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.

Desenvolvimento de procedimento em fluxo com detecção espectrofotométrica para análise de bromoprida em medicamentos e/ou fluido biológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Variações genéticas para caracteres silviculturais em progênies e procedências de Dipteryx alata Vogel

Pós-graduação em Ciência Florestal - FCA

Determinação da acurácia de instrumentos de medidas para obter a evapotranspiração de referência com erros fixados

Pós-graduação em Agronomia (Ciência do Solo) - FCAV

Accuracy of genomic predictions in Bos indicus (Nellore) cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Imputation of non-genotyped individuals using genotyped progeny in Nellore, a Bos indicus cattle breed

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Determination of pantoprazole in human plasma by LC-MS-MS using lansoprazole as internal standard

An analytical method based on liquid chromatography with positive ion electrospray ionization (ESI) coupled to tandem mass spectrometry detection was developed for the determination of pantoprazole (CAS 102625-70-7) in human plasma using lansoprazole (CAS 103577-45-3) as the internal standard. The analyte and internal standard were extracted from the plasma samples by liquid/liquid extraction using diethyl-ether/dichloromethane (70:30; v/v) and chromatographed on a C-8 analytical column. The mobile phase consisted of acetonitrile/water/methanol (57:25:18; v/v/v) + 10 mmol/l acetic acid + 20 mmol/l ammonium acetate. The method has a chromatographic total run time of 4.5 min and was linear within the range 5.0-5,000 ng/mL. Detection was performed on a triple quadrupole tandem mass spectrometer by Multiple Reaction Monitoring (MRM). The intra- and inter-run precisions calculated from quality control (QC) samples were 4.2% and 3.2%, respectively. The accuracies as determined from QC samples were -5.0% (intra-run) and 2.0% (inter-run). The method herein described was employed in a bioequivalence study of two tablet formulations of pantoprazole.

A multisensor, multitemporal approach for monitoring herbaceous vegetation growth in the Amazon floodplain

The Amazon River floodplain is an important source of atmospheric CO2 and CH4. Aquatic herbaceous vegetation (macrophytes) have been shown to contribute significantly to floodplain net primary productivity (NPP) and methane emission in the region. Their fast growth rates under both flooded and dry conditions make herbaceous vegetation the most variable element in the Amazon floodplain NPP budget, and the most susceptible to environmental changes. The present study combines multitemporal Radarsat-1 and MODIS images to monitor spatial and temporal changes in herbaceous vegetation cover in the Amazon floodplain. Radarsat-1 images were acquired from Dec/2003 to Oct/2005, and MODIS daily surface reflectance products were acquired for the two cloud-free dates closest to each Radarsat-1 acquisition. An object-based, hierarchical algorithm was developed using the temporal SAR information to discriminate Permanent Open Water (OW), Floodplain (FP) and Upland (UL) classes at Level 1, and then subdivide the FP class into Woody Vegetation (WV) and Possible Macrophytes (PM) at Level 2. At Level 3, optical and SAR information were combined to discriminate actual herbaceous cover at each date. The resulting maps had accuracies ranging from 80% to 90% for Level 1 and 2 classifications, and from 60% to 70% for Level 3 classifications, with kappa values ranging between 0.7 and 0.9 for Levels 1 and 2 and between 0.5 and 0.6 for Level 3. All study sites had noticeable variations in the extent of herbaceous cover throughout the hydrological year, with maximum areas up to four times larger than minimum areas. The proposed classification method was able to capture the spatial pattern of macrophyte growth and development in the studied area, and the multitemporal information was essential for both separating vegetation cover types and assessing monthly variation in herbaceous cover extent.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)