Influence of blood meal and mating in reproduction patterns of Triatoma brasiliensis females (Hemiptera: Reduviidae) under laboratory conditions

The influence of blood meal and mating on Triatoma brasiliensis (Neiva) female fecundity, fertility, life-span and the preoviposition period were investigated under laboratory conditions. Nourishment increased fecundity, fertility and adult lifespan, whereas mating increased fecundity, fertility and decreased the preoviposition period. Females also required more than one mating to reach their full reproductive potential. Results indicate that both nourishment and mating are important in T. brasiliensis proliferation. Such information will help towards developing effective control strategies of this vector of Chagas disease.

Circulating antioxidant defences are decreased in healthy people after a high-fat meal.

The aim of this study was to examine the responses of uric acid, antioxidant defences and pro-oxidant variables after a high-fat meal. Twenty-five healthy persons without criteria for the metabolic syndrome, underwent a high-fat meal with Supracal (60 g fat). Measurements were made at baseline and 3 h after the meal of TAG, uric acid, HDL-cholesterol, total proteins and oxidative stress. Following the high-fat meal, we detected a significant increase in pro-oxidative variables and a decrease in antioxidative variables. The uric acid concentrations were significantly lower after the high-fat meal and the reduction correlated significantly with the oxidative stress variables. The inverse relation between reduced uric acid and increased carbonylated proteins remained in multiple regression analysis. We conclude that uric acid is a powerful antioxidant and its reduction following a high-fat meal may be related with its acute antioxidative action.

Identification of blood meal sources of Lutzomyia longipalpis using polymerase chain reaction-restriction fragment length polymorphism analysis of the cytochrome B gene

An analysis of the dietary content of haematophagous insects can provide important information about the transmission networks of certain zoonoses. The present study evaluated the potential of polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis of the mitochondrial cytochrome B (cytb) gene to differentiate between vertebrate species that were identified as possible sources of sandfly meals. The complete cytb gene sequences of 11 vertebrate species available in the National Center for Biotechnology Information database were digested with Aci I, Alu I, Hae III and Rsa I restriction enzymes in silico using Restriction Mapper software. The cytb gene fragment (358 bp) was amplified from tissue samples of vertebrate species and the dietary contents of sandflies and digested with restriction enzymes. Vertebrate species presented a restriction fragment profile that differed from that of other species, with the exception of Canis familiaris and Cerdocyon thous. The 358 bp fragment was identified in 76 sandflies. Of these, 10 were evaluated using the restriction enzymes and the food sources were predicted for four: Homo sapiens (1), Bos taurus (1) and Equus caballus (2). Thus, the PCR-RFLP technique could be a potential method for identifying the food sources of arthropods. However, some points must be clarified regarding the applicability of the method, such as the extent of DNA degradation through intestinal digestion, the potential for multiple sources of blood meals and the need for greater knowledge regarding intraspecific variations in mtDNA.

A comparison of the alr and ilr transformations for kernel density estimation of compositional data

In a seminal paper, Aitchison and Lauder (1985) introduced classical kernel densityestimation techniques in the context of compositional data analysis. Indeed, they gavetwo options for the choice of the kernel to be used in the kernel estimator. One ofthese kernels is based on the use the alr transformation on the simplex SD jointly withthe normal distribution on RD-1. However, these authors themselves recognized thatthis method has some deficiencies. A method for overcoming these dificulties based onrecent developments for compositional data analysis and multivariate kernel estimationtheory, combining the ilr transformation with the use of the normal density with a fullbandwidth matrix, was recently proposed in Martín-Fernández, Chacón and Mateu-Figueras (2006). Here we present an extensive simulation study that compares bothmethods in practice, thus exploring the finite-sample behaviour of both estimators

Meal-induced thermogenesis in obese children with or without familial history of obesity.

