A5100602640- Single-cell and spatial transcriptomics
- Multi-Criteria Decision Making
- Chemical Synthesis and Characterization
- Impact of Technology on Adolescents
- Advanced Image and Video Retrieval Techniques
- Fuzzy Systems and Optimization
- Brain Tumor Detection and Classification
- Anomaly Detection Techniques and Applications
- Optimization and Mathematical Programming
- RNA Research and Splicing
- Luminescence Properties of Advanced Materials
- Neurological Disease Mechanisms and Treatments
- DNA and Biological Computing
- Advanced MRI Techniques and Applications
- Emergency and Acute Care Studies
- Speech and Audio Processing
- Blind Source Separation Techniques
- Neural Networks and Reservoir Computing
- Immune cells in cancer
- Plant Reproductive Biology
- Crystal Structures and Properties
- Neural dynamics and brain function
- Hearing, Cochlea, Tinnitus, Genetics
- Neuroinflammation and Neurodegeneration Mechanisms
- AI in cancer detection
Shanghai University of Electric Power
2024
University of Florida
2024
The Ohio State University
2024
Sichuan University
2020-2023
Tianjin Chengjian University
2023
Hangzhou Dianzi University
2022
Zhejiang Lab
2022
Flinders University
2022
Zhengzhou University
2022
University of Tasmania
2019
Microglia undergo two-stage activation in neurodegenerative diseases, known as disease-associated microglia (DAM). TREM2 mediates the DAM2 stage transition, but what regulates first DAM1 transition is unknown. We report that glucose dyshomeostasis inhibits and PKM2 plays a role. As tumors, was aberrantly elevated both male female human AD brains, unlike it expressed active tetramers, well among + surrounding plaques 5XFAD mice. snRNAseq analyses of without Pkm2 mice revealed significant...
Single-cell RNA sequencing (scRNA-seq) offers unprecedented insights into transcriptome-wide gene expression at the single-cell level. Cell clustering has been long established in analysis of scRNA-seq data to identify groups cells with similar profiles. However, cell is technically challenging, as raw have various analytical issues, including high dimensionality and dropout values. Existing research developed deep learning models, such graph machine models contrastive learning-based for...
Objectives. This study set out to develop and validate a risk prediction tool for the early detection of heart failure (HF) onset using real-world electronic health records (EHRs). Background. While existing HF assessment models have shown promise in clinical settings, they are often tailored specific medical conditions, limiting their generalizability. Moreover, most methods rely on hand-crafted features, making it difficult capture high-dimensional, sparse, temporal nature EHR data, thus...
<title>Abstract</title> Purpose: Understanding the heterogeneity of neurodegeneration in Alzheimer’s disease (AD) development, as well identifying AD progression pathways, is vital for enhancing diagnosis, treatment, prognosis, and prevention strategies. To identify subphenotypes patients with mild cognitive impairment (MCI) using electronic health records (EHRs). Methods: We identified from OneFlorida+ Clinical Research Consortium. proposed an outcome-oriented graph neural network-based...
Biometrics is attracting increasing attention in privacy and security concerned issues, such as access control remote financial transaction. However, advanced forgery spoofing techniques are threatening the reliability of conventional biometric modalities. This has been motivating our investigation a novel yet promising modality transient evoked otoacoustic emission (TEOAE), which an acoustic response generated from cochlea after click stimulus. Unlike modalities that easily accessible or...
Abstract Single-cell RNA sequencing (scRNA-seq) offers unprecedented insights into transcriptome-wide gene expression at the single-cell level. Cell clustering has been long established in analysis of scRNA-seq data to identify groups cells with similar profiles. However, cell is technically challenging, as raw have various analytical issues, including high dimensionality and dropout values. Existing research developed deep learning models, such graph machine models contrastive...
Herein, two new Sb3+-based phosphites, Sb2O2(HPO3) (I) and Sb2O(HPO3)2 (II), were successfully obtained by ingeniously combining polyhedra containing stereochemically active lone pair (SCALP) HPO3 polar groups. Both reported compounds exhibit unique 2D van der Waals layered structures, [Sb4O4(HPO3)2]∞ [Sb2O(HPO3)2]∞, respectively, which favors with large optical anisotropy. Interestingly, the different curvatures of layers resulted in title showing significantly birefringences (0.079@546...
Xiaohongshu mixes social networking and online purchasing, making it one of the most popular Chinese apps. The research seeks to analyze Xiaohongshu’s Internet marketing strategy. In meeting objective, would embrace use secondary data which involves collecting from websites journals. results show that company's success has been achieved through good communication, connections with content creators, embracing diversity community, managing build strong its users. Furthermore, company’s ability...
Due to the wide variety of medical images and complexity human body structure, characteristics manual extraction are difficult, adaptive ability is poor, classification effect needs be improved. Aiming at shortcomings traditional image recognition methods, this paper proposes an convolutional neural network model CNN-BN-PReLU based on method. The first performs batch normalization (BN) processing input each feature map layer network, then adaptively adjusts parameters by using Parametric...
An induced subgraph is called an matching if each vertex a degree-1 in the subgraph. The \textsc{Almost Induced Matching} problem asks whether we can delete at most $k$ vertices from input graph such that remaining matching. This paper studies parameterized algorithms for this by taking size of deletion set as parameter. First, prove $6k$-vertex kernel problem, improving previous result $7k$. Second, give $O^*(1.6957^k)$-time and polynomial-space algorithm, running-time bound $O^*(1.7485^k)$.