Data-driven and data-oriented methods for materials science and technologies

The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials. However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process. This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results. Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques. This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns.

Temporalities of migration: time, data infrastructures and intervention

The dissertation explores the intersections between the temporalities of migration management and border-crossers’ temporalities. First, I analyze the relation between acceleration and (non)knowledge production by focusing on the “accelerated procedures” for asylum. These procedures are applied to people whose asylum applications are deemed as suspicious and likely to be rejected. I argue that the shortened timeframes shaping these procedures are a tool for hindering asylum seekers’ possibilities to collect and produce evidence supporting their cases, eventually facilitating and speeding up their removal for Member States’ territory. Second, I analyze the encounters between migration management and border-crossers during the identification practices carried out the Hotspots and during the asylum process in terms of “temporal collisions”. I develop the notion of “hijacked knowledge” to illustrate how these “temporal collisions” negatively affect border-crossers’ possibilities of action, by producing a significant lack of knowledge and awareness about the procedures to which they are subjected and their temporal implications. With the concept of “reactive calibration”, on the other hand, I suggest that once migrants become aware of the temporalities of control, they try to appropriate them by aligning their bodies, narrations and identities to those temporalities. The third part of the dissertation describes the situated intervention developed as part of my ethnographic activity. Drawing on participatory design, design justice and STS making and doing, I designed a role-playing game - My documents, check them out - seeking to involve border-crossers in the re-design of the categories usually deployed in migration management.

Private sensors and crowdsourced rainfall data: accuracy and effectiveness for monitoring severe events and mapping pluvial flooding in urban areas

The increasing number of extreme rainfall events, combined with the high population density and the imperviousness of the land surface, makes urban areas particularly vulnerable to pluvial flooding. In order to design and manage cities to be able to deal with this issue, the reconstruction of weather phenomena is essential. Among the most interesting data sources which show great potential are the observational networks of private sensors managed by citizens (crowdsourcing). The number of these personal weather stations is consistently increasing, and the spatial distribution roughly follows population density. Precisely for this reason, they perfectly suit this detailed study on the modelling of pluvial flood in urban environments. The uncertainty associated with these measurements of precipitation is still a matter of research. In order to characterise the accuracy and precision of the crowdsourced data, we carried out exploratory data analyses. A comparison between Netatmo hourly precipitation amounts and observations of the same quantity from weather stations managed by national weather services is presented. The crowdsourced stations have very good skills in rain detection but tend to underestimate the reference value. In detail, the accuracy and precision of crowd- sourced data change as precipitation increases, improving the spread going to the extreme values. Then, the ability of this kind of observation to improve the prediction of pluvial flooding is tested. To this aim, the simplified raster-based inundation model incorporated in the Saferplaces web platform is used for simulating pluvial flooding. Different precipitation fields have been produced and tested as input in the model. Two different case studies are analysed over the most densely populated Norwegian city: Oslo. The crowdsourced weather station observations, bias-corrected (i.e. increased by 25%), showed very good skills in detecting flooded areas.

Iis--integrated Interactome System: A Web-based Platform For The Annotation, Analysis And Visualization Of Protein-metabolite-gene-drug Interactions By Integrating A Variety Of Data Sources And Tools.

High-throughput screening of physical, genetic and chemical-genetic interactions brings important perspectives in the Systems Biology field, as the analysis of these interactions provides new insights into protein/gene function, cellular metabolic variations and the validation of therapeutic targets and drug design. However, such analysis depends on a pipeline connecting different tools that can automatically integrate data from diverse sources and result in a more comprehensive dataset that can be properly interpreted. We describe here the Integrated Interactome System (IIS), an integrative platform with a web-based interface for the annotation, analysis and visualization of the interaction profiles of proteins/genes, metabolites and drugs of interest. IIS works in four connected modules: (i) Submission module, which receives raw data derived from Sanger sequencing (e.g. two-hybrid system); (ii) Search module, which enables the user to search for the processed reads to be assembled into contigs/singlets, or for lists of proteins/genes, metabolites and drugs of interest, and add them to the project; (iii) Annotation module, which assigns annotations from several databases for the contigs/singlets or lists of proteins/genes, generating tables with automatic annotation that can be manually curated; and (iv) Interactome module, which maps the contigs/singlets or the uploaded lists to entries in our integrated database, building networks that gather novel identified interactions, protein and metabolite expression/concentration levels, subcellular localization and computed topological metrics, GO biological processes and KEGG pathways enrichment. This module generates a XGMML file that can be imported into Cytoscape or be visualized directly on the web. We have developed IIS by the integration of diverse databases following the need of appropriate tools for a systematic analysis of physical, genetic and chemical-genetic interactions. IIS was validated with yeast two-hybrid, proteomics and metabolomics datasets, but it is also extendable to other datasets. IIS is freely available online at: http://www.lge.ibi.unicamp.br/lnbio/IIS/.

