The viewpoint of health professionals on the role of basic units in the healthcare network of the Brazilian Unified Health System

In the process of creation of the Unified Health System (SUS) as a universal policy seeking to ensure comprehensive care, unscheduled assistance in primary healthcare units (UBS) is an unresolved challenge. The scope of this paper is to analyze the viewpoint of health professionals on the role of primary healthcare units in meeting this demand. It is a transversal study of qualitative data obtained through questionnaires and interviews with 106 medical practitioners from 6 emergency medical services and 190 professionals from 30 units. They explained why people seek emergency care for occurrences pertaining to primary care. The content analysis technique with thematic categories was used for data analysis. Lack of resources and problems with primary health unit work processes (50.8%) were the reasons most frequently cited by emergency care physicians to explain this inadequate demand. Only 33.3% of the health unit professionals agreed that these occurrences should be attended in the primary healthcare services. The limited viewpoint of the role of health services on the unscheduled care, particularly among primary care professionals, possibly leads to restrictive practices for access by the population.

Structure and Ionic Conductivity in the Mixed-Network Former Chalcogenide Glass System [Na2S](2/3)[(B2S3)(x)(P2S5)(1-x)](1/3)

Glasses in the system [Na2S](2/3)[(B2S3)(x)(P2S5)(1-x)](1/3) (0.0 <= x <= 1.0) were prepared by the melt quenching technique, and their properties were characterized by thermal analysis and impedance spectroscopy. Their atomic-level structures were comprehensively characterized by Raman spectroscopy and B-11, P-31, and Na-23 high resolution solid state magic-angle spinning (MAS) NMR techniques. P-31 MAS NMR peak assignments were made by the presence or absence of homonuclear indirect P-31-P-31 spin-spin interactions as detected using homonuclear J-resolved and refocused INADEQUATE techniques. The extent of B-S-P connectivity in the glassy network was quantified by P-31{B-11} and B-11{P-31} rotational echo double resonance spectroscopy. The results clearly illustrate that the network modifier alkali sulfide, Na2S, is not proportionally shared between the two network former components, B and P. Rather, the thiophosphate (P) component tends to attract a larger concentration of network modifier species than predicted by the bulk composition, and this results in the conversion of P2S74-, pyrothiophosphate, Na/P = 2:1, units into PS43-, orthothiophosphate, Na/P = 3:1, groups. Charge balance is maintained by increasing the net degree of polymerization of the thioborate (B) units through the formation of covalent bridging sulfur (BS) units, B S B. Detailed inspection of the B-11 MAS NMR spectra reveals that multiple thioborate units are formed, ranging from neutral BS3/2 groups all the way to the fully depolymerized orthothioborate (BS33-) species. On the basis of these results, a comprehensive and quantitative structural model is developed for these glasses, on the basis of which the compositional trends in the glass transition temperatures (T-g) and ionic conductivities can be rationalized. Up to x = 0.4, the dominant process can be described in a simplified way by the net reaction equation P-1 + B-1 reversible arrow P-0 + B-4, where the superscripts denote the number of BS atoms for the respective network former species. Above x = 0.4, all of the thiophosphate units are of the P-0 type and both pyro-(B-1) and orthothioborate (B-0) species make increasing contributions to the network structure with increasing x. In sharp contrast to the situation in sodium borophosphate glasses, four-coordinated thioborate species are generally less abundant and heteroatomic B-S-P linkages appear to not exist. On the basis of this structural information, compositional trends in the ionic conductivities are discussed in relation to the nature of the charge-compensating anionic species and the spatial distribution of the charge carriers.

Quantification of Short and Medium Range Order in Mixed Network Former Glasses of the System GeO2-NaPO3: A Combined NMR and X-ray Photoelectron Spectroscopy Study

Glasses in the system xGeO(2)-(1-x)NaPO3 (0 <= x <= 0.50) were prepared by conventional melting quenching and characterized by thermal analysis, Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and P-31 nuclear magnetic resonance (MAS NMR) techniques. The deconvolution of the latter spectra was aided by homonuclear J-resolved and refocused INADEQUATE techniques. The combined analyses of P-31 MAS NMR and O-1s XPS lineshapes, taking charge and mass balance considerations into account, yield the detailed quantitative speciations of the phosphorus, germanium, and oxygen atoms and their respective connectivities. An internally consistent description is possible without invoking the formation of higher-coordinated germanium species in these glasses, in agreement with experimental evidence in the literature. The structure can be regarded, to a first approximation, as a network consisting of P-(2) and P-(3) tetrahedra linked via four-coordinate germanium. As implied by the appearance of P-(3) units, there is a moderate extent of network modifier sharing between phosphate and germanate network formers, as expressed by the formal melt reaction P-(2) + Ge-(4) -> P-(3) + Ge-(3). The equilibrium constant of this reaction is estimated as K = 0.52 +/- 0.11, indicating a preferential attraction of network modifier by the phosphorus component. These conclusions are qualitatively supported by Raman spectroscopy as well as P-31{Na-23} and P-31{Na-23} rotational echo double resonance (REDOR) NMR results. The combined interpretation of O-1s XPS and P-31 MAS NMR spectra shows further that there are clear deviations from a random connectivity scenario: heteroatomic P-O-Ge linkages are favored over homoatomic P-O-P and Ge-O-Ge linkages.

