Congenital toxoplasmosis: evaluation of serological methods for the detection of anti-Toxoplasma gondii IgM and IgA antibodies

A study was carried out to evaluate the presence of serological markers for the immunodiagnosis of the vertical transmission of toxoplasmosis. We tested the sensitivity, specificity and predictive values (positive and negative) of different serological methods for the early diagnosis of congenital toxoplasmosis. In a prospective longitudinal study, 50 infants with suspected congenital toxoplasmosis were followed up in the ambulatory care centre of Congenital Infections at University Hospital in Goiânia, Goiás, Brazil, from 1 January 2004-30 September 2005. Microparticle Enzyme Immunoassay (MEIA), Enzyme-Linked Fluorescent Assay (ELFA) and Immune-Fluorescent Antibody Technique (IFAT) were used to detect specific IgM anti-Toxoplasma gondii antibodies and a capture ELISA was used to detect specific IgA antibodies. The results showed that 28/50 infants were infected. During the neonatal period, IgM was detected in 39.3% (11/28) of those infected infants and IgA was detected in 21.4% (6/28). The sensitivity, specificity and predictive values (positive and negative) of each assay were, respectively: MEIA and ELFA: 60.9%, 100%, 100%, 55.0%; IFAT: 59.6%, 91.7%, 93.3%, 53.7%; IgA capture ELISA: 57.1%, 100%, 100%, 51.2%. The presence of specific IgM and IgA antibodies during the neonatal period was not frequent, although it was correlated with the most severe cases of congenital transmission. The results indicate that the absence of congenital disease markers (IgM and IgA) in newborns, even after confirming the absence with several techniques, does not constitute an exclusion criterion for toxoplasmosis.

Comparative study of two extraction methods for enteric virus recovery from sewage sludge by molecular methods

The aim of this study was to compare two nucleic acid extraction methods for the recovery of enteric viruses from activated sludge. Test samples were inoculated with human adenovirus (AdV), hepatitis A virus (HAV), poliovirus (PV) and rotavirus (RV) and were then processed by an adsorption-elution-precipitation method. Two extraction methods were used: an organic solvent-based method and a silica method. The organic-based method was able to recoup 20% of the AdV, 90% of the RV and 100% of both the PV and HAV from seeded samples. The silica method was able to recoup 1.8% of the AdV and 90% of the RV. These results indicate that the organic-based method is more suitable for detecting viruses in sewage sludge.

Scoring Methods for Ordinal Multidimensional Forced-Choice Items

In most psychological tests and questionnaires, a test score is obtained bytaking the sum of the item scores. In virtually all cases where the test orquestionnaire contains multidimensional forced-choice items, this traditionalscoring method is also applied. We argue that the summation of scores obtained with multidimensional forced-choice items produces uninterpretabletest scores. Therefore, we propose three alternative scoring methods: a weakand a strict rank preserving scoring method, which both allow an ordinalinterpretation of test scores; and a ratio preserving scoring method, whichallows a proportional interpretation of test scores. Each proposed scoringmethod yields an index for each respondent indicating the degree to whichthe response pattern is inconsistent. Analysis of real data showed that withrespect to rank preservation, the weak and strict rank preserving methodresulted in lower inconsistency indices than the traditional scoring method;with respect to ratio preservation, the ratio preserving scoring method resulted in lower inconsistency indices than the traditional scoring method

Comparing methods for dimensionality reduction when data are density functions

Functional Data Analysis (FDA) deals with samples where a whole function is observedfor each individual. A particular case of FDA is when the observed functions are densityfunctions, that are also an example of infinite dimensional compositional data. In thiswork we compare several methods for dimensionality reduction for this particular typeof data: functional principal components analysis (PCA) with or without a previousdata transformation and multidimensional scaling (MDS) for diferent inter-densitiesdistances, one of them taking into account the compositional nature of density functions. The difeerent methods are applied to both artificial and real data (householdsincome distributions)

Dynamic graphics of parametrically linked multivariate methods used in compositional data analysis

Many multivariate methods that are apparently distinct can be linked by introducing oneor more parameters in their definition. Methods that can be linked in this way arecorrespondence analysis, unweighted or weighted logratio analysis (the latter alsoknown as "spectral mapping"), nonsymmetric correspondence analysis, principalcomponent analysis (with and without logarithmic transformation of the data) andmultidimensional scaling. In this presentation I will show how several of thesemethods, which are frequently used in compositional data analysis, may be linkedthrough parametrizations such as power transformations, linear transformations andconvex linear combinations. Since the methods of interest here all lead to visual mapsof data, a "movie" can be made where where the linking parameter is allowed to vary insmall steps: the results are recalculated "frame by frame" and one can see the smoothchange from one method to another. Several of these "movies" will be shown, giving adeeper insight into the similarities and differences between these methods

Testing of Diagnostic Methods for Detection of Influenza Virusfor Optimal Performance in the Context of an Influenza Surveillance Network

Influenza surveillance networks must detect early the viruses that will cause the forthcoming annual epidemics and isolate the strains for further characterization. We obtained the highest sensitivity (95.4%) with a diagnostic tool that combined a shell-vial assay and reverse transcription-PCR on cell culture supernatants at 48 h, and indeed, recovered the strain

An hybrid methodology for RL-based behavior coordination in a target following mission with an AUV

Proposes a behavior-based scheme for high-level control of autonomous underwater vehicles (AUVs). Two main characteristics can be highlighted in the control scheme. Behavior coordination is done through a hybrid methodology, which takes in advantages of the robustness and modularity in competitive approaches, as well as optimized trajectories

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Methods for testing association between uncertain genotypes and quantitative traits.

