Income inequality and pecuniary crimes

This paper verifies the relationship between income inequality and pecuniary crimes. The elasticity of pecuniary crimes relative to inequality is 1.46, corroborating previous literature. Other factors important to decrease criminality are expanding job opportunities and a more efficient legal system, (C) 2009 Elsevier B.V. All rights reserved.

Neonatal endotoxin exposure changes neuroendocrine, cardiovascular function and mortality during polymicrobial sepsis in adult rats

Our aim was to investigate whether neonatal LPS challenge may improve hormonal, cardiovascular response and mortality, this being a beneficial adaptation when adult rats are submitted to polymicrobial sepsis by cecal ligation and puncture (CLP). Fourteen days after birth, pups received an intraperitoneal injection of lipopolysaccharide (LPS; 100 mu g/kg) or saline. After 8-12 weeks, they were submitted to CLP, decapitated 4,6 or 24 h after surgery and blood was collected for vasopressin (AVP), corticosterone and nitrate measurement, while AVP contents were measured in neurohypophysis, supra-optic (SON) and paraventricular (PVN) nuclei. Moreover, rats had their mean arterial pressure (MAP) and heart rate (HR) evaluated, and mortality and bacteremia were determined at 24 h. Septic animals with neonatal LPS exposure had higher plasma AVP and corticosterone levels, and higher c-Fos expression in SON and PVN at 24 h after surgery when compared to saline treated rats. The LPS pretreated group showed increased AVP content in SON and PVN at 6 h, while we did not observe any change in neurohypophyseal AVP content. The nitrate levels were significantly reduced in plasma at 6 and 24 h after surgery, and in both hypothalamic nuclei only at 6 h. Septic animals with neonatal LPS exposure showed increase in MAP during the initial phase of sepsis, but HR was not different from the neonatal saline group. Furthermore, neonatally LPS exposed rats showed a significant decrease in mortality rate as well as in bacteremia. These data suggest that neonatal LPS challenge is able to promote beneficial effects on neuroendocrine and cardiovascular responses to polymicrobial sepsis in adulthood. (C) 2011 Elsevier B.V. All rights reserved.

Anthropogenic influences in a lagoonal environment: a multiproxy approach at the valo grande mouth, Cananéia-Iguape system (SE Brazil)

The Cananéia-Iguape system, SE Brazil, consists of a complex of lagoonal channels, located in a United Nations Educational, Scientific and Cultural Organization (UNESCO) Biosphere Reserve. Nevertheless, important environmental changes have occurred in approximately the last 150 yrs due to the opening of an artificial channel, the Valo Grande, connecting the Ribeira de Iguape River to the lagoonal system. Our objective is to assess the historical record of the uppermost layers of the sedimentary column of the lagoonal system in order to determine the history of environmental changes caused by the opening of the artificial channel. In this sense, an integrated geochemical-faunal approach is used. The environmental changes led significant modifications in salinity, in changes of the depositional patterns of sediments and foraminiferal assemblages (including periods of defaunation), and, more drastically, in the input of heavy metals to the coastal environment. The concentrations Pb in the core analyzed here were up to two times higher than the values measured in contaminated sediments from the Santos estuary, the most industrialized coastal zone in Brazil.

System Identification and Prediction of Dengue Fever Incidence in Rio de Janeiro

Identification, prediction, and control of a system are engineering subjects, regardless of the nature of the system. Here, the temporal evolution of the number of individuals with dengue fever weekly recorded in the city of Rio de Janeiro, Brazil, during 2007, is used to identify SIS (susceptible-infective-susceptible) and SIR (susceptible-infective-removed) models formulated in terms of cellular automaton (CA). In the identification process, a genetic algorithm (GA) is utilized to find the probabilities of the state transition S -> I able of reproducing in the CA lattice the historical series of 2007. These probabilities depend on the number of infective neighbors. Time-varying and non-time-varying probabilities, three different sizes of lattices, and two kinds of coupling topology among the cells are taken into consideration. Then, these epidemiological models built by combining CA and GA are employed for predicting the cases of sick persons in 2008. Such models can be useful for forecasting and controlling the spreading of this infectious disease.

