Emprego de redes complexas para modelar e analisar a disseminação de microrganismos da familia enterobacteriaceae em pacientes mantidos em unidades de terapia intensiva

Nowadays the studies of different methodologies to interfere in the growing and spread of serious infections and systemic status in institutionalized patients those kept on intensive therapy units are relevant to understanding these complex systems and bring benefits to several health areas, particularly public health. In this study, it was analyzed the clinical and microbiological data from patients hospitalized in intensive therapy units. The interaction between patients and caregivers was modeled and analyzed using dynamic system model and complex network theory, identifying outbreaks values of microorganisms of Enterobacteriaceae Family.

Caos e complexidade: design de superficie e os novos paradigmas da ciencia

The expressive possibilities within the field of surface design come up with increasingly larger with the emergence of technologies that allow the construction of forms and structures of high complexity such as three-dimensional printing. Establishing a relationship between design and complex systems, this work seeks to address the significant interrelationship of new paradigms of science, designed from concepts such as chaos, complexity and self-organization along with the cyber and parametric design, assuming thus the consequent impact of these in the creation and construction of process surfaces. Starting from the investigation of the applicability of the aforementioned conceptual bases, will be exemplified prospects of surface, produced in the first instance through computer interfaces, assigning the emergence of new creative processes and technology. Furthermore, elucidating biomimetics and its importance in the design of the design as a means of inspiration in complex systems of nature.

Magnetic susceptibility in the prediction of soil attributes in two sugarcane harvesting management systems

This study aimed to investigate the potential use of magnetic susceptibility (MS) as pedotransfer function to predict soil attributes under two sugarcane harvesting management systems. For each area of 1 ha (one with green sugarcane mechanized harvesting and other one with burnt sugarcane manual harvesting), 126 soil samples were collected and subjected to laboratory analysis to determine soil physical, chemical and mineralogical attributes and for measuring of MS. Data were submitted to descriptive statistics by calculating the mean and coefficient of variation. In order to compare the means in the different harvesting management systems it was carried out the Tukey test at a significance level of 5%. In order to investigate the correlation of the MS with other soil properties it was made the correlation test and aiming to assess how the MS contributes to the prediction of soil complex attributes it was made the multiple linear regressions. The results demonstrate that MS showed, in both sugarcane harvesting management systems, statistical correlation with chemical, physical and mineralogical soil attributes and it also showed potential to be used as pedotransfer function to predict attributes of the studied oxisol.

Prediction of intake and average daily gain by different feeding systems for goats

A main purpose of a mathematical nutrition model (a.k.a., feeding systems) is to provide a mathematical approach for determining the amount and composition of the diet necessary for a certain level of animal productive performance. Therefore, feeding systems should be able to predict voluntary feed intake and to partition nutrients into different productive functions and performances. In the last decades, several feeding systems for goats have been developed. The objective of this paper is to compare and evaluate the main goat feeding systems (AFRC, CSIRO, NRC, and SRNS), using data of individual growing goat kids from seven studies conducted in Brazil. The feeding systems were evaluated by regressing the residuals (observed minus predicted) on the predicted values centered on their means. The comparisons showed that these systems differ in their approach for estimating dry matter intake (DMI) and energy requirements for growing goats. The AFRC system was the most accurate for predicting DMI (mean bias = 91 g/d, P < 0.001; linear bias 0.874). The average ADG accounted for a large part of the bias in the prediction of DMI by CSIRO, NRC, and, mainly, AFRC systems. The CSIRO model gave the most accurate predictions of ADG when observed DMI was used as input in the models (mean bias 12 g/d, P < 0.001; linear bias -0.229). while the AFRC was the most accurate when predicted DMI was used (mean bias 8g/d. P > 0.1; linear bias -0.347). (C) 2011 Elsevier B.V. All rights reserved.

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

Local dimension and finite time prediction in spatiotemporal chaotic systems

Predictability is related to the uncertainty in the outcome of future events during the evolution of the state of a system. The cluster weighted modeling (CWM) is interpreted as a tool to detect such an uncertainty and used it in spatially distributed systems. As such, the simple prediction algorithm in conjunction with the CWM forms a powerful set of methods to relate predictability and dimension.

The energetic cost of maintenance in ruminants: From classical to new concepts and prediction systems

The cost of maintenance makes up a large part of total energy costs in ruminants. Metabolizable energy (ME) requirement for maintenance (MEm) is the daily ME intake that exactly balances heat energy (HE). The net energy requirement for maintenance (NEm) is estimated subtracting MEm from the HE produced by the processing of the diet. Men cannot be directly measured experimentally and is estimated by measuring basal metabolism in fasted animals or by regression measuring the recovered energy in fed animals. MEm and NEm usually, but not always, are expressed in terms of BW0.75. However, this scaling factor is substantially empirical and its exponent is often inadequate, especially for growing animals. MEm estimated by different feeding systems (AFRC, CNCPS, CSIRO, INRA, NRC) were compared by using dairy cattle data. The comparison showed that these systems differ in the approaches used to estimate MEm and for its quantification. The CSIRO system estimated the highest MEm, mostly because it includes a correction factor to increase ME as the feeding level increases. Relative to CSIRO estimates, those of NRC, INRA, CNCPS, and AFRC were on average 0.92, 0.86, 0.84, and 0.78, respectively. MEm is affected by the previous nutritional history of the animals. This phenomenon is best predicted by dynamic models, of which several have been published in the last decades. They are based either on energy flows or on nutrient flows. Some of the different approaches used were described and discussed.

Cluster analysis and artificial neural network on the superovulatory response prediction in mice

Complex biological systems require sophisticated approach for analysis, once there are variables with distinct measure levels to be analyzed at the same time in them. The mouse assisted reproduction, e.g. superovulation and viable embryos production, demand a multidisciplinary control of the environment, endocrinologic and physiologic status of the animals, of the stressing factors and the conditions which are favorable to their copulation and subsequently oocyte fertilization. In the past, analyses with a simplified approach of these variables were not well succeeded to predict the situations that viable embryos were obtained in mice. Thereby, we suggest a more complex approach with association of the Cluster Analysis and the Artificial Neural Network to predict embryo production in superovulated mice. A robust prediction could avoid the useless death of animals and would allow an ethic management of them in experiments requiring mouse embryo.

On the Study of Complex Impulsive Systems

The article investigates complex impulsive systems in which the so-called controlling systems jumps effect emerges. In particular, this research includes the correctness of the solution to the impulsive control system and approximation lemmas. A 3D model example is provided which illustrates the relevance of the considered approach to the study of complex impulsive systems.

Controllability of control systems on complex simple lie groups and the topology of flag manifolds

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)