[consensus Of The Brazilian Society Of Rheumatology For The Diagnosis, Management And Treatment Of Lupus Nephritis].

To develop recommendations for the diagnosis, management and treatment of lupus nephritis in Brazil. Extensive literature review with a selection of papers based on the strength of scientific evidence and opinion of the Commission on Systemic Lupus Erythematosus members, Brazilian Society of Rheumatology. 1) Renal biopsy should be performed whenever possible and if this procedure is indicated; and, when the procedure is not possible, the treatment should be guided with the inference of histologic class. 2) Ideally, measures and precautions should be implemented before starting treatment, with emphasis on attention to the risk of infection. 3) Risks and benefits of treatment should be shared with the patient and his/her family. 4) The use of hydroxychloroquine (preferably) or chloroquine diphosphate is recommended for all patients (unless contraindicated) during induction and maintenance phases. 5) The evaluation of the effectiveness of treatment should be made with objective criteria of response (complete remission/partial remission/refractoriness). 6) ACE inhibitors and/or ARBs are recommended as antiproteinuric agents for all patients (unless contraindicated). 7) The identification of clinical and/or laboratory signs suggestive of proliferative or membranous glomerulonephritis should indicate an immediate implementation of specific therapy, including steroids and an immunosuppressive agent, even though histological confirmation is not possible. 8) Immunosuppressives must be used during at least 36 months, but these medications can be kept for longer periods. Its discontinuation should only be done when the patient achieve and maintain a sustained and complete remission. 9) Lupus nephritis should be considered as refractory when a full or partial remission is not achieved after 12 months of an appropriate treatment, when a new renal biopsy should be considered to assist in identifying the cause of refractoriness and in the therapeutic decision.

Fire management and the nesting of Athene cunicularia (Aves, Strigidae) in grasslands in central Cerrado, Brazil

Fire management is a common practice in several reserves in the Cerrado, but its influences on bird reproduction remain unknown. In addition, the nesting biology of the Burrowing Owl (Athene cunicularia) has been studied in numerous environments, but not in tropical grasslands managed by fire. This study examined the effects of fire management on the nesting biology of A. cunicularia in Emas National Park, State of Goias, central Brazilian Cerrado. We compared the number of breeding pairs and their burrows in October and November 2009 at 15 study sites in grasslands managed by fire (firebreaks) and unmanaged grasslands adjacent to and distant from firebreaks. We visited active burrows two-four times and described the burrow entrances and sentinel sites and counted and observed adults and young. A total of 19 burrows were found at firebreaks. One and two burrows were found in grasslands adjacent to and distant from firebreaks, respectively. For all burrows found, one to three young reached the adult size, being able to fly and/or run in early November. The 22 burrows found were in the ground, associated or not with termite and ant nests. Most (86.4%) burrows had only one entrance. Only three burrows had two or three entrances. Structures used as sentinel perches by adults were mounds in front of the burrow entrances, termite nests, shrubs and trees. Most of these sentinel sites were shorter than 2 m high and located less than 10 m from the burrow entrance. At Emas National Park, firebreaks appear to provide more attractive conditions to the nesting of A. cunicularia than unmanaged grasslands, likely because of the short herbaceous stratum due to frequent burning of firebreaks. This study suggests that fire management provides suitable conditions for the successful reproduction of A. cunicularia in firebreaks at Emas National Park.

Crystallization, data collection and data processing of maltose-binding protein (MalE) from the phytopathogen Xanthomonas axonopodis pv. citri

Maltose-binding protein is the periplasmic component of the ABC transporter responsible for the uptake of maltose/maltodextrins. The Xanthomonas axonopodis pv. citri maltose-binding protein MalE has been crystallized at 293 Kusing the hanging-drop vapour-diffusion method. The crystal belonged to the primitive hexagonal space group P6(1)22, with unit-cell parameters a = 123.59, b = 123.59, c = 304.20 angstrom, and contained two molecules in the asymetric unit. It diffracted to 2.24 angstrom resolution.

