Angiotensin and baroreflex control of the circulation

There is a close association between the location of angiotensin (Ang) receptors and many important brain nuclei involved in the regulation of the cardiovascular system. The present review encompasses the physiological role of Ang II in the brainstem, particularly in relation to its influence on baroreflex control of the heart and kidney. Activation of AT1 receptors in the brainstem by fourth ventricle (4V) administration to conscious rabbits or local administration of Ang II into the rostral ventrolateral medulla (RVLM) of anesthetized rabbits acutely increases renal sympathetic nerve activity (RSNA) and RSNA baroreflex responses. Administration of the Ang antagonist Sarile into the RVLM of anesthetized rabbits blocked the effects of Ang II on the RSNA baroreflex, indicating that the RVLM is the major site of sympathoexcitatory action of Ang II given into the cerebrospinal fluid surrounding the brainstem. However, in conscious animals, blockade of endogenous Ang receptors in the brainstem by the 4V AT1 receptor antagonist losartan resulted in sympathoexcitation, suggesting an overall greater activity of endogenous Ang II within the sympathoinhibitory pathways. However, the RSNA response to airjet stress in conscious rabbits was markedly attenuated. While we found no effect of acute central Ang on heart rate baroreflexes, chronic 4V infusion inhibited the baroreflex and chronic losartan increased baroreflex gain. Thus, brainstem Ang II acutely alters sympathetic responses to specific afferent inputs thus forming part of a potentially important mechanism for the integration of autonomic response patterns. The sympathoexcitatory AT1 receptors appear to be activated during stress, surgery and anesthesia.

Fuzzy expert system in the prediction of neonatal resuscitation

In view of the importance of anticipating the occurrence of critical situations in medicine, we propose the use of a fuzzy expert system to predict the need for advanced neonatal resuscitation efforts in the delivery room. This system relates the maternal medical, obstetric and neonatal characteristics to the clinical conditions of the newborn, providing a risk measurement of need of advanced neonatal resuscitation measures. It is structured as a fuzzy composition developed on the basis of the subjective perception of danger of nine neonatologists facing 61 antenatal and intrapartum clinical situations which provide a degree of association with the risk of occurrence of perinatal asphyxia. The resulting relational matrix describes the association between clinical factors and risk of perinatal asphyxia. Analyzing the inputs of the presence or absence of all 61 clinical factors, the system returns the rate of risk of perinatal asphyxia as output. A prospectively collected series of 304 cases of perinatal care was analyzed to ascertain system performance. The fuzzy expert system presented a sensitivity of 76.5% and specificity of 94.8% in the identification of the need for advanced neonatal resuscitation measures, considering a cut-off value of 5 on a scale ranging from 0 to 10. The area under the receiver operating characteristic curve was 0.93. The identification of risk situations plays an important role in the planning of health care. These preliminary results encourage us to develop further studies and to refine this model, which is intended to implement an auxiliary system able to help health care staff to make decisions in perinatal care.

Spatial and temporal variation in fish populations and assemblages in coastal waters of the northern Baltic Proper

The distribution and traits of fish are of interest both ecologically and socio-economically. In this thesis, phenotypic and structural variation in fish populations and assemblages was studied on multiple spatial and temporal scales in shallow coastal areas in the archipelago of the northern Baltic Proper. In Lumparn basin in Åland Islands, the fish assemblage displayed significant seasonal variation in depth zone distribution. The results indicate that investigating both spatial and temporal variation in small scale is crucial for understanding patterns in fish distribution and community structure in large scale. The local population of Eurasian perch Perca fluviatilis L displayed habitat-specific morphological and dietary variation. Perch in the pelagic zone were on average deeper in their body shape than the littoral ones and fed on fish and benthic invertebrates. The results differ from previous studies conducted in freshwater habitats, where the pelagic perch typically are streamlined in body shape and zooplanktivorous. Stable isotopes of carbon and nitrogen differed between perch with different stomach contents, suggesting differentiation of individual diet preferences. In the study areas Lumparn and Ivarskärsfjärden in Åland Islands and Galtfjärden in Swedish east coast, the development in fish assemblages during the 2000’s indicated a general shift towards higher abundances of small-bodied lower-order consumers, especially cyprinids. For European pikeperch Sander lucioperca L., recent declines in adult fish abundances and high mortalities (Z = 1.06–1.16) were observed, which suggests unsustainably high fishing pressure on pikeperch. Based on the results it can be hypothesized that fishing has reduced the abundances of large predatory fish, which together with bottom-up forcing by eutrophication has allowed the lower-order consumer species to increase in abundances. This thesis contributes to the scientific understanding of aquatic ecosystems with new descriptions on morphological and dietary adaptations in perch in brackish water, and on the seasonal variation in small-scale spatial fish distribution. The results also demonstrate anthropogenic effects on coastal fish communities and underline the urgency of further reducing nutrient inputs and regulating fisheries in the Baltic Sea region.

