Prediction of hyperglycemia in preterm newborn infants

Many conditions are associated with hyperglycemia in preterm neonates because they are very susceptible to changes in carbohydrate homeostasis. The purpose of this study was to evaluate the occurrence of hyperglycemia in preterm infants undergoing glucose infusion during the first week of life, and to enumerate the main variables predictive of hyperglycemia. This prospective study (during 1994) included 40 preterm neonates (gestational age <37 weeks); 511 determinations of glycemic status were made in these infants (average 12.8/infant), classified by gestational age, birth weight, glucose infusion rate and clinical status at the time of determination (based on clinical and laboratory parameters). The clinical status was classified as stable or unstable, as an indication of the stability or instability of the mechanisms governing glucose homeostasis at the time of determination of blood glucose; 59 episodes of hyperglycemia (11.5%) were identified. A case-control study was used (case = hyperglycemia; control = normoglycemia) to derive a model for predicting glycemia. The risk factors considered were gestational age (<=31 vs. >31 weeks), birth weight (<=1500 vs. >1500 g), glucose infusion rate (<=6 vs. >6 mg/kg/min) and clinical status (stable vs. unstable). Multivariate analysis by logistic regression gave the following mathematical model for predicting the probability of hyperglycemia: 1/exp{-3.1437 + 0.5819(GA) + 0.9234(GIR) + 1.0978(Clinical status)} The main predictive variables in our study, in increasing order of importance, were gestational age, glucose infusion rate and, the clinical status (stable or unstable) of the preterm newborn infant. The probability of hyperglycemia ranged from 4.1% to 36.9%.

Breastfeeding: making the difference in the development, health and nutrition of term and preterm newborns

Breastfeeding is the natural and safe way of feeding small infants, providing nutritional, immunological, psychological and economic recognized and unquestionable advantages. These qualities are especially important in premature infants, because of their vulnerability. Despite highly desirable, there is, in general, little success in breastfeeding preterm infants, especially in special care neonatal units. There are evidences that a high supportive hospital environment, with an interdisciplinary team, makes possible to these infants to be breastfed. In this article, the authors present an up-to-date review about the components of human milk and its unique characteristics, as well as describes aspects that make the breast milk particularly suitable for feeding the premature newborn.

Dynamic range prediction for an electric vehicle

Electric Vehicles (EVs) have limited energy storage capacity and the maximum autonomy range is strongly dependent of the driver's behaviour. Due to the fact of that batteries cannot be recharged quickly during a journey, it is essential that a precise range prediction is available to the driver of the EV. With this information, it is possible to check if the desirable destination is achievable without a stop to charge the batteries, or even, if to reach the destination it is necessary to perform an optimized driving (e.g., cutting the air-conditioning, among others EV parameters). The outcome of this research work is the development of an Electric Vehicle Assistant (EVA). This is an application for mobile devices that will help users to take efficient decisions about route planning, charging management and energy efficiency. Therefore, it will contribute to foster EVs adoption as a new paradigm in the transportation sector.

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Using customer lifetime value to improve the prediction of bank deposit subscription in telemarketing campaigns

Customer lifetime value (LTV) enables using client characteristics, such as recency, frequency and monetary (RFM) value, to describe the value of a client through time in terms of profitability. We present the concept of LTV applied to telemarketing for improving the return-on-investment, using a recent (from 2008 to 2013) and real case study of bank campaigns to sell long- term deposits. The goal was to benefit from past contacts history to extract additional knowledge. A total of twelve LTV input variables were tested, un- der a forward selection method and using a realistic rolling windows scheme, highlighting the validity of five new LTV features. The results achieved by our LTV data-driven approach using neural networks allowed an improvement up to 4 pp in the Lift cumulative curve for targeting the deposit subscribers when compared with a baseline model (with no history data). Explanatory knowledge was also extracted from the proposed model, revealing two highly relevant LTV features, the last result of the previous campaign to sell the same product and the frequency of past client successes. The obtained results are particularly valuable for contact center companies, which can improve pre- dictive performance without even having to ask for more information to the companies they serve.

Measurement of Spin Correlation in Top-Antitop Quark Events and Search for Top Squark Pair Production in pp Collisions at s√=8 TeV Using the ATLAS Detector

A measurement of spin correlation in tt¯ production is presented using data collected with the ATLAS detector at the Large Hadron Collider in proton-proton collisions at a center-of-mass energy of 8 TeV, corresponding to an integrated luminosity of 20.3 fb−1. The correlation between the top and antitop quark spins is extracted from dilepton tt¯ events by using the difference in azimuthal angle between the two charged leptons in the laboratory frame. In the helicity basis the measured degree of correlation corresponds to Ahelicity=0.38±0.04, in agreement with the Standard Model prediction. A search is performed for pair production of top squarks with masses close to the top quark mass decaying to predominantly right-handed top quarks and a light neutralino, the lightest supersymmetric particle. Top squarks with masses between the top quark mass and 191 GeV are excluded at the 95% confidence level.

Impact performance prediction of injection-molded talc-filled polypropylene through thermomechanical environment assessment

Due to the fact that different injection molding conditions tailor the mechanical response of the thermoplastic material, such effect must be considered earlier in the product development process. The existing approaches implemented in different commercial software solutions are very limited in their capabilities to estimate the influence of processing conditions on the mechanical properties. Thus, the accuracy of predictive simulations could be improved. In this study, we demonstrate how to establish straightforward processing-impact property relationships of talc-filled injection-molded polypropylene disc-shaped parts by assessing the thermomechanical environment (TME). To investigate the relationship between impact properties and the key operative variables (flow rate, melt and mold temperature, and holding pressure), the design of experiments approach was applied to systematically vary the TME of molded samples. The TME is characterized on computer flow simulation outputsanddefined bytwo thermomechanical indices (TMI): the cooling index (CI; associated to the core features) and the thermo-stress index (TSI; related to the skin features). The TMI methodology coupled to an integrated simulation program has been developed as a tool to predict the impact response. The dynamic impact properties (peak force, peak energy, and puncture energy) were evaluated using instrumented falling weight impact tests and were all found to be similarly affected by the imposed TME. The most important molding parameters affecting the impact properties were found to be the processing temperatures (melt andmold). CI revealed greater importance for the impact response than TSI. The developed integrative tool provided truthful predictions for the envisaged impact properties.

UV-visible scanning spectrophotometry and chemometric analysis as tools for carotenoids analysis in cassava genotypes (Manihot esculenta Crantz)

In this study, the metabolomics characterization focusing on the carotenoid composition of ten cassava (Manihot esculenta) genotypes cultivated in southern Brazil by UV-visible scanning spectrophotometry and reverse phase-high performance liquid chromatography was performed. Cassava roots rich in -carotene are an important staple food for populations with risk of vitamin A deficiency. Cassava genotypes with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The data set was used for the construction of a descriptive model by chemometric analysis. The genotypes of yellow-fleshed roots were clustered by the higher concentrations of cis--carotene and lutein. Inversely, cream-fleshed roots genotypes were grouped precisely due to their lower concentrations of these pigments, as samples rich in lycopene (redfleshed) differed among the studied genotypes. The analytical approach (UV-Vis, HPLC, and chemometrics) used showed to be efficient for understanding the chemodiversity of cassava genotypes, allowing to classify them according to important features for human health and nutrition.

Prediction of drug targets in human pathogens

The identification of new and druggable targets in bacteria is a critical endeavour in pharmaceutical research of novel antibiotics to fight infectious agents. The rapid emergence of resistant bacteria makes today's antibiotics more and more ineffective, consequently increasing the need for new pharmacological targets and novel classes of antibacterial drugs. A new model that combines the singular value decomposition technique with biological filters comprised of a set of protein properties associated with bacterial drug targets and similarity to protein-coding essential genes of E. coli has been developed to predict potential drug targets in the Enterobacteriaceae family [1]. This model identified 99 potential target proteins amongst the studied bacterial family, exhibiting eight different functions that suggest that the disruption of the activities of these proteins is critical for cells. Out of these candidates, one was selected for target confirmation. To find target modulators, receptor-based pharmacophore hypotheses were built and used in the screening of a virtual library of compounds. Postscreening filters were based on physicochemical and topological similarity to known Gram-negative antibiotics and applied to the retrieved compounds. Screening hits passing all filters were docked into the proteins catalytic groove and 15 of the most promising compounds were purchased from their chemical vendors to be experimentally tested in vitro. To the best of our knowledge, this is the first attempt to rationalize the search of compounds to probe the relevance of this candidate as a new pharmacological target.

Social entrepreneurship: does institutional environment make a difference?

The purpose of this paper is to contribute to a better understanding of the link between social entrepreneurs and institutional environment in Portugal. A quantitative approach is used in the study, and primary data were collected through an online survey. A questionnaire was emailed to, both, Portuguese Non-Governmental organizations and projects available on the Portuguese social stock exchange. In the analysis of the data were used descriptive statistics, factorial analysis and t-student tests to validate (or not) the research hypotheses. The results show that a favorable institutional environment has a low importance in the decision to develop social initiatives. This conclusion supports the idea that many social entrepreneurs can emerge even in developing regions where the institutional environment is weak. Therefore, social entrepreneurship could be an instrument of regional development and contribute to attenuate the social and economic differences among Portuguese regions.

