A5030911500- Topic Modeling
- Natural Language Processing Techniques
- Extracellular vesicles in disease
- Image and Signal Denoising Methods
- Advanced biosensing and bioanalysis techniques
- Advanced Graph Neural Networks
- Advanced Text Analysis Techniques
- Nanopore and Nanochannel Transport Studies
- High-Voltage Power Transmission Systems
- HVDC Systems and Fault Protection
- Power Systems and Renewable Energy
- Sparse and Compressive Sensing Techniques
- Multimodal Machine Learning Applications
- Nanoplatforms for cancer theranostics
- Microgrid Control and Optimization
- Text and Document Classification Technologies
- Imbalanced Data Classification Techniques
- Photoacoustic and Ultrasonic Imaging
- Data Mining Algorithms and Applications
- Geomechanics and Mining Engineering
- Concrete Properties and Behavior
- Image Processing Techniques and Applications
- Artificial Intelligence in Law
- Advanced Image Fusion Techniques
- Data Quality and Management
Shanghai Jiao Tong University
2009-2025
Renji Hospital
2023-2025
Yanshan University
2006-2024
Yangzhou University
2022-2023
Tsinghua University
2023
China State Construction Engineering (China)
2022
Shandong University
2015-2017
Chongqing University of Posts and Telecommunications
2015
To evade immune surveillance, tumor cells express ectonucleotide pyrophosphatase phosphodiesterase 1 (ENPP1) on the surface of their membrane, which degrades extracellular cyclic GMP-AMP (cGAMP), thereby inhibiting synthase (cGAS) stimulator interferon gene (STING) DNA-sensing pathway. fully understand this stealth mechanism, it is essential to determine whether other forms ENPP1 with hydrolytic cGAMP activity also are present in microenvironment regulate innate Herein, reported that various...
Isolation and analysis of tumor-derived extracellular vesicles (T-EVs) are important for clinical cancer management. Here, we develop a fluid multivalent magnetic interface (FluidmagFace) in microfluidic chip high-performance isolation, release, protein profiling T-EVs. The FluidmagFace increases affinity by 105-fold with fluidity-enhanced binding to improve isolation efficiency 13.9 % compared non-fluid interface. Its anti-adsorption property hydrodynamic shear minimize contamination,...
Tumor-derived extracellular vesicles (T-EVs) PD-L1 are an important biomarker for predicting immunotherapy response and can help us understand the mechanism of resistance to immunotherapy. However, this is due interference from a large proportion nontumor-derived EVs. It still challenging accurately analyze T-EVs in complex human fluids. Herein, simple ultrasensitive method based on dual-aptamer-proximity ligation assay (PLA)-guided rolling circle amplification (RCA) analysis was developed....
MicroRNAs (miRNAs) in tumor-derived extracellular vesicles (tEVs) are important cancer biomarkers for screening and early diagnosis. Multiplex detection of miRNAs tEVs facilitates accurate diagnosis but remains a challenge. Herein, we propose an encoded fusion strategy to profile the miRNA signature pancreatic A panel encoded-targeted-fusion beads was fabricated selective recognition tEVs, with turn-on fluorescence signals molecule beacons quantification barcode identification using readily...
Sparse signal representation from overcomplete dictionaries have been extensively investigated in recent research, leading to state-of-the-art results signal, image and video restoration. One of the most important issues is involved selecting proper size dictionary. However, related guidelines are still not established. In this paper, we tackle problem by proposing a so-called sub clustering K-SVD algorithm. This approach incorporates subtractive method into retain atom candidates. At same...
Extracellular vesicles (EVs) carry diverse biomolecules (e. g., nucleic acids, proteins) for intercellular communication, serving as important markers diseases. Analyzing acids derived from EVs enables non-invasive disease diagnosis and prognosis evaluation. Membrane fusion, a fundamental cellular process wherein two lipid membranes merge, facilitates cell communication cargo transport. Building on this natural phenomenon, recent years have witnessed the emergence of membrane fusion-based...