Efficient Forest Data Structure for Evolutionary Algorithms Applied to Network Design

The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree with n nodes, the most efficient data structures available in the literature require time O(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average time O(root n). Experiments with an EA based on NDDR applied to large-scale instances of the degree-constrained minimum spanning tree problem have shown that the implementation adds small constants and lower order terms to the theoretical bound.

Phenol biodegradation by a microbial consortium: application of artificial neural network (ANN) modelling

In this study, an effective microbial consortium for the biodegradation of phenol was grown under different operational conditions, and the effects of phosphate concentration (1.4 g L-1, 2.8 g L-1, 4.2 g L-1), temperature (25 degrees C, 30 degrees C, 35 degrees C), agitation (150 rpm, 200 rpm, 250 rpm) and pH (6, 7, 8) on phenol degradation were investigated, whereupon an artificial neural network (ANN) model was developed in order to predict degradation. The learning, recall and generalization characteristics of neural networks were studied using data from the phenol degradation system. The efficiency of the model generated by the ANN was then tested and compared with the experimental results obtained. In both cases, the results corroborate the idea that aeration and temperature are crucial to increasing the efficiency of biodegradation.

Optimization of Large-Scale Hydrothermal System Operation

This paper presents the development of a mathematical model to optimize the management and operation of the Brazilian hydrothermal system. The system consists of a large set of individual hydropower plants and a set of aggregated thermal plants. The energy generated in the system is interconnected by a transmission network so it can be transmitted to centers of consumption throughout the country. The optimization model offered is capable of handling different types of constraints, such as interbasin water transfers, water supply for various purposes, and environmental requirements. Its overall objective is to produce energy to meet the country's demand at a minimum cost. Called HIDROTERM, the model integrates a database with basic hydrological and technical information to run the optimization model, and provides an interface to manage the input and output data. The optimization model uses the General Algebraic Modeling System (GAMS) package and can invoke different linear as well as nonlinear programming solvers. The optimization model was applied to the Brazilian hydrothermal system, one of the largest in the world. The system is divided into four subsystems with 127 active hydropower plants. Preliminary results under different scenarios of inflow, demand, and installed capacity demonstrate the efficiency and utility of the model. From this and other case studies in Brazil, the results indicate that the methodology developed is suitable to different applications, such as planning operation, capacity expansion, and operational rule studies, and trade-off analysis among multiple water users. DOI: 10.1061/(ASCE)WR.1943-5452.0000149. (C) 2012 American Society of Civil Engineers.

The rapid atmospheric monitoring system of the Pierre Auger Observatory

The Pierre Auger Observatory is a facility built to detect air showers produced by cosmic rays above 10(17) eV. During clear nights with a low illuminated moon fraction, the UV fluorescence light produced by air showers is recorded by optical telescopes at the Observatory. To correct the observations for variations in atmospheric conditions, atmospheric monitoring is performed at regular intervals ranging from several minutes (for cloud identification) to several hours (for aerosol conditions) to several days (for vertical profiles of temperature, pressure, and humidity). In 2009, the monitoring program was upgraded to allow for additional targeted measurements of atmospheric conditions shortly after the detection of air showers of special interest, e. g., showers produced by very high-energy cosmic rays or showers with atypical longitudinal profiles. The former events are of particular importance for the determination of the energy scale of the Observatory, and the latter are characteristic of unusual air shower physics or exotic primary particle types. The purpose of targeted (or "rapid") monitoring is to improve the resolution of the atmospheric measurements for such events. In this paper, we report on the implementation of the rapid monitoring program and its current status. The rapid monitoring data have been analyzed and applied to the reconstruction of air showers of high interest, and indicate that the air fluorescence measurements affected by clouds and aerosols are effectively corrected using measurements from the regular atmospheric monitoring program. We find that the rapid monitoring program has potential for supporting dedicated physics analyses beyond the standard event reconstruction.

