Visual management of sags and incidents gathered in distribution substations for power quality management

Monitor a distribution network implies working with a huge amount of data coining from the different elements that interact in the network. This paper presents a visualization tool that simplifies the task of searching the database for useful information applicable to fault management or preventive maintenance of the network

Reverse immunology approach for the identification of CD8 T-cell-defined antigens: advantages and hurdles.

One of the challenges of tumour immunology remains the identification of strongly immunogenic tumour antigens for vaccination. Reverse immunology, that is, the procedure to predict and identify immunogenic peptides from the sequence of a gene product of interest, has been postulated to be a particularly efficient, high-throughput approach for tumour antigen discovery. Over one decade after this concept was born, we discuss the reverse immunology approach in terms of costs and efficacy: data mining with bioinformatic algorithms, molecular methods to identify tumour-specific transcripts, prediction and determination of proteasomal cleavage sites, peptide-binding prediction to HLA molecules and experimental validation, assessment of the in vitro and in vivo immunogenic potential of selected peptide antigens, isolation of specific cytolytic T lymphocyte clones and final validation in functional assays of tumour cell recognition. We conclude that the overall low sensitivity and yield of every prediction step often requires a compensatory up-scaling of the initial number of candidate sequences to be screened, rendering reverse immunology an unexpectedly complex approach.

Quantifying urban attractiveness from the distribution and density of digital footprints

In the past, sensors networks in cities have been limited to fixed sensors, embedded in particular locations, under centralised control. Today, new applications can leverage wireless devices and use them as sensors to create aggregated information. In this paper, we show that the emerging patterns unveiled through the analysis of large sets of aggregated digital footprints can provide novel insights into how people experience the city and into some of the drivers behind these emerging patterns. We particularly explore the capacity to quantify the evolution of the attractiveness of urban space with a case study of in the area of the New York City Waterfalls, a public art project of four man-made waterfalls rising from the New York Harbor. Methods to study the impact of an event of this nature are traditionally based on the collection of static information such as surveys and ticket-based people counts, which allow to generate estimates about visitors’ presence in specific areas over time. In contrast, our contribution makes use of the dynamic data that visitors generate, such as the density and distribution of aggregate phone calls and photos taken in different areas of interest and over time. Our analysis provides novel ways to quantify the impact of a public event on the distribution of visitors and on the evolution of the attractiveness of the points of interest in proximity. This information has potential uses for local authorities, researchers, as well as service providers such as mobile network operators.

Provision of health care actions and services for the management of HIV/AIDS from the users’ perspective

Objective To analyse the provision of health care actions and services for people living with AIDS and receiving specialised care in Ribeirão Preto, SP. Method A descriptive, exploratory, survey-type study that consisted of interviews with structured questionnaires and data analysis using descriptive statistics. Results The provision of health care actions and services is perceived as fair. For the 301 subjects, routine care provided by the reference team, laboratory tests and the availability of antiretroviral drugs, vaccines and condoms obtained satisfactory evaluations. The provision of tests for the prevention and diagnosis of comorbidities was assessed as fair, whereas the provisions of specialised care by other professionals, psychosocial support groups and medicines for the prevention of antiretroviral side effects were assessed as unsatisfactory. Conclusion Shortcomings were observed in follow-up and care management along with a predominantly biological, doctor-centred focus in which clinical control and access to antiretroviral therapy comprise the essential focus of the care provided.


Current approaches and challenges for the metabolite profiling of complex natural extracts.

