Uma das grandes dificuldades para o controle do rebanho em áreas tão extensas como o território brasileiro é a resistência por parte dos pecuaristas/proprietários de fazendas em usar a tecnologia. O não uso de tecnologias acaba tornando inviável a rastreabilidade e consequente controle da cadeia produtiva. A proposta deste trabalho é a implementação de um sistema, aqui denominado SARE (Sistema de Alerta de Risco Epidemiológico), para gerenciar informações de diagnóstico, comunicando em tempo real aos órgãos competentes de maneira hierarquizada as ocorrências de testes positivos e doenças, agregando confiabilidade e segurança na transmissão dos dados. O foco primário é agilizar o processo de comunicação com intuito de assegurar que medidas preventivas possam ser tomadas em tempo hábil. Considerando que a situação atual no Instituto Biológico (IB) é bastante burocrática e não existem processos informatizados integrados, o sistema proposto SARE foi desenvolvido para ser utilizado via web, de modo a facilitar a comunicação imediata dos órgãos fiscalizadores envolvidos no processo, que imediatamente podem intervir e tomar decisões para o controle de doenças zoossanitárias. A informatização do sistema de sanidade animal para um Centro de Referência como o Instituto Biológico em São Paulo aliado a implantação de um programa especialmente desenvolvido para gerenciar os dados e transmiti-los em tempo real aos órgãos de defesa sanitária contribuirá para a modernização dos processos de diagnóstico, facilitando a identificação de amostras, agilizando a comunicação e gerando rastreabilidade de todas as operações diagnósticas. O sistema proposto contempla também um módulo desenvolvido para auxiliar especialistas (veterinários e especialistas de campo) na solicitação de exames conforme situação detectada em campo. Para o desenvolvimento desse módulo foi proposto o uso de ontologias para a representação do conhecimento dos especialistas da área a fim de criar um vocabulário com termos formais e informais para auxílio na solicitação adequada de exames conforme sintomas aferidos em campo. / One of the major challenges for herd control in areas as large as the Brazilian territory is resistance by breeders/ranch owners to using technology. Failure to use technologies makes tracking and subsequent control of the productive chain unviable. The proposal of this work is to implement a system, hereby named Epidemiologic Risk Alert System (SARE), to manage diagnosis information, reporting occurrences of positive tests and diseases in real time and in a hierarchical manner to the competent agencies, adding reliability and security in data transmission. The primary focus is to expedite the communication process aiming to ensure that preventive measures can be taken in a timely manner. Considering the fact that the current situation at the Biological Institute (BI) is very bureaucratic and that there are no integrated computerized processes, the proposed SARE system was developed to be used via the web in order to facilitate immediate communication with the inspecting agencies involved in the process, which can immediately intervene and make decisions to control zoosanitary diseases. Computerization of the animal health system for a reference center such as the Biological Institute of São Paulo, together with implementation of a specially developed program to manage data and transmit it in real time to the health defense agencies will contribute to modernization of the processes of diagnosis, facilitating identification of samples, speeding communication and ensuring traceability of all the diagnostic operations. The proposed system also contains a module developed to assist specialists (veterinarians and field specialists) request exams depending on the situation detected in the field. To develop this module the use of ontologies was proposed, to represent the knowledge of specialists in the area in order to create a vocabulary with formal and informal terms to assist in the adequate requesting of exams depending on the symptoms noted in the field.

Consolidating patient and clinical data to support better-informed clinical decisions remains a primary function of electronic health records (EHRs). In the United States, nearly 6 million patients receive care from an accountable care organization (ACO). Knowledge of clinical decision support (CDS) tool design for use by physicians participating in ACOs remains limited. The purpose of this quantitative study was to examine whether a significant correlation exists between characteristics of alert content and alert timing (the independent variables) and physician perceptions of improved ACO quality measure adherence during electronic ordering (the dependent variable). Sociotechnical theory supported the theoretical framework for this research. Sixty-nine physician executives using either a Cerner Incorporated or Epic Systems EHR in a hospital or health system affiliated ACO participated in the online survey. The results of the regression analysis were statistically significant, R2 = .108, F(2,66) = 3.99, p = .023, indicating that characteristics of alert content and timing affect physician perceptions for improving their adherence to ACO quality measures. However, analysis of each independent variable showed alert content highly correlated with the dependent variable (p = .007) with no significant correlation found between workflow timing and the dependent variable (p = .724). Understanding the factors that support physician acceptance of alerts is essential to third-party software developers and health care organizations designing CDS tools. Providing physicians with improved EHR-integrated CDS tools supports the population health goal of ACOs in delivering better patient care.

This study describes how information spread on the internet by examining diffusion, framing and source use surrounding coverage of the 2010 best-selling book, The Immortal Life of Henrietta Lacks. The book presented a rare opportunity to view how a story about science, discovery and race became a best-seller within weeks after its publication. Through a mixed-methods and case study approach, the author examines patterns of coverage using Google Alerts that traced the book's online coverage in the first six months of its release. The author found that online information clustered around several themes with the most prominent describing aspects of science and scientific discovery, followed by the book's characterization as a "best seller" or "good read." Another recurring theme centered on issues surrounding exploitation in human research. In addition, the study reveals that sources who "set the frame" for coverage were most likely to be media figures, including Oprah Winfrey, Alan Ball and HBO films, in addition to newspapers and individual journalists and science writers. By examining the relationship of online frames with sources, the author found that a diversity of frames is paired with key sources: that is, multiple themes co-occur with source mentions, although the themes may not have been generated by the sources themselves. Rather, sources are linked to narrative frames by others who generate online coverage. The author concludes that, while key sources initially set a message's frame, once diffused, the message may take on other qualities.

The tremendous increase in usage and complexity of modern communication and network systems connected to the Internet, places demands upon security management to protect organisations¿ sensitive data and resources from malicious intrusion. Malicious attacks by intruders and hackers exploit flaws and weakness points in deployed systems through several sophisticated techniques that cannot be prevented by traditional measures, such as user authentication, access controls and firewalls. Consequently, automated detection and timely response systems are urgently needed to detect abnormal activities by monitoring network traffic and system events. Network Intrusion Detection Systems (NIDS) and Network Intrusion Prevention Systems (NIPS) are technologies that inspect traffic and diagnose system behaviour to provide improved attack protection. The current implementation of intrusion detection systems (commercial and open-source) lacks the scalability to support the massive increase in network speed, the emergence of new protocols and services. Multi-giga networks have become a standard installation posing the NIDS to be susceptible to resource exhaustion attacks. The research focuses on two distinct problems for the NIDS: missing alerts due to packet loss as a result of NIDS performance limitations; and the huge volumes of generated alerts by the NIDS overwhelming the security analyst which makes event observation tedious. A methodology for analysing alerts using a proposed framework for alert correlation has been presented to provide the security operator with a global view of the security perspective. Missed alerts are recovered implicitly using a contextual technique to detect multi-stage attack scenarios. This is based on the assumption that the most serious intrusions consist of relevant steps that temporally ordered. The pre- and post- condition approach is used to identify the logical relations among low level alerts. The alerts are aggregated, verified using vulnerability modelling, and correlated to construct multi-stage attacks. A number of algorithms have been proposed in this research to support the functionality of our framework including: alert correlation, alert aggregation and graph reduction. These algorithms have been implemented in a tool called Multi-stage Attack Recognition System (MARS) consisting of a collection of integrated components. The system has been evaluated using a series of experiments and using different data sets i.e. publicly available datasets and data sets collected using real-life experiments. The results show that our approach can effectively detect multi-stage attacks. The false positive rates are reduced due to implementation of the vulnerability and target host information.

Smartwatch is a technological product that has rekindled a lot of interest worldwide and areseen to be connected to our day-to-day lives. The recent growth on the adoption of thesmartwatch has posed challenges to researchers and other interest groups. Humans now live inmodern scientific societies that are heavily reliant on numerous smart devices of which thesmartwatch is a part of it. Past research has been done on the smartwatch phenomenon and itsassociated question that surround it. In this paper, we seek answers to the vital questionsbordering the usage of smartwatch and how those factors that are responsible for the increasein adoption of the smartwatch . The grounded theory research designed was used as ourmethodological approach and data were collected from twenty participants, some living inSweden and some in Norway. Analysis of data is also based upon, and informed by, theguidelines of grounded theory data coding steps. Further open coding & axial coding techniqueswere applied in data analysis section. Analysis of the data collected from the interviewsrevealed some interesting areas of smartwatch applications today. It also presented an insighton how consumers of smartwatch think about the product. Results from this study will be usefulfor future researchers as well as smartwatch merchants and makers.