Resting metabolic rate (RMR) and the thermic effect of a meal (TEM) were measured in a group of 26 prepubertal children divided into three groups: (1) children with both parents obese (n = 8, group OB2); (2) children with no obese parents and without familial history of obesity (n = 8, OB0); and (3) normal body weight children (n = 10, C). Average RMR was similar in OB2 and OB0 children (4785 +/- 274 kJ/day vs 5091 +/- 543 kJ/day), but higher (P < 0.05) than in controls (4519 +/- 322 kJ/day). Adjusted for fat-free mass (FFM) mean RMRs were comparable in the three groups of children (4891 +/- 451 kJ/day vs 5031 +/- 451 kJ/day vs 4686 +/- 451 kJ/day in OB2, OB0, and C, respectively). The thermic response to the mixed meal was similar in OB2, OB0 and C groups. The TEM calculated as the percentage of RMR was lower (P < 0.05) in obese than in control children: 10.2% +/- 3.1% vs 10.9% +/- 4.3% vs 14.0% +/- 4.3% in OB2, OB0, and C, respectively. The similar RMR as absolute value as well as adjusted for FFM, and the comparable thermic effect of food in the obese children with or without familial history of obesity, failed to support the view that family history of obesity can greatly influence the RMR and the TEM of the obese child with obese parents.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Time Series Input Selection using Multiple Kernel Learning

In this paper we study the relevance of multiple kernel learning (MKL) for the automatic selection of time series inputs. Recently, MKL has gained great attention in the machine learning community due to its flexibility in modelling complex patterns and performing feature selection. In general, MKL constructs the kernel as a weighted linear combination of basis kernels, exploiting different sources of information. An efficient algorithm wrapping a Support Vector Regression model for optimizing the MKL weights, named SimpleMKL, is used for the analysis. In this sense, MKL performs feature selection by discarding inputs/kernels with low or null weights. The approach proposed is tested with simulated linear and nonlinear time series (AutoRegressive, Henon and Lorenz series).

Exploração da Implementação Actual de IPv6 no Kernel do Linux

A presente dissertação visa retratar a exploração do suporte do protocolo internet versão seis (IPv6) no kernel do Linux, conjuntamente com a análise detalhada do estado de implementação dos diferentes aspectos em que se baseia o protocolo. O estudo incide na experimentação do funcionamento em geral do stack, a identificação de inconsistências deste em relação RFC’s respectivos, bem como a simulação laboratorial de cenários que reproduzam casos de utilização de cada uma das facilidades analisadas. O objecto desta dissertação não é explicar o funcionamento do novo protocolo IPv6, mas antes, centrar-se essencialmente na exploração do IPv6 no kernel do Linux. Não é um documento para leigos em IPv6, no entanto, optou-se por desenvolver uma parte inicial onde é abordado o essencial do protocolo: a sua evolução até à aprovação e a sua especificação. Com base no estudo realizado, explora-se o suporte do IPv6 no kernel do Linux, fazendo uma análise detalhada do estudo de implementação dos diferentes aspectos em que se baseia o protocolo. Bem como a realização de testes de conformidade IPv6 em relação aos RFC’s.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.

The role of mycorrhizas in more sustainable oil palm cultivation

Oil palm is a significant and developing crop in many developing countries. The introduction of oil palm puts pressure on natural resources because it is often planted in cleared-cut land that previously supported other crops or was forested. This has led to environmental concerns which require attention. Hence it is important that new plantations are managed in a sustainable way to reduce the impact of oil palm cultivation on ecosystems whilst maximising yield and productivity to farmers. The application of arbuscular mycorrhizal fungi (AMF) technology is one option that can benefit both agronomic plant health and ecosystems. AMF have the potential to increase conventional agricultural productivity and are crucial for the sustainable functioning of agricultural ecosystems. This paper provides an insight into how AMF application might benefit oil palm cultivation through more sustainable management and the practical use of AMF for oil palm plantations.

Variable Kernel estimates: On the impossibility of tuning the parameters

For the standard kernel density estimate, it is known that one can tune the bandwidth such that the expected L1 error is within a constant factor of the optimal L1 error (obtained when one is allowed to choose the bandwidth with knowledge of the density). In this paper, we pose the same problem for variable bandwidth kernel estimates where the bandwidths are allowed to depend upon the location. We show in particular that for positive kernels on the real line, for any data-based bandwidth, there exists a densityfor which the ratio of expected L1 error over optimal L1 error tends to infinity. Thus, the problem of tuning the variable bandwidth in an optimal manner is ``too hard''. Moreover, from the class of counterexamples exhibited in the paper, it appears thatplacing conditions on the densities (monotonicity, convexity, smoothness) does not help.

Weighted Kernel regression

In the fixed design regression model, additional weights areconsidered for the Nadaraya--Watson and Gasser--M\"uller kernel estimators.We study their asymptotic behavior and the relationships between new andclassical estimators. For a simple family of weights, and considering theIMSE as global loss criterion, we show some possible theoretical advantages.An empirical study illustrates the performance of the weighted estimatorsin finite samples.