Antioxidant, antihypertensive and antimicrobial properties of ovine milk caseinate hydrolyzed with a microbial protease

BACKGROUND: Bioactive peptides might be released from precursor proteins through enzymatic hydrolysis. These molecules could be potentially employed in health and food products. In this investigation, ovine milk caseinate hydrolysates obtained with a novel microbial protease derived from Bacillus sp. P7 were evaluated for antioxidant, antimicrobial, and angiotensin I‐converting enzyme (ACE)‐inhibitory activities. RESULTS: Antioxidant activity measured by the 2,2′‐azino‐bis‐(3‐ethylbenzothiazoline)‐6‐sulfonic acid method increased with hydrolysis time up to 2 h, remaining stable for up to 4 h. Hydrolysates showed low 2,2‐diphenyl‐1‐picrylhydrazyl radical‐scavenging abilities, with higher activity (31%) reached after 1 h of hydrolysis. Fe²⁺‐chelating ability was maximum for 0.5 h hydrolysates (83.3%), decreasing thereafter; and the higher reducing power was observed after 1 h of hydrolysis. ACE‐inhibitory activity was observed to increase up to 2 h of hydrolysis (94% of inhibition), declining afterwards. 3 h hydrolysates were shown to inhibit the growth of Bacillus cereus, Corynebacterium fimi, Aspergillus fumigatus, and Penicillium expansum. CONCLUSION: Ovine caseinate hydrolyzed with Bacillus sp. P7 protease presented antioxidant, antihypertensive, and antimicrobial activities. Hydrolysis time was observed to affect the evaluated bioactivities. Such hydrolysates might have potential applications in the food industry. Copyright © 2011 Society of Chemical Industry     

Influence of sodium profile in preventing complications during hemodialysis

Although a safe procedure, hemodialysis (HD) can cause numerous complications. The objective of this study was to evaluate the incidence of complications during dialysis, interdialytic weight gain, and the predialysis and postdialysis blood pressure in HD patients with and without variable sodium. Patients were observed during 12 HD sessions and those presenting with recurrent hypotension were selected for a step-wise model of variable sodium profiling. A total of 53 patients were evaluated; the mean-SD age was 53.7+/-16.3 years and 22 (41.5%) were male. Of these, 18 (34.0%) were selected to receive variable sodium profiling: the mean (SD) age was 59.9+/-12.6 years, and 10 (55.6%) were female. A significant decline in the occurrence of cramps (p<0.027), in the mean interdialytic weight gain (p<0.009), and a tendency to reduce the number of hypotensive episodes were detected in patients using variable sodium profiling. On the other hand, predialysis systolic blood pressure presented a significant increase (p<0.048). Using variable sodium, there was a statistically significant reduction in cramps and in the mean interdialytic weight gain. There was a significant increase in predialysis systolic pressure. Regarding hypotension episodes, only a tendency toward a reduction in the frequency of hypotension episodes could be detected.     

Automatic boosting of cross-product coverage using Bayesian networks

Closing the feedback loop from coverage data to the stimuli generator is one of the main challenges in the verification process. Typically, verification engineers with deep domain knowledge manually prepare a set of stimuli generation directives for that purpose. Bayesian networks based CDG (coverage directed generation) systems have been successfully used to assist the process by automatically closing this feedback loop. However, constructing these CDG systems requires manual effort and a certain amount of domain knowledge from a machine learning specialist. We propose a new method that boosts coverage in the early stages of the verification process with minimal effort, namely a fully automatic construction of a CDG system that requires no domain knowledge. Experimental results on a real-life cross-product coverage model demonstrate the efficiency of the proposed method.     

Catalytic activity evaluation for hydrogen production via autothermal reforming of methane

A nickel catalyst (5.75 wt.%) supported on gamma-alumina was evaluated through autothermal reforming of methane (ATR). The reforming process was pointed to hydrogen production, following thermodynamic and stoichiometric predictions. The catalyst was characterised by several methods including atomic absorption spectroscopy (AAS), B.E.T.-N₂, X-ray diffraction (XRD), scanning electron microscope (SEM) and thermal analyses (thermogravimetry, TG; derivate thermogravimetry, DTG; and differential thermal analysis, DTA). Experimental evaluations in a fixed-bed reactor (1023–1123 K, 1.00 bar, 150–400 cm³/min feed) presented methane conversions in the range of 40–65%. The effluent mixtures provided hydrogen yields in the range of 78–84%, carbon monoxide 3–14%, and carbon dioxide 5–18%. High molar H₂/CO ratios, ranging from 8 to 90, were obtained. Operating autothermal conditions (excess of steam, 1023–1123 K, 1.00 bar) provided low coke formation and high hydrogen selectivity (81%) for methane reforming.     

Actual car fleet emissions estimated from urban air quality measurements and street pollution models.