The immunomodulator glatiramer acetate influences spinal motoneuron plasticity during the course of multiple sclerosis in an animal model

The immunomodulador glatiramer acetate (GA) has been shown to significantly reduce the severity of symptoms during the course of multiple sclerosis and in its animal model - experimental autoimmune encephalomyelitis (EAE). Since GA may influence the response of non-neuronal cells in the spinal cord, it is possible that, to some extent, this drug affects the synaptic changes induced during the exacerbation of EAE. In the present study, we investigated whether GA has a positive influence on the loss of inputs to the motoneurons during the course of EAE in rats. Lewis rats were subjected to EAE associated with GA or placebo treatment. The animals were sacrificed after 15 days of treatment and the spinal cords processed for immunohistochemical analysis and transmission electron microscopy. A correlation between the synaptic changes and glial activation was obtained by performing labeling of synaptophysin and glial fibrillary acidic protein using immunohistochemical analysis. Ultrastructural analysis of the terminals apposed to alpha motoneurons was also performed by electron transmission microscopy. Interestingly, although the GA treatment preserved synaptophysin labeling, it did not significantly reduce the glial reaction, indicating that inflammatory activity was still present. Also, ultrastructural analysis showed that GA treatment significantly prevented retraction of both F and S type terminals compared to placebo. The present results indicate that the immunomodulator GA has an influence on the stability of nerve terminals in the spinal cord, which in turn may contribute to its neuroprotective effects during the course of multiple sclerosis.

Method for automatic detection of wheezing in lung sounds

The present report describes the development of a technique for automatic wheezing recognition in digitally recorded lung sounds. This method is based on the extraction and processing of spectral information from the respiratory cycle and the use of these data for user feedback and automatic recognition. The respiratory cycle is first pre-processed, in order to normalize its spectral information, and its spectrogram is then computed. After this procedure, the spectrogram image is processed by a two-dimensional convolution filter and a half-threshold in order to increase the contrast and isolate its highest amplitude components, respectively. Thus, in order to generate more compressed data to automatic recognition, the spectral projection from the processed spectrogram is computed and stored as an array. The higher magnitude values of the array and its respective spectral values are then located and used as inputs to a multi-layer perceptron artificial neural network, which results an automatic indication about the presence of wheezes. For validation of the methodology, lung sounds recorded from three different repositories were used. The results show that the proposed technique achieves 84.82% accuracy in the detection of wheezing for an isolated respiratory cycle and 92.86% accuracy for the detection of wheezes when detection is carried out using groups of respiratory cycles obtained from the same person. Also, the system presents the original recorded sound and the post-processed spectrogram image for the user to draw his own conclusions from the data.

Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram

The aim of the present study was to develop a classifier able to discriminate between healthy controls and dyspeptic patients by analysis of their electrogastrograms. Fifty-six electrogastrograms were analyzed, corresponding to 42 dyspeptic patients and 14 healthy controls. The original signals were subsampled, filtered and divided into the pre-, post-, and prandial stages. A time-frequency transformation based on wavelets was used to extract the signal characteristics, and a special selection procedure based on correlation was used to reduce their number. The analysis was carried out by evaluating different neural network structures to classify the wavelet coefficients into two groups (healthy subjects and dyspeptic patients). The optimization process of the classifier led to a linear model. A dimension reduction that resulted in only 25% of uncorrelated electrogastrogram characteristics gave 24 inputs for the classifier. The prandial stage gave the most significant results. Under these conditions, the classifier achieved 78.6% sensitivity, 92.9% specificity, and an error of 17.9 ± 6% (with a 95% confidence level). These data show that it is possible to establish significant differences between patients and normal controls when time-frequency characteristics are extracted from an electrogastrogram, with an adequate component reduction, outperforming the results obtained with classical Fourier analysis. These findings can contribute to increasing our understanding of the pathophysiological mechanisms involved in functional dyspepsia and perhaps to improving the pharmacological treatment of functional dyspeptic patients.

