Genotoxic effect of Physalis angulata L. (Solanaceae) extract on human lymphocytes treated in vitro

Physalis angulata L (Solanaceae) is a medicinal plant from North of Brazil, whose different extracts and infusions are commonly used in the popular medicine for the treatment of malaria, asthma, hepatitis, dermatitis and rheumatism. However, the genotoxic effects of P. angulata on human cells is not well known. The main purpose of the present study was to evaluate the in vitro genotoxic effects of aqueous extract of P. angulata using the comet assay and the micronucleus assay in human lymphocytes provided from 6 healthy donors. Treatments with P. angulata extracts were performed in vitro in order to access the extent of DNA damage. The comet assay has shown that treatments with P. angulata at 0.5, 1.0, 2.0, 3.0 and 6.0 microg/mL in culture medium were genotoxic. Lymphocytes treated with P. angulata at the concentrations of 3.0 and 6.0 microg/mL in culture medium showed a statistically significant increase in the frequency of micronucleus (p<0.05), however, the cytokinesis blocked proliferation index (CBPI) was not decreased after P. angulata treatment. In conclusion, the present work demonstrated the genotoxic effects of P. angulata extract on human lymphocytes in vitro.     

Fabry Disease and the Heart: A Comprehensive Review

Fabry disease (FD) is an X-linked lysosomal storage disorder caused by mutations of the GLA gene that result in a deficiency of the enzymatic activity of α-galactosidase A and consequent accumulation of glycosphingolipids in body fluids and lysosomes of the cells throughout the body. GB3 accumulation occurs in virtually all cardiac cells (cardiomyocytes, conduction system cells, fibroblasts, and endothelial and smooth muscle vascular cells), ultimately leading to ventricular hypertrophy and fibrosis, heart failure, valve disease, angina, dysrhythmias, cardiac conduction abnormalities, and sudden death. Despite available therapies and supportive treatment, cardiac involvement carries a major prognostic impact, representing the main cause of death in FD. In the last years, knowledge has substantially evolved on the pathophysiological mechanisms leading to cardiac damage, the natural history of cardiac manifestations, the late-onset phenotypes with predominant cardiac involvement, the early markers of cardiac damage, the role of multimodality cardiac imaging on the diagnosis, management and follow-up of Fabry patients, and the cardiac efficacy of available therapies. Herein, we provide a comprehensive and integrated review on the cardiac involvement of FD, at the pathophysiological, anatomopathological, laboratory, imaging, and clinical levels, as well as on the diagnosis and management of cardiac manifestations, their supportive treatment, and the cardiac efficacy of specific therapies, such as enzyme replacement therapy and migalastat.     

Determination of trace manganese and Ni in neem oil by ETA-AAS with emulsion sample introduction

An emulsion-based method was developed as an alternative for the introduction of oil-based samples into a graphite furnace to produce a more rapid, precise, and accurate method of analysis. The combination of emulsion sampling and electrothermal atomization for atomic absorption spectroscopy was developed and then applied to the determination of Ni and manganese in neem oil from Venezuela. The optimal concentration of neem oil in the emulsion was 30 and 4% for Ni and Mn, respectively, based on the metal concentration of the neem oil. The stability of the emulsion as a function of time was evaluated. The use of aqueous calibration solutions and the accuracy attained using this method make it attractive for the analysis of oil samples. The metal recovery was between 97 and 101%. Based on the emulsion method, the neem oil samples had 1.39 and 0.21 mg of Mn and Ni per kg of oil, respectively. The wet digestion method indicated a Mn and Ni concentration in the neem oil sample of 1.42 and 0.24 mg/kg, respectively.     

Measuring and improving the quality of development processes based on usability and accessibility

Universal Access in the Information Society - Measurement of both usability and accessibility is an important concern in software development today. However, while a great amount of attention has...     

Learning to rank academic experts in the DBLP dataset

Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state‐of‐the‐art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph‐structure of the citation patterns for the community of experts and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combining all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state‐of‐the‐art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.     

