Determination of trace elements in bovine semen samples by inductively coupled plasma mass spectrometry and data mining techniques for identification of bovine class

The reproductive performance of cattle may be influenced by several factors, but mineral imbalances are crucial in terms of direct effects on reproduction. Several studies have shown that elements such as calcium, copper, iron, magnesium, selenium, and zinc are essential for reproduction and can prevent oxidative stress. However, toxic elements such as lead, nickel, and arsenic can have adverse effects on reproduction. In this paper, we applied a simple and fast method of multi-element analysis to bovine semen samples from Zebu and European classes used in reproduction programs and artificial insemination. Samples were analyzed by inductively coupled plasma spectrometry (ICP-MS) using aqueous medium calibration and the samples were diluted in a proportion of 1:50 in a solution containing 0.01% (vol/vol) Triton X-100 and 0.5% (vol/vol) nitric acid. Rhodium, iridium, and yttrium were used as the internal standards for ICP-MS analysis. To develop a reliable method of tracing the class of bovine semen, we used data mining techniques that make it possible to classify unknown samples after checking the differentiation of known-class samples. Based on the determination of 15 elements in 41 samples of bovine semen, 3 machine-learning tools for classification were applied to determine cattle class. Our results demonstrate the potential of support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) chemometric tools to identify cattle class. Moreover, the selection tools made it possible to reduce the number of chemical elements needed from 15 to just 8.

Development of Nanoinjector Devices for Electrospray Ionization - Tandem Mass Spectrometry (ESI-MSn)

In mass spectrometric (MS) systems with electrospray ionization (ESI), the sample can be analyzed coupled to separation systems (such as liquid chromatography or capillary electrophoresis) or simply by direct infusion. The greatest benefit of the type of injection is the possibility of continuous use of small amounts of samples over a long period of time. This extended analysis time allows a complete study of fragmentation by mass spectrometry, which is critical for structure elucidation of new compounds, or when using an ion trap mass analyzer. The injector filled with the sample is placed at the ESI source inlet creating an electric field suitable for the continuous formation of a spray (solvent and sample) and consequently, the gradual and even release of the sample. For the formation of the spray, is necessary that the injector end is metalized. The formation of a bilayer of titanium and gold provided an excellent attachment of the film, resulting in a nanoinjector for ionization/spray formation in the system for MS. The nanoinjectors showed high repeatability and stability over 100 min by continuous sampling with 10 mu L of sample.

Multi-element determination in Brazilian honey samples by inductively coupled plasma mass spectrometry and estimation of geographic origin with data mining techniques

Multi-element analysis of honey samples was carried out with the aim of developing a reliable method of tracing the origin of honey. Forty-two chemical elements were determined (Al, Cu, Pb, Zn, Mn, Cd, Tl, Co, Ni, Rb, Ba, Be, Bi, U, V, Fe, Pt, Pd, Te, Hf, Mo, Sn, Sb, P, La, Mg, I, Sm, Tb, Dy, Sd, Th, Pr, Nd, Tm, Yb, Lu, Gd, Ho, Er, Ce, Cr) by inductively coupled plasma mass spectrometry (ICP-MS). Then, three machine learning tools for classification and two for attribute selection were applied in order to prove that it is possible to use data mining tools to find the region where honey originated. Our results clearly demonstrate the potential of Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Random Forest (RF) chemometric tools for honey origin identification. Moreover, the selection tools allowed a reduction from 42 trace element concentrations to only 5. (C) 2012 Elsevier Ltd. All rights reserved.

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

Structural, Spectroscopic (NMR, IR, and Raman), and DFT Investigation of the Self-Assembled Nanostructure of Pravastatin-LDH (Layered Double Hydroxides) Systems

