bioRxiv Science⌕ Search

Biology subjects

Johnson, L.

Publications and source records attributed to Johnson, L..

40 records · Page 3Linked to original sources

Dose-dependent effects of transcranial alternating current stimulation on spike timing in awake nonhuman primates

Weak extracellular electric fields can influence spike timing in neural networks. Approaches to impose such fields on the brain in a noninvasive manner have high potential for novel treatments of neurological and psychiatric disorders. One of these methods, transcranial alternating current stimulation (TACS), is hypothesized to affect spike timing and cause neural entrainment. However, the conditions under which these effects occur in-vivo are unknown. Here, we show that TACS modulates spike timing in awake nonhuman primates (NHPs) in a dose-dependent fashion. Recording single-unit activity from pre-and post-central gyrus regions in NHPs during TACS, we found that a larger population of neurons became entrained to the stimulation waveform for higher stimulation intensities. Performing a cluster analysis of changes in interspike intervals, we identified two main types of neural responses to TACS - increased burstiness and phase entrainment. Our results demonstrate the ability of TACS to affect spike-timing in the awake primate brain and identify fundamental neural mechanisms. Concurrent electric field recordings demonstrate that spike-timing changes occur with stimulation intensities readily achievable in humans. These results suggest that novel TACS protocols tailored to ongoing brain activity may be a potent tool to normalize spike-timing in maladaptive brain networks and neurological disease.

neuroscience↗

Bayesian abundance estimation from genetic mark-recapture data when not all sites are sampled: an example with the bowhead whale

Estimating abundance is one of the most fundamental and important aspects of population biology, with major implications on how the status of a population is perceived and thus on conservation and management efforts. Although typically based on one of two methods (distance sampling or mark-recapture), there are many individual identification methods that can be used for mark-recapture purposes. In recent years, the use of genetic data for individual identification and abundance estimation through mark-recapture analyses have increased, and in some situations such genetic identifications are more efficient than their field-based counterparts for population monitoring. One issue with mark-recapture analyses, regardless of which method of individual identification is used, is that the study area must provide adequate opportunities for "capturing" all individuals within a population. However, many populations are unevenly and widely distributed, making it unfeasible to adequately sample all necessary areas. Here we develop an analytical technique that accounts for unsampled locations, and provides a means to infer "missing" individuals from unsampled locations, and therefore obtain more accurate abundance estimates when it is not possible to sample all sites. This method is validated using simulations, and is used to estimate abundance of the Eastern Canada-West Greenland (EC-WG) bowhead whale population. Based on these analyses, the estimated size of this population is 9,089 individuals, with a 95% highest density interval of 5,107-17,079.

zoology↗

A Mathematical Modeling Approach to The Cort-Fitness Hypothesis

The Cort-Fitness hypothesis has generated much interest from investigators integrating field endocrinology with evolutionary biology, ecology, and conservation. The hypothesis was developed on the assumption that if glucocorticoid levels increase with environmental challenges and fitness decreases with environmental challenges, then there should be a negative relationship between glucocorticoid levels and fitness. However, studies across diverse taxa have found that the relationship between glucocorticoid levels and fitness is not consistent: some studies show a positive relationship, others negative, and some show no correlation. Hence, support for the hypothesis is not consistent and thus a deeper understanding of the mechanisms underlying the relationship between glucocorticoid levels, environmental pressures, and fitness is needed. We propose a mathematical model representing the links between glucocorticoid levels, environmental challenges, and fitness. Our model explores how variation in the predictability and intensity of environmental challenges, reproductive strategies, and fitness metrics can all contribute to the variability observed in empirical tests of the Cort-Fitness hypothesis. We provide qualitative results showing the Cort-Fitness relationship for different environmental scenarios and discuss how the model can be used to inform future Cort-Fitness studies.

animal behavior and cognition↗

A large scale joint analysis of flowering time reveals independent temperate adaptations in maize

Modulating days to flowering is a key mechanism in plants for adapting to new environments, and variation in days to flowering drives population structure by limiting mating. To elucidate the genetic architecture of flowering across maize, a quantitative trait, we mapped flowering in five global populations, a diversity panel (Ames) and four half-sib mapping designs, Chinese (CNNAM), US (USNAM), and European Dent (EUNAM-Dent) and Flint (EUNAM-Flint). Using whole-genome projected SNPs, we tested for joint association using GWAS, resampling GWAS and two regional approaches; Regional Heritability Mapping (RHM) (1, 2) and a novel method, Boosted Regional Heritability Mapping (BRHM). Direct overlap in significant regions detected between populations and flowering candidate genes was limited, but whole-genome cross-population predictive abilities were [≤]0.78. Poor predictive ability correlated with increased population differentiation (r = 0.41), unless the parents were broadly sampled from across the North American temperate-tropical germplasm gradient; uncorrected GWAS results from populations with broadly sampled parents were well predicted by temperate-tropical FSTs in machine learning. Machine learning between GWAS results also suggested shared architecture between the American panels and, more distantly, the European panels, but not the Chinese panel. Machine learning approaches can reconcile non-linear relationships, but the combined predictive ability of all of the populations did not significantly enhance prediction of physiological candidates. While the North American-European temperate adaption is well studied, this study suggest independent temperate adaptation evolved in the Chinese panel, most likely in China after 1500, a finding supported by differential gene ontology term enrichment between populations.

genetics↗