RESUMEN
EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support clinical decision-making, it is important to estimate not only the exact location of the source signal but also the extended source activation regions. Existing methods may render over-diffuse or sparse solutions, which limit the source extent estimation accuracy. In this work, we leverage the graph structures defined in the 3D mesh of the brain and the spatial graph Fourier transform (GFT) to decompose the spatial graph structure into sub-spaces of low-, medium-, and high-frequency basis. We propose to use the low-frequency basis of spatial graph filters to approximate the extended areas of brain activation and embed the GFT into the classical ESI methods. We validated the classical source localization methods with the corresponding improved version using GFT in both synthetic data and real data. We found the proposed method can effectively reconstruct focal source patterns and significantly improve the performance compared to the classical algorithms.
RESUMEN
For the analysis of cellular architecture during mitosis, nanometer resolution is needed to visualize the organization of microtubules in spindles. Here, we present a detailed protocol that can be used to produce 3D reconstructions of whole mitotic spindles in cells grown in culture. For this, we attach mammalian cells enriched in mitotic stages to sapphire discs. Our protocol further involves cryo-immobilization by high-pressure freezing, freeze-substitution, and resin embedding. We then use fluorescence light microscopy to stage select mitotic cells in the resin-embedded samples. This is followed by large-scale electron tomography to reconstruct the selected and staged mitotic spindles in 3D. The generated and stitched electron tomograms are then used to semi-automatically segment the microtubules for subsequent quantitative analysis of spindle organization. Thus, by providing a detailed correlative light and electron microscopy (CLEM) approach, we give cell biologists a toolset to streamline the 3D visualization and analysis of spindle microtubules (http://kiewisz.shinyapps.io/asga). In addition, we refer to a recently launched platform that allows for an interactive display of the 3D-reconstructed mitotic spindles (https://cfci.shinyapps.io/ASGA_3DViewer/). Key features ⢠High-throughput screening of mitotic cells by correlative light and electron microscopy (CLEM). ⢠Serial-section electron tomography of selected cells. ⢠Visualization of mitotic spindles in 3D and quantitative analysis of microtubule organization.
RESUMEN
Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.