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  1. Article ; Online: Semantics-Guided Hierarchical Feature Encoding Generative Adversarial Network for Visual Image Reconstruction From Brain Activity.

    Meng, Lu / Yang, Chuanhao

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

    2024  Volume 32, Page(s) 1267–1283

    Abstract: The utilization of deep learning techniques for decoding visual perception images from brain activity recorded by functional magnetic resonance imaging (fMRI) has garnered considerable attention in recent research. However, reconstructed images from ... ...

    Abstract The utilization of deep learning techniques for decoding visual perception images from brain activity recorded by functional magnetic resonance imaging (fMRI) has garnered considerable attention in recent research. However, reconstructed images from previous studies still suffer from low quality or unreliability. Moreover, the complexity inherent to fMRI data, characterized by high dimensionality and low signal-to-noise ratio, poses significant challenges in extracting meaningful visual information for perceptual reconstruction. In this regard, we proposes a novel neural decoding model, named the hierarchical semantic generative adversarial network (HS-GAN), inspired by the hierarchical encoding of the visual cortex and the homology theory of convolutional neural networks (CNNs), which is capable of reconstructing perceptual images from fMRI data by leveraging the hierarchical and semantic representations. The experimental results demonstrate that HS-GAN achieved the best performance on Horikawa2017 dataset (histogram similarity: 0.447, SSIM-Acc: 78.9%, Peceptual-Acc: 95.38%, AlexNet(2): 96.24% and AlexNet(5): 94.82%) over existing advanced methods, indicating improved naturalness and fidelity of the reconstructed image. The versatility of the HS-GAN was also highlighted, as it demonstrated promising generalization capabilities in reconstructing handwritten digits, achieving the highest SSIM (0.783±0.038), thus extending its application beyond training solely on natural images.
    MeSH term(s) Humans ; Semantics ; Image Processing, Computer-Assisted/methods ; Neural Networks, Computer ; Magnetic Resonance Imaging/methods ; Brain
    Language English
    Publishing date 2024-03-22
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1166307-8
    ISSN 1558-0210 ; 1063-6528 ; 1534-4320
    ISSN (online) 1558-0210
    ISSN 1063-6528 ; 1534-4320
    DOI 10.1109/TNSRE.2024.3377698
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Dual-Guided Brain Diffusion Model: Natural Image Reconstruction from Human Visual Stimulus fMRI.

    Meng, Lu / Yang, Chuanhao

    Bioengineering (Basel, Switzerland)

    2023  Volume 10, Issue 10

    Abstract: The reconstruction of visual stimuli from fMRI signals, which record brain activity, is a challenging task with crucial research value in the fields of neuroscience and machine learning. Previous studies tend to emphasize reconstructing pixel-level ... ...

    Abstract The reconstruction of visual stimuli from fMRI signals, which record brain activity, is a challenging task with crucial research value in the fields of neuroscience and machine learning. Previous studies tend to emphasize reconstructing pixel-level features (contours, colors, etc.) or semantic features (object category) of the stimulus image, but typically, these properties are not reconstructed together. In this context, we introduce a novel three-stage visual reconstruction approach called the Dual-guided Brain Diffusion Model (DBDM). Initially, we employ the Very Deep Variational Autoencoder (VDVAE) to reconstruct a coarse image from fMRI data, capturing the underlying details of the original image. Subsequently, the Bootstrapping Language-Image Pre-training (BLIP) model is utilized to provide a semantic annotation for each image. Finally, the image-to-image generation pipeline of the Versatile Diffusion (VD) model is utilized to recover natural images from the fMRI patterns guided by both visual and semantic information. The experimental results demonstrate that DBDM surpasses previous approaches in both qualitative and quantitative comparisons. In particular, the best performance is achieved by DBDM in reconstructing the semantic details of the original image; the Inception, CLIP and SwAV distances are 0.611, 0.225 and 0.405, respectively. This confirms the efficacy of our model and its potential to advance visual decoding research.
    Language English
    Publishing date 2023-09-24
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2746191-9
    ISSN 2306-5354
    ISSN 2306-5354
    DOI 10.3390/bioengineering10101117
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.

