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  1. Article ; Online: Preliminary study of silk fibroin porous scaffold for oral soft-tissue thickening.

    Li, Dexiong / Cao, Runyuan / Chen, Jiang

    Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology

    2024  Volume 40, Issue 5, Page(s) 513–521

    Abstract: Objectives: This study aimed to investigate the feasibility of three different concentrations of silk-fibroin porous scaffolds applied to oral soft-tissue thickening : Methods: Silk-fibroin scaffolds with three different concentrations (1 wt%, 3 wt%, ...

    Abstract Objectives: This study aimed to investigate the feasibility of three different concentrations of silk-fibroin porous scaffolds applied to oral soft-tissue thickening
    Methods: Silk-fibroin scaffolds with three different concentrations (1 wt%, 3 wt%, and 5 wt%; denoted as SF1, SF3, and SF5, respectively) were prepared by freeze drying and methanol enhancement. The scaffolds were characterized by scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), and thermogravimetric analysis. Pore size, porosity, and
    Results: SEM showed that the three groups of scaffolds were all cross-linked porous structures. XRD and FTIR showed that the three scaffolds were dominated by a relatively stable Silk Ⅱ structure, which degraded more slowly
    Conclusions: Silk-fibroin scaffolds can be applied to effectively thicken soft tissues, among which SF3 (3 wt%) silk fibroin scaffold exhibited the best physicochemical properties, histocompatibility, and mucosal-thickening effect.
    Language Chinese
    Publishing date 2024-04-10
    Publishing country China
    Document type Journal Article
    ZDB-ID 1202342-5
    ISSN 2618-0456 ; 1000-1182
    ISSN (online) 2618-0456
    ISSN 1000-1182
    DOI 10.7518/hxkq.2022.05.003
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Endowing Protein Language Models with Structural Knowledge

    Chen, Dexiong / Hartout, Philip / Pellizzoni, Paolo / Oliver, Carlos / Borgwardt, Karsten

    2024  

    Abstract: Understanding the relationships between protein sequence, structure and function is a long-standing biological challenge with manifold implications from drug design to our understanding of evolution. Recently, protein language models have emerged as the ... ...

    Abstract Understanding the relationships between protein sequence, structure and function is a long-standing biological challenge with manifold implications from drug design to our understanding of evolution. Recently, protein language models have emerged as the preferred method for this challenge, thanks to their ability to harness large sequence databases. Yet, their reliance on expansive sequence data and parameter sets limits their flexibility and practicality in real-world scenarios. Concurrently, the recent surge in computationally predicted protein structures unlocks new opportunities in protein representation learning. While promising, the computational burden carried by such complex data still hinders widely-adopted practical applications. To address these limitations, we introduce a novel framework that enhances protein language models by integrating protein structural data. Drawing from recent advances in graph transformers, our approach refines the self-attention mechanisms of pretrained language transformers by integrating structural information with structure extractor modules. This refined model, termed Protein Structure Transformer (PST), is further pretrained on a small protein structure database, using the same masked language modeling objective as traditional protein language models. Empirical evaluations of PST demonstrate its superior parameter efficiency relative to protein language models, despite being pretrained on a dataset comprising only 542K structures. Notably, PST consistently outperforms the state-of-the-art foundation model for protein sequences, ESM-2, setting a new benchmark in protein function prediction. Our findings underscore the potential of integrating structural information into protein language models, paving the way for more effective and efficient protein modeling Code and pretrained models are available at https://github.com/BorgwardtLab/PST.
    Keywords Quantitative Biology - Quantitative Methods ; Computer Science - Machine Learning ; Quantitative Biology - Biomolecules
    Subject code 612
    Publishing date 2024-01-26
    Publishing country us
    Document type Book ; Online
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  3. Book ; Online: Fisher Information Embedding for Node and Graph Learning

    Chen, Dexiong / Pellizzoni, Paolo / Borgwardt, Karsten

    2023  

    Abstract: Attention-based graph neural networks (GNNs), such as graph attention networks (GATs), have become popular neural architectures for processing graph-structured data and learning node embeddings. Despite their empirical success, these models rely on ... ...

    Abstract Attention-based graph neural networks (GNNs), such as graph attention networks (GATs), have become popular neural architectures for processing graph-structured data and learning node embeddings. Despite their empirical success, these models rely on labeled data and the theoretical properties of these models have yet to be fully understood. In this work, we propose a novel attention-based node embedding framework for graphs. Our framework builds upon a hierarchical kernel for multisets of subgraphs around nodes (e.g. neighborhoods) and each kernel leverages the geometry of a smooth statistical manifold to compare pairs of multisets, by "projecting" the multisets onto the manifold. By explicitly computing node embeddings with a manifold of Gaussian mixtures, our method leads to a new attention mechanism for neighborhood aggregation. We provide theoretical insights into generalizability and expressivity of our embeddings, contributing to a deeper understanding of attention-based GNNs. We propose both efficient unsupervised and supervised methods for learning the embeddings. Through experiments on several node classification benchmarks, we demonstrate that our proposed method outperforms existing attention-based graph models like GATs. Our code is available at https://github.com/BorgwardtLab/fisher_information_embedding.

