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Biology subjects

King, A. M.

Publications and source records attributed to King, A. M..

2 recordsLinked to original sources

Using Tools as Cues for Motor Adaptation in Virtual Reality

Humans are adept at using multiple tools, often switching between them even when each involves distinct and potentially conflicting motor demands. This study investigated how different features of a tool influence the formation of distinct motor memories during dual adaptation to opposing visuomotor perturbations. Using an immersive virtual reality (VR) setup, participants performed an aiming task in which they launched a ball toward a target using virtual tools, each consistently associated with a different visuomotor rotation. We manipulated tool features across three groups: one in which tools differed only in colour (Colour Control), one in which they differed in shape but involved similar movement patterns (Motor Congruent), and one in which they differed both in shape and in how they were operated--for example, a paddle-like forward swing to propel the ball versus a draw-and-release motion similar to a slingshot (Motor Incongruent). A fourth control group that adapted to a single perturbation with a single tool at a time was also included. Only the Motor Incongruent group demonstrated robust dual adaptation and clear aftereffects, comparable to those observed during single-tool learning. These results suggest that distinct modes of tool operation play a critical role in supporting the formation and retention of separate internal models during sensorimotor adaptation. New & NoteworthyHumans often switch between tools with conflicting motor demands. Using immersive virtual reality, we tested whether visual features or operational differences support dual adaptation to opposing visuomotor perturbations. Only when tools differed in both shape and mode of operation did participants show robust adaptation and aftereffects, comparable to single-tool learning. These findings highlight the critical role of movement-relevant cues in forming distinct motor memories for flexible tool use.

neuroscience↗

sRNA-target Prediction Organizing Tool (SPOT) integrates computational and experimental data to facilitate functional characterization of bacterial small RNAs

Small RNAs (sRNAs) post-transcriptionally regulate mRNA targets, typically under conditions of environmental stress. Although hundreds of sRNAs have been discovered in diverse bacterial genomes, most sRNAs remain uncharacterized, even in model organisms. Identification of mRNA targets directly regulated by sRNAs is rate-limiting for sRNA functional characterization. To address this, we developed a computational pipeline that we named SPOT for sRNA-target Prediction Organizing Tool. SPOT incorporates existing computational tools to search for sRNA binding sites, allows filtering based on experimental data, and organizes the results into a standardized report. SPOT sensitivity (Correctly Predicted Targets/Total Known Targets) was equal to or exceeded any individual method when used on 12 characterized sRNAs. Using SPOT, we generated a set of target predictions for the sRNA RydC, which was previously shown to positively regulate cfa mRNA, encoding cyclopropane fatty acid synthase. SPOT identified cfa along with additional putative mRNA targets, which we then tested experimentally. Our results demonstrated that in addition to cfa mRNA, RydC also regulates trpE and pheA mRNAs, which encode aromatic amino acid biosynthesis enzymes. Our results suggest that SPOT can facilitate elucidation of sRNA target regulons to expand our understanding of the many regulatory roles played by bacterial sRNAs.\n\nIMPORTANCESmall RNAs (sRNAs) regulate gene expression in diverse bacteria by interacting with mRNAs to change their structure, stability or translation. Hundreds of sRNAs have been identified in bacteria, but characterization of their regulatory functions is limited by difficulty with sensitive and accurate identification of mRNA targets. Thus, new robust methods of bacterial sRNA target identification are in demand. Here, we describe our Small RNA-target Prediction Organizing Tool, which streamlines the process of sRNA target prediction by providing a single pipeline that combines available computational prediction tools with customizable results filtering based on experimental data. SPOT allows the user to rapidly produce a prioritized list of predicted sRNA-target mRNA interactions that serves as a basis for further experimental characterization. This tool will facilitate elucidation of sRNA regulons in bacteria, allowing new discoveries regarding the roles of sRNAs in bacterial stress responses and metabolic regulation.

microbiology↗