Xi Chen

Background: Large-scale connectomics requires the nanometer-scale resolution of electron microscopy to resolve ultrastructural details, but its acquisition is time- and labor-intensive. In contrast, high-throughput light microscopy offers high throughput but lacks the spatial resolution required for fine-grained analysis. To bridge this gap, we propose a computational framework for cross-modal ultrastructural inference, which maps high-throughput light microscopy data to an electron microscopy-like structural representation to accelerate downstream workflows. Rather than performing de novo synthesis, our framework recovers ultrastructural representation from diffraction-limited optical signals by enhancing latent morphological cues within the light microscopy data. This workflow is powered by a novel deep learning framework, the Content-Decoupled Schrödinger Bridge, which disentangles modality-invariant physical content from imaging-specific attributes and incorporates a physics-informed perceptual loss to ensure structural plausibility. Results: Our approach accelerates the connectomics pipeline, demonstrated in three key applications. First, the enhanced clarity of the generated images reduced expert miss rates for region-of-interest selection. Second, the generated images are inherently aligned with the source light microscopy data while matching the appearance of target electron microscopy data, streamlining multi-modal registration. Third, they improve segmentation accuracy by allowing pre-trained electron microscopy models to be applied directly to light microscopy data. Conclusions: In summary, this work provides a computational solution for bridging the gap between imaging speed and resolution. By enhancing the analytical value of light microscopy data, our workflow accelerates key stages of large-scale connectomics mapping, from targeted acquisition to quantitative analysis.

Accurate measurement of biological slice thickness is essential for the precise reconstruction of neuronal connections in microscale connectomics. Conventional metrology often faces performance bottlenecks, struggling to balance measurement accuracy with system complexity and operational efficiency. This study proposes a novel, low-cost, and high-precision method for biological slice thickness measurement. The proposed data-driven approach eliminates the need for high-precision physical modeling and complex instrumentation. By utilizing a simple optical setup based on a standard RGB camera, the proposed method achieves a mean absolute error (MAE) of 0.77 nm over a wide measurement range of 30–1000 nm. To address the limited generalization capability of conventional deep learning (Conv. DL) methods under sparse training data conditions, a physics-inspired three-wavelength interferometric neural network (TWI-NN) framework is proposed. The architecture incorporates physics-based structural constraints, including task decoupling and input–output inversion, enabling the network to learn accurate mappings from limited data. Experimental results indicate that, under sparse training data conditions, the prediction accuracy of the TWI-NN is significantly better than that of Conv. DL models. Uncertainty analysis confirms that the total expanded uncertainty (k = 2) is less than 0.947 nm. Furthermore, compared with existing interferometric color measurement methods, the proposed method eliminates the need for repeated calibration and features a simpler implementation. Thickness distribution measurement at an effective resolution of 200 × 200 pixels can be completed in 0.73 s. Overall, the proposed method provides a scalable, cost-effective, and high-precision solution, demonstrating strong potential for high-throughput thickness measurement.

Carbon fiber-reinforced polymer (CFRP) composites have excellent mechanical properties, but their performance is hampered by delamination caused by weak interfacial bonding and resin-rich region (RRR). This research has proposed an interleaving film to improve interlaminar structure and mechanical properties by adding polyacrylonitrile (PAN) fiber into the epoxy interlayer of the CFRP laminates. The PAN fiber/epoxy resin (PANER) interleaving film could be prepared, which was beneficial to hinder crack initiation paths and improve the load transfer. Flexural and compression performance testing results showed optimum performance was obtained when 2 wt.% PAN fiber was added, and an increment of 28.6% was obtained in the flexural strength and 11.7% increment in compressive strength. The damaged energy absorption was improved up to 21.4% and 11.3% for the flexural and compressive properties, respectively. The overall thickness increments in the interlayer with PANER interleaving film were approximately 4–9 μm. X-Ray micro-computed tomography and scanning electron microscopy observations exhibited the potential of PAN fiber in the reduction of RRR, resulting in modes replacement from delamination-dominant failure to crossing-multi-layer failure. In all, PANER interleaving film at the interlayer has been confirmed to be an effective approach to produce a simple reinforcement technology for FRP laminates.

Advancements in animal behavior quantification methods have driven the development of computational ethology, enabling fully automated behavior analysis. Existing multi-animal pose estimation workflows rely on tracking-by-detection frameworks for either bottom-up or top-down approaches, requiring retraining to accommodate diverse animal appearances. This study introduces InteBOMB, an integrated workflow that enhances top-down approaches by incorporating generic object tracking, eliminating the need for prior knowledge of target animals while maintaining broad generalizability. InteBOMB includes two key strategies for tracking and segmentation in laboratory environments and two techniques for pose estimation in natural settings. The "background enhancement" strategy optimizes foreground-background contrastive loss, generating more discriminative correlation maps. The "online proofreading" strategy stores human-in-the-loop long-term memory and dynamic short-term memory, enabling adaptive updates to object visual features. The "automated labeling suggestion" technique reuses the visual features saved during tracking to identify representative frames for training set labeling. Additionally, the "joint behavior analysis" technique integrates these features with multimodal data, expanding the latent space for behavior classification and clustering. To evaluate the framework, six datasets of mice and six datasets of non-human primates were compiled, covering laboratory and natural scenes. Benchmarking results demonstrated a 24% improvement in zero-shot generic tracking and a 21% enhancement in joint latent space performance across datasets, highlighting the effectiveness of this approach in robust, generalizable behavior analysis.

Diffusion models have recently emerged as a powerful technique in image generation, especially for image super-resolution tasks. While 2D diffusion models significantly enhance the resolution of individual images, existing diffusion-based methods for 3D volume super-resolution often struggle with structure discontinuities in axial direction and high sampling costs. In this work, we present a novel approach that leverages the 2D diffusion model and lateral continuity within the volume to enhance 3D volume electron microscopy (vEM) super-resolution. We first simulate lateral degradation with slices in the XY plane and train a 2D diffusion model to learn how to restore the degraded slices. The model is then applied slice-by-slice in the lateral direction of low-resolution volume, recovering slices while preserving inherent lateral continuity. Following this, a high-frequency-aware 3D super-resolution network is trained on the recovery lateral slice sequences to learn spatial feature transformation across slices. Finally, the network is applied to infer high-resolution volumes in the axial direction, enabling 3D super-resolution. We validate our approach through comprehensive evaluations, including image similarity assessments, resolution analysis, and performance on downstream tasks. Our results on two publicly available focused ion beam scanning electron microscopy (FIB-SEM) datasets demonstrate the robustness and practical applicability of our framework for 3D volume super-resolution.