Quantification of chlordesmethyldiazepam by liquid chromatography-tandem mass spectrometry: application to a cloxazolam bioequivalence study

A rapid, sensitive and specific LC-MS/MS method was developed and validated for quantifying chlordesmethyldiazepam (CDDZ or delorazepam), the active metabolite of cloxazolam, in human plasma. In the analytical assay, bromazepam (internal standard) and CDDZ were extracted using a liquid-liquid extraction (diethyl-ether/hexane, 80/20, v/v) procedure. The LC-MS/MS method on a RP-C18 column had an overall run time of 5.0 min and was linear (1/x weighted) over the range 0.5-50 ng/mL (R > 0.999). The between-run precision was 8.0% (1.5 ng/mL), 7.6% (9 ng/mL), 7.4% (40 ng/mL), and 10.9% at the low limit of quantification-LLOQ (0.500 ng/mL). The between-run accuracies were 0.1, -1.5, -2.7 and 8.7% for the above mentioned concentrations, respectively. All current bioanalytical method validation requirements (FDA and ANVISA) were achieved and it was applied to the bioequivalence study (Cloxazolam-test, Eurofarma Lab. Ltda and Olcadil (R)-reference, Novartis Biociencias S/A). The relative bioavailability between both formulations was assessed by calculating individual test/reference ratios for Cmax, AUClast and AUCO-inf. The pharmacokinetic profiles indicated bioequivalence since all ratios were as proposed by FDA and ANVISA. Copyright (C) 2009 John Wiley & Sons, Ltd.

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.

Hypotheses testing for structural calibration model

The main objective of this paper is to discuss maximum likelihood inference for the comparative structural calibration model (Barnett, in Biometrics 25:129-142, 1969), which is frequently used in the problem of assessing the relative calibrations and relative accuracies of a set of p instruments, each designed to measure the same characteristic on a common group of n experimental units. We consider asymptotic tests to answer the outlined questions. The methodology is applied to a real data set and a small simulation study is presented.

Comparing diagnostic tests with missing data

When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive values on some subset of the data that fits into methods implemented in standard statistical packages. Such methods are usually valid only under the strong missing completely at random (MCAR) assumption and may generate biased and less precise estimates. We review some models that use the dependence structure of the completely observed cases to incorporate the information of the partially categorized observations into the analysis and show how they may be fitted via a two-stage hybrid process involving maximum likelihood in the first stage and weighted least squares in the second. We indicate how computational subroutines written in R may be used to fit the proposed models and illustrate the different analysis strategies with observational data collected to compare the accuracy of three distinct non-invasive diagnostic methods for endometriosis. The results indicate that even when the MCAR assumption is plausible, the naive partial analyses should be avoided.

Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease

The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

It is well known that cointegration between the level of two variables (e.g. prices and dividends) is a necessary condition to assess the empirical validity of a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts to investigate whether short-run co-movememts steming from common cyclical feature restrictions are also present in such a system. In this paper we test for the presence of such co-movement on long- and short-term interest rates and on price and dividend for the U.S. economy. We focuss on the potential improvement in forecasting accuracies when imposing those two types of restrictions coming from economic theory.

Construção e avaliação térmica de um fogão solar tipo caixa

The present work presents a contribution in the study of modelings of transference of heat for foods submitted to the experimental tests in the considered solar oven, where the best modeling for the beefburger of chicken in study was evaluated, comparing the results, considering this food as a half-infinite(1er object considered model) and,after that, considered the chicken beefburger as a plain plate in transient regimen in two distinct conditions: not considering and another model considering the contribution of the generation term, through the Criterion of Pomerantsev. The Sun, beyond life source, is the origin of all the energy forms that the man comes using during its history and can be the reply for the question of the energy supplying in the future, a time that learns to use to advantage in rational way the light that this star constantly special tax on our planet. Shining more than the 5 billion years, it is calculated that the Sun still in them will privilege for others 6 billion years, or either, it is only in the half of its existence and will launch on the Earth, only in this year, 4000 times more energy that we will consume. Front to this reality, would be irrational not to search, by all means technical possible, to use to advantage this clean, ecological and gratuitous power plant. In this dissertation evaluate the performance of solar cooker of the type box. Laboratory of Solar Energy of the Federal University of the Great River of North - UFRN was constructed by the group (LES) a model of solar stove of the type box and was tested its viability technique, considering modeling foods submitted when baking in the solar oven, the cooker has main characteristic the easiness of manufacture and assembly, the low cost (was used material accessible composition to the low income communities) and simplicity in the mechanism of movement of the archetype for incidence of the direct solar light. They had been proposals modeling for calculations of food the minimum baking time, considering the following models of transference of heat in the transient state: object the halfinfinite, plain plate and the model of the sphere to study the necessary temperature for the it bakes of bread (considering spherical geometry). After evaluate the models of transmission of heat will be foods submitted you the processes of to it bakes of, the times gotten for the modeling with the experimental times of it bakes in the solar oven had been compared, demonstrating the modeling that more good that it portraies the accuracies of the results of the model