Mineralogical and geochemical influences on sediment color of Amazon wetlands analyzed by visible spectrophotometry

Based on sedimentological and geochemical data, this work relates spectrophotometric measurements with sediment composition and its application in palaeoecological studies of Amazon wetlands. The CIELAB values are directly related to mineralogical and chemical composition, mostly involving quartz, iron oxyhydroxides and sulfides (e.g. pyrite), and total organic carbon. Total organic carbon contents between 0.4-1%, 1-2%, 3-5% and 15-40% were related to L* (lightness) data of 27, 26-15, 7-10 and 7 or less, respectively. The CIELAB values of a deposit in Marabá, Pará, were proportional to variations in quartz and total organic carbon contents, but changes in zones of similar color, mainly in the +a* (red) and +b* (yellow) values of deposits in Calçoene, Amapá and Soure, Pará, indicate a close relationship between total organic carbon content and iron oxyhydroxides and sulfides. Furthermore, the Q7/4 diagram (ratio between the % re?ectance value at 700 nm to that at 400 nm, coupled with L*) indicated iron-rich sediments in the bioturbated mud facies of the Amapá deposit, bioturbated mud and bioturbated sand facies of Soure deposit, and cross-laminated sand and massive sand facies of the Marabá core. Also, organic-rich sediments were found in the bioturbated mud facies of the Amapá deposit, lenticular heterolithic and bioturbated mud facies of the Soure deposit, and laminated mud and peat facies of the Marabá deposit. At the Marabá site, the data suggest an autochthonous influence with peat formation. The coastal wetland sites at Marajó and Amapá represent the development of a typical tidal flat setting with sulfide and iron oxyhydroxides formation during alternated flooding and drying.

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.

Difference in the in-hospital mortality of unstable angina pectoris between men and women

PURPOSE: To assess differences in the in-hospital mortality (HM) rate between men and women with unstable angina pectoris (UA) according to age, depression of the ST segment, history of previous acute myocardial infarction (AMI), and risk factors for coronary heart disease. METHODS: From October 96 to March 98, 261 patients with UA were selected. Logistic regression models were developed to adjust the association between sex and HM for possible influence of covariables, such as hypertension, diabetes mellitus, dyslipidemia, sedentary lifestyle, smoking, and familial history of early coronary heart disease. RESULTS: HM due to UA was approximately three times higher in women (9.3%; 12/129) than in men (3.0%; 4/132) accounting for a relative risk of 3.07; 95% confidence interval (CI) =1.02-9.27. In logistic regression models, the association between sex and death was not significantly altered when the following parameters were considered: age, depression of the ST segment, history of previous AMI and risk factors for coronary heart disease. The nonadjusted and adjusted odds ratio (OR) for the distinct covariables were 3.28 (CI 95%=1.03-10.45) and 3.14 (CI = 95% = 0.88-11.20), respectively. CONCLUSION: Similarly to AMI, HM in UA is higher in women than in men. Age, risk factors for coronary heart disease, and depression of the ST segment in the electrocardiogram on patients' admission to the hospital did not significantly influence the association between sex and death.

O autorretrato em literatura: ilustração e ruína

A aplicação ao caso literário do termo e do conceito de retrato, aqui incluindo a espécie mais tardia do autorretrato, não é hoje fluente, apesar da reconhecida permeabilidade do género a inúmeras linguagens artísticas e apesar de uma longa tradição de descrições de figura – particularmente no que toca a representação poético-retórica da beleza feminina – que recua à poesia clássica. Partindo de uma possível distinção entre autorretrato literário e variantes várias de escritas intimistas e autobiográficas com as quais delineia fronteiras nem sempre rigorosamente nítidas, é nosso propósito ilustrar, recorrendo a casos selecionados, modos de concretização verbal de autorretrato que se distanciam progressivamente de um paradigma representativo fundado na perceção e na semelhança, aproximando-se ao contrário de registos de rasura, apagamento, ruína, cegueira que questionam uma noção de identidade estabilizável em imagem (visual ou mental), encaminhando o gesto autorrepresentativo para uma meditação sobre a diferença e sobre o irreconhecimento; no limite, para uma condição fora do visível e porventura, arriscando os seus mais básicos pressupostos, para lá da representação.