Effects of commercial canning on SDS-PAGE patterns of the albumin fraction of four pea sizes

Size has been related with pea seed growth and maturation [1], and it is also considered an important parameter when peas are chosen for canning [2]. Some changes occur in the composition of vegetables during blanching, which are the partial loss of major components [3], that are further reduced during canning, mainly due to leaching into the water used for processing [4]. This leaching can be significant if the brine is discarded and not used as a part of the food consumed, as it is often done with most of canned vegetables [5]. Pea seed is an important source of protein for humans, and it has been suggested [6] that when considering pea seeds protein quality more emphasis should be given to the study of the albumin fraction, because it is the fraction rich in the sulphur amino acids. Although electrophoretic analysis of heat-treated proteins can be considered as time consuming it has been used to monitor changes in specific proteins and mixtures of protein subjected to a variety of heat treatments [7]. In general, heat treatments result in a drastic change in polyacrylamide gel electrophoresis (PAGE) patterns in the presence of the detergent sodium dodecyl sulfate (SDS) by reducing the number of bands compared to untreated samples [8]. The purpose of this study was to monitor changes in the albumin fraction of the protein of 4 sizes of peas in the raw, blanched and canned products, and the can brine solutions, using SDS-PAGE.     

Toward automatic phenotyping of developing embryos from videos.

We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fully automated phenotyping system. The system contains three modules 1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; 2) an energy-based model, which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; 3) a set of elastic models of the embryo at various stages of development that are matched to the label images.     

Specific consumption of liquid biofuels in gasoline fuelled engines

Oxygenated fuels increase fuel consumption due to their low enthalpy of combustion; however, their high antiknock index renders them suitable for use in engines with a high compression rate, increasing their thermal yield. This study evaluated the performance of biorenewable oxygenated fuels (ethanol and isoamyl alcohol) and partially renewable fuels (ETBE: ethyl tert-butyl ether, TAEE: tert-amyl ethyl ether and di-TAE: di-tert-amyl ether) with high degree of purity and in mixtures with automotive gasoline, based on tests with Otto cycle engines. Among the oxygenated fuels evaluated here, di-TAE was found to present the best characteristics of performance, both individually and in mixtures with gasoline.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Linear mixture model applied to Amazonian vegetation classification

Many research projects require accurate delineation of different secondary succession (SS) stages over large regions/subregions of the Amazon basin. However, the complexity of vegetation stand structure, abundant vegetation species, and the smooth transition between different SS stages make vegetation classification difficult when using traditional approaches such as the maximum likelihood classifier (MLC). Most of the time, classification distinguishes only between forest/non-forest. It has been difficult to accurately distinguish stages of SS. In this paper, a linear mixture model (LMM) approach is applied to classify successional and mature forests using Thematic Mapper (TM) imagery in the Rondônia region of the Brazilian Amazon. Three endmembers (i.e., shade, soil, and green vegetation or GV) were identified based on the image itself and a constrained least-squares solution was used to unmix the image. This study indicates that the LMM approach is a promising method for distinguishing successional and mature forests in the Amazon basin using TM data. It improved vegetation classification accuracy over that of the MLC. Initial, intermediate, and advanced successional and mature forests were classified with overall accuracy of 78.2% using a threshold method on the ratio of shade to GV fractions, a 7.4% increase over the MLC. The GV and shade fractions are sensitive to the change of vegetation stand structures and better capture biophysical structure information.     

Mapping of Trellises Associated with General Encoders onto High-Performance VLSI Architectures

A rate 1/n binary generic convolutional encoder is a shift-register circuit where the inputs are information bits and the outputs are blocks of n bits generated as linear combinations on the appropriate shift register contents. The decoding of the outputs of a convolutional encoder can be carried out by the well-known Viterbi algorithm. The communication pattern of the Viterbi Algorithm is given as a graph, called trellis, associated to the state diagram of the corresponding encoder. In this paper we present a methodology that permits the efficient mapping of the Viterbi algorithm onto a column of an arbitrary number of processors. This is done through the representation of the data flow by using mathematical operators which present an inmediate hardware projection. A single operator string has been obtained to represent a generic encoder through the study of the data flow of free-forward encoders and feed-back encoders. The formal model developed is employed for the partitioning of the computations among an arbitrary number of processors in such a way that the data are recirculated opimizing the use of the processors and the communications. As a result, we obtain a highly regular and modular architecture suitable for VLSI implementation.     

Mathematical modelling of the alcoholic fermentation of glucose by Zymomonas mobilis mobilis

The kinetics of alcoholic fermentation of a strain of Zymomonas mobilis, isolated from sugarcane juice, has been studied with the objective of determining the constansts of a non-structured mathematical model that represents the fermentation process. Assays in batch and in continuous culture have been carried out with different initial concentrations of glucose. The final concentrations of glucose, ethanol and biomass were determined. The following kinetic parameters were obtained: μ_max, 0·5 h^?1; K_s, 4·64 g dm^?3; P_max, 106 g dm^?3; Y_x/s, 0·0265 g g^?1; m, 1·4 g g^?1 h^?1; α, 17·38 g g^?1; β, 0·69 g g^?1 h^?1.     

Hull-form stochastic optimization via computational-cost reduction methods

In this paper, analytical functions for the estimation of the temperature-dependent behaviors of poorly and highly dispersed graphene oxide reinforced nanocomposite (GORNC) materials are studied in the framework of a machine learning-based approach. The validity of the presented models is shown comparing the results achieved from this modeling with those reported in the open literature. Also, the application of the obtained functions in solving the thermal buckling problem of beams constructed from such nanocomposites is demonstrated based on an energy-based method incorporated with a shear deformable beam hypothesis. The verification of the results indicates that the presented mechanical model can approximate the buckling behaviors of nanocomposite beams with remarkable precision. It can be realized from the results that the temperature plays an indispensable role in the determination of the buckling load which can be endured by the nanocomposite structure.