Using visual and text features for direct marketing on multimedia messaging services domain

Traditionally, direct marketing companies have relied on pre-testing to select the best offers to send to their audience. Companies systematically dispatch the offers under consideration to a limited sample of potential buyers, rank them with respect to their performance and, based on this ranking, decide which offers to send to the wider population. Though this pre-testing process is simple and widely used, recently the industry has been under increased pressure to further optimize learning, in particular when facing severe time and learning space constraints. The main contribution of the present work is to demonstrate that direct marketing firms can exploit the information on visual content to optimize the learning phase. This paper proposes a two-phase learning strategy based on a cascade of regression methods that takes advantage of the visual and text features to improve and accelerate the learning process. Experiments in the domain of a commercial Multimedia Messaging Service (MMS) show the effectiveness of the proposed methods and a significant improvement over traditional learning techniques. The proposed approach can be used in any multimedia direct marketing domain in which offers comprise both a visual and text component.

A synergy of multi-objective optimization and data mining for the analysis of a flexible flow shop

A method for analyzing production systems by applying multi-objective optimization and data mining techniques on discrete-event simulation models, the so-called Simulation-based Innovization (SBI) is presented in this paper. The aim of the SBI analysis is to reveal insight on the parameters that affect the performance measures as well as to gain deeper understanding of the problem, through post-optimality analysis of the solutions acquired from multi-objective optimization. This paper provides empirical results from an industrial case study, carried out on an automotive machining line, in order to explain the SBI procedure. The SBI method has been found to be particularly suitable in this case study as the three objectives under study, namely total tardiness, makespan and average work-in-process, are in conflict with each other. Depending on the system load of the line, different decision variables have been found to be influencing. How the SBI method is used to find important patterns in the explored solution set and how it can be valuable to support decision making in order to improve the scheduling under different system loadings in the machining line are addressed.     

Enhanced default risk models with SVM+

Default risk models have lately raised a great interest due to the recent world economic crisis. In spite of many advanced techniques that have extensively been proposed, no comprehensive method incorporating a holistic perspective has hitherto been considered. Thus, the existing models for bankruptcy prediction lack the whole coverage of contextual knowledge which may prevent the decision makers such as investors and financial analysts to take the right decisions. Recently, SVM+ provides a formal way to incorporate additional information (not only training data) onto the learning models improving generalization. In financial settings examples of such non-financial (though relevant) information are marketing reports, competitors landscape, economic environment, customers screening, industry trends, etc. By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed default risk model showed better predictability performance than the baseline SVM and multi-task learning with SVM.     

The ALTER-NATIVA knowledge management approach

Nowadays, it is commonly known that information systems need an agile capability of handling knowledge. To accomplish this, systems have to have a formal knowledge representation ability supported by specific and advanced reasoning features. This research work proposes a knowledge management approach with the purpose to gather, model and consume community knowledge for specific recommendation commitments. Such approach is accomplished by a semantic lexicon alignment between the various community knowledge assets, to facilitate collaborations establishment between people and systems in an interoperable fashion. Thus, a knowledge base supported by a thesaurus able to represent all the metadata needed to represent and characterize the various community stakeholders’ resources is proposed. The thesaurus represents the lexicon in the domain, which in the ALTER-NATIVA systems is mostly used to support the various e-Learning elements (e.g. courses) and users categorization, sustained by synchronization features to facilitate a constant update of its information. A set of services designed to recommend specific resources in relation to a determined profile of user is provided. Additionally, a discussion about how the ALTER-NATIVA knowledge management approach can be applied to industrial environments is presented.     

Characterization of a water-based paint for corrosion protection

Corrosion of steel rebars in reinforced concrete is one of the major problems in the construction industry. Carbonation reactions of concrete with carbon dioxide and, mainly, the chloride salts action are the main causes responsible for concrete degradation. Protective coatings help to improve the durability of concrete structures by acting as a physical barrier against the corrosion agents. Waterborne paints are usually used for concrete protection rather than solvent-based paints since they are less pollutant. The aim of this work is to investigate the influence of the pore size and porosity on the permeability of the paints films toward sodium chloride. Three characterization methods from membrane science were implemented to characterize paint coatings. The time-lag method was used to determine the permeability toward the sodium chloride and toward helium and argon, these for approximately 100% relative humidity. From the seven waterborne paints formulated, only one was found to be suitable for surface protection of reinforced concrete, since its permeability toward NaCl was smaller than 10⁻¹⁴ m² s⁻¹, the threshold value required by National Laboratory of Civil Engineering (LNEC) in Portugal. For the formulated paints, it was observed that the average pore size correlates well with the permeability toward sodium chloride. This is an important result since obtaining the permeability toward sodium chloride of corrosion protective paints is very time consuming, while the average pore size can be obtained in a much shorter time.