935 resultados para Secondary Data
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The secondary alcohol dehydrogenase from the thermophile Thermoanaerobacter ethanolicus 39E has been crystallized at 40 degrees C by vapour difussion using polyethelene glycol as a precipitant. The orthorhombic crystals belong to the space group P 2(1)2(1)2 with cell constants of a=170.0 Angstrom, b=125.7 Angstrom and c=80.5 Angstrom. A native X-ray diffraction data set has been collected to 2.7 Angstrom resolution.
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Background: Attempted suicide is a strong risk factor for subsequent suicidal behaviors. Innovative strategies to deal with people who have attempted suicide are needed, particularly in resource-poor settings. Aims: To evaluate a brief educational intervention and periodic follow-up contacts (BIC) for suicide attempters in five culturally different sites (Campinas, Brazil; Chennai, India; Colombo, Sri Lanka; Karaj, Islamic Republic of Iran; and Yuncheng, People's Republic of China) as part of the WHO Multisite Intervention Study on Suicidal Behaviors (SUPRE-MISS). Methods: Among the 1,867 suicide attempters enrolled in the emergency departments of the participating sites, 922 (49.4%) were randomly assigned to a brief intervention and contact (BIC) group and 945 (50.6%) to a treatment as usual (TAU) group. Repeated suicide attempts over the 18 months following the index attempt - the secondary outcome measure presented in this paper - were identified by follow-up calls or visits. Subsequent completed suicide - the primary outcome measure has been reported in a previous paper. Results: Overall, the proportion of subjects with repeated suicide attempts was similar in the BIC and TAU groups (7.6% vs. 7.5%, chi(2) = 0.013; p = .909), but there were differences in rates across the five sites. Conclusions: This study from five low-and middle-income countries does not confirm the effectiveness of brief educational intervention and follow-up contacts for suicide attempters in reducing subsequent repetition of suicide attempts up to 18 months after discharge from emergency departments.
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We present a semiempirical method to study the production and propagation of atmospheric secondary protons with energy>100 Mev, moving in the vertical direction. The derived production functions are fitted by the least-square method for the only previously published splash (SP) and return (RE) albedos observed data using the same instrument and measurement sites. The closed agreements between the measurement data and the calculations over a wide range of atmospheric depths lead to a possible extension of the method for other latitudes. The spectra of SP and RE intensities versus the geomagnetic cut-off reveal similar behaviour as assumed earlier by the theory for those components in the Earth's magnetic field. © 1993 Società Italiana di Fisica.
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Incentives for using wind power and the increasing price of energy might generate in a relatively short time a scenario where low voltage customers opt to install roof-top wind turbines. This paper focuses on evaluating the effects of such situation in terms of energy consumption, loss reduction, reverse power flow and voltage profiles. Various commercially-available roof-top wind turbines are installed in two secondary distribution circuits considering real-life wind speed data and seasonal load demand. Results are presented and discussed. © 2006 IEEE.
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In this paper, we show a local-in-time existence result for the 3D micropolar fluid system in the framework of Besov-Morrey spaces. The initial data class is larger than the previous ones and contains strongly singular functions and measures. © 2013 Springer Basel.