A method to determine emissions from the actual car fleet under realistic driving conditions has been developed. The method is based on air quality measurements, traffic counts and inverse application of street air quality models. Many pollutants are of importance for assessing the adverse impact of the air pollution, e.g. NO2, CO, lead, VOCs and particulate matter. Aromatic VOCs are of special great concern due to their adverse health effects. Measurements of benzene, toluene and xylenes were carried out in central Copenhagen since 1994. Significant correlation was observed between VOCs and CO concentrations, indicating that the petrol engine vehicles are the major sources of VOC air pollution in central Copenhagen. Hourly mean concentrations of benezene were observed to reach values of up to 20 ppb, what is critically high according to the WHOs recommendations. Based on inverse model calculation of dispersion of pollutants in street canyons, an average emission factor of benzene for the fleet of petrol fuelled vehicles was estimated to be 0.38 g/km in 1994 and 0.11 in 1997. This decrease was caused by the reduction of benzene content in Danish petrol since summer 1995 and increasing percentage of cars equipped with three-way catalysts. The emission factors for benzene for diesel-fuelled vehicles were low.     

Weighted least-square estimation of demand product mix and its applications to semiconductor demand

Estimation of demand product mix is important for effective production plans. Unlike most research in the literature where the product mix is either given or treated as a decision variable in optimization of the production efficiency, this paper focuses on the product mix itself and how to estimate it from the market demand. With more accurate information on the demand product mix, aggregate production plans for product families can be disaggregated into quality detailed plans for individual product items. In this paper, least-square estimates of demand product-mix proportions are first derived. To take into account the effect of the product life cycle, dynamic weighting schemes are then developed to improve the accuracy of the product-mix estimates. For applications, we concentrate particularly on semiconductor demand where new generations of semiconductor products emerge at the pace of every six months, as manifested by the celebrated Moore's laws. The proposed methodologies will be tested with simulated DRAM demands and actual semiconductor demands of different technology generations.     

Secretome Analysis of Arabidopsis–Trichoderma atroviride Interaction Unveils New Roles for the Plant Glutamate:Glyoxylate Aminotransferase GGAT1 in Plant Growth Induced by the Fungus and Resistance against Botrytis cinerea

The establishment of plant–fungus mutualistic interaction requires bidirectional molecular crosstalk. Therefore, the analysis of the interacting organisms secretomes would help to understand how such relationships are established. Here, a gel-free shotgun proteomics approach was used to identify the secreted proteins of the plant Arabidopsis thaliana and the mutualistic fungus Trichoderma atroviride during their interaction. A total of 126 proteins of Arabidopsis and 1027 of T. atroviride were identified. Among them, 118 and 780 were differentially modulated, respectively. Bioinformatic analysis unveiled that both organisms’ secretomes were enriched with enzymes. In T. atroviride, glycosidases, aspartic endopeptidases, and dehydrogenases increased in response to Arabidopsis. Additionally, amidases, protein-serine/threonine kinases, and hydro-lyases showed decreased levels. Furthermore, peroxidases, cysteine endopeptidases, and enzymes related to the catabolism of secondary metabolites increased in the plant secretome. In contrast, pathogenesis-related proteins and protease inhibitors decreased in response to the fungus. Notably, the glutamate:glyoxylate aminotransferase GGAT1 was secreted by Arabidopsis during its interaction with T. atroviride. Our study showed that GGAT1 is partially required for plant growth stimulation and on the induction of the plant systemic resistance by T. atroviride. Additionally, GGAT1 seems to participate in the negative regulation of the plant systemic resistance against B. cinerea through a mechanism involving H₂O₂ production.     

Comparative study about heating systems for pultrusion process

Different heating systems have been used in pultrusion, where the most widely used heaters are planar resistances. The primary objective of this study was to develop an improved heating system and compare its performance with that of a system with planar resistances. In this study, thermography was used to better understand the temperature profile along the die. Finite element analysis was performed to determine the amount of energy consumed by the heating systems. Improvements were made to the die to test the new heating system, and it was found that the new system reduced the setup time and energy consumption by approximately 57%.     

A measure of relative entropy between individual sequences withapplication to universal classification

A new notion of empirical informational divergence (relative entropy) between two individual sequences is introduced. If the two sequences are independent realizations of two finite-order, finite alphabet, stationary Markov processes, the empirical relative entropy converges to the relative entropy almost surely. This empirical divergence is based on a version of the Lempel-Ziv data compression algorithm. A simple universal algorithm for classifying individual sequences into a finite number of classes, which is based on the empirical divergence, is introduced. The algorithm discriminates between the classes whenever they are distinguishable by some finite-memory classifier for almost every given training set and almost any test sequence from these classes. It is universal in the sense that it is independent of the unknown sources