    Dong, Xiaoru / Leary, Jack R / Yang, Chuanhao / Brusko, Maigan A / Brusko, Todd M / Bacher, Rhonda

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Single-cell RNA sequencing (scRNA-seq) experiments have become instrumental in developmental and differentiation studies, enabling the profiling of cells at a single or multiple time-points to uncover subtle variations in expression profiles reflecting ... ...

    Abstract Single-cell RNA sequencing (scRNA-seq) experiments have become instrumental in developmental and differentiation studies, enabling the profiling of cells at a single or multiple time-points to uncover subtle variations in expression profiles reflecting underlying biological processes. Benchmarking studies have compared many of the computational methods used to reconstruct cellular dynamics, however researchers still encounter challenges in their analysis due to uncertainties in selecting the most appropriate methods and parameters. Even among universal data processing steps used by trajectory inference methods such as feature selection and dimension reduction, trajectory methods' performances are highly dataset-specific. To address these challenges, we developed Escort, a framework for evaluating a dataset's suitability for trajectory inference and quantifying trajectory properties influenced by analysis decisions. Escort navigates single-cell trajectory analysis through data-driven assessments, reducing uncertainty and much of the decision burden associated with trajectory inference. Escort is implemented in an accessible R package and R/Shiny application, providing researchers with the necessary tools to make informed decisions during trajectory analysis and enabling new insights into dynamic biological processes at single-cell resolution.
    Language English
    Publishing date 2023-12-19
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.12.18.572214
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Decoupling analysis to assess the impact of land use patterns on carbon emissions: A case study in the Yellow River Delta efficient eco-economic zone, China

    Wang, Qian / Yang, Chuan-hao / Wang, Ma-li / Zhao, Lin / Zhao, Yu-chen / Zhang, Qi-peng / Zhang, Chun-yan

    Journal of Cleaner Production. 2023 Aug., v. 412 p.137415-

    2023  

    Abstract: Investigating how land use patterns impact on carbon emissions is crucial, as land use change is a major cause of increased carbon emission. High Efficiency Eco-economic Zone of Yellow River Delta (HEEZ-YRD) are typical of land use patterns affecting ... ...

    Abstract Investigating how land use patterns impact on carbon emissions is crucial, as land use change is a major cause of increased carbon emission. High Efficiency Eco-economic Zone of Yellow River Delta (HEEZ-YRD) are typical of land use patterns affecting carbon emissions due to the fact they have greater dramatic land use changes. Here, we used carbon emission model and the decoupling analysis to assess the impact of land use patterns on carbon emissions based on the 30-m Global Land Cover Dataset (GlobeLand30) and socio-economic data from 2000 to 2019. The results showed that construction land increased while cropland decreased. The construction land area increased from cropland was 9.03 × 10⁴ha from 2000 to 2010 and 1.44 × 10⁵ha from 2010 to 2019, indicating that the increase in construction land mainly from cropland. Moreover, the HEEZ-YRD was the carbon source area. The total net carbon emissions increased from 9155.68 Gg CO₂ equivalents (CO₂e) in 2000–45817.44 Gg CO₂e in 2019, but the increase declined. In the HEEZ-YRD, the land-use mix degree was highest in the east and north and lowest in the west and south. Zouping had the highest carbon emissions of all the cities. The decoupling of land use patterns and carbon emissions was dynamic. During the study period, the decoupling between land use patterns and carbon emissions shifted from an extended negative decoupling to a weak or even a strong decoupling. The consequences will make contributions to the establishment and implementation of low-carbon insurance policies and supply a theoretical framework for sustainable land use.
    Keywords carbon ; carbon dioxide ; case studies ; cropland ; data collection ; insurance ; land cover ; land use change ; models ; river deltas ; socioeconomics ; sustainable land management ; China ; Yellow River ; Land intensive use ; Carbon emissions ; Decoupling relationship ; Local Moran's I
    Language English
    Dates of publication 2023-08
    Publishing place Elsevier Ltd
    Document type Article ; Online
    ISSN 0959-6526
    DOI 10.1016/j.jclepro.2023.137415
    Database NAL-Catalogue (AGRICOLA)

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