We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the fine-tuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S fine-tuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications.

Rail surface defect detection for traffic safety has received considerable attention. With the development of deep learning, numerous methods for combining RGB and depth information have been proposed. However, these methods directly fuse raw features extracted from the backbone, which can lead to ineffective use of the complementary information of the two modalities. In this study, we developed a contour and semantic feature alignment fusion network (CSANet) with bidirectional feature alignment to explore the internal consistency of cross-modal features from both contour and semantic perspectives. First, an adjacency contour feature extraction module was designed to capture high-quality contour information from adjacent low-level features. Second, an attention-aware graph convolution embedded semantic feature extraction module was designed to explore long-range dependencies and extract semantic information. Third, a bidirectional alignment mechanism was designed to explore the internal consistency of contours and semantics between bimodal features. Experimental results on the industrial RGB-D dataset (NEU RSDDS-AUG) revealed that the proposed CSANet outperformed 12 state-of-the-art algorithms in four evaluation metrics.