MOTIVATION: Spatial transcriptomics (ST) technologies measure gene expression together with spatial locations, but each spot typically contains a mixture of cell types, posing a challenge for downstream analysis. Cell-type deconvolution aims to infer spot-wise cell-type proportions by integrating single-cell RNA-seq (scRNA-seq) and ST data. Many existing methods construct cell-type signatures from predefined marker genes, which can limit performance when marker information is incomplete or unavailable. RESULTS: To address this limitation, we propose a spatial-aware auto-encoder framework (SA2E) for cell-type deconvolution without requiring predefined cell-type biomarkers. SA2E learns latent spot representations using a spatially regularized auto-encoder that preserves the local topology of the spot spatial graph. Based on these representations, SA2E learns cell-type signatures by enforcing them to reconstruct ST expression. In our framework, simulated ST data with known proportions are used for supervised pretraining, while real ST data are optimized using the reconstruction objective. Extensive experiments on simulated and real ST datasets demonstrate that SA2E outperforms state-of-the-art deconvolution baselines. AVAILABILITY AND IMPLEMENTATION: The code of SA2E is available at Github (https://github.com/xkmaxidian/SA2E) and Zenodo (DOI: 10.5281/zenodo.18765467).

Spatial transcriptomics (ST) technologies capture transcriptomics of genens with spatial context, enabling systematic exploration of micro-environment of tissues that is highly associated with spatial domains. And, noise of ST data poses a great challenge on designing algorithms for identifying spatial domains, whereas available methods remove noise by employing pre-processing procedure, resulting in the undesirable performance. To overcome this limitation, we propose a robust and joint framework, called jNFACL (joint Network-based Feature-Affinity Contrastive Learning), for identifying spatial domains of noised ST data, where deniosing of ST data and identifying spatial domains are simultaneously integrated. Specifically, jNFACL first constructs expression and spatial graphs with transcriptomics and spatial coordinates of spots, which removes heterogeneity of ST data. And, jNFACL separates noise of ST data by jointly projecting these constructed graphs into the shared subspace, where noise of ST data is separated from feature level with nonnegative matrix factorization. To further enhance quality of features of spots, contrastive learning is adopted to leverages spatial neighborhoods by pulling similar spots together and pushing dissimilar spots apart, where self-supervision information is incorporated, thereby improving the characterization and identification of spatial domains. Experiments on various datasets with different noise levels from multiple platforms and species demonstrate that jNFACL is much more accurate and robust than state-of-the-art methods, providing alternatives for analyzing noised ST data.

As the accumulation of multi-view data continues to grow, multi-view clustering has become increasingly important in research fields like data mining. However, current methods have been criticized for their unsatisfactory performance, such as insufficient exploration of intra-view high-order relationships and poor characterization of inter-view diverse features. To overcome these challenges, we propose a novel approach called Local High-order Graph Learning for Multi-View Clustering (LHGL_MVC). Our method aims to explore high-order relationships within a view while also considering diverse information between views. In LHGL_MVC, we learn the initial graphs of each view through self-representation, which are decomposed into consistent and diverse parts to better capture the diversity of different views. Based on consistent parts, we propose a novel local high-order graph learning approach to more effectively explore high-order relationships between samples within each view. At the same time, we leverage high-order relationships between views using the rotated tensor nuclear norm. Finally, we obtain a unified graph for clustering by fusing all consistent affinity graphs and their high-order graphs with adaptive weights. All procedures are integrated into an overall objective function, which mutually promotes during the optimization process. The comprehensive experiments conducted on eleven real-world datasets demonstrate that LHGL_MVC significantly outperforms existing algorithms in various measurements, highlighting the superiority of the proposed method.

Spatial omics technologies enable the measurement of multiple molecular characterizations from the same tissue section while preserving spatial information, providing unprecedented opportunities to elucidate the relationship between cellular localization and tissue function. Spatial domain identification, which segments intact tissues into functionally distinct regions, is a fundamental task in spatial omics analysis. However, existing approaches are often limited to single-omics data or neglect spatial context, facing substantial limitations when extended to spatial multi-omics data. In this paper, we propose SIMID (Spatial domain Identification via graph Mutual Information and Deep subspace learning), a framework that integrates heterogeneous molecular profiles with spatial information to identify spatial domains. Specifically, a graph mutual information encoder is employed to capture cellular spatial proximity and molecular profile similarity, generating omics-specific cell embeddings for each omics layer. The deep subspace learning is then employed to construct cell network for each omics layer, converting heterogeneous multi-omics data into a homogeneous cell multi-layer network. SIMID further employs the low-rank and discriminative constraints to decompose the cell multi-layer network into consistent and complementary structures, providing an effective strategy for domain identification from spatial multi-omics data. Experimental results on both simulated and real-world spatial multi-omics datasets demonstrate that SIMID consistently outperforms existing methods and precisely reveals spatial domains from spatial multi-omics data.

Palm oil and its derivative ingredients constitute a third of all global vegetable oils. The expanding palm oil cultivation in Indonesia and Malaysia is correlated with deforestation, increased habitat loss, and threats to endemic species in some biodiversity hotspots. Palm monoculture creates Greenhouse Gas (GHG) emissions, contributing to global climate change. Both domestic and foreign multinationals play a role in the cultivation and production of palm oil. These companies are responsible for reporting and helping to address the crisis of GHG and biodiversity loss stemming from deforestation in and around plantations. Such reporting is challenging due to the need for more data. Satellite data, including our work, presents a methodology that integrates remote sensing and geospatial data to map, measure, and analyze impacts and changes resulting from increasing palm oil cultivation. We measure key risk metrics related to biodiversity loss, forest morphology changes, and carbon loss from deforestation resulting from palm oil cultivation over twenty years, 2000-2021.

The in-network caching mechanism is one of the core technologies of the Content Centric Network (CCN) and has been increasingly concerned. In order to improve the cache hit ratio of the content centric network cache system and increase the content diversity of the cache system, this paper proposes a cooperative caching strategy based on content popularity and node centrality, called CCPNC. The CCPNC caching strategy comprehensively considers content popularity and node distribution rules. It can separately cache content objects based on different popularity and mobilize the core routing nodes in the network to work together with non-core routing nodes. The CCPNC caching strategy not only makes use of the core routing node cache resources to provide faster popular content services for a wide range of users, but also avoids unnecessary high-frequency cache replacement of the core routing nodes. Meanwhile, it utilizes the cache resources of non-core routing nodes to provide more convenient non-popular content services. Through simulation experiments, it is found that the CCPNC caching strategy can effectively balance the distribution of content objects in the cache system and improve the cache hit ratio of the content centric network, while reducing the average routing hop and average request latency of content backhaul.