Participation of neuronal nitric oxide synthase in experimental neuropathic pain induced by sciatic nerve transection

Nerve injury leads to a neuropathic pain state that results from central sensitization. This phenomenom is mediated by NMDA receptors and may involve the production of nitric oxide (NO). In this study, we investigated the expression of the neuronal isoform of NO synthase (nNOS) in the spinal cord of 3-month-old male, Wistar rats after sciatic nerve transection (SNT). Our attention was focused on the dorsal part of L3-L5 segments receiving sensory inputs from the sciatic nerve. SNT resulted in the development of neuropathic pain symptoms confirmed by evaluating mechanical hyperalgesia (Randall and Selitto test) and allodynia (von Frey hair test). Control animals did not present any alteration (sham-animals). The selective inhibitor of nNOS, 7-nitroindazole (0.2 and 2 µg in 50 µL), blocked hyperalgesia and allodynia induced by SNT. Immunohistochemical analysis showed that nNOS was increased (48% by day 30) in the lumbar spinal cord after SNT. This increase was observed near the central canal (Rexed’s lamina X) and also in lamina I-IV of the dorsal horn. Real-time PCR results indicated an increase of nNOS mRNA detected from 1 to 30 days after SNT, with the highest increase observed 1 day after injury (1469%). Immunoblotting confirmed the increase of nNOS in the spinal cord between 1 and 15 days post-lesion (20%), reaching the greatest increase (60%) 30 days after surgery. The present findings demonstrate an increase of nNOS after peripheral nerve injury that may contribute to the increase of NO production observed after peripheral neuropathy.

Central chemoreceptors and neural mechanisms of cardiorespiratory control

The arterial partial pressure (P CO2) of carbon dioxide is virtually constant because of the close match between the metabolic production of this gas and its excretion via breathing. Blood gas homeostasis does not rely solely on changes in lung ventilation, but also to a considerable extent on circulatory adjustments that regulate the transport of CO2 from its sites of production to the lungs. The neural mechanisms that coordinate circulatory and ventilatory changes to achieve blood gas homeostasis are the subject of this review. Emphasis will be placed on the control of sympathetic outflow by central chemoreceptors. High levels of CO2 exert an excitatory effect on sympathetic outflow that is mediated by specialized chemoreceptors such as the neurons located in the retrotrapezoid region. In addition, high CO2 causes an aversive awareness in conscious animals, activating wake-promoting pathways such as the noradrenergic neurons. These neuronal groups, which may also be directly activated by brain acidification, have projections that contribute to the CO2-induced rise in breathing and sympathetic outflow. However, since the level of activity of the retrotrapezoid nucleus is regulated by converging inputs from wake-promoting systems, behavior-specific inputs from higher centers and by chemical drive, the main focus of the present manuscript is to review the contribution of central chemoreceptors to the control of autonomic and respiratory mechanisms.

Brazilian scientific funding agency budgets have not matched the country's economic growth

The growth of the Brazilian economy in recent years has created an atmosphere of optimism in various segments of Brazilian society, with several important international repercussions. In this paper, we analyze in detail how this economic growth is reflected in investments in science and technology made by major academic funding agencies. As a result, we observed a discrepancy in the growth of funding input and the growth of the Brazilian gross domestic product. This fact associated with an increased academic output entails negative consequences for the system. This may be a symptom of an academic community not fully understood by society and vice versa. Finally, we believe that a long-lasting important change in investment policy in science is necessary in order to ensure financial security for the academic system as a whole.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

Estimating the average length of hospitalization due to pneumonia: a fuzzy approach

Exposure to air pollutants is associated with hospitalizations due to pneumonia in children. We hypothesized the length of hospitalization due to pneumonia may be dependent on air pollutant concentrations. Therefore, we built a computational model using fuzzy logic tools to predict the mean time of hospitalization due to pneumonia in children living in São José dos Campos, SP, Brazil. The model was built with four inputs related to pollutant concentrations and effective temperature, and the output was related to the mean length of hospitalization. Each input had two membership functions and the output had four membership functions, generating 16 rules. The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance. The values predicted by the model were significantly correlated with real data. Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2. This model can contribute to the care provided to children with pneumonia.

