Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Logistic Regression in Modelling Data for CVC - Related Infection

2000 Mathematics Subject Classification: 62J12, 62P10.

Modeling Data for Complications in Diabetics Using Logistic Regression

2010 Mathematics Subject Classification: 62P10.

Sequential Logistic Regression: A Method to Reveal Subtlety in Self-Efficacy

This paper uses self-efficacy to predict the success of women in introductory physics. We show how sequential logistic regression demonstrates the predictive ability of self-efficacy, and reveals variations with type of physics course. Also discussed are the sources of self-efficacy that have the largest impact on predictive ability.

Estimating Bat and Bird Mortality Occurring at Wind Energy Turbines from Covariates and Carcass Searches Using Mixture Models

Environmental impacts of wind energy facilities increasingly cause concern, a central issue being bats and birds killed by rotor blades. Two approaches have been employed to assess collision rates: carcass searches and surveys of animals prone to collisions. Carcass searches can provide an estimate for the actual number of animals being killed but they offer little information on the relation between collision rates and, for example, weather parameters due to the time of death not being precisely known. In contrast, a density index of animals exposed to collision is sufficient to analyse the parameters influencing the collision rate. However, quantification of the collision rate from animal density indices (e.g. acoustic bat activity or bird migration traffic rates) remains difficult. We combine carcass search data with animal density indices in a mixture model to investigate collision rates. In a simulation study we show that the collision rates estimated by our model were at least as precise as conventional estimates based solely on carcass search data. Furthermore, if certain conditions are met, the model can be used to predict the collision rate from density indices alone, without data from carcass searches. This can reduce the time and effort required to estimate collision rates. We applied the model to bat carcass search data obtained at 30 wind turbines in 15 wind facilities in Germany. We used acoustic bat activity and wind speed as predictors for the collision rate. The model estimates correlated well with conventional estimators. Our model can be used to predict the average collision rate. It enables an analysis of the effect of parameters such as rotor diameter or turbine type on the collision rate. The model can also be used in turbine-specific curtailment algorithms that predict the collision rate and reduce this rate with a minimal loss of energy production.

Determinantes individuais da utilização de serviços odontológicos por adultos e idosos de baixa renda

A utilização de serviços odontológicos resulta da interação de determinantes biológicos com fatores socioculturais, familiares e comunitários, bem como de características dos sistemas de saúde. O objetivo deste estudo foi identificar os fatores individuais associados à utilização de serviços odontológicos por parte de adultos e idosos de baixa renda residentes na área de abrangência da Estratégia Saúde da Família, em Ponta Grossa, PR. A amostra constou de 246 indivíduos, com 35 anos de idade ou mais, que responderam a um questionário sobre condições socioeconômicas, necessidade percebida e acesso a serviços odontológicos. A análise dos dados foi realizada por meio de regressão logística, segundo referencial teórico baseado no Modelo Comportamental de Andersen, considerando a consulta odontológica não recente como variável dependente. Verificou-se elevada prevalência de problemas bucais auto-referidos e de perdas dentárias. Cerca de 40% dos adultos e 67% dos idosos não iam ao dentista há mais de três anos. Indivíduos que não residiam em domicílios próprios, realizavam higiene bucal com menor frequência e utilizavam próteses totais apresentaram maiores chances de haver utilizado os serviços odontológicos há mais tempo. O fato de possuir um dentista regular foi identificado como fator de proteção na análise. Concluindo, os determinantes individuais mostraram-se importantes indicadores de acesso aos serviços de saúde bucal. O modelo teórico confirmou a presença de desigualdades sociais e psicossociais na utilização de serviços odontológicos entre os adultos e idosos de baixa renda.

Comorbidity adjustment index for the international classification of diseases, 10

OBJECTIVE: To develop a Charlson-like comorbidity index based on clinical conditions and weights of the original Charlson comorbidity index. METHODS: Clinical conditions and weights were adapted from the International Classification of Diseases, 10th revision and applied to a single hospital admission diagnosis. The study included 3,733 patients over 18 years of age who were admitted to a public general hospital in the city of Rio de Janeiro, southeast Brazil, between Jan 2001 and Jan 2003. The index distribution was analyzed by gender, type of admission, blood transfusion, intensive care unit admission, age and length of hospital stay. Two logistic regression models were developed to predict in-hospital mortality including: a) the aforementioned variables and the risk-adjustment index (full model); and b) the risk-adjustment index and patient's age (reduced model). RESULTS: Of all patients analyzed, 22.3% had risk scores >1, and their mortality rate was 4.5% (66.0% of them had scores >1). Except for gender and type of admission, all variables were retained in the logistic regression. The models including the developed risk index had an area under the receiver operating characteristic curve of 0.86 (full model), and 0.76 (reduced model). Each unit increase in the risk score was associated with nearly 50% increase in the odds of in-hospital death. CONCLUSIONS: The risk index developed was able to effectively discriminate the odds of in-hospital death which can be useful when limited information is available from hospital databases.

Novel methods improve prediction of species' distributions from occurrence data

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

Determinants of science-based cooperation: evidence in a sample of small and micro firms

We study the determining factors of cience-based cooperation in the case of small and micro firms. In this research, we propose an analytical framework based on the resource-based view of the firm and we identify a set of organisational characteristics, which we classify as internal, external and structural factors. Each factor can be linked to at least one reason, from the firm¿s point of view, to cooperate with universities and public research centres. Each reason can, in turn, be used as an indicator of a firm¿s organisational needs or organisational capacities. In order to validate the theoretical model, we estimate a logistic regression that models the propensity to participate in science-based cooperation activities within a sample of 285 small and micro firms located in Barcelona. The results show the key role played by the absorptive capacity of new and small companies.

Prediction of hypertensive disorders in pregnancy by combined uterine artery Doppler, serum biomarkers and maternal characteristics

Objectif: Évaluer l'efficacité du dépistage de l’hypertension gestationnelle par les caractéristiques démographiques maternelles, les biomarqueurs sériques et le Doppler de l'artère utérine au premier et au deuxième trimestre de grossesse. Élaborer des modèles prédictifs de l’hypertension gestationnelle fondées sur ces paramètres. Methods: Il s'agit d'une étude prospective de cohorte incluant 598 femmes nullipares. Le Doppler utérin a été étudié par échographie transabdominale entre 11 +0 à 13 +6 semaines (1er trimestre) et entre 17 +0 à 21 +6 semaines (2e trimestre). Tous les échantillons de sérum pour la mesure de plusieurs biomarqueurs placentaires ont été recueillis au 1er trimestre. Les caractéristiques démographiques maternelles ont été enregistrées en même temps. Des courbes ROC et les valeurs prédictives ont été utilisés pour analyser la puissance prédictive des paramètres ci-dessus. Différentes combinaisons et leurs modèles de régression logistique ont été également analysés. Résultats: Parmi 598 femmes, on a observé 20 pré-éclampsies (3,3%), 7 pré-éclampsies précoces (1,2%), 52 cas d’hypertension gestationnelle (8,7%) , 10 cas d’hypertension gestationnelle avant 37 semaines (1,7%). L’index de pulsatilité des artères utérines au 2e trimestre est le meilleur prédicteur. En analyse de régression logistique multivariée, la meilleure valeur prédictive au 1er et au 2e trimestre a été obtenue pour la prévision de la pré-éclampsie précoce. Le dépistage combiné a montré des résultats nettement meilleurs comparés avec les paramètres maternels ou Doppler seuls. Conclusion: Comme seul marqueur, le Doppler utérin du deuxième trimestre a la meilleure prédictive pour l'hypertension, la naissance prématurée et la restriction de croissance. La combinaison des caractéristiques démographiques maternelles, des biomarqueurs sériques maternels et du Doppler utérin améliore l'efficacité du dépistage, en particulier pour la pré-éclampsie nécessitant un accouchement prématuré.

Factores asociados a complicaciones post quirúrgicas en pacientes adultos con obstrucción intestinal mecánica en Bogotá, Colombia, período 2008 - 2014

Introducción: La obstrucción intestinal es una patología de alta prevalencia e impacto en los servicios de cirugía general a nivel mundial. El manejo de esta entidad puede ser médico o quirúrgico. Cuando se requiere intervención quirúrgica, se busca evitar el desarrollo de isquemia intestinal y resecciones intestinales; durante el postoperatorio, pueden existir complicaciones. El objetivo de este estudio es identificar los factores asociados al desarrollo de complicaciones post operatorias en un grupo de pacientes con obstrucción intestinal mecánica llevados a manejo quirúrgico. Metodología: Estudio analítico tipo casos y controles en un grupo de pacientes con diagnóstico de obstrucción intestinal mecánica llevados a manejo quirúrgico de su patología. Los casos corresponden a los pacientes con complicaciones postoperatorias y los controles aquellos que no presentaron complicaciones. Se identificaron factores asociados a complicación post operatoria mediante modelos estadísticos bivariados y multivariados de regresión logística para factores como edad, sexo, antecedente quirúrgico, presentación clínica, paraclínica y diagnóstico postoperatorio de malignidad, entre otras. Resultados: Se identificaron un total de 138 pacientes (54 casos y 129 controles). Los rangos de edad entre 55-66 años y mayor de 66 años fueron asociados con complicaciones postoperatorias (OR 3,87 IC95% 1,58-9,50 y OR 3,62 IC95% 1,45-9,08 respectivamente). El déficit de base inferior a 5 mEq/litro se relaciona con complicaciones postoperatorias (OR 2,64 IC95% 1.33-5,25) Otras pruebas de laboratorio, características radiológicas, hallazgos de malignidad en el postoperatorio y la evolución de los pacientes no fueron asociados con complicaciones. Conclusiones: Las disminución de las complicaciones durante el manejo quirúrgico de obstrucción intestinal mecánica continúa siendo un reto para la cirugía general. Factores no modificables como edad avanzada y modificables como el equilibrio ácido base deben ser tenidos en cuenta dada su correlación en el desarrollo de complicaciones postoperatorias.

Clinical risk factors for osteoporotic fractures in Brazilian women and men: the Brazilian Osteoporosis Study (BRAZOS)

The Brazilian Osteoporosis Study (BRAZOS) is the first epidemiological study carried out in a representative sample of Brazilian men and women aged 40 years or older. The prevalence of fragility fractures is about 15.1% in the women and 12.8% in the men. Moreover, advanced age, sedentarism, family history of hip fracture, current smoking, recurrent falls, diabetes mellitus and poor quality of life are the main clinical risk factors associated with fragility fractures. The Brazilian Osteoporosis Study (BRAZOS) is the first epidemiological study carried out in a representative sample of Brazilian men and women aged 40 years or older with the purpose of identifying the prevalence and the main clinical risk factors (CRF) associated with osteoporotic fracture in our population. A total of 2,420 individuals (women, 70%) from 150 different cities in the five geographic regions in Brazil, and all different socio-economical classes were selected to participate in the present survey. Anthropometrical data as well as life habits, fracture history, food intake, physical activity, falls and quality of life were determined by individual quantitative interviews. The representative sampling was based on Brazilian National data provided by the 2000 and 2003 census. Low trauma fracture was defined as that resulting of a fall from standing height or less in individuals 50 years or older at specific skeletal sites: forearm, femur, ribs, vertebra and humerus. Sampling error was 2.2% with 95% confidence intervals. Logistic regression analysis models were designed having the fragility fracture as the dependent variable and all other parameters as the independent variable. Significance level was set as p < 0.05. The average of age, height and weight for men and women were 58.4 +/- 12.8 and 60.1 +/- 13.7 years, 1.67 +/- 0.08 and 1.56 +/- 0.07 m and 73.3 +/- 14.7 and 64.7 +/- 13.7 kg, respectively. About 15.1% of the women and 12.8% of the men reported fragility fractures. In the women, the main CRF associated with fractures were advanced age (OR = 1.6; 95% CI 1.06-2.4), family history of hip fracture (OR = 1.7; 95% CI 1.1-2.8), early menopause (OR = 1.7; 95% CI 1.02-2.9), sedentary lifestyle (OR = 1.6; 95% CI 1.02-2.7), poor quality of life (OR = 1.9; 95% CI 1.2-2.9), higher intake of phosphorus (OR = 1.9; 95% CI 1.2-2.9), diabetes mellitus (OR = 2.8; 95% CI 1.01-8.2), use of benzodiazepine drugs (OR = 2.0; 95% CI 1.1-3.6) and recurrent falls (OR = 2.4; 95% CI 1.2-5.0). In the men, the main CRF were poor quality of life (OR = 3.2; 95% CI 1.7-6.1), current smoking (OR = 3.5; 95% CI 1.28-9.77), diabetes mellitus (OR = 4.2; 95% CI 1.27-13.7) and sedentary lifestyle (OR = 6.3; 95% CI 1.1-36.1). Our findings suggest that CRF may contribute as an important tool to identify men and women with higher risk of osteoporotic fractures and that interventions aiming at specific risk factors (quit smoking, regular physical activity, prevention of falls) may help to manage patients to reduce their risk of fracture.

Serum Metalloproteinases 2 and 9 as Predictors of Gait Status, Pressure Ulcer and Mortality after Hip Fracture

Introduction: The aim of this study is to evaluate the serum activity of metalloproteinases (MMPs) -2 and -9 as predictors of pressure ulcer (PU), gait status and mortality 6 months after hip fracture. Methods: Eighty-seven patients over the age of 65 admitted to the orthopedic unit from January to December 2010 with hip fracture were prospectively evaluated. Upon admission, patient demographic information, including age, gender and concomitant diseases, was recorded. Blood samples were taken for analysis of MMP -2 and -9 activity by gel zymography and for biochemical examination within the first 72 hours of the patient's admission, after clinical stabilization. The fracture pattern (neck, trochanteric or subtrochanteric), time from admission to surgery, surgery duration and length of hospital stay were also recorded. Results: Two patients were excluded due to the presence of pathological fractures (related to cancer), and three patients were excluded due to the presence of PU before admission. Eighty-two patients, with a mean age of 80.4 ± 7.3 years, were included in the analysis. Among these patients, 75.6% were female, 59.8% had PU, and 13.4% died 6 months after hip fracture. All patients underwent hip fracture repair. In a univariate analysis, there were no differences in serum MMP activity between hip fracture patients with or without PU. In addition, the multiple logistic regression analysis models, which were adjusted by age, gender, length of hospital stay and C-reactive protein, showed that the pro-MMP-9 complexed with neutrophil gelatinase-associated lipocalin form (130 kDa) was associated with gait status recovery 6 months after hip fracture. Conclusions: In conclusion, serum pro-MMP-9 is a predictor of gait status recovery 6 months after hip fracture. © 2013 Gumieiro et al.

Regressão logística aplicada na análise espacial de dados arqueológicos

Pós-graduação em Ciências Cartográficas - FCT