Shallow landslide prediction in the Serra do Mar, Sao Paulo, Brazil

Various methods are currently used in order to predict shallow landslides within the catchment scale. Among them, physically based models present advantages associated with the physical description of processes by means of mathematical equations. The main objective of this research is the prediction of shallow landslides using TRIGRS model, in a pilot catchment located at Serra do Mar mountain range, Sao Paulo State, southeastern Brazil. Susceptibility scenarios have been simulated taking into account different mechanical and hydrological values. These scenarios were analysed based on a landslide scars map from the January 1985 event, upon which two indexes were applied: Scars Concentration (SC - ratio between the number of cells with scars, in each class, and the total number of cells with scars within the catchment) and Landslide Potential (LP - ratio between the number of cells with scars, in each class, and the total number of cells in that same class). The results showed a significant agreement between the simulated scenarios and the scar's map. In unstable areas (SF <= 1), the SC values exceeded 50% in all scenarios. Based on the results, the use of this model should be considered an important tool for shallow landslide prediction, especially in areas where mechanical and hydrological properties of the materials are not well known.

Feature selection environment for genomic applications

Background: Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e. g., normal tissues and several types of cancer; or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application. Results: The intent of this work is to provide an open-source multiplataform graphical environment for bioinformatics problems, which supports many feature selection algorithms, criterion functions and graphic visualization tools such as scatterplots, parallel coordinates and graphs. A feature selection approach for growing genetic networks from seed genes ( targets or predictors) is also implemented in the system. Conclusion: The proposed feature selection environment allows data analysis using several algorithms, criterion functions and graphic visualization tools. Our experiments have shown the software effectiveness in two distinct types of biological problems. Besides, the environment can be used in different pattern recognition applications, although the main concern regards bioinformatics tasks.

Development of Ramulosis Disease of Cotton Under Controlled Environment and Field Conditions

Colletotrichum gossypii var. cephalosporioides, the fungus that causes ramulosis disease of cotton, is widespread in Brazil and can cause severe yield loss. Because weather conditions greatly affect disease development, the objective of this work was to develop weather-based models to assess disease favorability. Latent period, incidence, and severity of ramulosis symptoms were evaluated in controlled environment experiments using factorial combinations of temperature (15, 20, 25, 30, and 35 degrees C) and leaf wetness duration (0, 4, 8, 16, 32, and 64 h after inoculation). Severity was modeled as an exponential function of leaf wetness duration and temperature. At the optimum temperature of disease development, 27 degrees C, average latent period was 10 days. Maximum ramulosis severity occurred from 20 to 30 degrees C, with sharp decreases at lower and higher temperatures. Ramulosis severity increased as wetness periods were increased from 4 to 32 h. In field experiments at Piracicaba, Sao Paulo State, Brazil, cotton plots were inoculated (10(5) conidia ml(-1)) and ramulosis severity was evaluated weekly. The model obtained from the controlled environment study was used to generate a disease favorability index for comparison with disease progress rate in the field. Hourly measurements of solar radiation, temperature, relative humidity, leaf wetness duration, rainfall, and wind speed were also evaluated as possible explanatory variables. Both the disease favorability model and a model based on rainfall explained ramulosis growth rate well, with R(2) of 0.89 and 0.91, respectively. They are proposed as models of ramulosis development rate on cotton in Brazil, and weather-disease relationships revealed by this work can form the basis of a warning system for ramulosis development.

A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.

Dynamics of Aeromonas species isolated from wastewater treatment system

Aeromonas are widely distributed in the aquatic environment, and are considered to be emerging organisms that can produce a series of virulence factors. The present study was carried out in a sanitary sewage stabilization pond treatment system, located in Lins, State of Sao Paulo, Brazil. Most probable number was applied for estimation of the genus Aeromonas. Colony isolation was carried out on blood agar ampicillin and confirmed by biochemical characterization. Aeromonas species were isolated in 72.4% of influent samples, and in 55.2 and 48.3% of effluent from anaerobic and facultative lagoons, respectively. Thirteen Aeromonas species were isolated, representing most of the recognized species of these organisms. Even though it was possible to observe a tendency of decrease, total elimination of these organisms from the studied system was not achieved. Understanding of the pathogenic organism`s dynamics in wastewater treatment systems with a reuse potential is especially important because of the risk it represents.

Prediction of dynamical, nonlinear, and unstable process behavior

Process scheduling techniques consider the current load situation to allocate computing resources. Those techniques make approximations such as the average of communication, processing, and memory access to improve the process scheduling, although processes may present different behaviors during their whole execution. They may start with high communication requirements and later just processing. By discovering how processes behave over time, we believe it is possible to improve the resource allocation. This has motivated this paper which adopts chaos theory concepts and nonlinear prediction techniques in order to model and predict process behavior. Results confirm the radial basis function technique which presents good predictions and also low processing demands show what is essential in a real distributed environment.