    Comment: ICML 2023
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-05-12
    Publishing country us
    Document type Book ; Online
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  4. Article ; Online: Multifunctional Mesoporous Silica Nanoparticles Reinforced Silk Fibroin Composite with Antibacterial and Osteogenic Effects for Infectious Bone Rehabilitation.

    Li, Dexiong / Xie, Jing / Qiu, Yubei / Zhang, Sihui / Chen, Jiang

    International journal of nanomedicine

    2022  Volume 17, Page(s) 5661–5678

    Abstract: Background: Existing implant materials cannot meet the essential multifunctional requirements of repairing infected bone defects, such as antibacterial and osteogenesis abilities. A promising strategy to develop a versatile biomimicry composite of the ... ...

    Abstract Background: Existing implant materials cannot meet the essential multifunctional requirements of repairing infected bone defects, such as antibacterial and osteogenesis abilities. A promising strategy to develop a versatile biomimicry composite of the natural bone structure may be accomplished by combining a multifunctional nanoparticle with an organic scaffold.
    Methods: In this study, a quaternary ammonium silane-modified mesoporous silica containing nano silver (Ag@QHMS) was successfully synthesized and further combined with silk fibroin (SF) to fabricate the multifunctional nano-reinforced scaffold (SF-Ag@QHMS) using the freeze-drying method. Furthermore, the antibacterial and osteogenic effects of this composite were evaluated in vitro and in vivo.
    Results: SF-Ag@QHMS inherited a three-dimensional porous structure (porosity rate: 91.90 ± 0.62%) and better mechanical characteristics (2.11 ± 0.06 kPa) than that of the SF scaffold (porosity rate: 91.62 ± 1.65%; mechanic strength: 2.02 ± 0.01 kPa). Simultaneously, the introduction of versatile nanoparticles has provided the composite with additional antibacterial ability against
    Conclusion: The multifunctional silver-loaded mesoporous silica enhanced the mechanical strength of the composite while also ensuring greater and sustained antibacterial and osteogenic properties, allowing the SF-Ag@QHMS composite to be used to repair infected bone defects.
    MeSH term(s) Humans ; Osteogenesis ; Fibroins ; Multifunctional Nanoparticles ; Silicon Dioxide ; Communicable Diseases ; Anti-Bacterial Agents/pharmacology ; Nanoparticles
    Chemical Substances Fibroins (9007-76-5) ; Silicon Dioxide (7631-86-9) ; Anti-Bacterial Agents
    Language English
    Publishing date 2022-11-25
    Publishing country New Zealand
    Document type Journal Article
    ZDB-ID 2364941-0
    ISSN 1178-2013 ; 1176-9114
    ISSN (online) 1178-2013
    ISSN 1176-9114
    DOI 10.2147/IJN.S387347
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Corrigendum to "Estimating PM2.5 with high-resolution 1-km AOD data and an improved machine learning model over Shenzhen, China" [Sci. Total Environ. 746 (December 1, 2020) 141093].

    Chen, Wenqian / Ran, Haofan / Cao, Xiaoyi / Wang, Jingzhe / Teng, Dexiong / Chen, Jing / Zheng, Xuan

    The Science of the total environment

    2021  Volume 765, Page(s) 145325

    Language English
    Publishing date 2021-01-29
    Publishing country Netherlands
    Document type Published Erratum
    ZDB-ID 121506-1
    ISSN 1879-1026 ; 0048-9697
    ISSN (online) 1879-1026
    ISSN 0048-9697
    DOI 10.1016/j.scitotenv.2021.145325
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    Zs.A 949: Show issues Location:
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  6. Article: Predicting

    Kim, Taehoon / Chen, Dexiong / Hornauer, Philipp / Emmenegger, Vishalini / Bartram, Julian / Ronchi, Silvia / Hierlemann, Andreas / Schröter, Manuel / Roqueiro, Damian

    Frontiers in neuroinformatics

    2023  Volume 16, Page(s) 1032538

    Abstract: Modern Graph Neural Networks (GNNs) provide opportunities to study the determinants underlying the complex activity patterns of biological neuronal networks. In this study, we applied GNNs to a large-scale electrophysiological dataset of rodent primary ... ...