Layered double hydroxide (LDH) nanocontainers, suitable as carriers for anionic drugs, were intercalated with Pravastatin drug using magnesium-aluminum and zinc-aluminum in a M-II/Al molar ratio equal 2 and different Al3+/Pravastatin molar ratios. Postsynthesis treatments were used in order to increase the materials crystallinity. Hybrid materials were characterized by a set of physical chemical techniques: chemical elemental analysis, X-ray diffraction (XRD), mass coupled thermal analyses, vibrational infrared and Raman spectroscopies, and solid-state C-13 nuclear magnetic resonance (NMR). Results were interpreted in light of computational density functional theory (DFT) calculations performed for Sodium Pravastatin in order to assign the data obtained for the LDH intercalated materials. XRD peaks of LDH-Pravastatin material and the one-dimensional (1D) electron density map pointed out to a bilayer arrangement of Pravastatin in the interlayer region, where its associated carboxylate and vicinal hydroxyl groups are close to the positive LDH. The structural organization observed for the stacked assembly containing the unsymmetrical and bulky monoanion Pravastatin and LDH seems to be promoted by a self-assembling process, in which local interactions are maximized and chloride ion cointercalation is required. It is observed a high similarity among vibrational and C-13 NMR spectra of Na-Pravastatin and LDH-Pravastatin materials. Those features indicate that the intercalation preserves the drug structural integrity. Spectroscopic techniques corroborate the nature of the guest species and their arrangement between the inorganic layers. Changes related to carboxylate, alcohol, and olefinic moieties are observed in both vibrational Raman and C-13 NMR spectra after the drug intercalation. Thus, Pravastatin ions are forced to be arranged as head to tail through intermolecular hydrogen bonding between adjacent organic species. The thermal decomposition profile of the hybrid samples is distinct of that one observed for Na-Pravastatin salt, however, with no visible increase in the thermal behavior when the organic anion is sequestrated within LDH gap.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

EVOLUTIONARY TRAJECTORIES OF ULTRACOMPACT "BLACK WIDOW" PULSARS WITH VERY LOW MASS COMPANIONS

The existence of millisecond pulsars with planet-mass companions in close orbits is challenging from the stellar evolution point of view. We calculate in detail the evolution of binary systems self-consistently, including mass transfer, evaporation, and irradiation of the donor by X-ray feedback, demonstrating the existence of a new evolutionary path leading to short periods and compact donors as required by the observations of PSR J1719-1438. We also point out the alternative of an exotic nature of the companion planet-mass star.

Effect of Complete Revascularization on 10-Year Survival of Patients With Stable Multivessel Coronary Artery Disease MASS II Trial

Background-The importance of complete revascularization remains unclear and contradictory. This current investigation compares the effect of complete revascularization on 10-year survival of patients with stable multivessel coronary artery disease (CAD) who were randomly assigned to percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG). Methods and Results-This is a post hoc analysis of the Second Medicine, Angioplasty, or Surgery Study (MASS II), which is a randomized trial comparing treatments in patients with stable multivessel CAD, and preserved systolic ventricular function. We analyzed patients who underwent surgery (CABG) or stent angioplasty (PCI). The survival free of overall mortality of patients who underwent complete (CR) or incomplete revascularization (IR) was compared. Of the 408 patients randomly assigned to mechanical revascularization, 390 patients (95.6%) underwent the assigned treatment; complete revascularization was achieved in 224 patients (57.4%), 63.8% of those in the CABG group and 36.2% in the PCI group (P = 0.001). The IR group had more prior myocardial infarction than the CR group (56.2% X 39.2%, P = 0.01). During a 10-year follow-up, the survival free of cardiovascular mortality was significantly different among patients in the 2 groups (CR, 90.6% versus IR, 84.4%; P = 0.04). This was mainly driven by an increased cardiovascular specific mortality in individuals with incomplete revascularization submitted to PCI (P = 0.05). Conclusions-Our study suggests that in 10-year follow-up, CR compared with IR was associated with reduced cardiovascular mortality, especially due to a higher increase in cardiovascular-specific mortality in individuals submitted to PCI.

