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Received — 27 May 2026 ⏭
Received — 13 April 2026 ⏭

Genetic mutation and dysfunction of AT2 cells drive B(a)P/LPS-induced inflammation-related lung tumorigenesis: evidence and mechanism of autophagy

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zhihao Tang · Xianchao Zhou · Pingping Shang · Quanping Yan · Ping Lu · Yanting Lei · Chenfeng Hua · Xiaoshan Zhou · Qiao Zhang · Wei Wang · Huaiyong Chen · Jian He · Feifei Feng
9 April 2026 at 18:00

Acta Biochim Biophys Sin (Shanghai). 2026 Mar 25. doi: 10.3724/abbs.2025238. Online ahead of print.

ABSTRACT

The environmental pollutant benzo(a)pyrene (B(a)P), a representative polycyclic aromatic hydrocarbon (PAH), is a recognized carcinogen, and chronic pulmonary inflammation is closely associated with lung carcinogenesis. Although alveolar type 2 (AT2) cells are the origin of lung adenocarcinoma, the genetic and functional changes in AT2 cells and the mechanisms involved in inflammation-related lung tumorigenesis have not been elucidated. Here, C57BL/6J mice are exposed to B(a)P and the inflammatory irritant lipopolysaccharide (LPS) to establish a model of inflammation-related lung tumorigenesis. Single-cell RNA sequencing is performed on lung tissues. DNA mutations in AT2 cells are analyzed via whole-exome sequencing. The protein expression of AT2 cells in lung cancer tissue is determined by immunofluorescence staining. The results reveal that LPS promotes B(a)P-induced lung tumorigenesis; in the whole lungs of B(a)P/LPS, a decreased proportion, altered differentiation trajectory, and increased gene mutation number in AT2 cells are observed. Additionally, in B(a)P/LPS-treated lung cancer tissue, the levels of γ-H2AX DNA damage and the proliferation marker Ki67 in AT2 cells are increased, whereas the levels of differentiation markers are decreased. Single-cell RNA transcriptomics reveals that the autophagy-related genes Foxo3 and Ppp2r5, which are enriched in the PI3K-Akt pathway, and the autophagy-related genes in AT2 cells in lung cancer are decreased in the B(a)P/LPS group. Thus, chronic inflammation promotes DNA damage, gene mutation and dysfunction in AT2 cells, and decreased autophagy in AT2 cells may be an important mechanism for inflammation-related lung tumorigenesis.

PMID:41952558 | DOI:10.3724/abbs.2025238

Received — 26 March 2026 ⏭

SARE: Sample-wise Adaptive Reasoning for Training-free Fine-grained Visual Recognition

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jingxiao Yang · DaLin He · Miao Pan · Ge Su · Wenqi Zhang · Yifeng Hu · Tangwei Li · Yuke Li · Xuhong Zhang
25 March 2026 at 12:00
arXiv:2603.17729v2 Announce Type: replace-cross Abstract: Recent advances in Large Vision-Language Models (LVLMs) have enabled training-free Fine-Grained Visual Recognition (FGVR). However, effectively exploiting LVLMs for FGVR remains challenging due to the inherent visual ambiguity of subordinate-level categories. Existing methods predominantly adopt either retrieval-oriented or reasoning-oriented paradigms to tackle this challenge, but both are constrained by two fundamental limitations:(1) They apply the same inference pipeline to all samples without accounting for uneven recognition difficulty, thereby leading to suboptimal accuracy and efficiency; (2) The lack of mechanisms to consolidate and reuse error-specific experience causes repeated failures on similar challenging cases. To address these limitations, we propose SARE, a Sample-wise Adaptive textbfREasoning framework for training-free FGVR. Specifically, SARE adopts a cascaded design that combines fast candidate retrieval with fine-grained reasoning, invoking the latter only when necessary. In the reasoning process, SARE incorporates a self-reflective experience mechanism that leverages past failures to provide transferable discriminative guidance during inference, without any parameter updates. Extensive experiments across 14 datasets substantiate that SARE achieves state-of-the-art performance while substantially reducing computational overhead.
Received — 17 March 2026 ⏭

