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Abstract

European beaver (Castor fiber), the largest rodent species inhabiting a wide area of Eurasia, feeds mainly on dry parts of plants, bark or wood. Such kind of nourishment needs to be properly digested in each part of the gastrointestinal tract. The time of stomach digestion, which directly influences all the following steps of the digestion process, is precisely controlled by the pylorus and its innervation. However, virtually no data is available on the organization of the enteric nervous system in most of the wild animal species, including beavers. On the other hand, a pecu- liar diet consumed by beavers, suggests that the arrangement of their stomach intramural nerve elements can be atypical. Therefore, the present study investigated the distribution and chemical coding of neurons and nerve fibers in the pylorus of the European beaver. The experiment was performed on stomachs obtained from a group of 6 beavers caught in Northeastern region of Poland (due to beaver overpopulation). Pyloric wall tissue cryosections were double immunostained with a mixture of antibodies against pan-neuronal marker PGP 9.5 (to visualize enteric neurons) and ChAT (cholinergic marker), nNOS (nitrergic marker), SP, CGRP, Gal (peptidergic markers). Confocal microscopy analysis revealed that the majority of enteric nerve cells were clustered forming submucosal and myenteric ganglia and all the studied substances were expressed (in various amounts) in these neurons. We conclude, that the anatomical arrangement and chemical coding of intramural nerve elements in the beaver pylorus resemble those found in other mammalian species.
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Abstract

Combined retrograde tracing and double-labelling immunofluorescence were used to investi- gate the distribution and chemical coding of neurons in aorticoerenal (ARG) and testicular (TG) ganglia supplying the urinary bladder apex (UBA) in the juvenile male pig (n=4, 12 kg. body weight). Retrograde fluorescent tracer Fast Blue (FB) was injected into the wall of the bladder apex under pentobarbital anesthesia. After three weeks all the pigs were deeply anesthetized and transcardially perfused with 4% buffered paraformaldehyde. TG and ARG were collected and processed for double-labelling immunofluorescence. The presence of tyrosine hydroxylase (TH) or dopamine beta-hydroxylase (DBH), neuropeptide Y (NPY), somatostatin (SOM), galanin (GAL), nitric oxide synthase (NOS) and vesicular acetylcholine transporter (VAChT) were inves- tigated. The cryostat sections were examined with a Zeiss LSM 710 confocal microscope equipped with adequate filter blocks. The TG and ARG were found to contain many FB-positive neurons projecting to the UBA (UBA-PN). The UBA-PN were distributed in both TG and ARG. The majority were found in the left ganglia, mostly in TG. Immunohistochemistry disclosed that the vast majority of UBA-PN were noradrenergic (TH- and/or DBH-positive). Many noradrenergic neurons also contained immunoreactivity to NPY, SOM or GAL. Most of the UBA-PN were supplied with varicose VAChT-, or NOS- IR (immunoreactive) nerve fibres. This study has revealed a relatively large population of differently coded ARG and TG neu- rons projecting to the porcine urinary bladder. As judged from their neurochemical organization these nerve cells constitute an important element of the complex neuro-endocrine system involved in the regulation of the porcine urogenital organ function.
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Abstract

The paper presents a multi-scale mathematical model dedicated to a comprehensive simulation of resistance heating combined with the melting and controlled cooling of steel samples. Experiments in order to verify the formulated numerical model were performed using a Gleeble 3800 thermo-mechanical simulator. The model for the macro scale was based upon the solution of Fourier-Kirchhoff equation as regards predicting the distribution of temperature fields within the volume of the sample. The macro scale solution is complemented by a functional model generating voluminal heat sources, resulting from the electric current flowing through the sample. The model for the micro-scale, concerning the grain growth simulation, is based upon the probabilistic Monte Carlo algorithm, and on the minimization of the system energy. The model takes into account the forming mushy zone, where grains degrade at the melting stage – it is a unique feature of the micro-solution. The solution domains are coupled by the interpolation of node temperatures of the finite element mesh (the macro model) onto the Monte Carlo cells (micro model). The paper is complemented with examples of resistance heating results and macro- and micro-structural tests, along with test computations concerning the estimation of the range of zones with diverse dynamics of grain growth.
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