Mapping and signaling of neural pathways involved in the regulation of hydromineral homeostasis

Several forebrain and brainstem neurochemical circuitries interact with peripheral neural and humoral signals to collaboratively maintain both the volume and osmolality of extracellular fluids. Although much progress has been made over the past decades in the understanding of complex mechanisms underlying neuroendocrine control of hydromineral homeostasis, several issues still remain to be clarified. The use of techniques such as molecular biology, neuronal tracing, electrophysiology, immunohistochemistry, and microinfusions has significantly improved our ability to identify neuronal phenotypes and their signals, including those related to neuron-glia interactions. Accordingly, neurons have been shown to produce and release a large number of chemical mediators (neurotransmitters, neurohormones and neuromodulators) into the interstitial space, which include not only classic neurotransmitters, such as acetylcholine, amines (noradrenaline, serotonin) and amino acids (glutamate, GABA), but also gaseous (nitric oxide, carbon monoxide and hydrogen sulfide) and lipid-derived (endocannabinoids) mediators. This efferent response, initiated within the neuronal environment, recruits several peripheral effectors, such as hormones (glucocorticoids, angiotensin II, estrogen), which in turn modulate central nervous system responsiveness to systemic challenges. Therefore, in this review, we shall evaluate in an integrated manner the physiological control of body fluid homeostasis from the molecular aspects to the systemic and integrated responses.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Application of artificial neural networks as an alternative to volumetric water balance in drip irrigation management in watermelon crop

Precision irrigation seeks to establish strategies which achieve an efficient ratio between the volume of water used (reduction in input) and the productivity obtained (increase in production). There are several studies in the literature on strategies for achieving this efficiency, such as those dealing with the method of volumetric water balance (VWB). However, it is also of great practical and economic interest to set up versatile implementations of irrigation strategies that: (i) maintain the performance obtained with other implementations, (ii) rely on few computational resources, (iii) adapt well to field conditions, and (iv) allow easy modification of the irrigation strategy. In this study, such characteristics are achieved when using an Artificial Neural Network (ANN) to determine the period of irrigation for a watermelon crop in the Irrigation Perimeter of the Lower Acaraú, in the state of Ceará, Brazil. The Volumetric Water Balance was taken as the standard for comparing the management carried out with the proposed implementation of ANN. The statistical analysis demonstrates the effectiveness of the proposed management, which is able to replace VWB as a strategy in automation.

Classification of antimicrobial resistance using artificial neural networks and the relationship of 38 genes associated with the virulence of Escherichia coli isolates from broilers

Avian pathogenic Escherichia coli (APEC) is responsible for various pathological processes in birds and is considered as one of the principal causes of morbidity and mortality, associated with economic losses to the poultry industry. The objective of this study was to demonstrate that it is possible to predict antimicrobial resistance of 256 samples (APEC) using 38 different genes responsible for virulence factors, through a computer program of artificial neural networks (ANNs). A second target was to find the relationship between (PI) pathogenicity index and resistance to 14 antibiotics by statistical analysis. The results showed that the RNAs were able to make the correct classification of the behavior of APEC samples with a range from 74.22 to 98.44%, and make it possible to predict antimicrobial resistance. The statistical analysis to assess the relationship between the pathogenic index (PI) and resistance against 14 antibiotics showed that these variables are independent, i.e. peaks in PI can happen without changing the antimicrobial resistance, or the opposite, changing the antimicrobial resistance without a change in PI.

Synergistic control of forearm based on accelerometer data and artificial neural networks

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.

Benefits of exercise training in the treatment of heart failure: study with a control group

OBJECTIVE: Exercise training programs have been proposed as adjuncts to treatment of heart failure. The effects of a 3-month-exercise-training-program with 3 exercise sessions per week were assessed in patients with stable systolic chronic heart failure. METHODS: We studied 24 patients with final left ventricle diastolic diameter of 70±10mm and left ventricular ejection fraction of 37±4%. Mean age was 52±16 years. Twelve patients were assigned to an exercise training group (G1), and 12 patients were assigned to a control group (G2). Patients underwent treadmill testing, before and after exercise training, to assess distance walked, heart rate, systolic blood pressure, and double product. RESULTS: In G2 group, before and after 3 months, we observed, respectively distance walked, 623±553 and 561± 460m (ns); peak heart rate, 142±23 and 146± 33b/min (ns); systolic blood pressure, 154±36 and 164±26 mmHg (ns); and double product, 22211± 6454 and 24293±7373 (ns). In G1 group, before and after exercise, we observed: distance walked, 615±394 and 970± 537m (p<0.003) peak heart rate, 143±24 and 143±29b/min (ns); systolic blood pressure, 136±33 and 133±24 mmHg (ns); and double product, 19907± 7323 and 19115±5776, respectively. Comparing the groups, a significant difference existed regarding the variation in the double product, and in distance walked. CONCLUSION: Exercise training programs in patients with heart failure can bring about an improvement in physical capacity.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

