Camilleri-Carter, T.-L. | bioRxiv Science

bioRxiv2021

Maternal and paternal sugar consumption interact to modify offspring life history and physiology

Intergenerational effects on offspring phenotypes occur in response to variation in both maternal and paternal nutrition. Because the combined maternal and paternal effects are rarely considered together however, their relative contributions, and the capacity for interactions between parental diets to shape offspring life history and physiology are not understood. To address this, we altered sucrose levels of adult fruit flies (Drosophila melanogaster) prior to mating, across two generations, producing parent-parent and parent-offspring combinations that were either matched or mismatched in dietary sucrose. We then measured lifespan, fecundity, body mass, and triglyceride levels in parents and offspring. We reveal complex non-additive interactions, that involve diets of each parent and offspring to shape offspring phenotypes, but the effects were generally not consistent with an adaptive response to parental diet. Notably, we find that interacting parental flies (sires and dams) lived longer when their sucrose treatments were matched, but they produced shorter-lived offspring.

evolutionary biology↗

bioRxiv2020

Sex estimation in cranial remains: A comparison of machine learning and discriminant analysis in Italian populations

ObjectivesUsing cranial measurements in two Italian populations, we compare machine learning methods to the more traditional method of linear discriminant analysis in estimating sex. We use crania in sex estimation because it is useful especially when remains are fragmented or displaced, and the cranium may be the only remains found. Materials and MethodsUsing the machine learning methods of decision tree learning, support-vector machines, k-nearest neighbor algorithm, and ensemble methods we estimate the sex of two populations: Samples from Bologna and samples from the island of Sardinia. We used two datasets, one containing 17 cranial measurements, and one measuring the foramen magnum. Results and DiscussionOur results indicate that machine learning models produce similar results to linear discriminant analysis, but in some cases machine learning produces more consistent accuracy between the sexes. Our study shows that sex can be accurately predicted (> 80%) in Italian populations using the cranial measurements we gathered, except for the foramen magnum, which shows a level of accuracy of [~]70% accurate which is on par with previous geometric morphometrics studies using crania in sex estimation. We also find that our trained machine learning models produce population-specific results; we see that Italian crania are sexually dimorphic, but the features that are important to this dimorphism differ between the populations.

bioinformatics↗