Modeling and simulation of severe slugging with mass transfer effects

A mathematical model and numerical simulations are presented to investigate the dynamics of gas, oil and water flow in a pipeline-riser system. The pipeline is modeled as a lumped parameter system and considers two switchable states: one in which the gas is able to penetrate into the riser and another in which there is a liquid accumulation front, preventing the gas from penetrating the riser. The riser model considers a distributed parameter system, in which movable nodes are used to evaluate local conditions along the subsystem. Mass transfer effects are modeled by using a black oil approximation. The model predicts the liquid penetration length in the pipeline and the liquid level in the riser, so it is possible to determine which type of severe slugging occurs in the system. The method of characteristics is used to simplify the differentiation of the resulting hyperbolic system of equations. The equations are discretized and integrated using an implicit method with a predictor-corrector scheme for the treatment of the nonlinearities. Simulations corresponding to severe slugging conditions are presented and compared to results obtained with OLGA computer code, showing a very good agreement. A description of the types of severe slugging for the three-phase flow of gas, oil and water in a pipeline-riser system with mass transfer effects are presented, as well as a stability map. (C) 2011 Elsevier Ltd. All rights reserved.

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification.

CLASSIFICATION OF THE VIGOR OF SEEDS OF BEAN-KID BY THE RESPIRATORY ACTIVITY

Several tests to assess the vigor of seed lots are used by producing companies for internal quality control. The respiratory activity test determined in the Pettenkofer apparatus has potential to be used for this purpose. Therefore, this study aimed to analyze and compare the use of respiratory activity measured in the Pettenkofer apparatus with standard tests to assess the vigor, and classify seed lots of bean-kid in high, medium and low vigor. The respiratory activity of three lots of bean-kid seeds were related to the following tests: germination, first germination count, electrical conductivity, length of shoots and roots, and dry weight of seedlings shoots and roots. The results of germination tests, germination first count, seedling shoot and root length, seedling shoot and root dry mass, electrical conductivity and determination of respiratory activity the seeds, allowed the classification of seeds lots of bean-kid in levels of different vigor. It is concluded that the respiratory activity measured in the Pettenkofer apparatus is efficient for the classification of seed lots of bean-kid according to vigor, being a fast, effective and low cost procedure.

High precision astrometry mission for the detection and characterization of nearby habitable planetary systems with the Nearby Earth Astrometric Telescope (NEAT)

A complete census of planetary systems around a volume-limited sample of solar-type stars (FGK dwarfs) in the Solar neighborhood (d a parts per thousand currency signaEuro parts per thousand 15 pc) with uniform sensitivity down to Earth-mass planets within their Habitable Zones out to several AUs would be a major milestone in extrasolar planets astrophysics. This fundamental goal can be achieved with a mission concept such as NEAT-the Nearby Earth Astrometric Telescope. NEAT is designed to carry out space-borne extremely-high-precision astrometric measurements at the 0.05 mu as (1 sigma) accuracy level, sufficient to detect dynamical effects due to orbiting planets of mass even lower than Earth's around the nearest stars. Such a survey mission would provide the actual planetary masses and the full orbital geometry for all the components of the detected planetary systems down to the Earth-mass limit. The NEAT performance limits can be achieved by carrying out differential astrometry between the targets and a set of suitable reference stars in the field. The NEAT instrument design consists of an off-axis parabola single-mirror telescope (D = 1 m), a detector with a large field of view located 40 m away from the telescope and made of 8 small movable CCDs located around a fixed central CCD, and an interferometric calibration system monitoring dynamical Young's fringes originating from metrology fibers located at the primary mirror. The mission profile is driven by the fact that the two main modules of the payload, the telescope and the focal plane, must be located 40 m away leading to the choice of a formation flying option as the reference mission, and of a deployable boom option as an alternative choice. The proposed mission architecture relies on the use of two satellites, of about 700 kg each, operating at L2 for 5 years, flying in formation and offering a capability of more than 20,000 reconfigurations. The two satellites will be launched in a stacked configuration using a Soyuz ST launch vehicle. The NEAT primary science program will encompass an astrometric survey of our 200 closest F-, G- and K-type stellar neighbors, with an average of 50 visits each distributed over the nominal mission duration. The main survey operation will use approximately 70% of the mission lifetime. The remaining 30% of NEAT observing time might be allocated, for example, to improve the characterization of the architecture of selected planetary systems around nearby targets of specific interest (low-mass stars, young stars, etc.) discovered by Gaia, ground-based high-precision radial-velocity surveys, and other programs. With its exquisite, surgical astrometric precision, NEAT holds the promise to provide the first thorough census for Earth-mass planets around stars in the immediate vicinity of our Sun.

