HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS

This work presents the results of a Hybrid Neural Network (HNN) technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.

Persistent attenuation and enhancement of the earthworm main muscle contraction generator response induced by repeated stimulation of a peripheral neuron

Responses evoked in the earthworm, Amynthas hawayanus, main muscle contraction generator M-2 (postsynaptic mechanical-stimulus-sensitive) neuron by threshold mechanical stimuli in 2-s intertrial intervals (ITI) were used as the control or unconditioned responses (UR). Their attenuation induced by decreasing these intervals in non-associative conditioning and their enhancement induced by associating the unconditioned stimuli (US) to a train of short (0.1 s) hyperpolarizing electrical substitutive conditioning stimuli (SCS) in the Peri-Kästchen (PK) neuron were measured in four parameters, i.e., peak numbers (N) and amplitude ()averaged from 120 responses, sum of these amplitudes (SAMP) and the highest peak amplitude (V) over a period of 4 min. Persistent attenuation similar to habituation was induced by decreasing the control ITI to 0.5 s and 2.0 s in non-associative conditioning within less than 4 min. Dishabituation was induced by randomly pairing one of these habituated US to an electrical stimulus in the PK neuron. All four parameters of the UR were enhanced by forward (SCS-US), but not backward (US-SCS), association of the US with 25, 100 and 250-Hz trains of SCS with 40-ms interstimulus intervals (ISI) for 4 min and persisted for another 4 min after turning off the SCS. The enhancement of these parameters was proportional to the SCS frequencies in the train. No UR was evoked by the SCS when the US was turned off after 4 min of classical conditioning.

Synaptic vesicle pool size, release probability and synaptic depression are sensitive to Ca2+ buffering capacity in the developing rat calyx of Held

The calyx of Held, a specialized synaptic terminal in the medial nucleus of the trapezoid body, undergoes a series of changes during postnatal development that prepares this synapse for reliable high frequency firing. These changes reduce short-term synaptic depression during tetanic stimulation and thereby prevent action potential failures during a stimulus train. We measured presynaptic membrane capacitance changes in calyces from young postnatal day 5-7 (p5-7) or older (p10-12) rat pups to examine the effect of calcium buffer capacity on vesicle pool size and the efficiency of exocytosis. Vesicle pool size was sensitive to the choice and concentration of exogenous Ca2+ buffer, and this sensitivity was much stronger in younger animals. Pool size and exocytosis efficiency in p5-7 calyces were depressed by 0.2 mM EGTA to a greater extent than with 0.05 mM BAPTA, even though BAPTA is a 100-fold faster Ca2+ buffer. However, this was not the case for p10-12 calyces. With 5 mM EGTA, exocytosis efficiency was reduced to a much larger extent in young calyces compared to older calyces. Depression of exocytosis using pairs of 10-ms depolarizations was reduced by 0.2 mM EGTA compared to 0.05 mM BAPTA to a similar extent in both age groups. These results indicate a developmentally regulated heterogeneity in the sensitivity of different vesicle pools to Ca2+ buffer capacity. We propose that, during development, a population of vesicles that are tightly coupled to Ca2+ channels expands at the expense of vesicles more distant from Ca2+ channels.

Modeling the water uptake by chicken carcasses during cooling by immersion

In this study, water uptake by poultry carcasses during cooling by water immersion was modeled using artificial neural networks. Data from twenty-five independent variables and the final mass of the carcass were collected in an industrial plant to train and validate the model. Different network structures with one hidden layer were tested, and the Downhill Simplex method was used to optimize the synaptic weights. In order to accelerate the optimization calculus, Principal Component Analysis (PCA) was used to preprocess the input data. The obtained results were: i) PCA reduced the number of input variables from twenty-five to ten; ii) the neural network structure 4-6-1 was the one with the best result; iii) PCA gave the following order of importance: parameters of mass transfer, heat transfer, and initial characteristics of the carcass. The main contributions of this work were to provide an accurate model for predicting the final content of water in the carcasses and a better understanding of the variables involved.