Tuning metadata for better movie content-based recommendation systems

The increasing number of television channels, on-demand services and online content, is expected to contribute to a better quality of experience for a costumer of such a service. However, the lack of efficient methods for finding the right content, adapted to personal interests, may lead to a progressive loss of clients. In such a scenario, recommendation systems are seen as a tool that can fill this gap and contribute to the loyalty of users. Multimedia content, namely films and television programmes are usually described using a set of metadata elements that include the title, a genre, the date of production, and the list of directors and actors. This paper provides a deep study on how the use of different metadata elements can contribute to increase the quality of the recommendations suggested. The analysis is conducted using Netflix and Movielens datasets and aspects such as the granularity of the descriptions, the accuracy metric used and the sparsity of the data are taken into account. Comparisons with collaborative approaches are also presented.

Effects of prescribed fire on surface soil in a Pinus pinaster plantation, northern Portugal

In order to decrease the risk of severe wildfire, prescribed fire has recently been adopted in Portugal and elsewhere in the Mediterranean as a major tool for reducing the fuel load instead of manual or mechanical removal of vegetation. There has been some research into its impact on soils in shrublands and grasslands, but to date little research has been conducted in forested areas in the region. As a result, the impact of prescribed fire on the physico-chemical soil characteristics of forest soils has been assumed to be minimal, but this has not been demonstrated. In this study, we present the results of a monitoring campaign of a detailed pre- and post-prescribed fire assessment of soil properties in a long-unburnt P. pinaster plantation, NW Portugal. The soil characteristics examined were pH, total porosity, bulk density, moisture content, organic matter content and litter/ash quantity. The results show that there was no significant impact on the measured soil properties, the only effect being confined to minor changes in the upper 1 cm of soil. We conclude that provided the fire is carried out according to strict guidelines in P. pinaster forest, a minimal impact on soil properties can be expected.

ABCC Subfamily Vacuolar Transporters are Involved in Pb (Lead) Detoxification in Saccharomyces cerevisiae

The present work has as objective to contribute for the elucidation of the mechanism associated with Pb detoxification, using the yeast Saccharomyces cerevisiae as a model organism. The deletion of GTT1 or GTT2 genes, coding for functional glutathione transferases (GST) enzymes in S. cerevisiae, caused an increased susceptibility to high Pb concentrations (500-1000 μmol L(-1)). These results suggest that the formation of glutathione-Pb conjugate (GS-Pb), dependent of GSTs, is important in Pb detoxification. The involvement of ATP-binding cassette (ABC) vacuolar transporters, belonging to class C subfamily (ABCC) in vacuolar compartmentalization of Pb, was evaluated. For this purpose, mutant strains disrupted in YCF1, VMR1, YBT1 or BPT 1 genes were used. All mutants tested, without vacuolar ABCC transporters, presented an increased sensitivity to 500-1000 μmol L(-1) Pb comparative to wild-type strain. Taken together, the obtained results suggest that Pb detoxification, by vacuolar compartmentalization, can occur as a result of the concerted action of GSTs and vacuolar ABCC transporters. Pb is conjugated with glutathione, catalysed by glutathione transferases and followed to the transport of GS-Pb conjugate to the vacuole by ABCC transporters.

Quantification and viability analyses of Pseudokirchneriella subcapitata algal cells using image-based cytometry

This work aims to evaluate the feasibility of using image-based cytometry (IBC) in the analysis of algal cell quantification and viability, using Pseudokirchneriella subcapitata as a cell model. Cell concentration was determined by IBC to be in a linear range between 1 × 105 and 8 × 106 cells mL−1. Algal viability was defined on the basis that the intact membrane of viable cells excludes the SYTOX Green (SG) probe. The disruption of membrane integrity represents irreversible damage and consequently results in cell death. Using IBC, we were able to successfully discriminate between live (SG-negative cells) and dead algal cells (heat-treated at 65 °C for 60 min; SG-positive cells). The observed viability of algal populations containing different proportions of killed cells was well correlated (R 2 = 0.994) with the theoretical viability. The validation of the use of this technology was carried out by exposing algal cells of P. subcapitata to a copper stress test for 96 h. IBC allowed us to follow the evolution of cell concentration and the viability of copper-exposed algal populations. This technology overcomes several main drawbacks usually associated with microscopy counting, such as labour-intensive experiments, tedious work and lack of the representativeness of the cell counting. In conclusion, IBC allowed a fast and automated determination of the total number of algal cells and allowed us to analyse viability. This technology can provide a useful tool for a wide variety of fields that utilise microalgae, such as the aquatic toxicology and biotechnology fields.

Use of a fluorescence-based approach to assess short-term responses of the alga Pseudokirchneriella subcapitata to metal stress

This work explores the use of fluorescent probes to evaluate the responses of the green alga Pseudokirchneriella subcapitata to the action of three nominal concentrations of Cd(II), Cr(VI), Cu(II) and Zn(II) for a short time (6 h). The toxic effect of the metals on algal cells was monitored using the fluorochromes SYTOX Green (SG, membrane integrity), fluorescein diacetate (FDA, esterase activity) and rhodamine 123 (Rh123, mitochondrial membrane potential). The impact of metals on chlorophyll a (Chl a) autofluorescence was also evaluated. Esterase activity was the most sensitive parameter. At the concentrations studied, all metals induced the loss of esterase activity. SG could be used to effectively detect the loss of membrane integrity in algal cells exposed to 0.32 or 1.3 μmol L−1 Cu(II). Rh123 revealed a decrease in the mitochondrial membrane potential of algal cells exposed to 0.32 and 1.3 μmol L−1 Cu(II), indicating that mitochondrial activity was compromised. Chl a autofluorescence was also affected by the presence of Cr(VI) and Cu(II), suggesting perturbation of photosynthesis. In conclusion, the fluorescence-based approach was useful for detecting the disturbance of specific cellular characteristics. Fluorescent probes are a useful diagnostic tool for the assessment of the impact of toxicants on specific targets of P. subcapitata algal cells.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Electrochemical sensor for simultaneous determination of herbicide MCPA and its metabolite 4-chloro-2-methylphenol. Application to photodegradation environmental monitoring

The development and application of a polyaniline/carbon nanotube (CNT) cyclodextrin matrix (PANI-β-CD/MWCNT)-based electrochemical sensor for the quantitative determination of the herbicide 4-chloro-2-methylphenoxyacetic acid (MCPA) and its main transformation product 4-chloro-2-methylphenol in natural waters are described. A simple cyclic voltammetry-based electrochemical methodology, in phosphate buffer solution at pH 6.0, was used to develop a method to determine both MCPA and 4-chloro-2-methylphenol, without any previous extraction or derivatization steps. A linear concentration range (10 to 50 μmol L−1) and detection limits of 1.1 and 1.9 μmol L−1, respectively, were achieved using optimized cyclic voltammetric parameters. The proposed method was successfully applied to the determination of MCPA and 4-chloro-2-methylphenol in natural water samples with satisfactory recoveries (94 to 107 %) and in good agreement with the results obtained by an established high-performance liquid chromatography technique, no significant differences being found between the methods. Interferences from ionic species and other herbicides used for broad-leaf weed control were shown to be small. The newly developed methodology was also successfully applied to MCPA photodegradation environmental studies.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Comparing the performance of geostatistical models with additional information from covariates for sewage plume characterization

In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the Mat`ern models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.