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.

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

Subendocardial fibrosis in remote myocardium results from reduction of coronary driving pressure during acute infarction in rats

OBJECTIVE: To investigate the role of hemodynamic changes occurring during acute MI in subsequent fibrosis deposition within non-MI. METHODS: By using the rat model of MI, 3 groups of 7 rats each [sham, SMI (MI <30%), and LMI (MI >30%)] were compared. Systemic and left ventricular (LV) hemodynamics were recorded 10 minutes before and after coronary artery ligature. Collagen volume fraction (CVF) was calculated in picrosirius red-stained heart tissue sections 4 weeks later. RESULTS: Before surgery, all hemodynamic variables were comparable among groups. After surgery, LV end-diastolic pressure increased and coronary driving pressure decreased significantly in the LMI compared with the sham group. LV dP/dt max and dP/dt min of both the SMI and LMI groups were statistically different from those of the sham group. CVF within non-MI interventricular septum and right ventricle did not differ between each MI group and the sham group. Otherwise, subendocardial (SE) CVF was statistically greater in the LMI group. SE CVF correlated negatively with post-MI systemic blood pressure and coronary driving pressure, and positively with post-MI LV dP/dt min. Stepwise regression analysis identified post-MI coronary driving pressure as an independent predictor of SE CVF. CONCLUSION: LV remodeling in rats with MI is characterized by predominant SE collagen deposition in non-MI and results from a reduction in myocardial perfusion pressure occurring early on in the setting of MI.

Development of a life cycle management system for remote products.

The research described in this thesis has been developed as a part of the Reliability and Field Data Management for Multi-Component Products (REFIDAM) Project. This project was funded under the Applied Research Grants Scheme administered by Enterprise Ireland. The project was a partnership between Galway-Mayo Institute of Technology and an industrial company, Thermo King Europe. The project aimed to develop a system to manage the information required for maintenance costing, cost of ownership, reliability assessment and improvement of multi-component products, by establishing information flows between the customer network and across the Thermo King organisation.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Structured output SVM for remote sensing image classification

In the recent years, kernel methods have revealed very powerful tools in many application domains in general and in remote sensing image classification in particular. The special characteristics of remote sensing images (high dimension, few labeled samples and different noise sources) are efficiently dealt with kernel machines. In this paper, we propose the use of structured output learning to improve remote sensing image classification based on kernels. Structured output learning is concerned with the design of machine learning algorithms that not only implement input-output mapping, but also take into account the relations between output labels, thus generalizing unstructured kernel methods. We analyze the framework and introduce it to the remote sensing community. Output similarity is here encoded into SVM classifiers by modifying the model loss function and the kernel function either independently or jointly. Experiments on a very high resolution (VHR) image classification problem shows promising results and opens a wide field of research with structured output kernel methods.

Active mitral ring for post-surgical remote correction of residual mitral regurgitation on the beating heart.

OBJECTIVES: Residual mitral regurgitation after valve repair worsens patients' clinical outcome. Postimplant adjustable mitral rings potentially address this issue, allowing the reshaping of the annulus on the beating heart under echocardiography control. We developed an original mitral ring allowing valve geometry remodelling after the implantation and designed an animal study to assess device effectiveness in correcting residual mitral regurgitation. METHODS: The device consists of two concentric rings: one internal and flexible, sutured to the mitral annulus and a second external and rigid. A third conic element slides between the two rings, modifying the shape of the flexible ring. This sliding element is remotely activated with a rotating tool. Animal model: in adult swine, under cardio pulmonary bypass and cardiac arrest, we shortened the primary chordae of P2 segment to reproduce Type III regurgitation and implanted the active ring. We used intracardiac ultrasound to assess mitral regurgitation and the efficacy of the active ring to correct it. RESULTS: Severe mitral regurgitation (3+ and 4+) was induced in eight animals, 54 ± 6 kg in weight. Vena contracta width decreased from 0.8 ± 0.2 to 0.1 cm; proximal isovelocity surface area radius decreased from 0.8 ± 0.2 to 0.1 cm and effective regurgitant orifice area decreased from 0.50 ± 0.1 to 0.1 ± 0.1 cm(2). Six animals had a reversal of systolic pulmonary flow that normalized following the activation of the device. All corrections were reversible. CONCLUSIONS: Postimplant adjustable mitral ring corrects severe mitral regurgitation through the reversible modification of the annulus geometry on the beating heart. It addresses the frequent and morbid issue of recurrent mitral valve regurgitation.

Naturally acquired antibodies to merozoite surface protein (MSP)-1(19) and cumulative exposure to Plasmodium falciparum and Plasmodium vivax in remote populations of the Amazon Basin of Brazil

To infer recent patterns of malaria transmission, we measured naturally acquired IgG antibodies to the conserved 19-kDa C-terminal region of the merozoite surface protein (MSP)-1 of both Plasmodium vivax (PvMSP-1(19)) and Plasmodium falciparum (PfMSP-1(19)) in remote malaria-exposed populations of the Amazon Basin. Community-based cross-sectional surveys were carried out between 2002 and 2003 in subjects of all age groups living along the margins of the Unini and Jaú rivers, Northwestern Brazil. We found high prevalence rates of IgG antibodies to PvMSP-1(19) (64.0 - 69.6%) and PfMSP-1(19) (51.6 - 52.0%), with significant differences in the proportion of subjects with antibodies to PvMSP-1(19) according to age, place of residence and habitual involvement in high-risk activities, defining some groups of highly exposed people who might be preferential targets of malaria control measures. In contrast, no risk factor other than age was significantly associated with seropositivity to PfMSP-1(19). Only 14.1% and 19.3% of the subjects tested for antibodies to PvMSP-1(19) and PfMSP-1(19) in consecutive surveys (142 - 203 days apart) seroconverted or had a three fold or higher increase in the levels of antibodies to these antigens. We discuss the extent to which serological data correlated with the classical malariometric indices and morbidity indicators measured in the studied population at the time of the seroprevalence surveys and highlight some limitations of serological data for epidemiological inference.

Paint on whiteboard desktop

Es un proyecto para la creación de una herramienta aplicable al uso de pizarras digitales. Pintar sobre el escritorio o aplicaciones abiertas.

Schistosomiasis risk mapping in the state of Minas Gerais, Brazil, using a decision tree approach, remote sensing data and sociological indicators

Schistosomiasis mansoni is not just a physical disease, but is related to social and behavioural factors as well. Snails of the Biomphalaria genus are an intermediate host for Schistosoma mansoni and infect humans through water. The objective of this study is to classify the risk of schistosomiasis in the state of Minas Gerais (MG). We focus on socioeconomic and demographic features, basic sanitation features, the presence of accumulated water bodies, dense vegetation in the summer and winter seasons and related terrain characteristics. We draw on the decision tree approach to infection risk modelling and mapping. The model robustness was properly verified. The main variables that were selected by the procedure included the terrain's water accumulation capacity, temperature extremes and the Human Development Index. In addition, the model was used to generate two maps, one that included risk classification for the entire of MG and another that included classification errors. The resulting map was 62.9% accurate.