Molecular detection of Paracoccidioides brasiliensis in road-killed wild animals

Paracoccidioides brasiliensis infections have been little studied in wild and/or domestic animals, which may represent an important indicator of the presence of the pathogen in nature. Road-killed wild animals have been used for surveillance of vectors of zoonotic pathogens and may offer new opportunities for eco-epidemiological studies of paracoccidiodomycosis (PCM). The presence of P. brasiliensis infection was evaluated by Nested-PCR in tissue samples collected from 19 road-killed animals; 3 Cavia aperea (guinea pig), 5 Cerdocyon thous (crab-eating-fox), 1 Dasypus novemcinctus (nine-banded armadillo), 1 Dasypus septemcinctus (seven-banded armadillo), 2 Didelphis albiventris (white-eared opossum), 1 Eira barbara (tayra), 2 Gallictis vittata (grison), 2 Procyon cancrivorus (raccoon) and 2 Sphiggurus spinosus (porcupine). Specific P. brasiliensis amplicons were detected in (a) several organs of the two armadillos and one guinea pig, (b) the lung and liver of the porcupine, and (c) the lungs of raccoons and grisons. P. brasiliensis infection in wild animals from endemic areas might be more common than initially postulated. Molecular techniques can be used for detecting new hosts and mapping `hot spot` areas of PCM.

From EEG to BOLD: Brain mapping and estimating transfer functions in simultaneous EEG-fMRI acquisitions

Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. The integration of the information provided by the high spatial resolution of MR images and the high temporal resolution of EEG may be improved by referencing them by transfer functions, which allows the identification of neural driven areas without strong assumptions about haemodynamic response shapes or brain haemodynamic`s homogeneity. The difference on sampling rate is the first obstacle for a full integration of EEG and fMRI information. Moreover, a parametric specification of a function representing the commonalities of both signals is not established. In this study, we introduce a new data-driven method for estimating the transfer function from EEG signal to fMRI signal at EEG sampling rate. This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements. (C) 2010 Elsevier Inc. All rights reserved.

An fMRI Normative Database for Connectivity Networks Using One-Class Support Vector Machines

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal pattern, and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy Subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored. Hum Brain Mapp 30:1068-1076, 2009. (C) 2008 Wiley-Liss. Inc.

The impact of functional connectivity changes on support vector machines mapping of fMRI data

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called ""mass-univariate"" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate `approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM`s power to detect discriminative voxels. (C) 2008 Elsevier B.V. All rights reserved.

Extending the Phenotype of Monosomy 1p36 Syndrome and Mapping of a Critical Region for Obesity and Hyperphagia

Rearrangements of 1p36 are the most frequently detected abnormalities in diagnostic testing for chromosomal cryptic imbalances and include variably sized simple terminal deletions, derivative chromosomes, interstitial deletions, and complex rearrangements. These rearrangements result in the specific pattern of malformation and neurodevelopmental disabilities that characterizes monosomy 1p36 syndrome. Thus far, no individual gene within this region has been conclusively determined to be causative of any component of the phenotype. Nor is it known if the rearrangements convey phenotypes via a haploinsufficiency mechanism or through a position effect. We have used multiplex ligation-dependent probe amplification to screen for deletions of 1p36 in a group of 154 hyperphagic and overweight/obese, PWS negative individuals, and in a separate group of 83 patients initially sent to investigate a variety of other conditions. The strategy allowed the identification and delineation of rearrangements in nine subjects with a wide spectrum of clinical presentations. Our work reinforces the association of monosomy 1p36 and obesity and hyperphagia, and further suggests that these features may be associated with non-classical manifestations of this disorder in addition to a submicroscopic deletion of similar to 2-3 Mb in size. Multiplex ligation probe amplification using the monosomy 1p36 syndrome-specific kit coupled to the subtelomeric kit is an effective approach to identify and delineate rearrangements at 1p36. (C) 2009 Wiley-Liss, Inc.

Genes that code for T cell signaling proteins establish transcriptional regulatory networks during thymus ontogeny

Gene expression profiling by cDNA microarrays during murine thymus ontogeny has contributed to dissecting the large-scale molecular genetics of T cell maturation. Gene profiling, although useful for characterizing the thymus developmental phases and identifying the differentially expressed genes, does not permit the determination of possible interactions between genes. In order to reconstruct genetic interactions, on RNA level, within thymocyte differentiation, a pair of microarrays containing a total of 1,576 cDNA sequences derived from the IMAGE MTB library was applied on samples of developing thymuses (14-17 days of gestation). The data were analyzed using the GeneNetwork program. Genes that were previously identified as differentially expressed during thymus ontogeny showed their relationships with several other genes. The present method provided the detection of gene nodes coding for proteins implicated in the calcium signaling pathway, such as Prrg2 and Stxbp3, and in protein transport toward the cell membrane, such as Gosr2. The results demonstrate the feasibility of reconstructing networks based on cDNA microarray gene expression determinations, contributing to a clearer understanding of the complex interactions between genes involved in thymus/thymocyte development.