Metabolite profiling is critical in many aspects of the life sciences, particularly natural product research. Obtaining precise information on the chemical composition of complex natural extracts (metabolomes) that are primarily obtained from plants or microorganisms is a challenging task that requires sophisticated, advanced analytical methods. In this respect, significant advances in hyphenated chromatographic techniques (LC-MS, GC-MS and LC-NMR in particular), as well as data mining and processing methods, have occurred over the last decade. Together, these tools, in combination with bioassay profiling methods, serve an important role in metabolomics for the purposes of both peak annotation and dereplication in natural product research. In this review, a survey of the techniques that are used for generic and comprehensive profiling of secondary metabolites in natural extracts is provided. The various approaches (chromatographic methods: LC-MS, GC-MS, and LC-NMR and direct spectroscopic methods: NMR and DIMS) are discussed with respect to their resolution and sensitivity for extract profiling. In addition the structural information that can be generated through these techniques or in combination, is compared in relation to the identification of metabolites in complex mixtures. Analytical strategies with applications to natural extracts and novel methods that have strong potential, regardless of how often they are used, are discussed with respect to their potential applications and future trends.

OpenFluDB, a database for human and animal influenza virus.

Although research on influenza lasted for more than 100 years, it is still one of the most prominent diseases causing half a million human deaths every year. With the recent observation of new highly pathogenic H5N1 and H7N7 strains, and the appearance of the influenza pandemic caused by the H1N1 swine-like lineage, a collaborative effort to share observations on the evolution of this virus in both animals and humans has been established. The OpenFlu database (OpenFluDB) is a part of this collaborative effort. It contains genomic and protein sequences, as well as epidemiological data from more than 27,000 isolates. The isolate annotations include virus type, host, geographical location and experimentally tested antiviral resistance. Putative enhanced pathogenicity as well as human adaptation propensity are computed from protein sequences. Each virus isolate can be associated with the laboratories that collected, sequenced and submitted it. Several analysis tools including multiple sequence alignment, phylogenetic analysis and sequence similarity maps enable rapid and efficient mining. The contents of OpenFluDB are supplied by direct user submission, as well as by a daily automatic procedure importing data from public repositories. Additionally, a simple mechanism facilitates the export of OpenFluDB records to GenBank. This resource has been successfully used to rapidly and widely distribute the sequences collected during the recent human swine flu outbreak and also as an exchange platform during the vaccine selection procedure. Database URL: http://openflu.vital-it.ch.

Growth of seedlings of pigeon pea (Cajanus cajan (l.) millsp), wand riverhemp (Sesbania virgata (cav.) pers.), and lead tree (Leucaena leucocephala (lam.) de wit) in an arsenic-contaminated soil

Phytoremediation strategies utilize plants to decontaminate or immobilize soil pollutants. Among soil pollutants, metalloid As is considered a primary concern as a toxic element to organisms. Arsenic concentrations in the soil result from anthropogenic activities such as: the use of pesticides (herbicides and fungicides); some fertilizers; Au, Pb, Cu and Ni mining; Fe and steel production; coal combustion; and as a bi-product during natural gas extraction. This study evaluated the potential of pigeon pea (Cajanus cajan), wand riverhemp (Sesbania virgata), and lead tree (Leucaena leucocephala) as phytoremediators of soils polluted by As. Soil samples were placed in plastic pots, incubated with different As doses (0; 50; 100 and 200 mg dm-3) and then sown with seeds of the three species. Thirty (pigeon pea) and 90 days after sowing, the plants were evaluated for height, collar diameter and dry matter of young, intermediate and basal leaves, stems and roots. Arsenic concentration was determined in different aged leaves, stems and roots to establish the translocation index (TI) between the plant root system and aerial plant components and the bioconcentration factors (BF). The evaluated species showed distinct characteristics regarding As tolerance, since the lead tree and wand riverhemp were significantly more tolerant than pigeon pea. The high As levels found in wand riverhemp roots suggest the existence of an efficient accumulation and compartmentalization mechanism in order to reduce As translocation to shoot tissues. Pigeon pea is a sensitive species and could serve as a potential bioindicator plant, whereas the other two species have potential for phytoremediation programs in As polluted areas. However, further studies are needed with longer exposure times in actual field conditions to reach definite conclusions on relative phytoremediation potentials.

