Vector electrocardiographic alterations after percutaneous septal ablation in obstructive hypertrophic cardiomyopathy: possible anatomic causes

OBJECTIVE: Analyze the dromotropic disturbances (vector-electrocardiographic), and the possible anatomic causes, provoked by selective alcohol injection in the septal branch, for percutaneous treatment, of obstructive hypertrophic cardiomyopathy. METHODS: Ten patients with a mean age of 52.7 years underwent percutaneous septal ablation (PTSA) from october 1998; all in functional class III/IV). Twelve-lead electrocardiogram was performed prior to and during PTSA, and later electrocardiogram and vectorcardiogram according to Frank's method. The patients were followed up for 32 months. RESULTS: On electrocardiogram (ECG) prior to PTSA all patients had sinus rhythm and left atrial enlargement, 8 left ventricular hypertrophy of systolic pattern. On ECG immediately after PTSA, 8 had complete right bundle-branch block; 1 transient total atrioventricular block; 1 alternating transient bundle-branch block either right or hemiblock. On late ECG 8 had complete right bundle-branch block confirmed by vectorcardiogram, type 1 or Grishman. CONCLUSION: Septal fibrosis following alcohol injection caused a predominance of complete right bundle-branch block, different from surgery of myotomy/myectomy.

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.

Biological control of Biomphalaria tenagophila (Mollusca, Planorbidae), a schistosomiasis vector, using the fish Geophagus brasiliensis (Pisces, Cichlidae) in the laboratory or in a seminatural environment

In order to investigate a possible method of biological control of schistosomiasis, we used the fish Geophagus brasiliensis (Quoy & Gaimard, 1824) which is widely distributed throughout Brazil, to interrupt the life cycle of the snail Biomphalaria tenagophila (Orbigny, 1835), an intermediate host of Schistosoma mansoni. In the laboratory, predation eliminated 97.6% of the smaller snails (3-8 mm shell diameter) and 9.2% of the larger ones (12-14 mm shell diameter). Very promising results were also obtained in a seminatural environment. Studies of this fish in natural snail habitats should be further encouraged.

A subspace correction method for discontinuous Galerkin discretizations of linear elasticity equations

We study preconditioning techniques for discontinuous Galerkin discretizations of isotropic linear elasticity problems in primal (displacement) formulation. We propose subspace correction methods based on a splitting of the vector valued piecewise linear discontinuous finite element space, that are optimal with respect to the mesh size and the Lamé parameters. The pure displacement, the mixed and the traction free problems are discussed in detail. We present a convergence analysis of the proposed preconditioners and include numerical examples that validate the theory and assess the performance of the preconditioners.

How to increase the population of a Phlebotomus perniciosus (Diptera: Psychodidae) colony: a new method

The sandfly Phlebotomus perniciosus is the most widespread vector of Leishmania infantum in Spain. Laboratory colonisation represents the most feasible source of information on the biology of these insects, but in conducting any study, the density of individuals in the colony may drop to such an extent that it is sometimes difficult to recover the initial population levels. A new technique was tested for the recovery of sandfly eggs in three different colonies; the recovery rate was studied by comparing the standard method of mass rearing with this new method of colony management. The results demonstrate a mean increase of 18.4% in adult production, a growth in colony productivity that justifies the inclusion of this process in the routine maintenance of any colony of sandflies.

Vector control intervention towards interruption of transmission of Chagas disease by Rhodnius prolixus, main vector in Guatemala

In Guatemala, the Ministry of Health (MoH) began a vector control project with Japanese cooperation in 2000 to reduce the risk of Chagas disease infection. Rhodnius prolixus is one of the principal vectors and is targeted for elimination. The control method consisted of extensive residual insecticide spraying campaigns, followed by community-based surveillance with selective respraying. Interventions in nine endemic departments identified 317 villages with R. prolixus of 4,417 villages surveyed. Two cycles of residual insecticide spraying covered over 98% of the houses in the identified villages. Fourteen villages reinfestated were all resprayed. Between 2000-2003 and 2008, the number of infested villages decreased from 317 to two and the house infestation rate reduced from 0.86% to 0.0036%. Seroprevalence rates in 2004-2005, when compared with an earlier study in 1998, showed a significant decline from 5.3% to 1.3% among schoolchildren in endemic areas. The total operational cost was US$ 921,815, where the cost ratio between preparatory, attack and surveillance phases was approximately 2:12:1. In 2008, Guatemala was certified for interruption of Chagas disease transmission by R. prolixus. What facilitated the process was existing knowledge in vector control and notable commitment by the MoH, as well as political, managerial and technical support by external stakeholders.