Cost-Effectiveness Analysis for Surgical, Angioplasty, or Medical Therapeutics for Coronary Artery Disease 5-Year Follow-Up of Medicine, Angioplasty, or Surgery Study (MASS) II Trial

Background-The Second Medicine, Angioplasty, or Surgery Study (MASS II) included patients with multivessel coronary artery disease and normal systolic ventricular function. Patients underwent coronary artery bypass graft surgery (CABG, n = 203), percutaneous coronary intervention (PCI, n = 205), or medical treatment alone (MT, n = 203). This investigation compares the economic outcome at 5-year follow-up of the 3 therapeutic strategies. Methods and Results-We analyzed cumulative costs during a 5-year follow-up period. To analyze the cost-effectiveness, adjustment was made on the cumulative costs for average event-free time and angina-free proportion. Respectively, for event-free survival and event plus angina-free survival, MT presented 3.79 quality-adjusted life-years and 2.07 quality-adjusted life-years; PCI presented 3.59 and 2.77 quality-adjusted life-years; and CABG demonstrated 4.4 and 2.81 quality-adjusted life-years. The event-free costs were $9071.00 for MT; $19 967.00 for PCI; and $18 263.00 for CABG. The paired comparison of the event-free costs showed that there was a significant difference favoring MT versus PCI (P<0.01) and versus CABG (P<0.01) and CABG versus PCI (P<0.01). The event-free plus angina-free costs were $16 553.00, $25 831.00, and $24 614.00, respectively. The paired comparison of the event-free plus angina-free costs showed that there was a significant difference favoring MT versus PCI (P=0.04), and versus CABG (P<0.001); there was no difference between CABG and PCI (P>0.05). Conclusions-In the long-term economic analysis, for the prevention of a composite primary end point, MT was more cost effective than CABG, and CABG was more cost-effective than PCI.

Birth weight, current body mass index, and insulin sensitivity and secretion in young adults in two Latin American populations

Background and aims: Although studies have shown association of birth weight (BW) and adult body mass index (BMI) with insulin sensitivity in adults, there is limited evidence that BW is associated with insulin secretion. We assessed the associations between BW and current BMI with insulin sensitivity and secretion in young Latin American adults. Methods and results: Two birth cohorts, one from Ribeirao Preto, Brazil, based on 1984 participants aged 23-25 years, and another from Limache, Chile, based on 965 participants aged 22-28 years were studied. Weight and height at birth, and current fasting plasma glucose and insulin levels were measured. Insulin sensitivity (HOMA%S) and secretion (HOMA%beta) were estimated using the Homeostatic Model Assessment (HOMA2). Multiple linear regression analyses were carried out to test the associations between BW and adult BMI z-scores on log HOMA%S and log HOMA%beta. BW z-score was associated with HOMA%S in the two populations and HOMA%beta in Ribeirao Preto when adult BMI z-score was included in the model. BW z-score was associated with decreasing insulin secretion even without adjusting for adult BMI, but only in Ribeirao Preto. BMI z-score was associated with low HOMA%S and high HOMA%beta. No interactions between BW and BMI z-scores on insulin sensitivity were shown. Conclusions: This study supports the finding that BW may affect insulin sensitivity and secretion in young adults. The effect size of BW on insulin status is small in comparison to current BMI. (C) 2010 Elsevier B.V. All rights reserved.

Incorporating label dependency into the binary relevance framework for multi-label classification

In multi-label classification, examples can be associated with multiple labels simultaneously. The task of learning from multi-label data can be addressed by methods that transform the multi-label classification problem into several single-label classification problems. The binary relevance approach is one of these methods, where the multi-label learning task is decomposed into several independent binary classification problems, one for each label in the set of labels, and the final labels for each example are determined by aggregating the predictions from all binary classifiers. However, this approach fails to consider any dependency among the labels. Aiming to accurately predict label combinations, in this paper we propose a simple approach that enables the binary classifiers to discover existing label dependency by themselves. An experimental study using decision trees, a kernel method as well as Naive Bayes as base-learning techniques shows the potential of the proposed approach to improve the multi-label classification performance.