2-Methyl-3-oxo-4-phenyl-2,3-dihydrofuran-2-yl acetate: a fluorogenic reagent for detection and analysis of primary amines

A new fluorogenic reagent, 2-methyl-3-oxo-4-phenyl-2,3-dihydrofuran-2-yl acetate, has been developed for the analysis of primary amines and aminated carbohydrates by means of HPLC, CE, and MALDI/MS. Peptides at 1 pmol (2 x 10(-7) M) levels were successfully labeled and analyzed through CE. The fluorescent derivatives have good stability in both acidic and basic solutions, making their further manipulation and structural analysis possible. The derivatives can be analyzed in reversed-phase HPLC due to the hydrophobic nature of this fluorescent tag. Characteristic elution intervals between the diastereomeric peaks of the chiral peptide derivatives may be used in structural verification. The labeled peptides and neutral oligosaccharides are also readily detectable through MALDI/MS in its positive mode.     

Infiltration Variability in Furrow Irrigation

In this paper, the contribution of different sources of variability to irrigation water depth variability was quantified using a combination of variance techniques. This method was applied using field measurements from irrigation events performed on a loamy soil with a low-infiltration rate. Infiltration variability was estimated with blocked furrow infiltrometers. The assumptions made for the application of the combination of variance techniques proved to be valid. The major variability source turned out to be the soil intake characteristics, whose variance accounted for 45–71% of the variance in infiltrated depth under first irrigation conditions. Opportunity time and wetted perimeter were less variable in subsequent irrigations and the soil intake characteristics variability accounted for a percentage of total variance beyond 76%, being at times beyond 95%. The combination of variance techniques can be used to complement standard evaluation methods in order to take into account the influence of different variability sources.     

Retention of inorganic arsenic by coryneform mutant strains

The natural resistance mechanisms of corynebacteria to respond to the environments containing high levels of arsenic were successfully adopted to develop inexpensive and selective extractants for submicrogram amounts of arsenic. Kinetic and equilibrium characteristics were evaluated, and a preliminary exploration of the capability of these strains to be used for arsenic speciation was also made in this work. Three kinetics models were used to fit the experimental data. It was found that the pseudo-first-order kinetics model was not quite adequate to describe the retention process, while the intraparticle diffusion and the pseudo-second-order kinetics models provide the best fits. The equilibrium isotherm showed that the retention of arsenic was consistent with the Langmuir equation and that the Freundlich and Dubinin-Radushkevich models provided poorer fits to the experimental data. The maximum effective retention capacity for arsenic was about 15.4 ng As/mg biomass. The amount of arsenic retained was directly measured in the biomass by forward planning a slurry electrothermal atomic absorption spectrometric procedure.     

Bayesian and regularization methods for hyperparameter estimationin image restoration

In this paper, we propose the application of the hierarchical Bayesian paradigm to the image restoration problem. We derive expressions for the iterative evaluation of the two hyperparameters applying the evidence and maximum a posteriori (MAP) analysis within the hierarchical Bayesian paradigm. We show analytically that the analysis provided by the evidence approach is more realistic and appropriate than the MAP approach for the image restoration problem. We furthermore study the relationship between the evidence and an iterative approach resulting from the set theoretic regularization approach for estimating the two hyperparameters, or their ratio, defined as the regularization parameter. Finally the proposed algorithms are tested experimentally.     

SWAM: A logic-based mobile agent programming language for the Semantic Web

Once a big repository of static data, the Web has been gradually evolved into a worldwide network of information and services known as the Semantic Web. This environment allows programs to autonomously interact with Web-accessible information and services. In this sense, mobile agent technology could help in efficiently exploiting this relatively new Web in a fully automated way, since Semantic Web resources are described in a computer-understandable way. In this paper, we present SWAM, a platform for building and deploying Prolog-based intelligent mobile agents on the Semantic Web. The article also reports examples and experimental results in order to illustrate as well as to assess the benefits of SWAM.     

Super Resolution of Multispectral Images using ℓ1 Image Models and Interband Correlations

In this paper we propose a novel super-resolution based algorithm for the pansharpening of multispectral images. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images; that is, it imposes smoothness within each band by means of the energy associated with the ℓ1 norm of vertical and horizontal first order differences of image pixel values and also takes into account the correlation among the bands of the multispectral image. The observation process is modeled using the sensor characteristics of both panchromatic and multispectral images. The method is tested on real and synthetic images, compared with other pansharpening methods, and the quality of the results assessed both qualitatively and quantitatively.