Cano, A. V. | bioRxiv Science

bioRxiv2025

Detecting infection-related mortality using dynamical statistical indicators of high-resolution activity time series

Predicting transitions between health, disease, and death across biological systems remains an important challenge with significant implications for both ecological management and medical intervention. Although the principles underlying these transitions are increasingly recognised, accurate and tractable dynamical indicators of health-to-disease transitions remain rare, especially at the level of individual organisms. Here, we use dynamical statistical indicators of high-resolution activity time series to predict infection-related mortality. By analysing locomotor activity data from infected Drosophila melanogaster flies, we find that individual dynamical indicators, such as the mean, variance, autocorrelation, and permutation entropy, differed between flies that survived and those that died during the experiment. When these indicators were used to train a Random Forest model, the classifier performed well (AUC = 0.94), demonstrating an accuracy of 87.9% in discriminating between infected flies that would die from infection and those that would survive, with the strongest discriminatory power detected over 12 hours prior to death. Our findings show that combining these easy-to-compute, dynamical statistical indicators with machine learning enhances the ability to predict health deterioration in the Drosophila model. Conceptually, our findings emphasize that the integration of dynamical statistical metrics from physiological or behavioural time-series with machine learning approaches may offer a promising avenue for real-time health monitoring in both ecological and clinical settings. HighlightsO_LIHigh-resolution locomotor activity time series distinguish infected flies that live or die C_LIO_LISimple dynamical indicators (mean, SD, CV, lag-1 autocorrelation) jointly improve outcome discrimination C_LIO_LIPermutation entropy declines over time and is reduced in flies approaching acute infection-related death C_LIO_LIRandom Forest models classify infection survival outcomes with high accuracy (AUC 0.94) C_LIO_LIDivergence in activity dynamics is detectable over 12-16 hours before death in flies that succumb to infection C_LI

ecology↗

bioRxiv2023

Mutation and selection induce correlations between selection coefficients and mutation rates

The joint distribution of selection coefficients and mutation rates is a key determinant of the genetic architecture of molecular adaptation. Three different distributions are of immediate interest: (1) the nominal distribution of possible changes, prior to mutation or selection, (2) the de novo distribution of realized mutations, and (3) the fixed distribution of selectively established mutations. Here, we formally characterize the relationships between these joint distributions under the strong selection, weak mutation (SSWM) regime. The de novo distribution is enriched relative to the nominal distribution for the highest rate mutations, and the fixed distribution is further enriched for the most highly beneficial mutations. Whereas mutation rates and selection coefficients are often assumed to be uncorrelated, we show that even with no correlation in the nominal distribution, the resulting de novo and fixed distributions can have correlations with any combination of signs. Nonetheless, we suggest that natural systems with a finite number of beneficial mutations will frequently have the kind of nominal distribution that induces negative correlations in the fixed distribution. We apply our mathematical framework, along with population simulations, to explore joint distributions of selection coefficients and mutation rates from deep mutational scanning and cancer informatics. Finally, we consider the evolutionary implications of these joint distributions together with two additional joint distributions relevant to parallelism and the rate of adaptation.

evolutionary biology↗