Epidemiological and environmental aspects of visceral leishmaniasis in children under 15 years of age between 2007 and 2012 in the City of Araguaína, State of Tocantins, Brazil

Introduction Visceral leishmaniasis (VL) stands out as a zoonosis observed on four continents and also in urban expansion zones in several regions of Brazil. Methods A cross-sectional epidemiological study of VL cases in children under 15 years of age in the period from 2007 to 2012. Clinical data were gathered from medical reports; meteorological data were obtained at the Meteorological Measurement Department of UFT. Environmental variables were divided into two periods, rainy and dry. Results The study revealed no difference by gender (p=0.67) among the 821 patients. However, the most affected age group was between one and five years of age (58.6%; p<0.01); the highest prevalence of the disease (99.03%; p<0.01) occurred in urban zones; and the most affected ethnic group (85.5%; p<0.01) was mixed race. The highest incidence coefficients in this population occurred in 2007 and 2008 (578.39/100,000 inhabitants; 18.5/100,000 inhabitants, respectively), whereas the highest lethality coefficients occurred in 2008 and 2011 (0.85/100 deaths). There was no significant correlation between average rainfall and the number of VL cases. The correlation between temperature and number of VL cases was negative (r = -0.4039; p<0.01). Conclusions In Araguaína, visceral leishmaniasis in children under 15 years is an urban-based endemic disease distributed across all districts of the city wherein temperature as an environmental factor, a higher prevalence in mixed race children between one and five years of age, and a high incidence coefficient all strongly contribute to child mortality.

Studies on Phlebotominae (Diptera: Psychodidae) in the Campus FIOCRUZ Mata Atlântica, Jacarepaguá, in the City of Rio de Janeiro, Brazil

INTRODUCTION: The presence of American cutaneous leishmaniasis (ACL) in the communities of the Campus FIOCRUZ Mata Atlântica (CFMA) in the City of Rio de Janeiro initiated the investigation of the Phlebotominae fauna in the Atlantic Forest to determine the occurrence of putative ACL vectors associated with the enzootic cycle. METHODS: For 24 consecutive months, sand flies were captured inside the forest and in the border area near the communities. RESULTS: The following sand fly species were identified: Brumptomyia brumpti, Brumptomyia cunhai, Brumptomyia nitzulescui, Lutzomyia edwardsi, Lutzomyia pelloni, and Lutzomyia quinquefer. Other identified sand fly vectors, such as Lutzomyia intermedia (the predominant species), Lutzomyia migonei, Lutzomyia whitmani, Lutzomyia fischeri, and Lutzomyia hirsuta hirsuta, are associated with ACL transmission, and the vector for American visceral leishmaniases (AVL), Lutzomyia longipalpis, was also found. CONCLUSIONS: All sand fly vectors were found in both studied environments except for Lutzomyia whitmani, which was only identified in the forest. This study represents the first identification of Lutzomyia longipalpis in the CFMA, and the epidemiological implications are discussed.

Toxoplasma gondii infection: seroprevalence and associated risk factors among primary schoolchildren in Lagos City, Southern Nigeria

INTRODUCTION: Toxoplasma gondii infection has been described as the most widespread zoonotic infection of humans and other animals. Information concerning T. gondii infection among schoolchildren is unavailable in Lagos City, Nigeria. METHODS: This cross-sectional study investigated the seroprevalence and risk factors associated with T. gondii infection among primary schoolchildren (PSC) from a community located in the center of Lagos, southern Nigeria, from November 2013 to March 2014. A total of 382 PSC were screened for the presence of sera anti-T. gondii antibodies using a latex agglutination test (TOXO Test-MT, Tokyo, Japan). A cutoff titer of ≥ 1:32 was considered positive, while titers ≥ 1:1,024 indicated high responders. Questionnaires were also used to obtain data on possible risk factors from parents/guardians. RESULTS: The overall seroprevalence was 24% (91/382), and 83.5% (76/91) of seropositive PSC were classified as high responders. Among the risk factors tested, including contact with cats and soil, consumption of raw meat and vegetables, and drinking unboiled water, none showed statistical significance after multivariate adjustment. No associations were observed among age, gender, body mass index (BMI), and parents' occupation/educational level. CONCLUSIONS: The findings in this study show evidence of active infection, and hence, there is need for urgent preventive measures in this city. Further investigation is required to clarify the transmission routes. Policy makers also need to initiate prevention and control programs to protect pregnant women and immunocompromised patients in particular because they are more severely affected by T. gondii infection.