A scheme for synchronizing clocks connected by a packet communication network

Consider a communication system in which a transmitter equipment sends fixed-size packets of data at a uniform rate to a receiver equipment. Consider also that these equipments are connected by a packet-switched network, which introduces a random delay to each packet. Here we propose an adaptive clock recovery scheme able of synchronizing the frequencies and the phases of these devices, within specified limits of precision. This scheme for achieving frequency and phase synchronization is based on measurements of the packet arrival times at the receiver, which are used to control the dynamics of a digital phase-locked loop. The scheme performance is evaluated via numerical simulations performed by using realistic parameter values. (C) 2011 Elsevier By. All rights reserved.

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.

Modeling the Emergence of Circadian Rhythms in a Clock Neuron Network

Circadian rhythms in pacemaker cells persist for weeks in constant darkness, while in other types of cells the molecular oscillations that underlie circadian rhythms damp rapidly under the same conditions. Although much progress has been made in understanding the biochemical and cellular basis of circadian rhythms, the mechanisms leading to damped or self-sustained oscillations remain largely unknown. There exist many mathematical models that reproduce the circadian rhythms in the case of a single cell of the Drosophila fly. However, not much is known about the mechanisms leading to coherent circadian oscillation in clock neuron networks. In this work we have implemented a model for a network of interacting clock neurons to describe the emergence (or damping) of circadian rhythms in Drosophila fly, in the absence of zeitgebers. Our model consists of an array of pacemakers that interact through the modulation of some parameters by a network feedback. The individual pacemakers are described by a well-known biochemical model for circadian oscillation, to which we have added degradation of PER protein by light and multiplicative noise. The network feedback is the PER protein level averaged over the whole network. In particular, we have investigated the effect of modulation of the parameters associated with (i) the control of net entrance of PER into the nucleus and (ii) the non-photic degradation of PER. Our results indicate that the modulation of PER entrance into the nucleus allows the synchronization of clock neurons, leading to coherent circadian oscillations under constant dark condition. On the other hand, the modulation of non-photic degradation cannot reset the phases of individual clocks subjected to intrinsic biochemical noise.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.

Relating network rigidity, time scale hierarchies, and expression noise in gene networks

Fluctuation-dissipation theorems can be used to predict characteristics of noise from characteristics of the macroscopic response of a system. In the case of gene networks, feedback control determines the "network rigidity," defined as resistance to slow external changes. We propose an effective Fokker-Planck equation that relates gene expression noise to topology and to time scales of the gene network. We distinguish between two situations referred to as normal and inverted time hierarchies. The noise can be buffered by network feedback in the first situation, whereas it can be topology independent in the latter.

GeoHealth: A Georeferenced System for Health Data Analysis in Primary Care

The Primary Care Information System (SIAB) concentrates basic healthcare information from all different regions of Brazil. The information is collected by primary care teams on a paper-based procedure that degrades the quality of information provided to the healthcare authorities and slows down the process of decision making. To overcome these problems we propose a new data gathering application that uses a mobile device connected to a 3G network and a GPS to be used by the primary care teams for collecting the families' data. A prototype was developed in which a digital version of one SIAB form is made available at the mobile device. The prototype was tested in a basic healthcare unit located in a suburb of Sao Paulo. The results obtained so far have shown that the proposed process is a better alternative for data collecting at primary care, both in terms of data quality and lower deployment time to health care authorities.

Glia-Pinealocyte Network: The Paracrine Modulation of Melatonin Synthesis by Tumor Necrosis Factor (TNF)

The pineal gland, a circumventricular organ, plays an integrative role in defense responses. The injury-induced suppression of the pineal gland hormone, melatonin, which is triggered by darkness, allows the mounting of innate immune responses. We have previously shown that cultured pineal glands, which express toll-like receptor 4 (TLR4) and tumor necrosis factor receptor 1 (TNFR1), produce TNF when challenged with lipopolysaccharide (LPS). Here our aim was to evaluate which cells present in the pineal gland, astrocytes, microglia or pinealocytes produced TNF, in order to understand the interaction between pineal activity, melatonin production and immune function. Cultured pineal glands or pinealocytes were stimulated with LPS. TNF content was measured using an enzyme-linked immunosorbent assay. TLR4 and TNFR1 expression were analyzed by confocal microscopy. Microglial morphology was analyzed by immunohistochemistry. In the present study, we show that although the main cell types of the pineal gland (pinealocytes, astrocytes and microglia) express TLR4, the production of TNF induced by LPS is mediated by microglia. This effect is due to activation of the nuclear factor kappa B (NF-kB) pathway. In addition, we observed that LPS activates microglia and modulates the expression of TNFR1 in pinealocytes. As TNF has been shown to amplify and prolong inflammatory responses, its production by pineal microglia suggests a glia-pinealocyte network that regulates melatonin output. The current study demonstrates the molecular and cellular basis for understanding how melatonin synthesis is regulated during an innate immune response, thus our results reinforce the role of the pineal gland as sensor of immune status.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.