Analysis, modelling and visualisation of spatiotemporal data for urban studies

The proportion of population living in or around cites is more important than ever. Urban sprawl and car dependence have taken over the pedestrian-friendly compact city. Environmental problems like air pollution, land waste or noise, and health problems are the result of this still continuing process. The urban planners have to find solutions to these complex problems, and at the same time insure the economic performance of the city and its surroundings. At the same time, an increasing quantity of socio-economic and environmental data is acquired. In order to get a better understanding of the processes and phenomena taking place in the complex urban environment, these data should be analysed. Numerous methods for modelling and simulating such a system exist and are still under development and can be exploited by the urban geographers for improving our understanding of the urban metabolism. Modern and innovative visualisation techniques help in communicating the results of such models and simulations. This thesis covers several methods for analysis, modelling, simulation and visualisation of problems related to urban geography. The analysis of high dimensional socio-economic data using artificial neural network techniques, especially self-organising maps, is showed using two examples at different scales. The problem of spatiotemporal modelling and data representation is treated and some possible solutions are shown. The simulation of urban dynamics and more specifically the traffic due to commuting to work is illustrated using multi-agent micro-simulation techniques. A section on visualisation methods presents cartograms for transforming the geographic space into a feature space, and the distance circle map, a centre-based map representation particularly useful for urban agglomerations. Some issues on the importance of scale in urban analysis and clustering of urban phenomena are exposed. A new approach on how to define urban areas at different scales is developed, and the link with percolation theory established. Fractal statistics, especially the lacunarity measure, and scale laws are used for characterising urban clusters. In a last section, the population evolution is modelled using a model close to the well-established gravity model. The work covers quite a wide range of methods useful in urban geography. Methods should still be developed further and at the same time find their way into the daily work and decision process of urban planners. La part de personnes vivant dans une région urbaine est plus élevé que jamais et continue à croître. L'étalement urbain et la dépendance automobile ont supplanté la ville compacte adaptée aux piétons. La pollution de l'air, le gaspillage du sol, le bruit, et des problèmes de santé pour les habitants en sont la conséquence. Les urbanistes doivent trouver, ensemble avec toute la société, des solutions à ces problèmes complexes. En même temps, il faut assurer la performance économique de la ville et de sa région. Actuellement, une quantité grandissante de données socio-économiques et environnementales est récoltée. Pour mieux comprendre les processus et phénomènes du système complexe "ville", ces données doivent être traitées et analysées. Des nombreuses méthodes pour modéliser et simuler un tel système existent et sont continuellement en développement. Elles peuvent être exploitées par le géographe urbain pour améliorer sa connaissance du métabolisme urbain. Des techniques modernes et innovatrices de visualisation aident dans la communication des résultats de tels modèles et simulations. Cette thèse décrit plusieurs méthodes permettant d'analyser, de modéliser, de simuler et de visualiser des phénomènes urbains. L'analyse de données socio-économiques à très haute dimension à l'aide de réseaux de neurones artificiels, notamment des cartes auto-organisatrices, est montré à travers deux exemples aux échelles différentes. Le problème de modélisation spatio-temporelle et de représentation des données est discuté et quelques ébauches de solutions esquissées. La simulation de la dynamique urbaine, et plus spécifiquement du trafic automobile engendré par les pendulaires est illustrée à l'aide d'une simulation multi-agents. Une section sur les méthodes de visualisation montre des cartes en anamorphoses permettant de transformer l'espace géographique en espace fonctionnel. Un autre type de carte, les cartes circulaires, est présenté. Ce type de carte est particulièrement utile pour les agglomérations urbaines. Quelques questions liées à l'importance de l'échelle dans l'analyse urbaine sont également discutées. Une nouvelle approche pour définir des clusters urbains à des échelles différentes est développée, et le lien avec la théorie de la percolation est établi. Des statistiques fractales, notamment la lacunarité, sont utilisées pour caractériser ces clusters urbains. L'évolution de la population est modélisée à l'aide d'un modèle proche du modèle gravitaire bien connu. Le travail couvre une large panoplie de méthodes utiles en géographie urbaine. Toutefois, il est toujours nécessaire de développer plus loin ces méthodes et en même temps, elles doivent trouver leur chemin dans la vie quotidienne des urbanistes et planificateurs.

