Biology subjects

Bind, M.-A.

Publications and source records attributed to Bind, M.-A..

3 recordsLinked to original sources

bioRxiv2025

Divergent spatiotemporal integration of whole-field visual motion in medaka and zebrafish.

Cross-species comparisons offer leverage for identifying conserved and divergent neural computations underlying innate behavior. Visual motion integration is a fundamental operation that stabilizes position relative to the moving environment and supports object tracking, yet how its underlying algorithms vary across closely related vertebrate brains remains poorly understood. We investigated how zebrafish (Danio rerio) and medaka (Oryzias latipes) larvae implement visual motion integration using distinct spatiotemporal filters that trade speed for persistence through separable control modules. Using controlled whole-field motion stimuli, we found that medaka pool motion signals over visual fields nearly twice as large as those of zebrafish and exhibit enhanced weighting of peripheral inputs, whereas zebrafish rely more strongly on motion signals directly beneath the body. Temporally, zebrafish respond robustly to motion signals with lifetimes as short as 100 ms, whereas medaka require stimulus durations exceeding one second and maintain motion-driven activity for several seconds after stimulus offset. Decomposition of turning behavior revealed separable control modules for large and small corrective maneuvers, with species differences arising primarily from prolonged temporal integration in medaka small-turn control. Together, these differences reveal species-specific tuning of spatial kernels and temporal filters underlying visuomotor control. Our results demonstrate how alterations in basic computational motifs, spatiotemporal pooling, gain, and persistence, can generate divergent visuomotor strategies across closely related vertebrate brains. Significance StatementAnimals rely on visual motion to stabilize positions and interact with dynamic environments, yet how these computations vary across related species remains unclear. By comparing larval zebrafish and medaka, we show that visually similar vertebrates implement motion integration using distinct spatiotemporal strategies. Medaka integrate motion over larger visual fields and retain motion signals for seconds, whereas zebrafish favor rapid, spatially restricted integration. These differences arise from separable control modules governing fine and large motor adjustments. Our results reveal how small changes in core computational motifs--pooling, gain, and persistence--can generate divergent sensorimotor strategies across evolution.

neuroscience↗

bioRxiv2019

Human Aging DNA Methylation Signatures are Conserved but Accelerated in Cultured Fibroblasts

Aging is associated with progressive and site-specific changes in DNA methylation (DNAm). These global changes are captured by DNAm clocks that accurately predict chronological age in humans but relatively little is known about how clocks perform in vitro. Here we culture primary human fibroblasts across the cellular lifespan ([~]6 months) and use four different DNAm clocks to show that age-related DNAm signatures are conserved and accelerated in vitro. The Skin & Blood clock shows the best linear correlation with chronological time (r=0.90), including during replicative senescence. Although similar in nature, the rate of epigenetic aging is approximately 62x times faster in cultured cells than in the human body. Consistent with in vivo data, cells aged under hyperglycemic conditions exhibit an approximately three years elevation in baseline DNAm age. Moreover, candidate gene-based analyses further corroborate the conserved but accelerated biological aging process in cultured fibroblasts. Fibroblasts mirror the established DNAm topology of the age-related ELOVL2 gene in human blood and the rapid hypermethylation of its promoter cg16867657, which correlates with a linear decrease in ELOVL2 mRNA levels across the lifespan. Using generalized additive modeling on twelve timepoints across the lifespan, we also show how single CpGs exhibit loci-specific, linear and nonlinear trajectories that reach rates up to -47% (hypomethylation) to +23% (hypermethylation) per month. Together, these high temporal resolution global, gene-specific, and single CpG data highlight the conserved and accelerated nature of epigenetic aging in cultured fibroblasts, which may constitute a system to evaluate age-modifying interventions across the lifespan.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/605295_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (60K):\norg.highwire.dtl.DTLVardef@101e4f2org.highwire.dtl.DTLVardef@15df049org.highwire.dtl.DTLVardef@4a704dorg.highwire.dtl.DTLVardef@112f970_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract\n\nC_FIG

cell biology↗

bioRxiv2017

Combining Ensemble Learning Techniques and G-Computation to Investigate Chemical Mixtures in Environmental Epidemiology Studies

BackgroundAlthough biomonitoring studies demonstrate that the general population experiences exposure to multiple chemicals, most environmental epidemiology studies consider each chemical separately when assessing adverse effects of environmental exposures. Hence, the critical need for novel approaches to handle multiple correlated exposures.\n\nMethodsWe propose a novel approach using the G-formula, a maximum likelihood-based substitution estimator, combined with an ensemble learning technique (i.e. SuperLearner) to infer causal effect estimates for a multi-pollutant mixture. We simulated four continuous outcomes from real data on 5 correlated exposures under four exposure-response relationships with increasing complexity and 500 replications. The first simulated exposure-response was generated as a linear function depending on two exposures; the second was based on a univariate nonlinear exposure-response relationship; the third was generated as a linear exposure-response relationship depending on two exposures and their interaction; the fourth simulation was based on a non-linear exposure-response relationship with an effect modification by sex and a linear relationship with a second exposure. We assessed the method based on its predictive performance (Minimum Square error [MSE]), its ability to detect the true predictors and interactions (i.e. false discovery proportion, sensitivity), and its bias. We compared the method with generalized linear and additive models, elastic net, random forests, and Extreme gradient boosting. Finally, we reconstructed the exposure-response relationships and developed a toolbox for interactions visualization using individual conditional expectations.\n\nResultsThe proposed method yielded the best average MSE across all the scenarios, and was therefore able to adapt to the true underlying structure of the data. The method succeeded to detect the true predictors and interactions, and was less biased in all the scenarios. Finally, we could correctly reconstruct the exposure-response relationships in all the simulations.\n\nConclusionsThis is the first approach combining ensemble learning techniques and causal inference to unravel the effects of chemical mixtures and their interactions in epidemiological studies. Additional developments including high dimensional exposure data, and testing for detection of low to moderate associations will be carried out in future developments.

epidemiology↗