Comparing the performance of geostatistical models with additional information from covariates for sewage plume characterization

In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the Mat`ern models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.

Performance of state space and ARIMA models for consumer retail sales forecasting

Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.

Tuple-based morphisms for interoperability establishment of financial information models

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e Computadores

Oral Contraceptive Use and Clinical Outcomes in Patients with Multiple Sclerosis

Experimental and clinical data suggest a role of sex steroids in the pathogenesis of multiple sclerosis (MS). Scant information is available about the potential effect of oral contraceptive (OC) use on the prognosis of the disease. We aimed to evaluate this. The study population consisted of 132 women with relapsing-remitting MS before receiving disease modifying treatment and a mean disease duration 6.2 (SD 5.1) years. Three groups of patients were distinguished according to their OC behavior: [1] never-users, patients who never used OC [2] past-users, patients who stopped OC use before disease onset, and [3] after-users, those who used these drugs after disease onset. Multiple linear and logistic regression models were used to analyze the association between oral contraceptive use and annualized relapse rates, disability accumulation and severity of the disease. After-user patients had lower Expanded Disability Status Scale (EDSS) and Multiple Sclerosis Severity Score (MSSS) values than never users (p<0.001 and p=0.002, respectively) and past users (p=0.010 and p=0.002, respectively). These patients were also more likely to have a benign disease course (MSSS<2.5) than never and past users together (OR: 4.52, 95%CI: 2.13-9.56, p<0.001). This effect remained significant after adjustment for confounders, including smoking and childbirths (OR: 2.97, 95%CI: 1.24, 6.54, p=0.011 and for MSSS β: -1.04; 95% C.I. -1.78, -0.30, p=0.006). These results suggest that OC use in women with relapsing-remitting MS is possible associated with a milder disabling disease course.

Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.

ANIMAL MODELS FOR THE STUDY OF LEISHMANIASIS IMMUNOLOGY

Leishmaniasis remains a major public health problem worldwide and is classified as Category I by the TDR/WHO, mainly due to the absence of control. Many experimental models like rodents, dogs and monkeys have been developed, each with specific features, in order to characterize the immune response to Leishmania species, but none reproduces the pathology observed in human disease. Conflicting data may arise in part because different parasite strains or species are being examined, different tissue targets (mice footpad, ear, or base of tail) are being infected, and different numbers (“low” 1×102 and “high” 1×106) of metacyclic promastigotes have been inoculated. Recently, new approaches have been proposed to provide more meaningful data regarding the host response and pathogenesis that parallels human disease. The use of sand fly saliva and low numbers of parasites in experimental infections has led to mimic natural transmission and find new molecules and immune mechanisms which should be considered when designing vaccines and control strategies. Moreover, the use of wild rodents as experimental models has been proposed as a good alternative for studying the host-pathogen relationships and for testing candidate vaccines. To date, using natural reservoirs to study Leishmania infection has been challenging because immunologic reagents for use in wild rodents are lacking. This review discusses the principal immunological findings against Leishmania infection in different animal models highlighting the importance of using experimental conditions similar to natural transmission and reservoir species as experimental models to study the immunopathology of the disease.

HCV INFECTION THROUGH PERFORATING AND CUTTING MATERIAL AMONG CANDIDATES FOR BLOOD DONATION IN BELÉM, BRAZILIAN AMAZON

This study evaluated epidemiological factors for HCV infection associated with sharing perforating and cutting instruments among candidates for blood donation (CBD) in the city of Belém, Pará, Brazilian Amazon. Two definitions of HCV infection cases were used: anti-HCV positivity shown by EIA, and HCV-RNA detection by PCR. Infected and uninfected CBD completed a questionnaire about possible risk factors associated with sharing perforating and cutting instruments. The information was evaluated using simple and multiple logistic regressions. Between May and November 2010, 146 (1.1%) persons with anti-HCV antibodies and 106 (0.8%) with HCV-RNA were detected among 13,772 CBD in Belém. Risk factors associated with HCV infection based on the EIA (model 1) and PCR (model 2) results were: use of needles and syringes sterilized at home; shared use of razors at home, sharing of disposable razors in barbershops, beauty salons etc.; and sharing manicure and pedicure material. The models of HCV infection associated with sharing perforating and cutting instruments should be taken into account by local and regional health authorities and by those of other countries with similar cultural practices, in order to provide useful information to guide political and public strategies to control HCV transmission.

Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Development of novel human cellular models for neurotoxicity studies

Dissertation to obtain master degree in Genética Molecular e Biomedicina

CHICKEN COOPS, Triatoma dimidiata INFESTATION AND ITS INFECTION WITH Trypanosoma cruzi IN A RURAL VILLAGE OF YUCATAN, MEXICO

This study longitudinally investigated the association between Triatoma dimidiata infestation, triatomine infection with Trypanosoma cruzi and household/backyard environmental characteristics in 101 homesteads in Molas and Yucatan, Mexico, between November 2009 (rainy season) and May 2010 (dry season). Logistic regression models tested the associations between insect infestation/infection and potential household-level risk factors. A total of 200 T. dimidiata were collected from 35.6% of the homesteads, mostly (73%) from the peridomicile. Of all the insects collected, 48% were infected with T. cruzi. Infected insects were collected in 31.6% of the homesteads (54.1% and 45.9% intra- and peridomiciliary, respectively). Approximately 30% of all triatomines collected were found in chicken coops. The presence of a chicken coop in the backyard of a homestead was significantly associated with both the odds of finding T. dimidiata (OR = 4.10, CI 95% = 1.61-10.43, p = 0.003) and the presence of triatomines infected with T. cruzi (OR = 3.37, CI 95% = 1.36-8.33, p = 0.006). The results of this study emphasize the relevance of chicken coops as a putative source of T. dimidiata populations and a potential risk for T. cruzi transmission.

Is It Possible to Simplify Risk Stratification Scores for Patients with ST-Segment Elevation Myocardial Infarction Undergoing Primary Angioplasty?

INTRODUCTION: There are several risk scores for stratification of patients with ST-segment elevation myocardial infarction (STEMI), the most widely used of which are the TIMI and GRACE scores. However, these are complex and require several variables. The aim of this study was to obtain a reduced model with fewer variables and similar predictive and discriminative ability. METHODS: We studied 607 patients (age 62 years, SD=13; 76% male) who were admitted with STEMI and underwent successful primary angioplasty. Our endpoints were all-cause in-hospital and 30-day mortality. Considering all variables from the TIMI and GRACE risk scores, multivariate logistic regression models were fitted to the data to identify the variables that best predicted death. RESULTS: Compared to the TIMI score, the GRACE score had better predictive and discriminative performance for in-hospital mortality, with similar results for 30-day mortality. After data modeling, the variables with highest predictive ability were age, serum creatinine, heart failure and the occurrence of cardiac arrest. The new predictive model was compared with the GRACE risk score, after internal validation using 10-fold cross validation. A similar discriminative performance was obtained and some improvement was achieved in estimates of probabilities of death (increased for patients who died and decreased for those who did not). CONCLUSION: It is possible to simplify risk stratification scores for STEMI and primary angioplasty using only four variables (age, serum creatinine, heart failure and cardiac arrest). This simplified model maintained a good predictive and discriminative performance for short-term mortality.

Analysis of mental health service financial models with a particular emphasis of Ireland

ABSTRACT: Financing is a critical factor in ensuring the optimal development and delivery of a mental health system. The primary method of financing worldwide is tax-based. However many low income countries depend on out-of-pocket payments. There is a report on Irish Health Care funding but none that deals exclusively with mental health care. This paper analyses the various financial models that exist globally with respect to financing the mental health sector, examines the impact of various models on service users, especially in terms of relative ‘financial burden’ and provides a more detailed examination of the current mental health funding situation in Ireland After extensive internet and hardcopy research on the above topics, the findings were analysed and a number of recommendations were reached. Mental health service should be free at the point of delivery to achieve universal coverage. Government tax-based funding or mandatory social insurance with government top-ups, as required, appears the optimal option, although there is no one funding system applicable everywhere. Out-of-pocket funding can create a crippling financial burden for service users. It is important to employ improved revenue collection systems, eliminate waste, provide equitable resource distribution, ring fence mental health funding and cap the number of visits, where necessary. Political, economic, social and cultural factors play a role in funding decisions and this can be clearly seen in the context of the current economic recession in Ireland. Only 33% of the Irish population has access to free public health care and the number health insurance policy holders has dramatically declined, resulting in increased out-of-pocket payments. This approach risks negatively impacting on the social determinants of health, increasing health inequalities and negatively affecting economic productivity. It is therefore important the Irish government examines other options to provide funding for mental health services.

Empirical models for quantification of machining damage in composite materials

Dissertação para obtenção do Grau de Doutor em Engenharia Mecânica

Derivation and consistency checking of models in early software product line engineering

Dissertação para obtenção do Grau de Doutor em Engenharia Informática

Business models in the portuguese football clubs

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics