Hoffman, C. | bioRxiv Science

bioRxiv2022

Pattern dynamics and stochasticity of the brain rhythms

Our current understanding of brain rhythms is based on quantifying their instantaneous or time-averaged characteristics. What remains unexplored, is the actual structure of the waves--their shapes and patterns over finite timescales. To address this, we used two independent approaches to link wave forms to their physiological functions: the first is based on quantifying their consistency with the underlying mean behavior, and the second assesses "orderliness" of the waves features. The corresponding measures capture the waves characteristic and abnormal behaviors, such as atypical periodicity or excessive clustering, and demonstrate coupling between the patterns dynamics and the animals location, speed and acceleration. Specifically, we studied patterns of{theta} and{gamma} waves, and Sharp Wave Ripples, and observed speed-modulated changes of the waves cadence, an antiphase relationship between orderliness and acceleration, as well as spatial selectiveness of patterns. Further-more, we found an interdependence between orderliness and regularity: larger deviations from steady oscillatory behavior tend to accompany disarrayed temporal cluttering of peaks and troughs. Taken together, our results offer a complementary--mesoscale--perspective on brain wave structure, dynamics, and functionality.

neuroscience↗

bioRxiv2020

Species level resolution of female bladder microbiota from marker gene surveys

The human bladder contains bacteria in the absence of infection. Interest in studying these bacteria and their association with bladder conditions is increasing. However, the chosen experimental method can limit the resolution of the taxonomy that can be assigned to the bacteria found in the bladder. 16S rRNA amplicon sequencing is commonly used to identify bacteria in urinary specimens, but is typically restricted to genus-level identification. Our primary aim was to determine if accurate species-level identification of bladder bacteria is possible using 16S rRNA amplicon sequencing. We evaluated the ability of different classification schemes, each consisting of combinations of a reference database, a 16S rRNA gene variable region and a taxonomic classification algorithm to correctly classify bladder bacteria. We show that species-level identification is possible, and that the reference database chosen is the most important component, followed by the 16S variable region sequenced. ImportanceSpecies-level information may deepen our understanding of associations between bladder microbiota and bladder conditions, such as lower urinary tract symptoms and urinary tract infections. The capability to identify bacterial species depends on large databases of sequences, algorithms that leverage statistics and available computer hardware, and knowledge of bacterial genetics and classification. Taken together, this is a daunting body of knowledge to become familiar with before the simple question of bacterial identity can be answered. Our results show the choice of taxonomic database and variable region of the 16S rRNA gene sequence makes species level identification possible. We also show this improvement can be achieved through the more careful application of existing methods and use of existing resources.

bioinformatics↗