Bimodal polypropylene through binary metallocene catalytic systems: comparison between hybrid and mixed heterogeneous catalysts

Polypropylene with wider or bimodal molecular weight distribution is required for numerous applications since low molecular weight chains improve processability and high molecular weight fraction is required to get good mechanical properties. There are several routes to achieve a bimodal resin but the use of a binary catalytic system seems to be the most attractive, particularly with metallocenes combination. From a previous work two metallocenes were selected because they lead to polypropylenes with average molecular weights that differ in one order of magnitude. Two types of binary systems have been investigated, hybrid catalysts (two metallocenes loaded on the same support) and physical mixtures (two independent supported metallocenes that are introduced to the reactor and start the polymerization together), using different ratios, i.e., 25–75, 50–50, 75–25, at three reaction temperatures, i.e., 30, 50 and 70 °C. Most of the binary catalytic systems lead to bimodal molecular weight distributions. Polypropylenes produced by mixed catalysts are greatly influenced by the most active catalyst, while in PP coming from hybrid catalysts, as there is a strong interaction between both metallocenes, each one contributes according to its presence in the hybrid catalyst. Therefore, properties of obtained bimodal polypropylenes are quite influenced by the ratio between both metallocenes.     

Neuro-semantic prediction of user decisions to contribute content to online social networks

Neural Computing and Applications - Understanding at microscopic level the generation of contents in an online social network (OSN) is highly desirable for an improved management of the OSN and the...     

Effects of Human and Porcine Adipose Extracellular Matrices Decellularized by Enzymatic or Chemical Methods on Macrophage Polarization and Immunocompetence

The decellularized extracellular matrix (ECM) obtained from human and porcine adipose tissue (AT) is currently used to prepare regenerative medicine bio-scaffolds. However, the influence of these natural biomaterials on host immune response is not yet deeply understood. Since macrophages play a key role in the inflammation/healing processes due to their high functional plasticity between M1 and M2 phenotypes, the evaluation of their response to decellularized ECM is mandatory. It is also necessary to analyze the immunocompetence of macrophages after contact with decellularized ECM materials to assess their functional role in a possible infection scenario. In this work, we studied the effect of four decellularized adipose matrices (DAMs) obtained from human and porcine AT by enzymatic or chemical methods on macrophage phenotypes and fungal phagocytosis. First, a thorough biochemical characterization of these biomaterials by quantification of remnant DNA, lipids, and proteins was performed, thus indicating the efficiency and reliability of both methods. The proteomic analysis evidenced that some proteins are differentially preserved depending on both the AT origin and the decellularization method employed. After exposure to the four DAMs, specific markers of M1 proinflammatory and M2 anti-inflammatory macrophages were analyzed. Porcine DAMs favor the M2 phenotype, independently of the decellularization method employed. Finally, a sensitive fungal phagocytosis assay allowed us to relate the macrophage phagocytosis capability with specific proteins differentially preserved in certain DAMs. The results obtained in this study highlight the close relationship between the ECM biochemical composition and the macrophage’s functional role.     

A fuzzy optimization model for planning integrated terrestrial carbon management networks

Clean Technologies and Environmental Policy - Biochar application and enhanced weathering are negative emission technologies (NETs) with the potential for large-scale deployment for the removal of...     

A review of adaptive online learning for artificial neural networks

In real applications learning algorithms have to address several issues such as, huge amount of data, samples which arrive continuously and underlying data generation processes that evolve over time. Classical learning is not always appropriate to work in these environments since independent and indentically distributed data are assumed. Taking into account the requirements of the learning process, systems should be able to modify both their structures and their parameters. In this survey, our aim is to review the developed methodologies for adaptive learning with artificial neural networks, analyzing the strategies that have been traditionally applied over the years. We focus on sequential learning, the handling of the concept drift problem and the determination of the network structure. Despite the research in this field, there are currently no standard methods to deal with these environments and diverse issues remain an open problem.     

Brazil’s new national policy on solid waste: challenges and opportunities

Brazil, one of the world’s largest developing countries, has recently introduced a new solid waste management regulatory policy. This new regulatory policy will have implications for a wide variety of stakeholders and sets the stage for opportunities and lessons to be learned. These issues are discussed in this article.     

Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction

The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density (MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool. Although most automated methods rely on supervised approaches based on a global labeling of the mammogram, the proposed method relies on pixel-level labeling, allowing better tissue classification and density measurement on a continuous scale. The fully automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case–control dataset consisting of 230 mammograms. The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC = 0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC = 0.838. In the case–control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessments present a good correlation. Both the methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available.     

The effect of probiotics (Lactobacillus rhamnosus HN001, Lactobacillus paracasei LPC-37, and Lactobacillus acidophilus NCFM) on the availability of minerals from Dutch-type cheese

The use of probiotic cultures in the production of Dutch-type cheeses did not lead to significant changes in their chemical composition but it lowered their acidity. The availability of calcium and magnesium analyzed by in vitro enzymatic hydrolysis was 19 and 35%, respectively; the availability of phosphorus was significantly higher, at >90%. The use of probiotic cultures significantly increased the availability of calcium (~2.5%), phosphorus (~6%), and magnesium (~18%). The in vitro method supports accurate determination of the effect of the Lactobacillus spp. cultures on the availability of mineral compounds ingested with Dutch-type cheese.