Log-modified Weibull regression models with censored data: Sensitivity and residual analysis

This paper proposes a regression model considering the modified Weibull distribution. This distribution can be used to model bathtub-shaped failure rate functions. Assuming censored data, we consider maximum likelihood and Jackknife estimators for the parameters of the model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and we also present some ways to perform global influence. Besides, for different parameter settings, sample sizes and censoring percentages, various simulations are performed and the empirical distribution of the modified deviance residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for a martingale-type residual in log-modified Weibull regression models with censored data. Finally, we analyze a real data set under log-modified Weibull regression models. A diagnostic analysis and a model checking based on the modified deviance residual are performed to select appropriate models. (c) 2008 Elsevier B.V. All rights reserved.

Regression models for grouped survival data: Estimation and sensitivity analysis

In this study, regression models are evaluated for grouped survival data when the effect of censoring time is considered in the model and the regression structure is modeled through four link functions. The methodology for grouped survival data is based on life tables, and the times are grouped in k intervals so that ties are eliminated. Thus, the data modeling is performed by considering the discrete models of lifetime regression. The model parameters are estimated by using the maximum likelihood and jackknife methods. To detect influential observations in the proposed models, diagnostic measures based on case deletion, which are denominated global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to those measures, the local influence and the total influential estimate are also employed. Various simulation studies are performed and compared to the performance of the four link functions of the regression models for grouped survival data for different parameter settings, sample sizes and numbers of intervals. Finally, a data set is analyzed by using the proposed regression models. (C) 2010 Elsevier B.V. All rights reserved.

Brazilian Network of Food Data Systems and LATINFOODS Regional Technical Compilation Committee: Food composition activities (2006-2009)

The Brazilian Network of Food Data Systems (BRASILFOODS) has been keeping the Brazilian Food Composition Database-USP (TBCA-USP) (http://www.fcf.usp.br/tabela) since 1998. Besides the constant compilation, analysis and update work in the database, the network tries to innovate through the introduction of food information that may contribute to decrease the risk for non-transmissible chronic diseases, such as the profile of carbohydrates and flavonoids in foods. In 2008, data on carbohydrates, individually analyzed, of 112 foods, and 41 data related to the glycemic response produced by foods widely consumed in the country were included in the TBCA-USP. Data (773) about the different flavonoid subclasses of 197 Brazilian foods were compiled and the quality of each data was evaluated according to the USDAs data quality evaluation system. In 2007, BRASILFOODS/USP and INFOODS/FAO organized the 7th International Food Data Conference ""Food Composition and Biodiversity"". This conference was a unique opportunity for interaction between renowned researchers and participants from several countries and it allowed the discussion of aspects that may improve the food composition area. During the period, the LATINFOODS Regional Technical Compilation Committee and BRASILFOODS disseminated to Latin America the Form and Manual for Data Compilation, version 2009, ministered a Food Composition Data Compilation course and developed many activities related to data production and compilation. (C) 2010 Elsevier Inc. All rights reserved.

Overview: Individual and organizational perspectives on emotion management and display

As reported in Volume 1 of Research on Emotions in Organizations (Ashkanasy, Zerbe, & Härtel, 2005), the chapters in this volume are drawn from the best contributions to the 2004 International Conference on Emotion and Organizational Life held at Birkbeck College, London, complemented by additional, invited chapters. (This biannual conference has come to be known as the “Emonet” conference, after the listserv of members.) Previous edited volumes (Ashkanasy, Härtel, & Zerbe, 2000; Ashkanasy, Zerbe, & Härtel, 2002; Härtel, Zerbe, & Ashkanasy, 2004) were published every two years following the Emonet conference. With the birth of this annual Elsevier series came the opportunity for greater focus in the theme of each volume, and for greater scope for invited contributions. This volume contains eight chapters selected from conference contributions for their quality, interest, and appropriateness to the theme of this volume, as well as four invited chapters. We again acknowledge in particular the assistance of the conference paper reviewers (see the appendix). In the year of publication of this volume the 2006 Emonet conference will be held in Atlanta, USA and will be followed by Volumes 3 and 4 of Research on Emotions in Organizations. Readers interested in learning more about the conferences or the Emonet list should check the Emonet website http://www.uq.edu.au/emonet/.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).