Characterization and nutrient release from silicate rocks and influence on chemical changes in soil

The expansion of Brazilian agriculture has led to a heavy dependence on imported fertilizers to ensure the supply of the growing food demand. This fact has contributed to a growing interest in alternative nutrient sources, such as ground silicate rocks. It is necessary, however, to know the potential of nutrient release and changes these materials can cause in soils. The purpose of this study was to characterize six silicate rocks and evaluate their effects on the chemical properties of treated soil, assessed by chemical extractants after greenhouse incubation. The experimental design consisted of completely randomized plots, in a 3 x 6 factorial scheme, with four replications. The factors were potassium levels (0-control: without silicate rock application; 200; 400; 600 kg ha-1 of K2O), supplied as six silicate rock types (breccia, biotite schist, ultramafic rock, phlogopite schist and two types of mining waste). The chemical, physical and mineralogical properties of the alternative rock fertilizers were characterized. Treatments were applied to a dystrophic Red-Yellow Oxisol (Ferralsol), which was incubated for 100 days, at 70 % (w/w) moisture in 3.7 kg/pots. The soil was evaluated for pH; calcium and magnesium were extracted with KCl 1 mol L-1; potassium, phosphorus and sodium by Mehlich 1; nickel, copper and zinc with DTPA; and the saturation of the cation exchange capacity was calculated for aluminum, calcium, magnesium, potassium, and sodium, and overall base saturation. The alternative fertilizers affected soil chemical properties. Ultramafic rock and Chapada mining byproduct (CMB) were the silicate rocks that most influenced soil pH, while the mining byproduct (MB) led to high K levels. Zinc availability was highest in the treatments with mining byproduct and Cu in soil fertilized with Chapada and mining byproduct.

A Confluence of Risks: Control and Compliance in the World of Unstructured Data, Big Data and the Cloud

The emergence of powerful new technologies, the existence of large quantities of data, and increasing demands for the extraction of added value from these technologies and data have created a number of significant challenges for those charged with both corporate and information technology management. The possibilities are great, the expectations high, and the risks significant. Organisations seeking to employ cloud technologies and exploit the value of the data to which they have access, be this in the form of "Big Data" available from different external sources or data held within the organisation, in structured or unstructured formats, need to understand the risks involved in such activities. Data owners have responsibilities towards the subjects of the data and must also, frequently, demonstrate that they are in compliance with current standards, laws and regulations. This thesis sets out to explore the nature of the technologies that organisations might utilise, identify the most pertinent constraints and risks, and propose a framework for the management of data from discovery to external hosting that will allow the most significant risks to be managed through the definition, implementation, and performance of appropriate internal control activities.

Pedotransfer functions to predict water retention for soils of the humid tropics: a review

Over the past three decades, pedotransfer functions (PTFs) have been widely used by soil scientists to estimate soils properties in temperate regions in response to the lack of soil data for these regions. Several authors indicated that little effort has been dedicated to the prediction of soil properties in the humid tropics, where the need for soil property information is of even greater priority. The aim of this paper is to provide an up-to-date repository of past and recently published articles as well as papers from proceedings of events dealing with water-retention PTFs for soils of the humid tropics. Of the 35 publications found in the literature on PTFs for prediction of water retention of soils of the humid tropics, 91 % of the PTFs are based on an empirical approach, and only 9 % are based on a semi-physical approach. Of the empirical PTFs, 97 % are continuous, and 3 % (one) is a class PTF; of the empirical PTFs, 97 % are based on multiple linear and polynomial regression of n th order techniques, and 3 % (one) is based on the k-Nearest Neighbor approach; 84 % of the continuous PTFs are point-based, and 16 % are parameter-based; 97 % of the continuous PTFs are equation-based PTFs, and 3 % (one) is based on pattern recognition. Additionally, it was found that 26 % of the tropical water-retention PTFs were developed for soils in Brazil, 26 % for soils in India, 11 % for soils in other countries in America, and 11 % for soils in other countries in Africa.

