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Received — 13 April 2026 ⏭

Chuanminshen violaceum (Apiaceae) as a medicinal-and-edible resource: phytochemical diversity, bioactivities, and routes to standardized products

✇Omics In Lung
By: Bin Zhu · Yu-Jia Zhou · Xiang Li · Zhong-Qi Shi · An-Guo Wu · Lu Yu · Jian-Feng Ye · Jian-Ming Wu · Fei-Hong Huang · Xiao-Gang Zhou
8 April 2026 at 18:00

J Ethnopharmacol. 2026 Apr 6:121628. doi: 10.1016/j.jep.2026.121628. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Chuanminshen violaceum Sheh et Shan is a medicinal-and-edible Apiaceae plant in China recorded for yin nourishment, lung/spleen tonification, and phlegm resolution, and used for cough and chronic respiratory complaints.

STUDY AIM: To synthesize current evidence on botanical resources, chemistry, pharmacology, and applications of C. violaceum, and to define priorities for standardized and safe development.

MATERIALS AND METHODS: This review integrates studies on resource distribution and ecological adaptability, multi-fraction phytochemistry, extraction-purification and formulation technologies, preclinical pharmacology, and quality, safety, and regulatory considerations.

RESULTS: C. violaceum contains structurally diverse polysaccharides plus volatile oils (often polyacetylene-rich), phenolics (e.g., chlorogenic acid and rutin), PUFA-rich lipids, and newly reported minor constituents. Polysaccharides show variable monosaccharide profiles, molecular-weight ranges, and linkage/branching patterns, strongly influenced by extraction-purification; derivatization (e.g., sulfation/selenization) and delivery systems can further tune physicochemical properties. Preclinical studies report antioxidant, anti-inflammatory, immunomodulatory, cardioprotective, and antiviral effects, commonly linked to Nrf2/Keap1 redox defense, inflammatory signaling control, TLR2/4-related immune regulation, gut-barrier reinforcement with microbiota remodeling, and anti-ferroptotic protection in myocardial ischemia-reperfusion models. Applications span traditional dosage forms and functional foods, but translation is limited by origin/process variability, incomplete long-term safety and ADME data, and regulatory uncertainty.

CONCLUSIONS: C. violaceum is a promising ethnomedicinal resource with clear part-specific features and polysaccharide-centered potential. Future work should combine multi-omics with target validation, fingerprint-guided QC and traceability, greener scalable processing, and regulatory-aligned safety packages to enable reproducible products.

PMID:41951195 | DOI:10.1016/j.jep.2026.121628

Received — 4 April 2026 ⏭

Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianhao Shi · Yang Zhang · Xiaoyan Zhao · Fengbin Zhu · Chenyi Lei · Han Li · Wenwu Ou · Yang Song · Yongdong Zhang · Fuli Feng
3 April 2026 at 12:00
arXiv:2604.01690v1 Announce Type: new Abstract: The rapid proliferation of Artificial Intelligence-Generated Content (AIGC) is fundamentally restructuring online content ecologies, necessitating a rigorous examination of its behavioral and distributional implications. Leveraging a comprehensive longitudinal dataset comprising tens of millions of users from a leading Chinese video-sharing platform, this study elucidated the distinct creation and consumption behaviors characterizing AIGC versus Human-Generated Content (HGC). We identified a prevalent scale-over-preference dynamic, wherein AIGC creators achieve aggregate engagement comparable to HGC creators through high-volume production, despite a marked consumer preference for HGC. Deeper analysis uncovered the ability of the algorithmic content distribution mechanism in moderating these competing interests regarding AIGC. These findings advocated for the implementation of AIGC-sensitive distribution algorithms and precise governance frameworks to ensure the long-term health of the online content platforms.
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 ⏭

PulseLM: A Foundation Dataset and Benchmark for PPG-Text Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hung Manh Pham · Jinyang Wu · Xiao Ma · Yiming Zhang · Yixin Xu · Aaqib Saeed · Bin Zhu · Zhou Pan · Dong Ma
5 March 2026 at 13:00
arXiv:2603.03331v1 Announce Type: cross Abstract: Photoplethysmography (PPG) is a widely used non-invasive sensing modality for continuous cardiovascular and physiological monitoring across clinical, laboratory, and wearable settings. While existing PPG datasets support a broad range of downstream tasks, they typically provide supervision in the form of numerical measurements or task-specific labels, limiting their suitability for language-based physiological reasoning and multimodal foundation models. In this work, we introduce PulseLM, a large-scale PPG-text dataset designed to bridge raw PPG waveforms and natural language through a unified, closed-ended question answering (QA) formulation. PulseLM aggregates PPG recordings from fifteen publicly available sources and harmonizes heterogeneous annotations into twelve common physiologically QA tasks. The dataset comprises 1.31 million standardized 10-second PPG segments, associated with 3.15 million question-answer pairs. We further define reproducible preprocessing, supervision, and evaluation protocols and establish baseline benchmarks using multimodal PPG-aware large language models. PulseLM provides a standardized foundation for studying multimodal physiological reasoning, cross-dataset generalization, and scalable benchmarking of PPG-based language models. The data and code can be found publicly available at: https://github.com/manhph2211/PulseLM.
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.

UniWeTok: An Unified Binary Tokenizer with Codebook Size $\mathit{2^{128}}$ for Unified Multimodal Large Language Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shaobin Zhuang · Yuang Ai · Jiaming Han · Weijia Mao · Xiaohui Li · Fangyikang Wang · Xiao Wang · Yan Li · Shanchuan Lin · Kun Xu · Zhenheng Yang · Huaibo Huang · Xiangyu Yue · Hao Chen · Yali Wang
17 February 2026 at 13:00
arXiv:2602.14178v1 Announce Type: cross Abstract: Unified Multimodal Large Language Models (MLLMs) require a visual representation that simultaneously supports high-fidelity reconstruction, complex semantic extraction, and generative suitability. However, existing visual tokenizers typically struggle to satisfy these conflicting objectives within a single framework. In this paper, we introduce UniWeTok, a unified discrete tokenizer designed to bridge this gap using a massive binary codebook ($\mathit{2^{128}}$). For training framework, we introduce Pre-Post Distillation and a Generative-Aware Prior to enhance the semantic extraction and generative prior of the discrete tokens. In terms of model architecture, we propose a convolution-attention hybrid architecture with the SigLu activation function. SigLu activation not only bounds the encoder output and stabilizes the semantic distillation process but also effectively addresses the optimization conflict between token entropy loss and commitment loss. We further propose a three-stage training framework designed to enhance UniWeTok's adaptability cross various image resolutions and perception-sensitive scenarios, such as those involving human faces and textual content. On ImageNet, UniWeTok achieves state-of-the-art image generation performance (FID: UniWeTok 1.38 vs. REPA 1.42) while requiring a remarkably low training compute (Training Tokens: UniWeTok 33B vs. REPA 262B). On general-domain, UniWeTok demonstrates highly competitive capabilities across a broad range of tasks, including multimodal understanding, image generation (DPG Score: UniWeTok 86.63 vs. FLUX.1 [Dev] 83.84), and editing (GEdit Overall Score: UniWeTok 5.09 vs. OmniGen 5.06). We release code and models to facilitate community exploration of unified tokenizer and MLLM.
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