The present study was conducted to test whether the ascending auditory projection from the superior olivary complex (SOC) of the brainstem to the inferior colliculus (IC) may use nitric oxide (NO) as a neuroactive compound. We identified olivo-collicular projection neurons in subnuclei of the SOC by retrograde neuronal tracing with Fluoro-Gold (FG) injected into the central nucleus of the IC. Sections containing retrograde labelled neurons were subjected to immunohistochemical incubation in an antiserum directed against the enzyme responsible for NO production in nerve cells, neuronal NO synthase (nNOS). The analysis showed that FG-containing neurons as well as nNOS-immunoreactive neurons were present in the lateral superior olive (LSO), superior paraolivary nucleus (SPO), ventral nucleus of the trapezoid body (VNTB), medial superior olive (MSO) and in dorsal and ventral periolivary regions to different amounts. However, only in the LSO, SPO and VNTB double-labelled neurons were found. They made up to less than 10% of all nNOS neurons in the SOC. Considering that only about 5% of the nNOS cells in the SOC are olivocochlear neurons [Riemann and Reuss, 1999], it is still open whether the majority of nitrergic neurons of the SOC project to other sites or whether they rather have intrinsic actions in providing NO to the SOC.
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http://dx.doi.org/10.1016/s0378-5955(03)00219-3 | DOI Listing |
J Anat
January 2025
Laboratory of Neurochemical Studies, Department of Physiology and Behavior, Bioscience Center, Federal University of Rio Grande do Norte, Natal, Brazil.
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Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada.
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Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
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Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC, 29209, USA.
Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. Based on the algebraic derivative estimation, we present a novel method to forecast the energy consumption in real time. In contrast to the mainstream machine-learning-based methods, the proposed method does not require access to the historical energy consumption data.
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