Behavior of Eucalyptus urophylla and Eucalyptus citriodora Seedlings Grown in Soil Contaminated by Arsenate

ABSTRACT Persistent areas of tailings and deposits from coal and gold mining may present high levels of arsenic (As), mainly in the arsenate form, endangering the environment and human health. The establishment of vegetation cover is a key step to reclaiming these environments. Thus, this study aimed to evaluate the potential of Eucalyptus urophylla and E. citriodora seedlings for use in phytoremediation programs of arsenate-contaminated areas. Soil samples were incubated at increasing rates (0, 50, 100, 200 and 400 mg dm-3) of arsenic (arsenate form, using Na2HAsO4) for 15 days. The seedlings were produced in a substrate (vermiculite + sawdust) and were transplanted to the pots with soil three months after seed germination. The values of plant height and diameter were taken during transplanting and 30, 60 and 90 days after transplanting. In the last evaluation, the total leaf area and biomass of shoots and roots were also determined. The values of available As in soil which caused a 50 % dry matter reduction (TS50%), the As translocation index (TI) from the roots to the shoot of the plants, and its bioconcentration factor (BF) were also calculated. Higher levels of arsenate in the soil significantly reduced the dry matter production of roots and shoots and the height of both species, most notably in E. urophylla plants. The highest levels of As were found in the root, with higher values for E. citriodora (ranging from 253.86 to 400 mg dm-3). The TI and BF were also reduced with As doses, but the values found in E. citriodora were significantly higher than in E. urophylla. E. citriodora plants presented a higher capacity to tolerate As and translocate it to the shoot than E. urophylla. Although these species cannot be considered as hyperaccumulators of As, E. citriodora presented the potential to be used in phytoremediation programs in arsenate-contaminated areas due to the long-term growth period of this species.

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

Combined use of the GGSFT data base and on Board Marine Collected Data to Model the Moho Beneath the Powell Basin, Antarctica

The Powell Basin is a small oceanic basin located at the NE end of the Antarctic Peninsula developed during the Early Miocene and mostly surrounded by the continental crusts of the South Orkney Microcontinent, South Scotia Ridge and Antarctic Peninsula margins. Gravity data from the SCAN 97 cruise obtained with the R/V Hespérides and data from the Global Gravity Grid and Sea Floor Topography (GGSFT) database (Sandwell and Smith, 1997) are used to determine the 3D geometry of the crustal-mantle interface (CMI) by numerical inversion methods. Water layer contribution and sedimentary effects were eliminated from the Free Air anomaly to obtain the total anomaly. Sedimentary effects were obtained from the analysis of existing and new SCAN 97 multichannel seismic profiles (MCS). The regional anomaly was obtained after spectral and filtering processes. The smooth 3D geometry of the crustal mantle interface obtained after inversion of the regional anomaly shows an increase in the thickness of the crust towards the continental margins and a NW-SE oriented axis of symmetry coinciding with the position of an older oceanic spreading axis. This interface shows a moderate uplift towards the western part and depicts two main uplifts to the northern and eastern sectors.

A comparison of mean phase difference and generalized least squares for analyzing single-case data

The present study focuses on single-case data analysis and specifically on two procedures for quantifying differences between baseline and treatment measurements The first technique tested is based on generalized least squares regression analysis and is compared to a proposed non-regression technique, which allows obtaining similar information. The comparison is carried out in the context of generated data representing a variety of patterns (i.e., independent measurements, different serial dependence underlying processes, constant or phase-specific autocorrelation and data variability, different types of trend, and slope and level change). The results suggest that the two techniques perform adequately for a wide range of conditions and researchers can use both of them with certain guarantees. The regression-based procedure offers more efficient estimates, whereas the proposed non-regression procedure is more sensitive to intervention effects. Considering current and previous findings, some tentative recommendations are offered to applied researchers in order to help choosing among the plurality of single-case data analysis techniques.