Background: Scoring models are widely established in the intensive care unit (ICU). However, the importance in patients with ruptured abdominal aortic aneurysm (RAAA) remains unclear. Our aim was to analyze scoring systems as predictors of survival in patients undergoing open surgical repair (OSR) for RAAA. Methods: This is a retrospective study in critically ill patients in a surgical ICU at a university hospital. Sixty-eight patients with RAAA were treated between February 2005 and June 2013. Serial measurements of Sequential Organ Failure Assessment score (SOFA), Simplified Acute Physiology Score II (SAPS II) and Simplified Therapeutic Intervention Scoring System-28 (TISS-28) were evaluated with respect to in-hospital mortality. Eleven patients had to be excluded from this study because 6 underwent endovascular repair and 5 died before they could be admitted to the ICU. Results: All patients underwent OSR. The initial, highest, and mean of SOFA and SAPS II scores correlated significant with in-hospital mortality. In contrast, TISS-28 was inferior and showed a smaller area under the receiver operating curve. The cut-off point for SOFA showed the best performance in terms of sensitivity and specificity. An initial SOFA score below 9 predicted an in-hospital mortality of 16.2% (95% CI, 4.3–28.1) and a score above 9 predicted an in-hospital mortality of 73.7% (95% CI, 53.8–93.5, p < 0.01). Trend analysis showed the largest effect on SAPS II. When the score increased or was unchanged within the first 48 h (score >45), the in-hospital mortality rate was 85.7% (95% CI, 67.4–100, p < 0.01) versus 31.6% (95% CI, 10.7–52.5, p = 0.01) when it decreased. On multiple regression analysis, only the mean of the SOFA score showed a significant predictive capacity with regards to mortality (odds ratio 1.77; 95% CI, 1.19–2.64; p < 0.01). Conclusion: SOFA and SAPS II scores were able to predict in-hospital mortality in RAAA within 48 h after OSR. According to cut-off points, an increase or decrease in SOFA and SAPS II scores improved sensitivity and specificity.

Indirect reciprocity describes a class of reputation-based mechanisms which may explain the prevalence of cooperation in groups where partners meet only once. The first model for which this has analytically been shown was the binary image scoring mechanism, where one's reputation is only based on one's last action. But this mechanism is known to fail if errors in implementation occur. It has thus been claimed that for indirect reciprocity to stabilize cooperation, reputation assessments must be of higher order, i.e. contingent not only on past actions, but also on the reputations of the targets of these actions. We show here that this need not be the case. A simple image scoring mechanism where more than just one past action is observed provides ample possibilities for stable cooperation to emerge even under substantial rates of implementation errors. (authors' abstract) / Series: Department of Economics Working Paper Series

The goal of the following thesis research was to develop a systematic approach for the refinement of low-resolution protein models, as a part of the protein structure prediction procedure. Significant progress has been made in the field of protein structure prediction and the contemporary methods are able to assemble correct topology for a large fraction of protein domains. But such approximate models are often not detailed enough for some important applications, including studies of reaction mechanisms, functional annotation, drug design or virtual ligand screening. The development of a method that could bring those structures closer to the native is then of great importance. The minimal requirements for a potential that can refine protein structures is the existence of a correlation between the energy with native similarity and the scoring of the native structure as being lowest in energy. Extensive tests of the contemporary all-atom physics-based force fields were conducted to assess their applicability for refinement. The tests revealed flatness of such potentials and enabled the identification of the key problems in the current approaches. Guided by these results, the optimization of the AMBER (ff03) force field was performed that aimed at creating a funnel shape of the potential, with the native structure at the global minimum. Such shape should facilitate the conformational search during refinement and drive it towards the native conformation. Adjusting the relative weights of particular energy components, and adding an explicit hydrogen bond potential significantly improved the average correlation coefficient of the energy with native similarity (from 0.25 for the original ff03 potential to 0.65 for the optimized force field). The fraction of proteins for which the native structure had lowest energy increased from 0.22 to 0.90. The new, optimized potential was subsequently used to refine protein models of various native-similarity. The test employed 47 proteins and 100 decoy structures per protein. When the lowest energy structure from each trajectory was compared with the starting decoy, we observed structural improvement for 70% of the models on average. Such an unprecedented result of a systematic refinement is extremely promising in the context of high-resolution structure prediction.

Submitted by Aelson Maciera (aelsoncm@terra.com.br) on 2017-08-16T20:00:07Z No. of bitstreams: 1 DissDMBS.pdf: 821487 bytes, checksum: 497fc9b102478d03042a1c3d10a45c19 (MD5) / Approved for entry into archive by Ronildo Prado (bco.producao.intelectual@gmail.com) on 2018-01-29T18:10:09Z (GMT) No. of bitstreams: 1 DissDMBS.pdf: 821487 bytes, checksum: 497fc9b102478d03042a1c3d10a45c19 (MD5) / Approved for entry into archive by Ronildo Prado (bco.producao.intelectual@gmail.com) on 2018-01-29T18:10:17Z (GMT) No. of bitstreams: 1 DissDMBS.pdf: 821487 bytes, checksum: 497fc9b102478d03042a1c3d10a45c19 (MD5) / Made available in DSpace on 2018-01-29T18:14:39Z (GMT). No. of bitstreams: 1 DissDMBS.pdf: 821487 bytes, checksum: 497fc9b102478d03042a1c3d10a45c19 (MD5) Previous issue date: 2017-06-13 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Regression models for binary response variables are very common in several areas of knowledge. The most used model in these situations is the logistic regression model, which assumes that the logit of the probability of a certain event is a linear function of the predictors variables. When this assumption is not reasonable, it is common to make some changes in the model, such as: transformation of predictor variables and/or add quadratic or cubic terms to the model. The problem with this approach is that it hinders parameter interpretation, and in some areas it is fundamental to interpret the parameters. Thus, a common approach is to categorize the quantitative covariates. This work aims to propose two new classes of categorization methods for continuous variables in binary regression models. The first class of methods is univariate and seeks to maximize the association between the response variable and the categorized covariate using measures of association for qualitative variables. The second class of methods is multivariate and incorporates the predictor variables correlation structure through the joint categorization of all covariates. To evaluate the performance, we applied the proposed methods and four existing categorization methods in 3 credit scoring databases and in two simulated cenarios. The results in the real databases suggest that the proposed univariate class of categorization methods performs better than the existing methods when we compare the predictive power of the logistic regression model. The results in the simulated databases suggest that both proposed classes perform better than the existing methods. Regarding computational performance, the multivariate method is inferior and the univariate method is superior to the existing methods. / Modelos de regressão para variáveis resposta binárias são muito comuns em diversas áreas do conhecimento. O modelo mais utilizado nessas situações é o modelo de regressão logística, que assume que o logito da probabilidade de ocorrência de um dos valores da variável resposta é uma função linear das variáveis preditoras. Quando essa suposição não é razoável, algumas possíveis alternativas são: realizar transformação das variáveis preditoras e/ou inserir termos quadráticos ou cúbicos no modelo. O problema dessa abordagem é que ela dificulta bastante a interpretação dos parâmetros do modelo e, em algumas áreas, é fundamental que eles sejam interpretáveis. Assim, uma abordagem muitas vezes utilizada é a categorização das variáveis preditoras quantitativas do modelo. Sendo assim, este trabalho tem como objetivo propor duas novas classes de métodos de categorização de variáveis contínuas em modelos de regressão para variáveis resposta binárias. A primeira classe de métodos é univariada e busca maximizar a associação entre a variável resposta e a covariável categorizada utilizando medidas de associação para variáveis qualitativas. Já a classe de métodos multivariada tenta incorporar a estrutura de dependência entre as covariáveis do modelo através da categorização conjunta de todas as variáveis preditoras. Para avaliar o desempenho, aplicamos as classes de métodos propostas e quatro métodos de categorização existentes em 3 bases de dados relacionadas à área de risco de crédito e a dois cenários de dados simulados. Os resultados nas bases reais sugerem que a classe univariada proposta têm um desempenho superior aos métodos existentes quando comparamos o poder preditivo do modelo de regressão logística. Já os resultados nas bases de dados simuladas sugerem que ambas as classes propostas possuem um desempenho superior aos métodos existentes. Em relação ao desempenho computacional, o método multivariado mostrou-se inferior e o univariado é superior aos métodos existentes.

Malware is still one of the most prominent vectors through which computer networks and systems are compromised. A compromised computer system or network provides data and or processing resources to the world of cybercrime. With cybercrime projected to cost the world $6 trillion by 2021, malware is expected to continue being a growing challenge. Statistics around malware growth over the last decade support this theory as malware numbers enjoy almost an exponential increase over the period. Recent reports on the complexity of the malware show that the fight against malware as a means of building more resilient cyberspace is an evolving challenge. Compounding the problem is the lack of cyber security expertise to handle the expected rise in incidents. This thesis proposes advancing automation of the malware static analysis and detection to improve the decision-making confidence levels of a standard computer user in regards to a file’s malicious status. Therefore, this work introduces a framework that relies on two novel approaches to score the malicious intent of a file. The first approach attaches a probabilistic score to heuristic anomalies to calculate an overall file malicious score while the second approach uses fuzzy hashes and evidence combination theory for more efficient malware detection. The approaches’ resultant quantifiable scores measure the malicious intent of the file. The designed schemes were validated using a dataset of “clean” and “malicious” files. The results obtained show that the framework achieves true positive – false positive detection rate “trade-offs” for efficient malware detection.

Indirect reciprocity describes a class of reputation-based mechanisms which may explain the prevalence of cooperation in large groups where partners meet only once. The first model for which this has been demonstrated was the image scoring mechanism. But analytical work on the simplest possible case, the binary scoring model, has shown that even small errors in implementation destabilize any cooperative regime. It has thus been claimed that for indirect reciprocity to stabilize cooperation, assessments of reputation must be based on higher-order information. Is indirect reciprocity relying on frst-order information doomed to fail? We use a simple analytical model of image scoring to show that this need not be the case. Indeed, in the general image scoring model the introduction of implementation errors has just the opposite effect as in the binary scoring model: it may stabilize instead of destabilize cooperation.

Made available in DSpace on 2016-06-02T20:06:10Z (GMT). No. of bitstreams: 1 6034.pdf: 941825 bytes, checksum: 6d06b85571d5cab86cee2ed1c1d699da (MD5) Previous issue date: 2014-03-28 / Universidade Federal de Sao Carlos / Granting credit to an applicant is a decision made in a context of uncertainty. At the moment the lender decides to grant a loan or credit sale there is always the possibility of loss, and, if it is associated with a probability, the decision to grant or not credit will be more reliable. In order to aid the decision to accept or not the request for applicants are used the credit scoring models, which estimate the probability of loss associated with granting credit. But one of the problems involving these models is that only information about the applicants accepted are used, which causes a sampling bias, because the rejected applicants are discarded. With the aim to solve this problem it can use rejected inference, which are considered individuals who have had credit application rejected. However, only considering rejected inference and one method of modeling data, usually, is not sufficient to get satisfactory predictive measures, and thus, were used combined results of three methods, logistic regression, analysis probit and decision tree. The purpose of this combination were to increase the predictive perfomance and the metrics used were sensitivity, specificity , positive predictive value, negative predictive value and accuracy. Through the application in data sets we concluded that the use of the combined results increased the predictive performance, specially regarding to sensitivity. / A concessão de crédito e uma decisão a ser tomada num contexto de incertezas. No momento em que o credor decide conceder um empréstimo, realizar um financiamento ou venda a prazo sempre existe a possibilidade de perda, e, se for atribuída uma probabilidade a esta perda, a decisão de conceder ou não credito será mais confiável. Com o objetivo de auxiliar a tomada de decisão em relação ao pedido de credito dos solicitantes são utilizados os modelos credit scoring, os quais estimam a probabilidade de perda associada a concessão de credito. Um dos problemas envolvendo estes modelos e que somente informações a respeito dos proponentes aceitos são utilizadas, o que causa um viés amostral, pois, os solicitantes recusados são descartados no processo de modelagem. Com intuito de solucionar este problema tem-se a inferência dos rejeitados, em que são considerados os indívíduos que tiveram pedido de credito rejeitado. No entanto, considerar a inferência dos rejeitados e o uso de somente um método de modelagem de dados, muitas vezes, não e suficiente para que se tenha medidas preditivas satisfatórias. Desta forma, foram utilizados resultados combinados de três metodologias, regressão logística, probit e árvore de decisão/classificação concomitantemente a utilização dos métodos de inferência dos rejeitados que incluem o uso de variável latente, reclassificação, parcelamento e ponderação. O objetivo dessa combinação foi aumentar a capacidade preditiva e as métricas utilizadas foram a sensibilidade, especificidade, valor preditivo positivo, valor preditivo negativo e acurácia. Através da aplicação em conjuntos de dados concluiu-se que a utilização dos resultados combinados aumentou a capacidade preditiva, principalmente, em relação a sensibilidade.

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Multiple trauma is characterized by a simultaneous injury to multiple body systems, at least one from them directly affects by weakening injured patient or faillure of basic life functions - ventilation, bloodstream and consciousness. Accidents are, despite all prevention measures, one of the important cause of death. Multiple traumas are leading cause of death in the age group up to 45 years in the developed countries. The multiple trauma therapy is continues long and complete process which needs individual approach. The therapy starts at the place of accident and then during the transport and continue in the trauma center. The trauma center is able to provide complete therapy including treatment conditions, which require multidisciplinary coordinated cooperation. The diploma thesis deals with the issue of admission of patient with polytraumate into the traumatic center. The aim of the thesis is to approach the readers the multiple trauma, the possible causes of multiple trauma, which are the treatments of algorithms, how is the role and specification of trauma team during incoming of patient. Practical part of the thesis is about cause study of patient with the multiple trauma which complicated fat embolism. This part describes all processes from incoming patient with multiple trauma, including...

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