Silencing miR-146a-5p protects against injury-induced osteoarthritis in mice

Osteoarthritis (OA), the most prevalent joint disease and the leading cause of disability, remains an incurable disease largely because the etiology and pathogenesis underlying this degenerative process are poorly understood. Low-grade inflammation within joints is a well-established factor that disturbs joint homeostasis and leads to an imbalance between anabolic and catabolic processes in articular cartilage; however, the complexity of the network between inflammatory factors that often involves positive and negative feedback loops makes current anti-cytokine therapy ineffective. MicroRNAs (miRNAs) have emerged as key regulators to control inflammation, and aberrant miRNAs expression has recently been linked to OA pathophysiology. In the present study, we characterized transcriptomic profiles of miRNAs in primary murine articular chondrocytes in response to a proinflammatory cytokine, IL-1β, and identifie

DPL: Decoupled Prompt Learning for Vision-Language Models

Prompt learning has emerged as an efficient and effective approach for transferring foundational Vision-Language Models (e.g., CLIP) to downstream tasks. However, current methods tend to overfit to seen categories, thereby limiting their generalization ability for unseen classes. In this paper, we propose a new method, Decoupled Prompt Learning (DPL), which reformulates the attention in prompt learning to alleviate this problem. Specifically, we theoretically investigate the collaborative process between prompts and instances (i.e., image patches/text tokens) by reformulating the original self-attention into four separate sub-processes. Through detailed analysis, we observe that certain sub-processes can be strengthened to bolster robustness and generalizability by some approximation techniques. Furthermore, we introduce language-conditioned textual prompting based on decoupled attention to naturally preserve the generalization of text input. Our approach is flexible for both visual and textual modalities, making it easily extendable to multi-modal prompt learning. By combining the proposed techniques, our approach achieves state-of-the-art performance on three representative benchmarks encompassing 15 image recognition datasets, while maintaining parameter-efficient. Moreover, our DPL does not rely on any auxiliary regularization task or extra training data, further demonstrating its remarkable generalization ability.Comment: 11 pages, 5 figures, 8 table

DNA methylation-mediated Rbpjk suppression protects against fracture nonunion caused by systemic inflammation

Challenging skeletal repairs are frequently seen in patients experiencing systemic inflammation. To tackle the complexity and heterogeneity of the skeletal repair process, we performed single-cell RNA sequencing and revealed that progenitor cells were one of the major lineages responsive to elevated inflammation and this response adversely affected progenitor differentiation by upregulation of Rbpjk in fracture nonunion. We then validated the interplay between inflammation (via constitutive activation of Ikk2, Ikk2ca) and Rbpjk specifically in progenitors by using genetic animal models. Focusing on epigenetic regulation, we identified Rbpjk as a direct target of Dnmt3b. Mechanistically, inflammation decreased Dnmt3b expression in progenitor cells, consequently leading to Rbpjk upregulation via hypomethylation within its promoter region. We also showed that Dnmt3b loss-of-function mice phenotypically recapitulated the fracture repair defects observed in Ikk2ca-transgenic mice, whereas Dnmt3b-transgenic mice alleviated fracture repair defects induced by Ikk2ca. Moreover, Rbpjk ablation restored fracture repair in both Ikk2ca mice and Dnmt3b loss-of-function mice. Altogether, this work elucidates a common mechanism involving a NF-κB/Dnmt3b/Rbpjk axis within the context of inflamed bone regeneration. Building on this mechanistic insight, we applied local treatment with epigenetically modified progenitor cells in a previously established mouse model of inflammation-mediated fracture nonunion and showed a functional restoration of bone regeneration under inflammatory conditions through an increase in progenitor differentiation potential

PVT: Point-Voxel Transformer for Point Cloud Learning

The recently developed pure Transformer architectures have attained promising accuracy on point cloud learning benchmarks compared to convolutional neural networks. However, existing point cloud Transformers are computationally expensive since they waste a significant amount of time on structuring the irregular data. To solve this shortcoming, we present Sparse Window Attention (SWA) module to gather coarse-grained local features from non-empty voxels, which not only bypasses the expensive irregular data structuring and invalid empty voxel computation, but also obtains linear computational complexity with respect to voxel resolution. Meanwhile, to gather fine-grained features about the global shape, we introduce relative attention (RA) module, a more robust self-attention variant for rigid transformations of objects. Equipped with the SWA and RA, we construct our neural architecture called PVT that integrates both modules into a joint framework for point cloud learning. Compared with previous Transformer-based and attention-based models, our method attains top accuracy of 94.0% on classification benchmark and 10x inference speedup on average. Extensive experiments also valid the effectiveness of PVT on part and semantic segmentation benchmarks (86.6% and 69.2% mIoU, respectively).Comment: 29 page