The evolution of controlled multitasked gene networks: The role of introns and other noncoding RNAs in the development of complex organisms

Eukaryotic phenotypic diversity arises from multitasking of a core proteome of limited size. Multitasking is routine in computers, as well as in other sophisticated information systems, and requires multiple inputs and outputs to control and integrate network activity. Higher eukaryotes have a mosaic gene structure with a dual output, mRNA (protein-coding) sequences and introns, which are released from the pre-mRNA by posttranscriptional processing. Introns have been enormously successful as a class of sequences and comprise up to 95% of the primary transcripts of protein-coding genes in mammals. In addition, many other transcripts (perhaps more than half) do not encode proteins at all, but appear both to be developmentally regulated and to have genetic function. We suggest that these RNAs (eRNAs) have evolved to function as endogenous network control molecules which enable direct gene-gene communication and multitasking of eukaryotic genomes. Analysis of a range of complex genetic phenomena in which RNA is involved or implicated, including co-suppression, transgene silencing, RNA interference, imprinting, methylation, and transvection, suggests that a higher-order regulatory system based on RNA signals operates in the higher eukaryotes and involves chromatin remodeling as well as other RNA-DNA, RNA-RNA, and RNA-protein interactions. The evolution of densely connected gene networks would be expected to result in a relatively stable core proteome due to the multiple reuse of components, implying,that cellular differentiation and phenotypic variation in the higher eukaryotes results primarily from variation in the control architecture. Thus, network integration and multitasking using trans-acting RNA molecules produced in parallel with protein-coding sequences may underpin both the evolution of developmentally sophisticated multicellular organisms and the rapid expansion of phenotypic complexity into uncontested environments such as those initiated in the Cambrian radiation and those seen after major extinction events.

Current density mapping approach for design of clinical magnetic resonance imaging magnets

Novel current density mapping (CDM) schemes are developed for the design of new actively shielded, clinical magnetic resonance imaging (MRI) magnets. This is an extended inverse method in which the entire potential solution space for the superconductors has been considered, rather than single current density layers. The solution provides an insight into the required superconducting coil pattern for a desired magnet configuration. This information is then used as an initial set of parameters for the magnet structure, and a previously developed hybrid numerical optimization technique is used to obtain the final geometry of the magnet. The CDM scheme is applied to the design of compact symmetric, asymmetric, and open architecture 1.0-1.5 T MRI magnet systems of novel geometry and utility. A new symmetric 1.0-T system that is just I m in length with a full 50-cm diameter of the active, or sensitive, volume (DSV) is detailed, as well as an asymmetric system in which a 50-cm DSV begins just 14 cm from the end of the coil structure. Finally a 1.0-T open magnet system with a full 50-cm DSV is presented. These new designs provide clinically useful homogeneous regions and have appropriately restricted stray fields but, in some of the designs, the DSV is much closer to the end of the magnet system than in conventional designs. These new designs have the potential to reduce patient claustrophobia and improve physician access to patients undergoing scans. (C) 2002 Wiley Periodicals, Inc.

Prospects for whole genome linkage disequilibrium mapping in domestic dog breeds

Linkage disequilibrium (LD) mapping is commonly used as a fine mapping tool in human genome mapping and has been used with some success for initial disease gene isolation in certain isolated inbred human populations. An understanding of the population history of domestic dog breeds suggests that LID mapping could be routinely utilized in this species for initial genome-wide scans. Such an approach offers significant advantages over traditional linkage analysis. Here, we demonstrate, using canine copper toxicosis in the Bedlington terrier as the model, that LID mapping could be reasonably expected to be a useful strategy in low-resolution, genome-wide scans in pure-bred dogs. Significant LID was demonstrated over distances up to 33.3 cM. It is very unlikely, for a number of reasons discussed, that this result could be extrapolated to the rest of the genome. It is, however, consistent with the expectation given the population structure of canine breeds and, in this breed at least, with the hypothesis that it may be possible to utilize LID in a genome-wide scan. In this study, LD mapping confirmed the location of the copper toxicosis in Bedlington terrier gene (CT-BT) and was able to do so in a population that was refractory to traditional linkage analysis.

