Papain hydrolysates of casein: molecular weight profile and encapsulation in lipospheres

Some reaction parameters were tested in the hydrolysis of casein by papain, in order to prepare hydrolysates with high oligopeptide contents, for either dietetic or pharmaceutical purposes. Five casein hydrolysates were prepared and then fractionated by size‐exclusion HPLC. The rapid correct fraction area method was used for quantifying peptides and free amino acids. Among the five reaction conditions tested, three produced similar peptide profiles. However, the use of a temperature of 37°C and an E:S ratio of 2% is probably the most economical condition for use in large‐scale manufacture. With the aim of masking the bitterness of these preparations, a new method, based on the encapsulation in lipospheres, was used. Also, second derivative spectrophotometry was used for the first time to measure the extent of encapsulation of protein hydrolysates, which changed from 50% to 83%. Moreover, the efficiency of this system was evaluated by analysing other parameters, which showed a reduction of hydrophobicity and bitterness of all samples, as well as good chemical stability during 60 days of storage under refrigeration. The electron microscopical analysis of liposheres showed an average size around 5.0 ± 1.0 µm. Copyright © 2004 Society of Chemical Industry     

Constructing nurse schedules at large hospitals

Several heuristics, based on evolutive algorithms and local search, are used to solve the nurse scheduling problem at a large hospital. Due to several intricate and specific restrictions imposed on the schedules, the problem is a difficult one to solve by hand. Moreover, some of the restrictions have a subjective value attached to them, and this constrains the use of exact methods that search for global optima. In order to facilitate the use of the solver modules by the hospital staff, a user interface was also implemented.     

A hybrid tool for detection of incipient faults in transformers based on the dissolved gas analysis of insulating oil

This paper describes the development and implementation of a tool for the diagnosis of faults in power transformers through the analysis of dissolved gases in oil. The computational system approach is based on the combined use of some traditional criteria of the dissolved gas analysis published in standards, an artificial neural network, and a fuzzy logic system. The objective of the tool is to provide the user with an answer obtained from analysis not only of the traditional methods already consolidated in the technical literature, but also via artificial-intelligence techniques, reaching a higher degree of reliability with respect to each technique individually. The results obtained with this tool are promising in the diagnosis of incipient faults in transformers, reaching success levels of more than 80%.     

Detection of sulphathiazole in honey samples using a lateral flow immunoassay

A lateral flow immunoassay (LFIA) was developed in the competitive reaction format and applied to test sulphathiazole (STZ) residues in honey samples. To prepare the assay test, a hapten conjugate and goat antirabbit antiserum as capture and control reagent, respectively, were dispensed on nitrocellulose membrane. Polyclonal antiserum against sulphathiazole was conjugated to colloidal gold nanoparticles and used as the detection reagent. The visual limit of detection (cut-off value) of the sulphathiazole LFIA was 15 ng/g, reaching qualitative results within 10 min. The assay was evaluated with STZ spiked honey samples from different geographical origins (n = 25). The results were in good agreement with those obtained from liquid chromatography separation and mass spectroscopy detection (LC–MS), indicating that the LFIA test might be used as a qualitative method for the determination of sulphathiazole residues without expensive equipment. The test was also highly specific, showing no cross-reactivity to other chemically similar antibiotics. To our knowledge, this is the only work where a development of LFIA tests for the detection of sulphathiazole residues is performed.     

In vivo antitumoural activity and composition of an oil extract of Brazilian propolis

The present study aimed to evaluate in vivo and in vitro the antitumoural activity of a propolis extract obtained with edible vegetable oil and its fractions and also to investigate its chemical composition by LC–MS and LC–MS/MS. To evaluate the toxicological aspects related to the propolis extract treatment, hematological, biochemical, histopathological and morphological analyses of treated animals were performed. All propolis extracts showed an in vivo antitumour activity in the experimental model with a moderate toxicity effect at experimental exposure levels. The oil extract was as effective as the ethanolic extract at inhibiting tumour growth. In vitro assays showed that the whole oil extract produced better inhibition of tumour cells than its fractions. LC–MS and LC–MS/MS identified four phenolic acids and three flavonoids. The anticancer potential of the oil extract of propolis has been demonstrated and the edible vegetable oil was shown as an attractive alternative solvent to extract bioactive natural propolis components.     

Foliar Spraying of Silicon Associated with Salicylic Acid Increases Silicon Absorption and Peanut Growth

Silicon - Silicon (Si) combined with foliar spraying of salicylic acid (SA) can affect the absorption of this beneficial element. A study with Si non-accumulators plants (soybean and bean) observed...     

A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.