Novel control, haptic and calibration methods for teleoperated electrohydraulic servo systems

The aim of this thesis is to propose a novel control method for teleoperated electrohydraulic servo systems that implements a reliable haptic sense between the human and manipulator interaction, and an ideal position control between the manipulator and the task environment interaction. The proposed method has the characteristics of a universal technique independent of the actual control algorithm and it can be applied with other suitable control methods as a real-time control strategy. The motivation to develop this control method is the necessity for a reliable real-time controller for teleoperated electrohydraulic servo systems that provides highly accurate position control based on joystick inputs with haptic capabilities. The contribution of the research is that the proposed control method combines a directed random search method and a real-time simulation to develop an intelligent controller in which each generation of parameters is tested on-line by the real-time simulator before being applied to the real process. The controller was evaluated on a hydraulic position servo system. The simulator of the hydraulic system was built based on Markov chain Monte Carlo (MCMC) method. A Particle Swarm Optimization algorithm combined with the foraging behavior of E. coli bacteria was utilized as the directed random search engine. The control strategy allows the operator to be plugged into the work environment dynamically and kinetically. This helps to ensure the system has haptic sense with high stability, without abstracting away the dynamics of the hydraulic system. The new control algorithm provides asymptotically exact tracking of both, the position and the contact force. In addition, this research proposes a novel method for re-calibration of multi-axis force/torque sensors. The method makes several improvements to traditional methods. It can be used without dismantling the sensor from its application and it requires smaller number of standard loads for calibration. It is also more cost efficient and faster in comparison to traditional calibration methods. The proposed method was developed in response to re-calibration issues with the force sensors utilized in teleoperated systems. The new approach aimed to avoid dismantling of the sensors from their applications for applying calibration. A major complication with many manipulators is the difficulty accessing them when they operate inside a non-accessible environment; especially if those environments are harsh; such as in radioactive areas. The proposed technique is based on design of experiment methodology. It has been successfully applied to different force/torque sensors and this research presents experimental validation of use of the calibration method with one of the force sensors which method has been applied to.

Power Consumption Measurement and Scenario Based Energy Model in Wearable Computing Applications

The power is still today an issue in wearable computing applications. The aim of the present paper is to raise awareness of the power consumption of wearable computing devices in specific scenarios to be able in the future to design energy efficient wireless sensors for context recognition in wearable computing applications. The approach is based on a hardware study. The objective of this paper is to analyze and compare the total power consumption of three representative wearable computing devices in realistic scenarios such as Display, Speaker, Camera and microphone, Transfer by Wi-Fi, Monitoring outdoor physical activity and Pedometer. A scenario based energy model is also developed. The Samsung Galaxy Nexus I9250 smartphone, the Vuzix M100 Smart Glasses and the SimValley Smartwatch AW-420.RX are the three devices representative of their form factors. The power consumption is measured using PowerTutor, an android energy profiler application with logging option and using unknown parameters so it is adjusted with the USB meter. The result shows that the screen size is the main parameter influencing the power consumption. The power consumption for an identical scenario varies depending on the wearable devices meaning that others components, parameters or processes might impact on the power consumption and further study is needed to explain these variations. This paper also shows that different inputs (touchscreen is more efficient than buttons controls) and outputs (speaker sensor is more efficient than display sensor) impact the energy consumption in different way. This paper gives recommendations to reduce the energy consumption in healthcare wearable computing application using the energy model.

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.

Characterization of biomasses from the north and northeast regions of Brazil for processes in biorefineries

Abstract In search for renewable energy sources, the Brazilian residual biomasses stand out due to their favorable physical and chemical properties, low cost, and their being less pollutant. Therefore, they are likely to be used in biorefineries in the production of chemical inputs to substitute fossil fuels. This substitution is possible due to the high contents of carbohydrates (>40%), low contents of extractives (<20%), ashes (<8%) and moisture (<8%) found in these residual biomasses. High calorific values of all residues also offer them the chance to be used in combustion. A principal components analysis (PCA) was performed for better understanding of the samples and their hysic-chemical properties. Thus, this study aimed to characterize biomasses from the north (babassu residues, such as mesocarp and endocarp; pequi and Brazil nut) and northeast (agave and coconut) regions of Brazil, in order to contribute to the preservation of the environment and strengthen the economy of the region.