Caracterização molecular de três espécies de Trachycephalus (Anura: Hylidae) : investigando potenciais híbridos interespecíficos

Animais híbridos representam um desafio à taxonomia e sistemática, pois correspondem a unidades evolutivas geralmente sem clara delimitação morfológica, comportamental e genética. Híbridos podem ser morfologicamente intermediários aos parentais ou, devido à introgressão e retrocruzamentos, suas características podem se misturar tornando difícil sua identificação. Uma das formas de identificação de híbridos é por meio de ferramentas de biologia molecular, que ao utilizarem marcadores de DNA mitocondrial (herança exclusiva materna) e DNA nuclear (herança materna e paterna), permitem a comparação entre informações genéticas. Além da hibridização existem outras fontes de conflito entre dados moleculares provenientes do DNA mitocondrial e DNA nuclear, como por exemplo a retenção de polimorfismos ancentrais. Em localidades do Espírito Santo, Brasil, foram coletados indivíduos de morfologia distinta de Trachycephalus mesophaeus e T. nigromaculatus, que são as únicas espécies do gênero conhecidas nesse estado. Porém, estudos piloto usando o gene mitocondrial Citocromo Oxidase subunidade I (COI) agruparam esses espécimes com amostras de T. typhonius. Devido a estas incongruências, foram sequenciados fragmentos de dois genes mitocondriais - COI e Nicotinamida Desidrogenase subunidade 2 (ND2) e um exon nuclear (tirosinase) de 173 indivíduos de Trachycephalus, de forma a esclarecer as identificações taxonômicas e investigar a correspondência entre caracteres morfológicos e genéticos nesta linhagem, na sua área de ocorrência As filogenias moleculares, divergências genéticas, redes de haplótipos e polimorfismos de nucleotídeos únicos (SNPs) confirmaram as três espécies acima mencionadas como linhagens evolutivas distintas e revelaram mais sete indivíduos potencialmente híbridos, mas morfologicamente assinalados a T. mesophaeus, T. nigromaculatus ou T. typhonius.. Devido à taxa de evolução lenta da tirosinase, as espécies mais recentes T. typhonius e T. nigromaculatus parecem não terem sido sorteadas completamente nesse gene. Já T. mesophaeus, que é a espécie mais antiga das três, foi recuperada inequivocamente em todas as análises. De forma inédita, as análises moleculares evidenciaram a ocorrência de introgressão bidirecional entre T. nigromaculatus e T. typhonius e entre T. nigromaculatus e T. mesophaeus, sendo que há indícios de indivíduos F1 (cruzamentos entre espécies parentais puras gerando híbridos). A utilização do gene ND2 mostrou-se mais eficiente do que o gene COI nas filogenias e, apesar da tirosinase ser um gene nuclear de evolução lenta, contribuiu para a identificação de incongruências citonucleares. Nossos resultados mostram que a história filogenética de Trachycephalus é complexa e que o uso de marcadores nucleares de evolução mais rápida e ampliação dessas análises para outras espécies do gênero podem revelar mais eventos de hibridização.

Mapping multi-species habitat use for marine conservation planning

Tese de Doutoramento, Ciências do Mar (Biologia Marinha)

sMapReduce: a programming pattern for wireless sensor networks

Wireless Sensor Networks (WSNs) are increasingly used in various application domains like home-automation, agriculture, industries and infrastructure monitoring. As applications tend to leverage larger geographical deployments of sensor networks, the availability of an intuitive and user friendly programming abstraction becomes a crucial factor in enabling faster and more efficient development, and reprogramming of applications. We propose a programming pattern named sMapReduce, inspired by the Google MapReduce framework, for mapping application behaviors on to a sensor network and enabling complex data aggregation. The proposed pattern requires a user to create a network-level application in two functions: sMap and Reduce, in order to abstract away from the low-level details without sacrificing the control to develop complex logic. Such a two-fold division of programming logic is a natural-fit to typical sensor networking operation which makes sensing and topological modalities accessible to the user.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

Model morphisms (MoMo) to enable language independent information models and interoperable business networks

MSc. Dissertation presented at Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa to obtain the Master degree in Electrical and Computer Engineering

A cellulosic liquid crystal pool for cellulose nanocrystals: structure and molecular dynamics at high shear rates

Cellulose and its derivatives, such as hydroxypropylcellulose (HPC) have been studied for a long time but they are still not well understood particularly in liquid crystalline solutions. These systems can be at the origin of networks with properties similar to liquid crystalline (LC) elastomers. The films produced from LC solutions can be manipulated by the action of moisture allowing for instance the development of a soft motor (Geng et al., 2013) driven by humidity. Cellulose nanocrystals (CNC), which combine cellulose properties with the specific characteristics of nanoscale materials, have been mainly studied for their potential as a reinforcing agent. Suspensions of CNC can also self-order originating a liquid-crystalline chiral nematic phases. Considering the liquid crystalline features that both LC-HPC and CNC can acquire, we prepared LC-HPC/CNC solutions with different CNC contents (1,2 and 5 wt.%). The effect of the CNC into the LC-HPC matrix was determined by coupling rheology and NMR spectroscopy - Rheo-NMR a technique tailored to analyse orientational order in sheared systems. (C) 2015 Elsevier Ltd. All rights reserved.