Biology subjects

Rogers, E.

Publications and source records attributed to Rogers, E..

5 recordsLinked to original sources

bioRxiv2026

Serum UCHL1, GFAP, and NfL track tyrosine hydroxylase loss in substantia nigra in two Rat Models of Parkinsons Disease

In Parkinsons disease (PD), blood-based (BB) biomarkers ubiquitin c-terminal hydrolase L 1 (UCHL-1), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) correlate with motor or cognitive impairment. However, it is unclear if blood levels of these biomarkers represent changes in nigrostriatal neuron viability or dopamine (DA) signaling. In 6-OHDA and Pink1 knockout (KO) rat models that showed progressive loss of DA tissue and tyrosine hydroxylase (TH) protein, we quantified UCHL-1, GFAP, and NfL expression in striatum and substantia nigra (SN) at 7- and 28-days in the 6-OHDA model and 7- and 18-month old in Pink 1 KO. Substantial changes in all biomarkers occurred with TH loss in SN, but not striatum, in both models. UCHL-1 levels increased against remaining TH protein. Accordingly, serum UCHL-1 levels increased 25% at 28 days post-6-OHDA and 18-month old Pink1 KO. GFAP and NfL levels increased in SN 28 days post-6-OHDA and 18 month-old Pink1 KO. Serum GFAP levels increased 28 days post-6-OHDA and 18 month-old Pink1 KO. Serum levels of NfL increased 28 days post-6-OHDA, and in 18 month-old Pink1 KO and wild-type, without influence by genotype. Expression levels of each biomarker were greater in the SN vs striatum, suggesting the SN contributes greater quantities of biomarkers to the blood and reflect TH loss therein. Taken together, our preclinical results show alignment between serum levels of UCHL-1, GFAP, and NfL and loss of TH and DA in the SN. As such, these biomarkers may be relevant peripheral indicators of deficient nigrostriatal DA signaling, and reflect nigrostriatal function in PD.

neuroscience↗

bioRxiv2025

Parallel neuronal ensembles control behavior across sensorimotor levels in Drosophila

Nervous systems can process information in serial or in parallel, trading off efficiency for flexibility and speed. How these network architectures are implemented across sensorimotor pathways to control behavior is unclear. We investigate this tradeoff directly in Drosophila by comparing neuronal circuits underlying landing and takeoff, behaviors transforming similar visual cues to whole-body motor output. Using a whole-CNS connectome, electrophysiology, and behavioral analysis, we reconstruct the complete feedforward pathway for landing, including visual feature detectors, a dedicated ensemble of descending neurons (DNs), and a core premotor circuit in the nerve cord. Comparison to the takeoff pathway reveals that, despite encoding the same sensory feature and engaging similar muscle groups, neuronal circuits controlling the two behaviors are separated at every sensorimotor level. Extending this analysis to the complete DN population reveals a blueprint for descending motor control: DNs across the behavioral space utilized by the fly are organized as a set of parallel, loosely-overlapping ensembles that form a continuum from command-like control, with individual DNs determining behavioral output, to population coding, with multiple DNs controlling behavior synergistically. Distinct combinations of sensory feature detectors differentially recruit DN ensembles to enable flexible, context-dependent behavioral control.

neuroscience↗

bioRxiv2025

HazardPyMatch: A Tool for identifying reproductive and other hazards in scientific laboratories

Understanding and mitigating laboratory hazards is essential for fostering safe and inclusive research environments. However, conducting risk assessments can be challenging and time-consuming, especially for scientists who have new or specific concerns about hazard susceptibility, such as pregnant women. In response, using reproductive hazards as our primary example, we developed HazardPyMatch, a laboratory hazard screening tool designed to be implemented in laboratories across scientific disciplines to support efficient hazard management. HazardPyMatch is an accessible and user-friendly tool that enables scientists to quickly and easily systematically identify chemical hazards in laboratory chemical inventories and categorize these hazards in laboratory protocols. MotivationA publicly accessible and easy-to-use resource did not exist to identify reproductive hazards in neuroscience laboratories. In response we developed the python workflow HazardPyMatch to address the need for a data-driven tool to catalog hazardous materials in laboratory settings.

bioinformatics↗

bioRxiv2020

Capturing in-field root system dynamics with the RootTracker

Optimizing root system architecture offers a promising approach to developing stress tolerant cultivars in the face of climate change, as root systems are critical for water and nutrient uptake as well as mechanical stability. However, breeding for optimal root system architecture has been hindered by the difficulty in measuring root growth in the field. Here, we describe a technology, the RootTracker (RT), which employs capacitance touch sensors to monitor in-field root growth over time. Configured in a cylindrical shutter-like fashion around a planted seed, 264 electrodes are individually charged multiple times over the course of an experiment. Signature changes in the measured capacitance and resistance readings indicate when a root has touched or grown close to an electrode. Using the RootTracker, we have measured root system dynamics of commercial maize hybrids growing in both typical Midwest field conditions and under different irrigation regimes. We observed rapid responses of root growth to water deficits and found evidence for a "priming response" in which an early water deficit causes more and deeper roots to grow at later time periods. There was genotypic variation among hybrid maize lines in their root growth in response to drought, indicating a potential to breed for root systems adapted for different environments.

plant biology↗

bioRxiv2019

Realistic Anatomically Detailed Open-Source Spinal Cord Stimulation (RADO-SCS) Model

ObjectiveComputational current flow models of spinal cord stimulation (SCS) are widely used in device development, clinical trial design, and patient programming. Proprietary models of varied sophistication have been developed. An open-source model with state-of-the-art precision would serve as a standard for SCS simulation. ApproachWe developed a sophisticated SCS modeling platform, named Realistic Anatomically Detailed Open-Source Spinal Cord Stimulation (RADO-SCS) model. This platform consists of realistic and detailed spinal cord and ancillary tissues anatomy derived based on prior imaging and cadaveric studies. Represented tissues within the T9-T11 spine levels include vertebrae, intravertebral discs, epidural space, dura, CSF, white-matter, gray-matter, dorsal and ventral roots and rootlets, dorsal root ganglion, sympathetic chain, thoracic aorta, epidural space vasculature, white-matter vasculature, and thorax. As an exemplary, a bipolar SCS montage was simulated to illustrate the model workflow from the electric field calculated from a finite element model (FEM) to activation thresholds predicted for individual axons populating the spinal cord. Main ResultsCompared to prior models, RADO-SCS meets or exceeds detail for every tissue compartment. The resulting electric fields in white and gray-matter, and axon model activation thresholds are broadly consistent with prior stimulations. SignificanceThe RADO-SCS can be used to simulate any SCS approach with both unprecedented resolution (precision) and transparency (reproducibility). Freely available online, the RADO-SCS will be updated continuously with version control.

neuroscience↗