Effects of gastrointestinal digestion models in vitro on phenolic compounds and antioxidant activity of juçara (Euterpe edulis)

This study evaluated the effects of different gastrointestinal digestion models in vitro on the bioaccessibility of phenolics and antioxidant activity in juçara frozen pulp. In the sequence, method 3 was applied to juçara fruit in three different stages of maturation (vitrin – reddish fruits, mature – purple fruits, tuíra – deep purple fruits). In the method applied, the final pH adopted was 5.0, in order to avoid interference in the assay used to determine the antioxidant activity, and BHT was used to prevent excessive oxidation in the system. In this method, higher values for antioxidant activity were obtained (3574.95–3719.10 μmol L^?1 Trolox 100 g^?1 pulp) compared with the other two methods tested (1969.14–3034.74 μmol L^?1 Trolox 100 g^?1 pulp). In relation to juçara fruit, the mature stage was found to be ideal for processing, showing generally higher values of the bioaccessibility for phenolics and antioxidant activity compared to other maturation stages.     

Fault Tolerance Mechanisms for FPGA-Based Regular Expression Matching

Traditional fault-tolerance techniques relying on spatial and temporal redundancy typically imply high power, delay, and area overheads. Cost-effective solutions often depend on system’s design and hardware platform at hand. Particularly for Field-Programmable Gate Arrays (FPGAs), soft errors on the configuration memory are a significant dependability threat. In this work, we present an extended and comprehensive fault tolerance mechanism especially suited for dealing with configuration faults on FPGA-based systems that must deal multiple failure modes. Each failure mode may present different criticality and probability of occurrence, and these properties are measured and exploited to provide low-cost solutions when compared to standard approaches such as triple modular redundancy. The exploited properties are typically found in critical monitoring systems that may trigger security- or safety-critical alarms and warnings in general. In such systems, failing to trigger an alarm when necessary is frequently regarded as more critical than providing an occasional false alarm. For instance, Regular Expression Matching (REM), a compute-intensive mechanism heavily used to perform Deep Packet Inspection in critical network applications, presents such properties, and it can be greatly accelerated by FPGAs to meet performance constraints in high-throughput networks. Therefore, we use FPGA-based REM engines as a case study to demonstrate the effectiveness of the proposed techniques. Additionally, a mutually-aware placement and scrubbing mechanism is introduced to reduce the repair time, improving the system reliability and availability. Experimental results show that the failure rate and the repair time can be reduced by 95 and 90% respectively while avoiding the costs of triplication.     

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10?000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.     

A comparative study of the behavior of Chlamydomonas reinhardtii and Spirulina platensis in solar catalytic pyrolysis

The aim of this study was to investigate the behavior of two distinct microalgae species during solar catalytic pyrolysis and the influence of their chemical composition and the process variables (biomass charge, reaction time, and catalyst percentage) on the product yields and bio-oil composition. For this purpose, solar catalytic pyrolysis of Spirulina platensis and Chlamydomonas reinhardtii was performed using hydrotalcite-derived mixed oxides as the catalyst. To gain more insight into the effect of composition on pyrolysis behavior, the biomasses were analyzed using various analytical techniques. The results indicated that a high percentage of catalyst (47.1%) culminated in liquid yields of 42.48% and 21.31% for Chlamydomonas pyrolysis and Spirulina pyrolysis, respectively. Additionally, Spirulina pyrolysis resulted in higher solid yields compared with Chlamydomonas pyrolysis. The results also showed that Spirulina bio-oil was rich in oxygenated compounds, probably due to its high carbohydrate content, whereas Chlamydomonas bio-oil was rich in nitrogenated compounds because of its higher protein content. The microalgae composition (lipids, protein, carbohydrates) exerted a large influence on the catalytic pathways and led to differences in yield and product distribution. A high percentage of catalysts preferentially promoted a deoxygenation of the bio-oil obtained from Spirulina solar pyrolysis compared with that obtained from Chlamydomonas pyrolysis.     

Prebiotic Origin of Pre-RNA Building Blocks in a Urea “Warm Little Pond” Scenario

Urea appears to be a key intermediate of important prebiotic synthetic pathways. Concentrated pools of urea likely existed on the surface of the early Earth, as urea is synthesized in significant quantities from hydrogen cyanide or cyanamide (widely accepted prebiotic molecules), it has extremely high water solubility, and it can concentrate to form eutectics from aqueous solutions. We propose a model for the origin of a variety of canonical and non-canonical nucleobases, including some known to form supramolecular assemblies that contain Watson-Crick-like base pairs.The dual nucleophilic-electrophilic character of urea makes it an ideal precursor for the formation of nitrogenous heterocycles. We propose a model for the origin of a variety of canonical and noncanonical nucleobases, including some known to form supramolecular assemblies that contain Watson-Crick-like base pairs. These reactions involve urea condensation with other prebiotic molecules (e. g., malonic acid) that could be driven by environmental cycles (e. g., freezing/thawing, drying/wetting). The resulting heterocycle assemblies are compatible with the formation of nucleosides and, possibly, the chemical evolution of molecular precursors to RNA. We show that urea eutectics at moderate temperature represent a robust prebiotic source of nitrogenous heterocycles. The simplicity of these pathways, and their independence from specific or rare geological events, support the idea of urea being of fundamental importance to the prebiotic chemistry that gave rise to life on Earth.     

Theoretical and experimental studies of a new aniline derivative corrosion inhibitor for mild steel in acid medium

4-Chloro-N-(pyridin-2-ylmethyl)aniline (CPYA) was synthesized by a simple and inexpensive method and tested as a corrosion inhibitor in acid medium for mild steel by using gravimetric studies and electrochemical measurements. An average maximum efficiency of 96.0% was achieved at 4.59 mmol/L. Corrosion kinetic and thermodynamic parameters were also analyzed. Surface analyses (atomic force microscopy and scanning electron microscopy) show that protection is enabled by adsorption on the metal, forming a film. Quantum chemical calculations were performed to access information regarding the molecular structure in the corrosive medium and to support interpretation of the results obtained by experimental methods.     

Technological and sensorial properties of liquid nitrogen ice cream enriched with protein from brewing waste (trub)

Debittered trub (brewing waste) is an important source of protein source (70.26%). Trub and whey protein were used for 5% protein enrichment of ice cream frozen by liquid nitrogen. Three formulations were elaborated: ice cream standard (ICS), ice cream with whey protein (ICW) and ice cream with trub (ICT). Chemical composition, rheological properties, texture, overrun, melting rate, scanning electron microscopy and a sensorial test were performed. Results showed that ICT presented a higher viscosity, obtained on the upward curve up to 6.76 Pa s⁻¹, consistency index (22.96 (Pa s⁻¹)ⁿ), hysteresis area (140.40 mPa s⁻¹) and hardness (31113.33 g) but a lower melting rate (0.38 g min⁻¹), overrun (13.92%) and sensorial acceptability than the other formulations. The addition of trub debittered for protein enrichment improved ice cream properties and demonstrated that it could be used as a food ingredient.