Description of epidemics of coffee rust with neural networks

The objective of this paper was to evaluate the potential of neural networks (NN) as an alternative method to the basic epidemiological approach to describe epidemics of coffee rust. The NN was developed from the intensities of coffee (Coffea arabica) rust along with the climatic variables collected in Lavras-MG between 13 February 1998 and 20 April 2001. The NN was built with climatic variables that were either selected in a stepwise regression analysis or by the Braincel® system, software for NN building. Fifty-nine networks and 26 regression models were tested. The best models were selected based on small values of the mean square deviation (MSD) and of the mean prediction error (MPE). For the regression models, the highest coefficients of determination (R²) were used. The best model developed with neural networks had an MSD of 4.36 and an MPE of 2.43%. This model used the variables of minimum temperature, production, relative humidity of the air, and irradiance 30 days before the evaluation of disease. The best regression model was developed from 29 selected climatic variables in the network. The summary statistics for this model were: MPE=6.58%, MSE=4.36, and R²=0.80. The elaborated neural networks from a time series also were evaluated to describe the epidemic. The incidence of coffee rust at four previous fortnights resulted in a model with MPE=4.72% and an MSD=3.95.

Effect of inhibitory avoidance training on [3H]-glutamate binding in the hippocampus and parietal cortex of rats

Glutamate receptors have been implicated in memory formation. The aim of the present study was to determine the effect of inhibitory avoidance training on specific [3H]-glutamate binding to membranes obtained from the hippocampus or parietal cortex of rats. Adult male Wistar rats were trained (0.5-mA footshock) in a step-down inhibitory avoidance task and were sacrificed 0, 5, 15 or 60 min after training. Hippocampus and parietal cortex were dissected and membranes were prepared and incubated with 350 nM [3H]-glutamate (N = 4-6 per group). Inhibitory avoidance training induced a 29% increase in glutamate binding in hippocampal membranes obtained from rats sacrificed at 5 min (P<0.01), but not at 0, 15, or 60 min after training, and did not affect glutamate binding in membranes obtained from the parietal cortex. These results are consistent with previous evidence for the involvement of glutamatergic synaptic modification in the hippocampus in the early steps of memory formation.

An immunohistochemical, clinical and electroneuromyographic correlative study of the neural markers in the neuritic form of leprosy

The nerve biopsies of 11 patients with pure neuritic leprosy were submitted to routine diagnostic procedures and immunoperoxidase staining with antibodies against axonal (neurofilament, nerve growth factor receptor (NGFr), and protein gene product (PGP) 9.5) and Schwann cell (myelin basic protein, S-100 protein, and NGFr) markers. Two pairs of non-adjacent histological cross-sections of the peripheral nerve were removed for quantification. All the fascicles of the nerve were examined with a 10X-ocular and 40X-objective lens. The immunohistochemistry results were compared to the results of semithin section analysis and clinical and electroneuromyographic data. Neurofilament staining was reduced in 100% of the neuritic biopsies. NGFr positivity was also reduced in 81.8%, PGP staining in 100% of the affected nerves, S100 positivity in 90.9%, and myelin basic protein immunoreactivity in 90.9%. Hypoesthesia was associated with decreased NGFr (81.8%) and PGP staining (90.9%). Reduced potential amplitudes (electroneuromyographic data) were found to be associated with reduced PGP 9.5 (63.6%) and nerve fiber neurofilament staining (45.4%) by immunohistochemistry and with loss of myelinated fibers (100%) by semithin section analysis. On the other hand, the small fibers (immunoreactive dots) seen amid inflammatory cells continued to be present even after 40% of the larger myelinated fibers had disappeared. The present study shows an in-depth view of the destructive effects of leprosy upon the expression of neural markers and the integrity of nerve fiber. The association of these structural changes with the clinical and electroneuromyographic manifestations of leprosy peripheral neuropathy was also discussed.

Training-related changes in the R-R interval at the onset of passive movements in humans

The aim of the present study was to determine whether training-related alterations in muscle mechanoreflex activation affect cardiac vagal withdrawal at the onset of exercise. Eighteen male volunteers divided into 9 controls (26 ± 1.9 years) and 9 racket players (25 ± 1.9 years) performed 10 s of voluntary and passive movement characterized by the wrist flexion of their dominant and non-dominant limbs. The respiratory cycle was divided into four phases and the phase 4 R-R interval was measured before and immediately following the initiation of either voluntary or passive movement. At the onset of voluntary exercise, the decrease in R-R interval was similar between dominant and non-dominant forearms in both controls (166 ± 20 vs 180 ± 34 ms, respectively; P > 0.05) and racket players (202 ± 29 vs 201 ± 31 ms, respectively; P > 0.05). Following passive movement, the non-dominant forearm of racket players elicited greater changes than the dominant forearm (129 ± 30 vs 77 ± 17 ms; P < 0.05), as well as both the dominant (54 ± 20 ms; P < 0.05) and non-dominant (59 ± 14 ms; P < 0.05) forearms of control subjects. In contrast, changes in R-R interval elicited by the racket players' dominant forearm were similar to that observed in the control group, indicating that changes in R-R interval at the onset of passive exercise were not attenuated in the dominant forearm of racket players. In summary, cardiac vagal withdrawal induced by muscle mechanoreflex stimulation is well-maintained, despite long-term exposure to training.