues, we propose a new method named multi-frequency decomposition entropy (MDE) learning for inferring nonlinear functional connectivity between brain regions.

MHNet: Multi-view High-Order Network for Diagnosing Neurodevelopmental Disorders Using Resting-State fMRI.

Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction.

Dual-scale modified BiOBr with enhanced structural self-transformation at wide pH for bifunctional treatment of Cr(VI).

Hexavalent chromium (Cr(VI)) poses threat to both ecosystems and human health. Complex pollution conditions, particularly the pH levels, significantly influence the treatment process of Cr(VI). In this study, BiOBr materials were synthesized with exposed (110) facets and Bi vacancies through dual modifications at both grain and atomic scales.

Resistance mechanisms and prospects of trastuzumab.

Breast cancer that overexpresses Human Epidermal Growth Factor Receptor 2 (HER2+) due to gene amplification or overexpression constitutes 15-20% of all breast cancer cases. Trastuzumab, the first FDA-approved monoclonal antibody targeting HER2, serves as the standard first-line treatment for HER2-positive advanced breast cancer, as recommended by multiple clinical guidelines.Currently, accumulated clinical evidence reveals a considerable degree of variability in the response of HER2+ breast cancer to trastuzumab treatment.

New species group and two new species of the genus Hesperus from China (Coleoptera: Staphylinidae: Philonthina).

A new species group, Hesperus babai group, is proposed based on characters of the subserrate antennae and apical portion of median lobe hatchet-like, along with description of a new species: H. pengi sp. n.

A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6.

Background: Breast cancer is a significant public health issue worldwide, being the most prevalent cancer among women and a leading cause of death related to this disease. The molecular processes that propel breast cancer progression are not fully elucidated, highlighting the intricate nature of the underlying biology and its crucial impact on global health. The objective of this research was to perform bioinformatics analyses on breast cancer-related datasets to gain a comprehensive understanding of the molecular mechanisms at play and to identify key genes associated with the disease.

Angelic acid triggers ferroptosis in colorectal cancer cells via targeting and impairing NRF2 protein stability.

Ferroptosis is a unique programmed cell death driven by iron-dependent phospholipid peroxidation. Tumor cells that escape from the conventional therapies appear more sensitive to ferroptosis. Therefore, it is extremely urgent to find safe and efficient active ingredients that induce ferroptosis in tumor cells.

Detecting thyroid nodules along with surrounding tissues and tracking nodules using motion prior in ultrasound videos.

Ultrasound examination plays a crucial role in the clinical diagnosis of thyroid nodules. Although deep learning technology has been applied to thyroid nodule examinations, the existing methods all overlook the prior knowledge of nodules moving along a straight line in the video. We propose a new detection model, DiffusionVID-Line, and design a novel tracking algorithm, ByteTrack-Line, both of which fully leverage the prior knowledge of linear motion of nodules in thyroid ultrasound videos.