Bo Zhou

Yaoyu Fang, Jiahe Qian, Xinkun Wang et al.

Spatial transcriptomics (ST) has revolutionized biomedical research by enabling high resolution gene expression profiling within tissues. However, the high cost and scarcity of high resolution ST data remain significant challenges. We present Single-shot Sparser-to-Sparse (S2S-ST), a novel framework for accurate ST imputation that requires only a single and low-cost sparsely sampled ST dataset alongside widely available natural images for co-training. Our approach integrates three key innovations: (1) a sparser-to-sparse self-supervised learning strategy that leverages intrinsic spatial patterns in ST data, (2) cross-domain co-learning with natural images to enhance feature representation, and (3) a Cascaded Data Consistent Imputation Network (CDCIN) that iteratively refines predictions while preserving sampled gene data fidelity. Extensive experiments on diverse tissue types, including breast cancer, liver, and lymphoid tissue, demonstrate that our method outperforms state-of-the-art approaches in imputation accuracy. By enabling robust ST reconstruction from sparse inputs, our framework significantly reduces reliance on costly high resolution data, facilitating potential broader adoption in biomedical research and clinical applications.

Ziqiao Weng, Yaoyu Fang, Jiahe Qian et al.

Spatial transcriptomics (ST) bridges gene expression and tissue morphology but faces clinical adoption barriers due to technical complexity and prohibitive costs. While computational methods predict gene expression from H&E-stained whole-slide images (WSIs), existing approaches often fail to capture the intricate biological heterogeneity within spots and are susceptible to morphological noise when integrating contextual information from surrounding tissue. To overcome these limitations, we propose HiFusion, a novel deep learning framework that integrates two complementary components. First, we introduce the Hierarchical Intra-Spot Modeling module that extracts fine-grained morphological representations through multi-resolution sub-patch decomposition, guided by a feature alignment loss to ensure semantic consistency across scales. Concurrently, we present the Context-aware Cross-scale Fusion module, which employs cross-attention to selectively incorporate biologically relevant regional context, thereby enhancing representational capacity. This architecture enables comprehensive modeling of both cellular-level features and tissue microenvironmental cues, which are essential for accurate gene expression prediction. Extensive experiments on two benchmark ST datasets demonstrate that HiFusion achieves state-of-the-art performance across both 2D slide-wise cross-validation and more challenging 3D sample-specific scenarios. These results underscore HiFusion's potential as a robust, accurate, and scalable solution for ST inference from routine histopathology.

Jiahe Qian, Yaoyu Fang, Ziqiao Weng et al.

Spatial transcriptomics aims to connect high-resolution histology images with spatially resolved gene expression. To achieve better performance on downstream tasks such as gene expression prediction, large-scale pre-training is required to obtain generalisable representations that can bridge histology and transcriptomics across tissues, protocols, and laboratories. Existing cross-modal pre-training approaches for spatial transcriptomics rely on either gene names or expression values in isolation, which strips the gene branch of essential semantics and breaks the association between each gene and its quantitative magnitude. In addition, by restricting supervision to image-text alignment, these methods ignore intrinsic visual cues that are critical for learning robust image features. We present CoMTIP, the first Contrastive Masked Text-Image Pretraining framework that jointly learns from images, gene names, and expression values while capturing fine-grained visual context for spatial transcriptomics. The vision branch uses Masked Feature Modeling to reconstruct occluded patches and learn context-aware image embeddings. The text branch applies a scalable Gene-Text Encoder that processes all gene sentences in parallel, enriches each gene and its numerical value with dedicated embeddings, and employs Pair-aware Adversarial Training (PAAT) to preserve correct gene-value associations. Image and text representations are aligned in a shared InfoNCE-optimised space. Experiments on public spatial transcriptomics datasets show that CoMTIP not only surpasses previous methods on diverse downstream tasks but also achieves zero-shot gene expression prediction, a capability that existing approaches do not provide.

Bo Zhou, Hongsheng Zeng, Yuecheng Liu et al.

Maintaining the stability of the modern power grid is becoming increasingly difficult due to fluctuating power consumption, unstable power supply coming from renewable energies, and unpredictable accidents such as man-made and natural disasters. As the operation on the power grid must consider its impact on future stability, reinforcement learning (RL) has been employed to provide sequential decision-making in power grid management. However, existing methods have not considered the environmental constraints. As a result, the learned policy has risk of selecting actions that violate the constraints in emergencies, which will escalate the issue of overloaded power lines and lead to large-scale blackouts. In this work, we propose a novel method for this problem, which builds on top of the search-based planning algorithm. At the planning stage, the search space is limited to the action set produced by the policy. The selected action strictly follows the constraints by testing its outcome with the simulation function provided by the system. At the learning stage, to address the problem that gradients cannot be propagated to the policy, we introduce Evolutionary Strategies (ES) with black-box policy optimization to improve the policy directly, maximizing the returns of the long run. In NeurIPS 2020 Learning to Run Power Network (L2RPN) competition, our solution safely managed the power grid and ranked first in both tracks.