Paper List
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Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically d...
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Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
This paper addresses the critical gap in evaluating how AI-generated images can effectively support cross-cultural mental distress communication, part...
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Collective adsorption of pheromones at the water-air interface
This paper addresses the core challenge of understanding how amphiphilic pheromones, previously assumed to be transported in the gas phase, can be sta...
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pHapCompass: Probabilistic Assembly and Uncertainty Quantification of Polyploid Haplotype Phase
This paper addresses the core challenge of accurately assembling polyploid haplotypes from sequencing data, where read assignment ambiguity and an exp...
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Setting up for failure: automatic discovery of the neural mechanisms of cognitive errors
This paper addresses the core challenge of automating the discovery of biologically plausible recurrent neural network (RNN) dynamics that can replica...
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Influence of Object Affordance on Action Language Understanding: Evidence from Dynamic Causal Modeling Analysis
This study addresses the core challenge of moving beyond correlational evidence to establish the *causal direction* and *temporal dynamics* of how obj...
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.