Is there an efficient trap or collection method for sampling Anopheles darlingi and other malaria vectors that can describe the essential parameters affecting transmission dynamics as effectively as human landing catches? - A Review

Distribution, abundance, feeding behaviour, host preference, parity status and human-biting and infection rates are among the medical entomological parameters evaluated when determining the vector capacity of mosquito species. To evaluate these parameters, mosquitoes must be collected using an appropriate method. Malaria is primarily transmitted by anthropophilic and synanthropic anophelines. Thus, collection methods must result in the identification of the anthropophilic species and efficiently evaluate the parameters involved in malaria transmission dynamics. Consequently, human landing catches would be the most appropriate method if not for their inherent risk. The choice of alternative anopheline collection methods, such as traps, must consider their effectiveness in reproducing the efficiency of human attraction. Collection methods lure mosquitoes by using a mixture of olfactory, visual and thermal cues. Here, we reviewed, classified and compared the efficiency of anopheline collection methods, with an emphasis on Neotropical anthropophilic species, especially Anopheles darlingi, in distinct malaria epidemiological conditions in Brazil.

Evaluation of oviposition traps as an entomological surveillance method for Aedes aegypti (Diptera, Culicidae)

This study aimed to describe the behavior of oviposition traps for Aedes aegypti over time, to compare it with the larval survey and to investigate the association with climatic variables. It was conducted in São José do Rio Preto city, São Paulo. Daily climatic data and fortnightly measurements for oviposition traps and larval infestation were collected from October 2003 to September 2004. Three different periods were identified in the behavior of oviposition traps' positivity and mean number of eggs: increase, plateau and decrease in values. These measurements followed the variation of climatic data from the first and third periods. High correlation was obtained between the positivity and the mean number of eggs. The oviposition traps showed higher capacity to detect the vector than did larval survey. It was observed that the first (October to December) and third (May to September) periods were considered to be the most suitable to use oviposition traps than larval surveys.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem.

Personalized drug administrations using Support Vector Machine

The decision-making process regarding drug dose, regularly used in everyday medical practice, is critical to patients' health and recovery. It is a challenging process, especially for a drug with narrow therapeutic ranges, in which a medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of available patient features and doctor's clinical experience (a priori adaptation). Computer support in drug dose administration makes the prescription procedure faster, more accurate, objective, and less expensive, with a tendency to reduce the number of invasive procedures. This paper presents an advanced integrated Drug Administration Decision Support System (DADSS) to help clinicians/patients with the dose computing. Based on a support vector machine (SVM) algorithm, enhanced with the random sample consensus technique, this system is able to predict the drug concentration values and computes the ideal dose amount and dose interval for a new patient. With an extension to combine the SVM method and the explicit analytical model, the advanced integrated DADSS system is able to compute drug concentration-to-time curves for a patient under different conditions. A feedback loop is enabled to update the curve with a new measured concentration value to make it more personalized (a posteriori adaptation).

Prevalence of iodine inadequacy in Switzerland assessed by the estimated average requirement cut-point method in relation to the impact of iodized salt.

OBJECTIVE: To assess the iodine status of Swiss population groups and to evaluate the influence of iodized salt as a vector for iodine fortification. DESIGN: The relationship between 24 h urinary iodine and Na excretions was assessed in the general population after correcting for confounders. Single-day intakes were estimated assuming that 92 % of dietary iodine was excreted in 24 h urine. Usual intake distributions were derived for male and female population groups after adjustment for within-subject variability. The estimated average requirement (EAR) cut-point method was applied as guidance to assess the inadequacy of the iodine supply. SETTING: Public health strategies to reduce the dietary salt intake in the general population may affect its iodine supply. SUBJECTS: The study population (1481 volunteers, aged ≥15 years) was randomly selected from three different linguistic regions of Switzerland. RESULTS: The 24 h urine samples from 1420 participants were determined to be properly collected. Mean iodine intakes obtained for men (n 705) and women (n 715) were 179 (sd 68.1) µg/d and 138 (sd 57.8) µg/d, respectively. Urinary Na and Ca, and BMI were significantly and positively associated with higher iodine intake, as were men and non-smokers. Fifty-four per cent of the total iodine intake originated from iodized salt. The prevalence of inadequate iodine intake as estimated by the EAR cut-point method was 2 % for men and 14 % for women. CONCLUSIONS: The estimated prevalence of inadequate iodine intake was within the optimal target range of 2-3 % for men, but not for women.

Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective

Approximate Approximations and a Boundary Point Method for the Linearized Stokes System

The method of approximate approximations, introduced by Maz'ya [1], can also be used for the numerical solution of boundary integral equations. In this case, the matrix of the resulting algebraic system to compute an approximate source density depends only on the position of a finite number of boundary points and on the direction of the normal vector in these points (Boundary Point Method). We investigate this approach for the Stokes problem in the whole space and for the Stokes boundary value problem in a bounded convex domain G subset R^2, where the second part consists of three steps: In a first step the unknown potential density is replaced by a linear combination of exponentially decreasing basis functions concentrated near the boundary points. In a second step, integration over the boundary partial G is replaced by integration over the tangents at the boundary points such that even analytical expressions for the potential approximations can be obtained. In a third step, finally, the linear algebraic system is solved to determine an approximate density function and the resulting solution of the Stokes boundary value problem. Even not convergent the method leads to an efficient approximation of the form O(h^2) + epsilon, where epsilon can be chosen arbitrarily small.