Aedes aegypti eggs oviposited on water surface collected from field ovitraps in Nova Iguaçu City, Brazil

ABSTRACT INTRODUCTION: Aedes aegypti eggs can be collected from the water surface. METHODS: Aedes aegypti oviposition from 97 field ovitraps was studied. RESULTS: Of the 16,016 eggs collected, 11,439 were obtained from paddles in ovitraps and 4,577 from water. Further, 89 (91.8%) traps contained eggs on water and 22 (22.7%) traps contained eggs only on water. CONCLUSIONS: In field traps, Aedes aegypti females usually oviposit some eggs on water surface suggesting that they might also oviposit on water of some natural breeding, and this possibility needs to be investigated. Eggs oviposited on water need to be considered for collecting trap data.

Prevalence of intestinal parasites and risk factors forspecific and multiple helminth infections in a remote city of the Brazilian Amazon

Abstract: INTRODUCTION: Few studies have described the risk factors of intestinal parasitic infections in the Amazon. METHODS: A cross-sectional survey was performed in a City of the State of Amazonas (Brazil) to estimate the prevalence of intestinal parasites and determine the risk factors for helminth infections. RESULTS: Ascaris lumbricoides was the most prevalent parasite. The main risk factors determined were: not having a latrine for A. lumbricoides infection; being male and having earth or wood floors for hookworm infection; and being male for multiple helminth infections. CONCLUSIONS: We reported a high prevalence of intestinal parasites and determined some poverty-related risk factors.

Two decades of tuberculosis in a city in Northeastern Brazil: advances and challenges in time and space

Abstract: INTRODUCTION This study presents two decades of epidemiological data on tuberculosis (TB), in order to understanding the disease profile and its spatiotemporal dynamics. METHODS This descriptive study was performed in the City of Olinda/Pernambuco, Brazil, from 1991-2010, and it analyzed new patients with TB living in the city. We used the χ²-test with a p-value <0.05 to identify differences in trends. Incidence and cluster distribution were identified using spatial scan statistics. RESULTS In total, 6202 new cases were recorded during the two decades. The highest incidence occurred in 1995 (110 cases/100,000 inhabitants), and the lowest occurred in 2009 (65 cases/100,000 inhabitants) (β=-1.44; R²=0.43; p=0.0018). The highest mortality occurred in 1998 (16 deaths/100,000 inhabitants), and the lowest occurred in 2008 (5 deaths/100,000 inhabitants) (β=-0.19; R²=0.17; p=0.07). There was a male predominance (65%), and ages ranged from 20-49 years (65%). There was a substantial increase in the number of patients that were cured after treatment (60% to 67%; p<0.001) as well as those tested for HIV (1.9% to 58.5%; p<0.001). During the first decade, clusters with p-values <0.05 included 29% of the total notified cases, and in the second decade, that percentage was 12%. CONCLUSIONS We observed a decreasing trend in incidence, which was significant, and mortality rates, which was not significant. The increased number of laboratory tests performed reflects advances in surveillance, and a reduction in the proportion of cases in primary clusters suggests, among other things, that the disease is spreading across the region.

Building 3D City Models: Testing and Comparing Laser Scanning and Low-Cost UAV Data Using FOSS Technologies

Contém resumo

Text mining e twitter : o poder das redes sociais num mercado competitivo

Actualmente, com a massificação da utilização das redes sociais, as empresas passam a sua mensagem nos seus canais de comunicação, mas os consumidores dão a sua opinião sobre ela. Argumentam, opinam, criticam (Nardi, Schiano, Gumbrecht, & Swartz, 2004). Positiva ou negativamente. Neste contexto o Text Mining surge como uma abordagem interessante para a resposta à necessidade de obter conhecimento a partir dos dados existentes. Neste trabalho utilizámos um algoritmo de Clustering hierárquico com o objectivo de descobrir temas distintos num conjunto de tweets obtidos ao longo de um determinado período de tempo para as empresas Burger King e McDonald’s. Com o intuito de compreender o sentimento associado a estes temas foi feita uma análise de sentimentos a cada tema encontrado, utilizando um algoritmo Bag-of-Words. Concluiu-se que o algoritmo de Clustering foi capaz de encontrar temas através do tweets obtidos, essencialmente ligados a produtos e serviços comercializados pelas empresas. O algoritmo de Sentiment Analysis atribuiu um sentimento a esses temas, permitindo compreender de entre os produtos/serviços identificados quais os que obtiveram uma polaridade positiva ou negativa, e deste modo sinalizar potencias situações problemáticas na estratégia das empresas, e situações positivas passíveis de identificação de decisões operacionais bem-sucedidas.