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The 18S rDNA phylogeny of Class Armophorea, a group of anaerobic ciliates, is proposed based on an analysis of 44 sequences (out of 195) retrieved from the NCBI/GenBank database. Emphasis was placed on the use of two nucleotide alignment criteria that involved variation in the gap-opening and gap-extension parameters and the use of rRNA secondary structure to orientate multiple-alignment. A sensitivity analysis of 76 data sets was run to assess the effect of variations in indel parameters on tree topologies. Bayesian inference, maximum likelihood and maximum parsimony phylogenetic analyses were used to explore how different analytic frameworks influenced the resulting hypotheses. A sensitivity analysis revealed that the relationships among higher taxa of the Intramacronucleata were dependent upon how indels were determined during multiple-alignment of nucleotides. The phylogenetic analyses rejected the monophyly of the Armophorea most of the time and consistently indicated that the Metopidae and Nyctotheridae were related to the Litostomatea. There was no consensus on the placement of the Caenomorphidae, which could be a sister group of the Metopidae + Nyctorheridae, or could have diverged at the base of the Spirotrichea branch or the Intramacronucleata tree.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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The teacher-librarian and organization of private secondary school libraries in Ondo West Local Government Area of Ondo State was the focus of the study. A structured questionnaire was the instrument used for data collection. Copies of questionnaires were administered to staff of six school libraries surveyed. The study revealed that none of the staff were professionally qualified, which resulted in poor and haphazard organization of the resources in all the schools surveyed. Recommendations were made to improve library services, including pr
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BACKGROUND Thrombin potently activates platelets through the protease-activated receptor PAR-1. Vorapaxar is a novel antiplatelet agent that selectively inhibits the cellular actions of thrombin through antagonism of PAR-1. METHODS We randomly assigned 26,449 patients who had a history of myocardial infarction, ischemic stroke, or peripheral arterial disease to receive vorapaxar (2.5 mg daily) or matching placebo and followed them for a median of 30 months. The primary efficacy end point was the composite of death from cardiovascular causes, myocardial infarction, or stroke. After 2 years, the data and safety monitoring board recommended discontinuation of the study treatment in patients with a history of stroke owing to the risk of intracranial hemorrhage. RESULTS At 3 years, the primary end point had occurred in 1028 patients (9.3%) in the vorapaxar group and in 1176 patients (10.5%) in the placebo group (hazard ratio for the vorapaxar group, 0.87; 95% confidence interval [CI], 0.80 to 0.94; P<0.001). Cardiovascular death, myocardial infarction, stroke, or recurrent ischemia leading to revascularization occurred in 1259 patients (11.2%) in the vorapaxar group and 1417 patients (12.4%) in the placebo group (hazard ratio, 0.88; 95% CI, 0.82 to 0.95; P=0.001). Moderate or severe bleeding occurred in 4.2% of patients who received vorapaxar and 2.5% of those who received placebo (hazard ratio, 1.66; 95% CI, 1.43 to 1.93; P<0.001). There was an increase in the rate of intracranial hemorrhage in the vorapaxar group (1.0%, vs. 0.5% in the placebo group; P<0.001). CONCLUSIONS Inhibition of PAR-1 with vorapaxar reduced the risk of cardiovascular death or ischemic events in patients with stable atherosclerosis who were receiving standard therapy. However, it increased the risk of moderate or severe bleeding, including intracranial hemorrhage. (Funded by Merck; TRA 2P-TIMI 50 ClinicalTrials.gov number, NCT00526474.)
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Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model (Hosmer & Lemeshow, 1989) and a logistic regression with state-dependent sample selection model (Cramer, 2004) applied to simulated data. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian bank portfolio. Our simulation results so far revealed that there is no statistically significant difference in terms of predictive capacity between the naive logistic regression models and the logistic regression with state-dependent sample selection models. However, there is strong difference between the distributions of the estimated default probabilities from these two statistical modeling techniques, with the naive logistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. (C) 2012 Elsevier Ltd. All rights reserved.
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In this paper the influence of a secondary variable as a function of the correlation with the primary variable for collocated cokriging is examined. For this study five exhaustive data sets were generated in computer, from which samples with 60 and 104 data points were drawn using the stratified random sampling method. These exhaustive data sets were generated departing from a pair of primary and secondary variables showing a good correlation. Then successive sets were generated by adding an amount of white noise in such a way that the correlation gets poorer. Using these samples, it was possible to find out how primary and secondary information is used to estimate an unsampled location according to the correlation level.
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[EN] Phytochemical research of two Tolpis species, T. proustii and T. lagopoda, led to the isolation of three new compounds: 30-chloro-3beta-acetoxy-22alfa-hydroxyl-20(21)- taraxastene (1), 3beta,22alfa-diacetoxy-30-ethoxy-20(21)-taraxastene (2) and 3beta,28-dihydroxy- 11alfa-hydroperoxy-12-ursene (3). The structures of the new compounds were elucidated by means of extensive IR, NMR, and MS data and by comparison of data reported in the literature. The in vitro antioxidant activities of the extracts were assessed by the DPPH and ABTS scavenging methods. The cytotoxicity of several known compounds and its derivatives was also assessed against human myeloid leukemia K-562 and K-562/ADR cell lines.
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[EN]Zooplankton growth and secondary production are key input parameters in marine ecosystem modelling, but their direct measurement is difficult to make. Accordingly, zooplanktologists have developed several statistical-based secondary production models. Here, three of these secondary production models are tested in Leptomysis lingvura (Mysidacea, Crustacea). Mysid length was measured in two cultures grown on two different food concentrations. The relationship between length and dry-mass was determined in a pilot study and used to calculate dry-mass from the experimental length data. Growth rates ranged from 0.11 to 0.64 , while secondary production rates ranged from 1.77 to 12.23 mg dry-mass . None of the three selected models were good predictors of growth and secondary production in this species of mysid.
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A study of maar-diatreme volcanoes has been perfomed by inversion of gravity and magnetic data. The geophysical inverse problem has been solved by means of the damped nonlinear least-squares method. To ensure stability and convergence of the solution of the inverse problem, a mathematical tool, consisting in data weighting and model scaling, has been worked out. Theoretical gravity and magnetic modeling of maar-diatreme volcanoes has been conducted in order to get information, which is used for a simple rough qualitative and/or quantitative interpretation. The information also serves as a priori information to design models for the inversion and/or to assist the interpretation of inversion results. The results of theoretical modeling have been used to roughly estimate the heights and the dip angles of the walls of eight Eifel maar-diatremes — each taken as a whole. Inversemodeling has been conducted for the Schönfeld Maar (magnetics) and the Hausten-Morswiesen Maar (gravity and magnetics). The geometrical parameters of these maars, as well as the density and magnetic properties of the rocks filling them, have been estimated. For a reliable interpretation of the inversion results, beside the knowledge from theoretical modeling, it was resorted to other tools such like field transformations and spectral analysis for complementary information. Geologic models, based on thesynthesis of the respective interpretation results, are presented for the two maars mentioned above. The results gave more insight into the genesis, physics and posteruptive development of the maar-diatreme volcanoes. A classification of the maar-diatreme volcanoes into three main types has been elaborated. Relatively high magnetic anomalies are indicative of scoria cones embeded within maar-diatremes if they are not caused by a strong remanent component of the magnetization. Smaller (weaker) secondary gravity and magnetic anomalies on the background of the main anomaly of a maar-diatreme — especially in the boundary areas — are indicative for subsidence processes, which probably occurred in the late sedimentation phase of the posteruptive development. Contrary to postulates referring to kimberlite pipes, there exists no generalized systematics between diameter and height nor between geophysical anomaly and the dimensions of the maar-diatreme volcanoes. Although both maar-diatreme volcanoes and kimberlite pipes are products of phreatomagmatism, they probably formed in different thermodynamic and hydrogeological environments. In the case of kimberlite pipes, large amounts of magma and groundwater, certainly supplied by deep and large reservoirs, interacted under high pressure and temperature conditions. This led to a long period phreatomagmatic process and hence to the formation of large structures. Concerning the maar-diatreme and tuff-ring-diatreme volcanoes, the phreatomagmatic process takes place due to an interaction between magma from small and shallow magma chambers (probably segregated magmas) and small amounts of near-surface groundwater under low pressure and temperature conditions. This leads to shorter time eruptions and consequently to structures of smaller size in comparison with kimberlite pipes. Nevertheless, the results show that the diameter to height ratio for 50% of the studied maar-diatremes is around 1, whereby the dip angle of the diatreme walls is similar to that of the kimberlite pipes and lies between 70 and 85°. Note that these numerical characteristics, especially the dip angle, hold for the maars the diatremes of which — estimated by modeling — have the shape of a truncated cone. This indicates that the diatreme can not be completely resolved by inversion.
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In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.