Recursive hierarchic segmentation analysis of bone mineral density changes on digital panoramic images

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Correlação entre métodos tradicionais e espectroscopia de ultra-som na determinação de características físico-químicas do leite

Compararam-se a espectroscopia de ultra-som (US) e os métodos tradicionais (MT) utilizados para a determinação de características físico-químicas do leite e estimaram-se as correlações entre esses métodos e suas acurácias. As características densidade, extrato seco desengordurado (ESD), teor de proteínas e teor de gordura foram determinadas em 65 amostras de leite cru por ambos os métodos. As densidades médias determinadas pelo US e pelos MT não diferiram entre si (P=0,14), e a correlação encontrada entre os dois métodos para a determinação da densidade não foi significativa (P= 0,08). Os teores médios de ESD, proteína e gordura encontrados pelo US e pelos MT foram diferentes (P=0,04, P<0,0001 e P<0,0001, respectivamente), as correlações entre os dois métodos utilizados para a análise dessas características foram positivas e significativas (r=0,0109, r=0,0007, r= <0,0001, respectivamente) e as acurácias dos métodos para essas determinações foram de 0,160, 0,062 e 0,145, respectivamente. Foi determinada a equação de regressão linear, que associa o teor de gordura obtido no método de espectroscopia de ultra-som ao do método butirométrico, que apresentou coeficiente de determinação de 0,5936.

High precision GPS network with precise ephemerides and earth body tide model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multi-trait and random regression mature weight heritability and breeding value estimates in Nelore cattle

Mature weight breeding values were estimated using a multi-trait animal model (MM) and a random regression animal model (RRM). Data consisted of 82 064 weight records from 8 145 animals, recorded from birth to eight years of age. Weights at standard ages were considered in the MM. All models included contemporary groups as fixed effects, and age of dam (linear and quadratic effects) and animal age as covariates. In the RRM, mean trends were modelled through a cubic regression on orthogonal polynomials of animal age and genetic maternal and direct and maternal permanent environmental effects were also included as random. Legendre polynomials of orders 4, 3, 6 and 3 were used for animal and maternal genetic and permanent environmental effects, respectively, considering five classes of residual variances. Mature weight (five years) direct heritability estimates were 0.35 (MM) and 0.38 (RRM). Rank correlation between sires' breeding values estimated by MM and RRM was 0.82. However, selecting the top 2% (12) or 10% (62) of the young sires based on the MM predicted breeding values, respectively 71% and 80% of the same sires would be selected if RRM estimates were used instead. The RRM modelled the changes in the (co)variances with age adequately and larger breeding value accuracies can be expected using this model. © South African Society for Animal Science.

A study on automatic methods based on mathematical morphology for Martian dust devil tracks detection

This paper presents three methods for automatic detection of dust devils tracks in images of Mars. The methods are mainly based on Mathematical Morphology and results of their performance are analyzed and compared. A dataset of 21 images from the surface of Mars representative of the diversity of those track features were considered for developing, testing and evaluating our methods, confronting their outputs with ground truth images made manually. Methods 1 and 3, based on closing top-hat and path closing top-hat, respectively, showed similar mean accuracies around 90% but the time of processing was much greater for method 1 than for method 3. Method 2, based on radial closing, was the fastest but showed worse mean accuracy. Thus, this was the tiebreak factor. © 2011 Springer-Verlag.

The accuracy potential of Galileo E5/E1 pseudoranges for surveying and mapping

In the paper we discuss the potential of the new Galileo signals for pseudorange based surveying and mapping in open areas under optimal reception conditions (open sky scenarios) and suboptimal ones (multipath created by moderate to thick tree coverage). The paper reviews the main features of the Galileo E5 AltBOC and E1 CBOC signals; describes the simulation strategy, models and algorithms to generate realistic E5 and E1 pseudoranges with and without multipath sources; describes the ionosphere modeling strategy, models and algorithms and discusses and presents the expected positioning accuracy and precision results. According to the simulations performed, pseudoranges can be extracted from the Galileo E5 AltBOC signals with tracking errors (1-σ level) ranging from 0.02 m (open sky scenarios) to 0.08 m (tree covered scenarios) whereas for the Galileo E1 CBOC signals the tracking errors range between 0.25 m to 2.00 m respectively. With these tracking errors and with the explicit estimation of the ionosphere parameters, simulations indicate real-time open sky cm-level horizontal positioning precisions and dm-level vertical ones and dm-level accuracies for both the horizontal and vertical position components.

Automatic classification of fish germ cells through optimum-path forest

The spermatogenesis is crucial to the species reproduction, and its monitoring may shed light over some important information of such process. Thus, the germ cells quantification can provide useful tools to improve the reproduction cycle. In this paper, we present the first work that address this problem in fishes with machine learning techniques. We show here how to obtain high recognition accuracies in order to identify fish germ cells with several state-of-the-art supervised pattern recognition techniques. © 2011 IEEE.