Saúde em Portugal : estudo das urgências hospitalares através do Data Mining

Qualquer assunto relacionado com a saúde é sempre um tema sensível, pela importância que tem junto da população, já que interage diretamente com o bem-estar das pessoas e, essencialmente, com a sensação de segurança que as estas pretendem ter na prestação dos cuidados básicos de saúde. Dados estatísticos mostram que a população está cada vez mais envelhecida, reforçando a importância da existência de bons centros hospitalares e de um bom Sistema Nacional de Saúde (SNS) (Plano Nacional de Saúde, 2010). Em Portugal, caso os pacientes necessitem de cuidados mais urgentes, podem recorrer ao Serviço de Urgências disponibilizado para toda a população através do SNS. No entanto, a gestão e planeamento deste serviço é complexa, dado este serviço ser frequentemente utilizado por pacientes que não necessitam de cuidados urgentes, levando a que os hospitais deixem de conseguir dar a resposta esperada, implicando a prestação por vezes um serviço de menor qualidade. Neste sentido, analisaram-se dados de um hospital do norte do país com o intuito de perceber o ponto de situação das urgências, de forma a encontrar padrões relevantes através da análise de clusters e de regras de associação. Começando pela análise de clusters, utilizaram-se apenas as variáveis que foram consideradas importantes para o problema, resultando da análise final 3 clusters. O primeiro cluster é constituído por elementos do sexo masculino de todas as idades, o segundo cluster por elementos do sexo masculino mais jovens e por elementos do sexo feminino até aos 60 anos e o terceiro cluster apenas por elementos do sexo feminino a partir dos 40 anos. No final verificaram-se muitas semelhanças entre os clusters 1 e 3, pois ambos continham os pacientes mais idosos, havendo um padrão comum no seu comportamento. No ano 2012 não houve registo de nenhuma epidemia, não havendo por isso nenhuma doença que se destacasse comparativamente às restantes. Concluiu-se também que na maior parte dos casos houve a necessidade de uma intervenção urgente (pulseira de cor Amarela), no entanto a maioria dos pacientes observados conseguiu regressar às suas habitações após as consultas nas Urgências Hospitalares, sem intervenções médicas adicionais. Relativamente às regras de associação, houve a necessidade de transformar e eliminar algumas variáveis que enviesassem o estudo. Após o processo da criação das regras de associação, percebeu-se que as regras eram muito similares entre si, apresentando uma maior confiança nas variáveis que apareceram em maior número (“Pacientes com pulseira de cor Amarela”, “distrito do Porto” ou “Alta Médica para a Residência”).

Contributions towards smart cities : exploring block level census data for the characterization of change in Lisbon

The interest in using information to improve the quality of living in large urban areas and its governance efficiency has been around for decades. Nevertheless, the improvements in Information and Communications Technology has sparked a new dynamic in academic research, usually under the umbrella term of Smart Cities. This concept of Smart City can probably be translated, in a simplified version, into cities that are lived, managed and developed in an information-saturated environment. While it makes perfect sense and we can easily foresee the benefits of such a concept, presently there are still several significant challenges that need to be tackled before we can materialize this vision. In this work we aim at providing a small contribution in this direction, which maximizes the relevancy of the available information resources. One of the most detailed and geographically relevant information resource available, for the study of cities, is the census, more specifically the data available at block level (Subsecção Estatística). In this work, we use Self-Organizing Maps (SOM) and the variant Geo-SOM to explore the block level data from the Portuguese census of Lisbon city, for the years of 2001 and 2011. We focus on gauging change, proposing ways that allow the comparison of the two time periods, which have two different underlying geographical bases. We proceed with the analysis of the data using different SOM variants, aiming at producing a two-fold portrait: one, of the evolution of Lisbon during the first decade of the XXI century, another, of how the census dataset and SOM’s can be used to produce an informational framework for the study of cities.

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Life cycle analysis of zero waste management for Jonggol green city

A Zero waste management is believed to be one of methods to gain sustainability in urban areas. Take advantages of resources as enough as the needs and process it until the last part to be wasted is a contribution to take care the environment for the next generation. Reduce, reuse, and recycle are three simplesactivities which are until nowadays consideredas the back bone of zero waste. Jonggolgreen city is a new urban area in Indonesia with a 100 ha of surface area zoned as education tourism area. It is an independent area with pure natural resources of water, air, and land to be managed and protected. It is planned as green city through zero waste management since2013. In this preliminary period, a monitoring tool is being prepared by applying a Life Cycle Analysis (LCA) for urban areas [1]. This paper will present an explanatory assessment ofthe zero waste management for Jonggolgreen city. The existing situation will be examined through LCA and afterwards,the new program and the proposed green design to gain the next level of zero waste will be discussed. The purpose is to track the persistence of the commitment and the perception of the necessary innovationsin order to achieve the ideal behavior level of LCA.

Business intelligence in banking: A literature analysis from 2002 to 2013 using Text Mining and latent Dirichlet allocation

telligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of ar- ticles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

Predicting preterm birth in maternity care by means of data mining

Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively.