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 ...
可变食性范围模型中向泛化主义的缓慢演化
Department of Mathematical Sciences, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom
30秒速读
IN SHORT: 通过证明是种群统计噪声（而非确定性动力学）驱动了模式形成和泛化食性的演化，解决了间接竞争下物种形成的悖论。
核心创新
- Methodology Develops a continuous-space resource-consumer model with explicit resource dynamics and evolvable dietary range, extending beyond fixed-preference Lotka-Volterra frameworks.
- Theory Demonstrates that deterministic analysis predicts homogeneous steady states (no species), but stochastic simulations with demographic noise induce persistent pattern formation interpreted as species.
- Methodology Uses Fourier analysis of linearized dynamics to predict the dominant perturbation modes (e.g., number of species) from the power spectrum, linking analytical predictions to simulation outcomes.
主要结论
- 对于固定食性范围（w=0.2），傅里叶分析预测在 kL/2π=5 处存在主导模式，这对应于在随机模拟中观察到的5个等间距物种的形成（图2，3A）。
- 在可演化食性范围模型中，动力学发生在两个时间尺度上：快速协同演化到准稳态流形，随后缓慢弛豫向均匀态。种群统计噪声阻止了这种弛豫，维持了模式。
- 泛化食性（宽w）在长时间尺度上随机演化，因为与由相同资源生产率支持的、种群规模较小的专化集群相比，它们更不易受到灭绝风险的影响。
摘要: 共享栖息地的物种会协同演化以利用可用资源，因为消费受到消费者与资源之间竞争和负反馈回路的调节。给定物种的食性范围决定了其可获取的资源，从而决定了与之竞争的其他物种。狭窄的食性范围以过度依赖少量资源为代价避免竞争；相反，广泛的食性范围提供了更多替代选择，但也增加了与其他物种竞争的机会。在此，我们研究了生态位形成数学模型中食性范围的演化。我们发现了高度路径依赖的协同演化动力学，其特征是长寿命的准稳态。最终，随机效应驱动了泛化食性的演化，正如我们在分析和模拟中所揭示的。