Interpretability and power of genome-wide association studies can be increased by imputing unobserved genotypes, using a reference panel of individuals genotyped at higher marker density. For many markers, genotypes cannot be imputed with complete certainty, and the uncertainty needs to be taken into account when testing for association with a given phenotype. In this paper, we compare currently available methods for testing association between uncertain genotypes and quantitative traits. We show that some previously described methods offer poor control of the false-positive rate (FPR), and that satisfactory performance of these methods is obtained only by using ad hoc filtering rules or by using a harsh transformation of the trait under study. We propose new methods that are based on exact maximum likelihood estimation and use a mixture model to accommodate nonnormal trait distributions when necessary. The new methods adequately control the FPR and also have equal or better power compared to all previously described methods. We provide a fast software implementation of all the methods studied here; our new method requires computation time of less than one computer-day for a typical genome-wide scan, with 2.5 M single nucleotide polymorphisms and 5000 individuals.

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

Accuracy of phenotypic methicillin susceptibility methods in the detection of Staphylococcus aureus isolates carrying different SCCmec types

A total of 138 isolates, 118 methicillin-resistant Staphylococcus aureus (MRSA) isolates (staphylococcal cassette chromosome type II, 20 isolates, type III, 39 isolates and type IV, 59 isolates) and 20 methicillin-sensitive S. aureus isolates were evaluated by phenotypic methods: cefoxitin and oxacillin disk diffusion (DD), agar dilution (AD), latex agglutination (LA), oxacillin agar screening (OAS) and chromogenic agar detection. All methods showed 100% specificity, but only the DD tests presented 100% sensitivity. The sensitivity of the other tests ranged from 82.2% (OAS)-98.3% (AD). The LA test showed the second lowest sensitivity (86.4%). The DD test showed high accuracy in the detection of MRSA isolates, but there was low precision in the detection of type IV isolates by the other tests, indicating that the genotypic characteristics of the isolates should be considered.

Methods for the ex vivo characterization of human CD8+ T subsets based on gene expression and replicative history analysis.

The generation of an antigen-specific T-lymphocyte response is a complex multi-step process. Upon T-cell receptor-mediated recognition of antigen presented by activated dendritic cells, naive T-lymphocytes enter a program of proliferation and differentiation, during the course of which they acquire effector functions and may ultimately become memory T-cells. A major goal of modern immunology is to precisely identify and characterize effector and memory T-cell subpopulations that may be most efficient in disease protection. Sensitive methods are required to address these questions in exceedingly low numbers of antigen-specific lymphocytes recovered from clinical samples, and not manipulated in vitro. We have developed new techniques to dissect immune responses against viral or tumor antigens. These allow the isolation of various subsets of antigen-specific T-cells (with major histocompatibility complex [MHC]-peptide multimers and five-color FACS sorting) and the monitoring of gene expression in individual cells (by five-cell reverse transcription-polymerase chain reaction [RT-PCR]). We can also follow their proliferative life history by flow-fluorescence in situ hybridization (FISH) analysis of average telomere length. Recently, using these tools, we have identified subpopulations of CD8+ T-lymphocytes with distinct proliferative history and partial effector-like properties. Our data suggest that these subsets descend from recently activated T-cells and are committed to become differentiated effector T-lymphocytes.

Molecular evidence for a functional ecdysone signaling system in Brugia malayi.

BACKGROUND: Filarial nematodes, including Brugia malayi, the causative agent of lymphatic filariasis, undergo molting in both arthropod and mammalian hosts to complete their life cycles. An understanding of how these parasites cross developmental checkpoints may reveal potential targets for intervention. Pharmacological evidence suggests that ecdysteroids play a role in parasitic nematode molting and fertility although their specific function remains unknown. In insects, ecdysone triggers molting through the activation of the ecdysone receptor: a heterodimer of EcR (ecdysone receptor) and USP (Ultraspiracle). METHODS AND FINDINGS: We report the cloning and characterization of a B. malayi EcR homologue (Bma-EcR). Bma-EcR dimerizes with insect and nematode USP/RXRs and binds to DNA encoding a canonical ecdysone response element (EcRE). In support of the existence of an active ecdysone receptor in Brugia we also cloned a Brugia rxr (retinoid X receptor) homolog (Bma-RXR) and demonstrate that Bma-EcR and Bma-RXR interact to form an active heterodimer using a mammalian two-hybrid activation assay. The Bma-EcR ligand-binding domain (LBD) exhibits ligand-dependent transactivation via a GAL4 fusion protein combined with a chimeric RXR in mammalian cells treated with Ponasterone-A or a synthetic ecdysone agonist. Furthermore, we demonstrate specific up-regulation of reporter gene activity in transgenic B. malayi embryos transfected with a luciferase construct controlled by an EcRE engineered in a B. malayi promoter, in the presence of 20-hydroxy-ecdysone. CONCLUSIONS: Our study identifies and characterizes the two components (Bma-EcR and Bma-RXR) necessary for constituting a functional ecdysteroid receptor in B. malayi. Importantly, the ligand binding domain of BmaEcR is shown to be capable of responding to ecdysteroid ligands, and conversely, ecdysteroids can activate transcription of genes downstream of an EcRE in live B. malayi embryos. These results together confirm that an ecdysone signaling system operates in B. malayi and strongly suggest that Bma-EcR plays a central role in it. Furthermore, our study proposes that existing compounds targeting the insect ecdysone signaling pathway should be considered as potential pharmacological agents against filarial parasites.