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2017-06-12T15:19:44Z No. of bitstreams: 2 Dissertação - Vinicius de Medeiros Alves - 2014.pdf: 3082084 bytes, checksum: da4838d5fe24841429f43de84204d98a (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-06-12T15:21:40Z (GMT) No. of bitstreams: 2 Dissertação - Vinicius de Medeiros Alves - 2014.pdf: 3082084 bytes, checksum: da4838d5fe24841429f43de84204d98a (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-06-12T15:21:40Z (GMT). No. of bitstreams: 2 Dissertação - Vinicius de Medeiros Alves - 2014.pdf: 3082084 bytes, checksum: da4838d5fe24841429f43de84204d98a (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-05-12 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Introduction: Skin sensitization is a major environmental and human health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. Skin sensitization is commonly evaluated using structural alerts. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. The main goal of this thesis was to develop and apply new cheminformatics methods to predict skin sensitization of chemical compounds that lack experimental data. Methodology: It has been compiled, curated, analyzed, and compared the available human data and the murine (performed in mice) animal model data, named LLNA (local lymph node assay). Using these data, it was developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. It was developed a freely accessible web-based application for the identification of potential skin sensitizers. In addition, it was demonstrated that contrary to the common perception of QSAR models as “black boxes” they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. Results and discussion: The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher correct classification rate of 82% but at the expense of the reduced external dataset coverage (52 %). We used the developed QSAR models for virtual screening of CosIng database and identified 1,061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. The developed Pred-Skin web app (http://www.labmol.com.br/predskin/) is based on binary QSAR models of human (109 compounds) and LLNA (515 compounds) data with good external correct classification rate (70-81% and 72-84%, respectively). It is also included a multiclass potency model based on LLNA data (accuracy ranging between 73-76%). Conclusions: Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential. The Pred-Skin web app is a fast, reliable, and user-friendly tool for early assessment of chemically-induced skin sensitization. A new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals was also proposed. / Introdução: A sensibilização cutânea é um importante parâmetro de avaliação de toxicidade humana e ambiental. Embora muitos compostos tenham sido avaliados em seres humanos, não foi reportado até o momento modelos de QSAR (do inglês, quantitative structure-activity relationships) gerados com esses dados. Comumente, a sensibilização cutânea é avaliada computacionalmente usando-se alertas estruturais. No entanto, tem havido uma preocupação crescente de que alertas sinalizam a maioria dos compostos como tóxicos, o que questiona sua confiabilidade como marcadores de toxicidade. O objetivo geral do presente trabalho foi desenvolver e aplicar novos métodos de quimioinformática para predizer a sensibilização cutânea de compostos químicos que carecem de dados experimentais. Metodologia: Foram compilados, preparados, analisados e comparados os dados de sensibilização cutânea de pele humana e do modelo animal murino (realizado em camundongos), denominado LLNA (local lymph node assay). Modelos de QSAR foram desenvolvidos utilizando esses dados e aplicados para a triagem de quimiotecas virtuais para identificar potenciais sensibilizadores. Foi desenvolvido um aplicativo gratuito para a identificação de potenciais sensibilizadores cutâneos. Além disso, foi demonstrado que modelos de QSAR podem ser usados para identificar subestruturas químicas estatisticamente significativas (alertas estruturais baseados em QSAR) que influenciam a toxicidade. Resultados e discussão: A concordância global (R) entre respostas de sensibilização cutânea humana e murina para um conjunto de 135 substâncias químicas únicas foi baixa (R = 28-43%), embora várias classes químicas apresentassem alta concordância. Foi possível desenvolver modelos de QSAR preditivos com taxa de classificação correta externa de 71%. Um modelo de consenso que integrava predições concordantes de QSAR e dados de LLNA proporcionaram uma acurácia 82%. Utilizou-se os modelos de QSAR desenvolvidos para a triagem virtual da base de dados CosIng e foram identificados 1061 potenciais sensibilizadores cutâneos. Para dezessete desses compostos, encontrou-se evidências publicadas de seus efeitos de sensibilização cutânea em seres humanos. O aplicativo desenvolvido, Pred-Skin (http://www.labmol.com.br/predskin/), baseia-se em modelos de QSAR classificatórios de dados humanos (109 compostos) e murinos (515 compostos) com boa taxa de classificação correta externa (70-81% e 72-84%, respectivamente). Esse aplicativo também possui um modelo de multiclassificatório desenvolvido com dados de LLNA (precisão que varia entre 73-76%). Conclusões: Os modelos de QSAR desenvolvidos forneceram uma alternativa mais precisa do que o modelo animal para avaliação da sensibilização cutânea humana. Além disso, a interpretação dos modelos de QSAR permitem orientar a otimização estrutural de compostos tóxicos para reduzir o potencial de toxicidade. O aplicativo Pred-Skin é uma ferramenta rápida, confiável e de fácil utilização para a avaliação da sensibilização cutânea de compostos químicos. Foi também proposta uma nova abordagem que integra sinergicamente alertas estruturais e modelos de QSAR rigorosamente validados para uma avaliação de toxicidade mais transparente e precisa de novos produtos químicos.