Evaluation of novel bitter cassava film for equilibrium modified atmosphere packaging of cherry tomatoes

This is a research article on Equilibrium modified atmosphere packaging (EMAP) technology that offers the possibility to maintain produce postharvest quality and extend its shelf-life.Equilibrium modified atmosphere packaging (EMAP) technology offers the possibility to maintain produce postharvest quality and extend its shelf-life. However, EMAP stability depends on well-tuned packaging design parameters to match environmental conditions. This study defined the design requirements of a biobased film EMAP that can preserve the quality and prolong the shelf-life of fresh cherry tomatoes under recommended and simulated abuse supply chain conditions. Optimum EMAP was evaluated based on headspace gas composition at 10–20 °C, 75–95% RH and verified by determining quality changes of packed cherry tomatoes in using a continuous or micro-perforated (0.27 μm) bio-based intact bitter cassava (IBC) film. This was compared with a non-bio-based polymer film (oriented polypropylene, OPP). The IBC film attained equilibrium O2 (2–3%) after 180 h at 10 °C, with 0 and 1 perforation, for 75 and 95% RH while OPP film maintained a downward O2 fall. Continuous and micro-perforated IBC film did not show any major differences in equilibrium headspace O2, thus perforation can be neglected. Based on desirability optimisation results, biobased IBC film demonstrated a better optimized EMAP system in attaining recommended gas and stretching cherry tomato shelf-life as compared to non-biobased (OPP) film. The application of bio-based IBC film offers new possibilities in packaging fresh produce under an equilibrium modified atmosphere without compromising its quality

Recommendations to assess the shear and punching capacity of one-way slabs under concentrated loads according to the ABNT NBR 6118:2014

This study describes a set of recommendations to improve the precision of shear and punching shear capacity predictions for one-way slabs under concentrated loads, regardless of the governing failure mechanism, using the ABNT NBR 6118:2014 code provisions. For this purpose, a database of 143 test results was developed, including one-way slabs with different support conditions and loading layouts and that failed by different shear failure mechanisms: one-way shear, punching shear or a mixed mode. The key parameters influencing the load capacity and failure mechanism of these slabs were considered for the proposed recommendations: load position and slab width. Adjustments to the effective shear width definition and shear resisting control perimeter were described. Arching action for loads close to the support was also considered in both one-way shear and punching shear predictions. Considering the whole database and without separation by the failure mechanism, the ratio between tested and predicted resistances with the one-way shear expressions shows an average value of 1.22 and a coefficient of variation of 18.3%. The respective ratio between tested and predicted resistances with the punching shear expressions reached an average ratio of 1.23 with a coefficient of variation of 21.3%. Therefore, the proposed recommendations allow for reaching enhanced levels of precision in assessing the shear and punching shear capacity of one-way slabs under concentrated loads, regardless of the governing failure mechanism of the slabs

The saliva proteome of the blood-feeding insect Triatoma infestans is rich in platelet-aggregation inhibitors

The saliva of the bloodsucking bug Triatoma infestans vector of Chagas disease contains an anti-hemostatic molecular cocktail that prevents coagulation, vasoconstriction and platelet aggregation in a vertebrate prey. In order to characterize T. infestans saliva proteome, we separated the secreted saliva by two-dimensional gel electrophoresis (2-DE). More than 200 salivary proteins were detected on the 2-DE map, mainly in the alkaline region. By nanoLC–MS/MS analysis using a LTQ–Orbitrap equipment followed by a combination of conventional and sequence-similarity searches, we identified 58 main protein spots. Most of such proteins possess potential blood-feeding associated functions, particularly anti-platelet aggregation proteins belonging to lipocalin and apyrase families. The saliva protein composition indicates a highly specific molecular mechanism of early response to platelet aggregation. This first proteome analysis of the T. infestans secreted saliva provides a basis for a better understanding of this fluid protein composition highly directed to counterpart hemostasis of the prey, thus promoting the bug’s blood-feeding

Standardized production of a homogeneous latex enzyme source overcoming seasonality and microenvironmental variables

Calotropis procera produces a milky sap containing proteolytic enzymes. At low concentrations, they induce milk-clotting (60 µg/ml) and to dehair hides (0.05 and 0.1%). A protocol for obtaining the enzymes is reported. The latex was mixed with distilled water and the mixture was cleaned through centrifugation. It was dialyzed with distilled water and centrifuged again to recover the soluble fraction [EP]. The dialyze is a key feature of the process. EP was characterized in terms of protein profile, chemical stability, among other criteria. Wild plants belonging to ten geographic regions and grown in different ecological conditions were used as latex source. Collections were carried out, spaced at three-month, according to the seasons at the site of the study. Proteolytic activity was measured as an internal marker and for determining stability of the samples. EP was also analyzed for metal content and microbiology. EP showed similar magnitude of proteolysis, chromatographic and electrophoretic profiles of proteins. Samples stored at 25 °C exhibited reduced solubility (11%) and proteolytic capacity (11%) after six months. Enzyme autolysis was negligible. Microbiological and metal analyses revealed standard quality of all the samples tested. EP induced milk clotting and hide dehairing after storage for up to six months.Fil: Rios Silveiraa, Sandro. Universidade Federal do Ceara; BrasilFil: Alves Coelhoa, Raphael. Universidade Federal do Ceara; BrasilFil: Ferraz e Sousaa, Brandon. Universidade Federal do Ceara; BrasilFil: Soares de Oliveira, Jefferson. Universidade Federal do Delta do Parnaíba; BrasilFil: Lopez, Laura Maria Isabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional Arturo Jauretche; Argentina. Instituto Nacional de Tecnología Industrial. Centro de Investigación y Desarrollo del Cuero. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Centíficas. Centro de Investigación y Desarrollo del Cuero; ArgentinaFil: Moreira Lima Filhod, José Vitor. Universidad Federal Rural Pernambuco; BrasilFil: Vieira Rocha Júnior, Pedro Abílio. Universidade Federal do Ceara; BrasilFil: Pereira de Souzaa, Diego. Universidade Federal do Ceara; BrasilFil: Teixeira de Freitas Cléverson Diniz. Universidade Federal do Ceara; BrasilFil: Viana Ramos, Marcio. Universidade Federal do Ceara; Brasi

Generation of synthetic benchmark electrical load profiles using publicly available load and weather data

Electrical load profiles of a particular region are usually required in order to study the performance of renewable energy technologies and the impact of different operational strategies on the power grid. Load profiles are generally constructed based on measurements and load research surveys which are capital and labour-intensive. In the absence of true load profiles, synthetically generated load profiles can be a viable alternative to be used as benchmarks for research or renewable energy investment planning. In this paper, the feasibility of using publicly available load and weather data to generate synthetic load profiles is investigated. An artificial neural network (ANN) based method is proposed to synthesize load profiles for a target region using its typical meteorological year 2 (TMY2) weather data as the input. To achieve this, the proposed ANN models are first trained using TMY2 weather data and load profile data of neighbouring regions as the input and targeted output. The limited number of data points in the load profile dataset and the consequent averaging of TMY2 weather data to match its period resulted in limited data availability for training. This challenge was tackled by incorporating generalization using Bayesian regularization into training. The other major challenge was facilitating ANN extrapolation and this was accomplished by the incorporation of domain knowledge into the input weather data for training. The performance of the proposed technique has been evaluated by simulation studies and tested on three real datasets. Results indicate that the generated synthetic load profiles closely resemble the real ones and therefore can be used as benchmarks