    Abstract Modern Graph Neural Networks (GNNs) provide opportunities to study the determinants underlying the complex activity patterns of biological neuronal networks. In this study, we applied GNNs to a large-scale electrophysiological dataset of rodent primary neuronal networks obtained by means of high-density microelectrode arrays (HD-MEAs). HD-MEAs allow for long-term recording of extracellular spiking activity of individual neurons and networks and enable the extraction of physiologically relevant features at the single-neuron and population level. We employed established GNNs to generate a combined representation of single-neuron and connectivity features obtained from HD-MEA data, with the ultimate goal of predicting changes in single-neuron firing rate induced by a pharmacological perturbation. The aim of the main prediction task was to assess whether single-neuron and functional connectivity features, inferred under baseline conditions, were informative for predicting changes in neuronal activity in response to a perturbation with Bicuculline, a GABA
    Language English
    Publishing date 2023-01-11
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2452979-5
    ISSN 1662-5196
    ISSN 1662-5196
    DOI 10.3389/fninf.2022.1032538
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  7. Book ; Online: Unsupervised Manifold Alignment with Joint Multidimensional Scaling

    Chen, Dexiong / Fan, Bowen / Oliver, Carlos / Borgwardt, Karsten

    2022  

    Abstract: We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, which maps datasets from two different domains, without any known correspondences between data instances across the datasets, to a common low-dimensional ... ...

    Abstract We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, which maps datasets from two different domains, without any known correspondences between data instances across the datasets, to a common low-dimensional Euclidean space. Our approach integrates Multidimensional Scaling (MDS) and Wasserstein Procrustes analysis into a joint optimization problem to simultaneously generate isometric embeddings of data and learn correspondences between instances from two different datasets, while only requiring intra-dataset pairwise dissimilarities as input. This unique characteristic makes our approach applicable to datasets without access to the input features, such as solving the inexact graph matching problem. We propose an alternating optimization scheme to solve the problem that can fully benefit from the optimization techniques for MDS and Wasserstein Procrustes. We demonstrate the effectiveness of our approach in several applications, including joint visualization of two datasets, unsupervised heterogeneous domain adaptation, graph matching, and protein structure alignment. The implementation of our work is available at https://github.com/BorgwardtLab/JointMDS

    Comment: ICLR 2023, see https://openreview.net/forum?id=lUpjsrKItz4
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning
    Subject code 004
    Publishing date 2022-07-06
    Publishing country us
    Document type Book ; Online
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  8. Book ; Online: Structure-Aware Transformer for Graph Representation Learning

    Chen, Dexiong / O'Bray, Leslie / Borgwardt, Karsten

    2022  

    Abstract: The Transformer architecture has gained growing attention in graph representation learning recently, as it naturally overcomes several limitations of graph neural networks (GNNs) by avoiding their strict structural inductive biases and instead only ... ...

    Abstract The Transformer architecture has gained growing attention in graph representation learning recently, as it naturally overcomes several limitations of graph neural networks (GNNs) by avoiding their strict structural inductive biases and instead only encoding the graph structure via positional encoding. Here, we show that the node representations generated by the Transformer with positional encoding do not necessarily capture structural similarity between them. To address this issue, we propose the Structure-Aware Transformer, a class of simple and flexible graph Transformers built upon a new self-attention mechanism. This new self-attention incorporates structural information into the original self-attention by extracting a subgraph representation rooted at each node before computing the attention. We propose several methods for automatically generating the subgraph representation and show theoretically that the resulting representations are at least as expressive as the subgraph representations. Empirically, our method achieves state-of-the-art performance on five graph prediction benchmarks. Our structure-aware framework can leverage any existing GNN to extract the subgraph representation, and we show that it systematically improves performance relative to the base GNN model, successfully combining the advantages of GNNs and Transformers. Our code is available at https://github.com/BorgwardtLab/SAT.

    Comment: To appear in ICML 2022
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-02-07
    Publishing country us
    Document type Book ; Online
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  9. Article ; Online: Estimating PM

    Chen, Wenqian / Ran, Haofan / Cao, Xiaoyi / Wang, Jingzhe / Teng, Dexiong / Chen, Jing / Zheng, Xuan

    The Science of the total environment

    2020  Volume 746, Page(s) 141093

    Abstract: Studies on fine particulate matter with an aerodynamic diameter of 2.5 μm or smaller ( ... ...

    Abstract Studies on fine particulate matter with an aerodynamic diameter of 2.5 μm or smaller (PM
    Language English
    Publishing date 2020-07-21
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 121506-1
    ISSN 1879-1026 ; 0048-9697
    ISSN (online) 1879-1026
    ISSN 0048-9697
    DOI 10.1016/j.scitotenv.2020.141093
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  10. Article ; Online: The dynamics of antibodies to SARS-CoV-2 in a case of SARS-CoV-2 infection.

    Xia, Yong / Hong, Honghai / Feng, Yao / Liu, Meiling / Pan, Xingfei / Chen, Dexiong

    International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases

    2020  Volume 96, Page(s) 359–360

    Keywords covid19
    Language English
    Publishing date 2020-05-17
    Publishing country Canada
    Document type Letter ; Research Support, Non-U.S. Gov't
    ZDB-ID 1331197-9
    ISSN 1878-3511 ; 1201-9712
    ISSN (online) 1878-3511
    ISSN 1201-9712
    DOI 10.1016/j.ijid.2020.05.042
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    Zs.A 4516: Show issues Location:
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