Paper List
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Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
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CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
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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 ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
Fluctuating Environments Favor Extreme Dormancy Strategies and Penalize Intermediate Ones
Universidad de Córdoba, Spain | Istituto Nazionale di Oceanografia e Geofisica Sperimentale (OGS), Italy | Universidad de Granada, Spain
The 30-Second View
IN SHORT: This paper addresses the core challenge of determining how organisms should tune dormancy duration to match the temporal autocorrelation of their environment, revealing that intermediate dormancy times are systematically maladaptive.
Innovation (TL;DR)
- Theory Identifies a generic mechanism where the interplay between demographic delay (dormancy) and environmental autocorrelation generates a strongly non-monotonic fitness landscape.
- Methodology Develops a parsimonious delayed-logistic model with colored multiplicative noise (dichotomous Markov noise) to analytically and numerically dissect the three-regime population performance.
- Biology Demonstrates evolutionary bistability, where selection favors either very short or very long dormancy strategies, systematically avoiding the maladaptive intermediate regime, as confirmed by an agent-based model.
Key conclusions
- For a population near the critical threshold (b ≳ d), the mean linear growth rate G(α) exhibits a local minimum at intermediate dormancy durations when noise amplitude σ or correlation time τ exceed a threshold, making this strategy globally least favorable (Figure 4).
- The stationary mean population density x* shows a pronounced depression (a 'valley') for intermediate α combined with strong environmental noise (σ > 0), which deepens and broadens as σ increases, potentially driving extinction (Figure 3).
- Evolutionary simulations confirm bistable selection: populations evolve towards either very short (α → 0) or very long (α ≳ 5) dormancy extremes, with the intermediate regime (e.g., α = 1) consistently leading to population collapse.
Abstract: Dormancy is a widespread adaptive strategy that enables biological populations to persist in fluctuating environments. Yet how its evolutionary benefits depend on the temporal structure of environmental variability, and whether dormancy can become systematically maladaptive, remains poorly understood. Here we examine how dormancy interacts with environmental correlation times using a parsimonious delayed-logistic model in which dormant individuals reactivate after a fixed lag while birth rates fluctuate under temporally correlated stochasticity. Numerical simulations and analytical calculations reveal that the joint effect of demographic memory and colored multiplicative noise generates a strongly non-monotonic dependence of fitness on dormancy duration, with three distinct regimes of population performance. Very short dormancy maximizes linear growth but amplifies fluctuations and extinction risk. Very long dormancy buffers environmental variability, substantially increasing mean extinction times despite slower growth. Strikingly, and central to our results, there is a broad band of intermediate dormancy durations that is maladaptive, simultaneously reducing both growth and persistence—an effect that arises generically from the mismatch between delay times and environmental autocorrelation. The predicted bistability between short- and long-dormancy strategies is confirmed in an evolutionary agent-based model, which avoids intermediate lag times and selects for evolutionarily stable extremes. Our results show that dormancy duration is not merely a life-history parameter but an adaptive mechanism tuned to environmental timescales, and that “dangerous middle” dormancy times can be inherently disfavored, with implications for understanding persistence in seed banks, microbial persisters, and cancer cell dormancy. More broadly, this work identifies a general mechanism by which demographic delays interacting with correlated environmental variability generate a non-monotonic fitness landscape that selects for extreme timing strategies, and raises fundamental questions on analyzing delayed, non-Markovian dynamics driven by correlated multiplicative noise near absorbing boundaries.