Schistosoma mansoni antigenic extracts obtained by different extraction procedures

Solubilization of Schistosoma mansoni antigens was obtained by agitation of adult worms in a 3M KCl solution. The protein contents of the KCl extrats varied from 0.35 to 0.96 mg/ml. Sera from 97 patients with hepatointestinal shistosomiasis and viable eggs in stools from a Brazilian endemic area were studied by immunoelectroomophoresis and Ouchterlony immunodiffusion methods with the KCl extract and with another antigen, obtained by homogenization of adult schistosomes in saline. The rate of positiveness of immunoprecipitation deterctions by immunoelectroomophoresis with the KCl extract was 53.5%. A correlation was verified between methods of detection and extration procedures, resulting in a better association of the extract obtained by agitation in 3M KCl and immunoelectroomophoresis.

A Model-to-model analysis of the repeated Prisoners' Dilemma: genetic algorithms vs. evolutionary dynamics

We study the properties of the well known Replicator Dynamics when applied to a finitely repeated version of the Prisoners' Dilemma game. We characterize the behavior of such dynamics under strongly simplifying assumptions (i.e. only 3 strategies are available) and show that the basin of attraction of defection shrinks as the number of repetitions increases. After discussing the difficulties involved in trying to relax the 'strongly simplifying assumptions' above, we approach the same model by means of simulations based on genetic algorithms. The resulting simulations describe a behavior of the system very close to the one predicted by the replicator dynamics without imposing any of the assumptions of the analytical model. Our main conclusion is that analytical and computational models are good complements for research in social sciences. Indeed, while on the one hand computational models are extremely useful to extend the scope of the analysis to complex scenar

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

In situ evaluation of single-cell lysis by cytosol extraction observation through fluorescence decay and dielectrophoretic trapping time

We present a new method for lysis of single cells in continuous flow, where cells are sequentially trapped, lysed and released in an automatic process. Using optimized frequencies, dielectrophoretic trapping allows exposing cells in a reproducible way to high electrical fields for long durations, thereby giving good control on the lysis parameters. In situ evaluation of cytosol extraction on single cells has been studied for Chinese hamster ovary (CHO) cells through out-diffusion of fluorescent molecules for different voltage amplitudes. A diffusion model is proposed to correlate this out-diffusion to the total area of the created pores, which is dependent on the potential drop across the cell membrane and enables evaluation of the total pore area in the membrane. The dielectrophoretic trapping is no longer effective after lysis because of the reduced conductivity inside the cells, leading to cell release. The trapping time is linked to the time required for cytosol extraction and can thus provide additional validation of the effective cytosol extraction for non-fluorescent cells. Furthermore, the application of one single voltage for both trapping and lysis provides a fully automatic process including cell trapping, lysis, and release, allowing operating the device in continuous flow without human intervention.

Fast numerical algorithms for the computation of invariant tori in Hamiltonian systems

In this paper, we develop numerical algorithms that use small requirements of storage and operations for the computation of invariant tori in Hamiltonian systems (exact symplectic maps and Hamiltonian vector fields). The algorithms are based on the parameterization method and follow closely the proof of the KAM theorem given in [LGJV05] and [FLS07]. They essentially consist in solving a functional equation satisfied by the invariant tori by using a Newton method. Using some geometric identities, it is possible to perform a Newton step using little storage and few operations. In this paper we focus on the numerical issues of the algorithms (speed, storage and stability) and we refer to the mentioned papers for the rigorous results. We show how to compute efficiently both maximal invariant tori and whiskered tori, together with the associated invariant stable and unstable manifolds of whiskered tori. Moreover, we present fast algorithms for the iteration of the quasi-periodic cocycles and the computation of the invariant bundles, which is a preliminary step for the computation of invariant whiskered tori. Since quasi-periodic cocycles appear in other contexts, this section may be of independent interest. The numerical methods presented here allow to compute in a unified way primary and secondary invariant KAM tori. Secondary tori are invariant tori which can be contracted to a periodic orbit. We present some preliminary results that ensure that the methods are indeed implementable and fast. We postpone to a future paper optimized implementations and results on the breakdown of invariant tori.

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Introduction: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints. Methods: We used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set. Results: A ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models. Conclusions: We showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.

Intestinal content detection in capsule endoscopy using robust features

This work covers two aspects. First, it generally compares and summarizes the similarities and differences of state of the art feature detector and descriptor and second it presents a novel approach of detecting intestinal content (in particular bubbles) in capsule endoscopy images. Feature detectors and descriptors providing invariance to change of perspective, scale, signal-noise-ratio and lighting conditions are important and interesting topics in current research and the number of possible applications seems to be numberless. After analysing a selection of in the literature presented approaches, this work investigates in their suitability for applications information extraction in capsule endoscopy images. Eventually, a very good performing detector of intestinal content in capsule endoscopy images is presented. A accurate detection of intestinal content is crucial for all kinds of machine learning approaches and other analysis on capsule endoscopy studies because they occlude the field of view of the capsule camera and therefore those frames need to be excluded from analysis. As a so called “byproduct” of this investigation a graphical user interface supported Feature Analysis Tool is presented to execute and compare the discussed feature detectors and descriptor on arbitrary images, with configurable parameters and visualized their output. As well the presented bubble classifier is part of this tool and if a ground truth is available (or can also be generated using this tool) a detailed visualization of the validation result will be performed.

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.

Effect of magnesium chloride and guanidinum chloride on the extraction of components of extracellular matrix from chicken cartilage

In order to evaluate the effect of chaotropic agents on proteoglycan and non-collagenous proteins, chicken xiphoid cartilage was treated with guanidine-HCI and MgCl2 in different concentrations (1M to 5M), and different periods of time (12, 24, 48 and 72hr). The maximum yield of uronic acid was obtained with 3M MgCl2 (73.3 per cent). Concentrations of 4M and 5M of MgCl2 showed that much less uronic acid was removed, 55.3 per cent and 38.1 respectively. Extraction with 3M MgCl2 and 3M guanidine-HCl resulted better efficiency when performed for 48 hr. Analysis by SDS-PAGE of the extracts obtained with guanidine-HCl and MgCl, in different concentrations pointed out that most components are equally removed with the two solvents, showing that the extraction with MgCl2 is an alternative assay to remove non-collagenous proteins from extracellular matrix.

Hard instances of algorithms and proof systems

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