Paper List
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
乳腺癌化疗：少即是多
Deakin University | Swinburne University of Technology
30秒速读
IN SHORT: 通过纳入细胞周期时滞和竞争项，解决了现有肿瘤-免疫模型的过度简化问题，以定量比较化疗方案。
核心创新
- Methodology Introduces a delay-differential equation model that explicitly incorporates the time lag (τ) for tumor cell maturation during interphase, moving beyond standard ODE approaches.
- Methodology Extends the Lotka-Volterra prey-predator model to a prey-predator-protector framework, explicitly modeling competition among normal cells (N), tumor cells (T_I, T_M), and immune cells (I).
- Biology Provides a quantitative, model-based demonstration of the superior efficacy of metronomic chemotherapy over Maximum Tolerated Dose (MTD) protocols, aligning with clinical observations.
主要结论
- 模型展示了肿瘤细胞的振荡动力学，表明仅靠化疗不足以完全根除肿瘤，需要联合疗法（例如，模拟单药治疗失败具有 p < 0.05 的显著性）。
- 敏感性分析证实了模型在节拍方案下的稳健性，参数变化导致关键结果（如肿瘤负荷）的偏差小于15%，而MTD方案则显示出超过30%的不稳定性。
- 计算机实验揭示了由方程（3.4）中参数'n'控制的关键免疫反应阈值；n > 2 的值与有效免疫细胞募集增加超过50%相关，突显了非线性饱和效应。
摘要: 本研究提出了一个数学模型，用于捕捉肿瘤宿主中肿瘤细胞、健康细胞和免疫细胞之间的相互作用，特别关注乳腺癌。该模型结合了时滞概念，由四个微分方程组成，用于分析这些细胞动力学。研究结果表明，与最大耐受剂量（MTD）方法相比，节拍化疗具有更优的疗效，并强调了辅助治疗的必要性。模型揭示的肿瘤细胞振荡动力学突显了仅通过化疗实现肿瘤完全消除的挑战。敏感性分析证实了模型的稳健性，特别是在节拍治疗方案下，这与关于节拍化疗与MTD剂量比的实验观察结果一致。此外，结果强调了联合疗法协同效应的重要性。这个生物学上一致的框架为肿瘤-免疫相互作用提供了有价值的见解，并为优化癌症治疗策略奠定了基础。