BitDance: Scaling Autoregressive Generative Models with Binary Tokens

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuang Ai · Jiaming Han · Shaobin Zhuang · Weijia Mao · Xuefeng Hu · Ziyan Yang · Zhenheng Yang · Yali Wang · Huaibo Huang · Xiangyu Yue · Hao Chen
16 March 2026 at 12:00
arXiv:2602.14041v2 Announce Type: replace-cross Abstract: We present BitDance, a scalable autoregressive (AR) image generator that predicts binary visual tokens instead of codebook indices. With high-entropy binary latents, BitDance lets each token represent up to $2^{256}$ states, yielding a compact yet highly expressive discrete representation. Sampling from such a huge token space is difficult with standard classification. To resolve this, BitDance uses a binary diffusion head: instead of predicting an index with softmax, it employs continuous-space diffusion to generate the binary tokens. Furthermore, we propose next-patch diffusion, a new decoding method that predicts multiple tokens in parallel with high accuracy, greatly speeding up inference. On ImageNet 256x256, BitDance achieves an FID of 1.24, the best among AR models. With next-patch diffusion, BitDance beats state-of-the-art parallel AR models that use 1.4B parameters, while using 5.4x fewer parameters (260M) and achieving 8.7x speedup. For text-to-image generation, BitDance trains on large-scale multimodal tokens and generates high-resolution, photorealistic images efficiently, showing strong performance and favorable scaling. When generating 1024x1024 images, BitDance achieves a speedup of over 30x compared to prior AR models. We release code and models to facilitate further research on AR foundation models. Code and models are available at: https://github.com/shallowdream204/BitDance.
Received — 5 March 2026 ⏭

Q-BERT4Rec: Quantized Semantic-ID Representation Learning for Multimodal Recommendation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haofeng Huang · Ling Gai
4 March 2026 at 13:00
arXiv:2512.02474v2 Announce Type: replace-cross Abstract: Sequential recommendation plays a critical role in modern online platforms such as e-commerce, advertising, and content streaming, where accurately predicting users' next interactions is essential for personalization. Recent Transformer-based methods like BERT4Rec have shown strong modeling capability, yet they still rely on discrete item IDs that lack semantic meaning and ignore rich multimodal information (e.g., text and image). This leads to weak generalization and limited interpretability. To address these challenges, we propose Q-Bert4Rec, a multimodal sequential recommendation framework that unifies semantic representation and quantized modeling. Specifically, Q-Bert4Rec consists of three stages: (1) cross-modal semantic injection, which enriches randomly initialized ID embeddings through a dynamic transformer that fuses textual, visual, and structural features; (2) semantic quantization, which discretizes fused representations into meaningful tokens via residual vector quantization; and (3) multi-mask pretraining and fine-tuning, which leverage diverse masking strategies -- span, tail, and multi-region -- to improve sequential understanding. We validate our model on public Amazon benchmarks and demonstrate that Q-Bert4Rec significantly outperforms many strong existing methods, confirming the effectiveness of semantic tokenization for multimodal sequential recommendation. Our source code will be publicly available on GitHub after publishing.
Received — 17 February 2026 ⏭

BitDance: Scaling Autoregressive Generative Models with Binary Tokens

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuang Ai · Jiaming Han · Shaobin Zhuang · Weijia Mao · Xuefeng Hu · Ziyan Yang · Zhenheng Yang · Huaibo Huang · Xiangyu Yue · Hao Chen
17 February 2026 at 13:00
arXiv:2602.14041v1 Announce Type: cross Abstract: We present BitDance, a scalable autoregressive (AR) image generator that predicts binary visual tokens instead of codebook indices. With high-entropy binary latents, BitDance lets each token represent up to $2^{256}$ states, yielding a compact yet highly expressive discrete representation. Sampling from such a huge token space is difficult with standard classification. To resolve this, BitDance uses a binary diffusion head: instead of predicting an index with softmax, it employs continuous-space diffusion to generate the binary tokens. Furthermore, we propose next-patch diffusion, a new decoding method that predicts multiple tokens in parallel with high accuracy, greatly speeding up inference. On ImageNet 256x256, BitDance achieves an FID of 1.24, the best among AR models. With next-patch diffusion, BitDance beats state-of-the-art parallel AR models that use 1.4B parameters, while using 5.4x fewer parameters (260M) and achieving 8.7x speedup. For text-to-image generation, BitDance trains on large-scale multimodal tokens and generates high-resolution, photorealistic images efficiently, showing strong performance and favorable scaling. When generating 1024x1024 images, BitDance achieves a speedup of over 30x compared to prior AR models. We release code and models to facilitate further research on AR foundation models. Code and models are available at: https://github.com/shallowdream204/BitDance.
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