Sex differences in variability across timescales in BALB/c mice.

BackgroundFemales are markedly underinvestigated in the biological and behavioral sciences due to the presumption that cyclic hormonal changes across the ovulatory cycle introduce excess variability to measures of interest in comparison to males. However, recent analyses indicate that male and female mice and rats exhibit comparable variability across numerous physiological and behavioral measures, even when the stage of the estrous cycle is not considered. Hormonal changes across the ovulatory cycle likely contribute cyclic, intra-individual variability in females, but the source(s) of male variability has, to our knowledge, not been investigated. It is unclear whether male variability, like that of females, is temporally structured and, therefore, quantifiable and predictable. Finally, whether males and females exhibit variability on similar time scales has not been explored.MethodsThese questions were addressed by collecting chronic, high temporal resolution locomotor activity (LA) and core body temperature (CBT) data from male and female BALB/c mice.ResultsContrary to expectation, males are more variable than females over the course of the day (diel variability) and exhibit higher intra-individual daily range than females in both LA and CBT. Between mice of a given sex, variability is comparable for LA but the inter-individual daily range in CBT is greater for males. To identify potential rhythmic processes contributing to these sex differences, we employed wavelet transformations across a range of periodicities (1-39 h).ConclusionsAlthough variability in circadian power is comparable between the sexes for both LA and CBT, infradian variability is greater in females and ultradian variability is greater in males. Thus, exclusion of female mice from studies because of estrous cycle variability may increase variance in investigations where only male measures are collected over a span of several hours and limit generalization of findings from males to females

Full-Water Column Turbulence Parameterization of Stratified Waters in Southern Lake Michigan

Full water column mean flow and turbulence structure was characterized at two stratified locations in Lake Michigan (a. Muskegon, MI; b. Michigan City, IN) in order to better understand the filtration potential of invasive quagga mussels. Invasive quagga mussels in Lake Michigan are filter feeders and can dramatically alter clarity as well as the biological/chemical characteristics of the water column. This filtering capacity is highly contingent on turbulence characteristics throughout the water column, which is poorly understood in the Great Lakes. Using velocity, temperature, and turbulence data collected from these locations, the structure of the water column turbulence was modeled for site (a) using data from 2011 and measured for site (b) in 2017. The data from 2017 was collected as a test run of a new acoustic Doppler current profiler, the Nortek Signature500, that will be utilized in future experiments on Lake Michigan. This data was analyzed to better characterize the turbulence structure of Lake Michigan and how it is affected by wind events and wave trends. Using power spectra and turbulence structure function, the turbulent kinetic energy dissipation of the full water column was analyzed from these two locations. This analysis provides insight into the turbulence structure of the full-water column in a stratified lake and will be utilized to prepare for the execution of future sampling events in Lake Michigan

Using machine learning to detect the differential usage of novel gene isoforms

BACKGROUND: Differential isoform usage is an important driver of inter-individual phenotypic diversity and is linked to various diseases and traits. However, accurately detecting the differential usage of different gene transcripts between groups can be difficult, in particular in less well annotated genomes where the spectrum of transcript isoforms is largely unknown. RESULTS: We investigated whether machine learning approaches can detect differential isoform usage based purely on the distribution of reads across a gene region. We illustrate that gradient boosting and elastic net approaches can successfully identify large numbers of genes showing potential differential isoform usage between Europeans and Africans, that are enriched among relevant biological pathways and significantly overlap those identified by previous approaches. We demonstrate that diversity at the 3′ and 5′ ends of genes are primary drivers of these differences between populations. CONCLUSION: Machine learning methods can effectively detect differential isoform usage from read fraction data, and can provide novel insights into the biological differences between groups. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04576-3