Sparse signal representation based on redundant dictionaries contributed to much progress in image processing the past decades. But common overcomplete dictionary model is not well structured and there still no guideline for selecting proper size. In this paper, we propose a new algorithm learning subspace segmentation. Our divides training data into sub-spaces constructs by extracting shared basis from multiple subspaces. The learned its size adaptive data. We analyze demonstrate ability...
Temporal psychovisual modulation (TPVM) is a newly proposed information display paradigm, which can be implemented by nonnegative matrix factorization (NMF) with additional upper bound constraints on the variables. In this paper, we study all state-of-the-art algorithms in NMF, extend them to incorporate bounds and discuss their potential use TPVM. By comparing NMF extended versions, find that: 1) error of truncated alternating least squares algorithm always fluctuates throughout iterations,...
Knowledge graph completion (KGC) can solve the problem of data sparsity in knowledge graph. A large number models for KGC task have been proposed recent years. However, underutilisation structure information around nodes is one main problems previous model, which leads to relatively single encoding information. To this end, a new model that encodes and decodes feature proposed. First, we adopt subgraph sampling method extract node structure. Moreover, convolutional network (GCN) introduced...
Most existing patch-based image denoising algorithms filter overlapping patches and aggregate multiple estimates for the same pixel via weighting. Current weighting approaches always assume restored as independent random variables, which is inconsistent with reality. In this letter, we analyze correlation among propose a bias-variance model to estimate Mean Squared Error (MSE) under various weights. The new exploits information of patches; it then utilizes optimization try minimize estimated...
Expected Patch Log Likelihood (EPLL) framework using Gaussian Mixture Model (GMM) prior for image restoration was recently proposed with its performance comparable to the state-of-the-art algorithms. However, EPLL uses generic trained from offline patches, which may not correctly represent statistics of current patches. In this paper, we extend an adaptive one, named A-EPLL, only concerns likelihood restored but also trains GMM fit degraded image. To efficiently estimate parameters in A-EPLL...
Currently, more and services are built on the cloud platform, in area of QoS-based Cloud composition, some QoS attributes user preferences not suitable for accurate representation. This paper introduces fuzzy sets theory into composition to solve above difficulties, uses triangular number describe uncertain information. Then total goals calculated based weighted-sum approach. Based new approach comparison, Pareto dominance relationship is redesigned, single objective optimization problem...
Abstract Few-shot relation extraction is one of the current research focuses. The key to this fully extract semantic information through very little training data. Intuitively, raising semantics awareness in sentences can improve efficiency model features alleviate overfitting problem few-shot learning. Therefore, we propose an enhanced feature based on prototype network relations from texts. Firstly, design a multi-level embedding encoder with position and Transformer, which uses local text...
Abstract Pre-trained language models achieve high performance on machine reading comprehension task, but these lack robustness and are vulnerable to adversarial samples. Most of the current methods for improving model based data enrichment. However, do not solve problem poor context representation model. We find that plays a key role in model, dense space results robustness. To deal with this, we propose Multi-task Reading Comprehension learning framework via Contrastive Learning. Its main...
Two control strategies for MMC in rectifier side based on arm current are proposed this paper. The work well both under balanced and unbalanced voltage conditions. have the following advantages: firstly, ac is condition by adjusting active power flowing from arms to dc bus; secondly, reference obtained using multi-hierarchy method, which keeps balance of SM capacitors better than that with nearest level control; thirdly, circulating suppressed inherently, removing need standalone suppressing...
When the multilevel modular matrix converters (MMMC) operate at low frequency, sub-module (SM) capacitors voltage mainly contains doubling-output frequency component inversely proportion to output especially when MMMC zero ripple of SM capacitor can be infinite in theory. This paper proposes two methods solve problem operating even near zero. The first one injects high zero-sequence on neutral point and negative-sequence currents each phase. To suppress fluctuation, magnitude phase angle...