High specificity of line-immunoassay based algorithms for recent HIV-1 infection independent of viral subtype and stage of disease.

ABSTRACT: BACKGROUND: Serologic testing algorithms for recent HIV seroconversion (STARHS) provide important information for HIV surveillance. We have shown that a patient's antibody reaction in a confirmatory line immunoassay (INNO-LIATM HIV I/II Score, Innogenetics) provides information on the duration of infection. Here, we sought to further investigate the diagnostic specificity of various Inno-Lia algorithms and to identify factors affecting it. METHODS: Plasma samples of 714 selected patients of the Swiss HIV Cohort Study infected for longer than 12 months and representing all viral clades and stages of chronic HIV-1 infection were tested blindly by Inno-Lia and classified as either incident (up to 12 m) or older infection by 24 different algorithms. Of the total, 524 patients received HAART, 308 had HIV-1 RNA below 50 copies/mL, and 620 were infected by a HIV-1 non-B clade. Using logistic regression analysis we evaluated factors that might affect the specificity of these algorithms. RESULTS: HIV-1 RNA <50 copies/mL was associated with significantly lower reactivity to all five HIV-1 antigens of the Inno-Lia and impaired specificity of most algorithms. Among 412 patients either untreated or with HIV-1 RNA ≥50 copies/mL despite HAART, the median specificity of the algorithms was 96.5% (range 92.0-100%). The only factor that significantly promoted false-incident results in this group was age, with false-incident results increasing by a few percent per additional year. HIV-1 clade, HIV-1 RNA, CD4 percentage, sex, disease stage, and testing modalities exhibited no significance. Results were similar among 190 untreated patients. CONCLUSIONS: The specificity of most Inno-Lia algorithms was high and not affected by HIV-1 variability, advanced disease and other factors promoting false-recent results in other STARHS. Specificity should be good in any group of untreated HIV-1 patients.

Global tractography with embedded anatomical priors for quantitative connectivity analysis.

Tractography algorithms provide us with the ability to non-invasively reconstruct fiber pathways in the white matter (WM) by exploiting the directional information described with diffusion magnetic resonance. These methods could be divided into two major classes, local and global. Local methods reconstruct each fiber tract iteratively by considering only directional information at the voxel level and its neighborhood. Global methods, on the other hand, reconstruct all the fiber tracts of the whole brain simultaneously by solving a global energy minimization problem. The latter have shown improvements compared to previous techniques but these algorithms still suffer from an important shortcoming that is crucial in the context of brain connectivity analyses. As no anatomical priors are usually considered during the reconstruction process, the recovered fiber tracts are not guaranteed to connect cortical regions and, as a matter of fact, most of them stop prematurely in the WM; this violates important properties of neural connections, which are known to originate in the gray matter (GM) and develop in the WM. Hence, this shortcoming poses serious limitations for the use of these techniques for the assessment of the structural connectivity between brain regions and, de facto, it can potentially bias any subsequent analysis. Moreover, the estimated tracts are not quantitative, every fiber contributes with the same weight toward the predicted diffusion signal. In this work, we propose a novel approach for global tractography that is specifically designed for connectivity analysis applications which: (i) explicitly enforces anatomical priors of the tracts in the optimization and (ii) considers the effective contribution of each of them, i.e., volume, to the acquired diffusion magnetic resonance imaging (MRI) image. We evaluated our approach on both a realistic diffusion MRI phantom and in vivo data, and also compared its performance to existing tractography algorithms.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Tc-99m-MAA SPECT-derived tumor-to-normal liver ratios for partition model dosimetry in selective internal radiation therapy (SIRT)

Aim: When planning SIRT using 90Y microspheres, the partition model is used to refine the activity calculated by the body surface area (BSA) method to potentially improve the safety and efficacy of treatment. For this partition model dosimetry, accurate determination of mean tumor-to-normal liver ratio (TNR) is critical since it directly impacts absorbed dose estimates. This work aimed at developing and assessing a reliable methodology for the calculation of 99mTc-MAA SPECT/CT-derived TNR ratios based on phantom studies. Materials and methods: IQ NEMA (6 hot spheres) and Kyoto liver phantoms with different hot/background activity concentration ratios were imaged on a SPECT/CT (GE Infinia Hawkeye 4). For each reconstruction with the IQ phantom, TNR quantification was assessed in terms of relative recovery coefficients (RC) and image noise was evaluated in terms of coefficient of variation (COV) in the filled background. RCs were compared using OSEM with Hann, Butterworth and Gaussian filters, as well as FBP reconstruction algorithms. Regarding OSEM, RCs were assessed by varying different parameters independently, such as the number of iterations (i) and subsets (s) and the cut-off frequency of the filter (fc). The influence of the attenuation and diffusion corrections was also investigated. Furthermore, both 2D-ROIs and 3D-VOIs contouring were compared. For this purpose, dedicated Matlab© routines were developed in-house for automatic 2D-ROI/3D-VOI determination to reduce intra-user and intra-slice variability. Best reconstruction parameters and RCs obtained with the IQ phantom were used to recover corrected TNR in case of the Kyoto phantom for arbitrary hot-lesion size. In addition, we computed TNR volume histograms to better assess uptake heterogeneityResults: The highest RCs were obtained with OSEM (i=2, s=10) coupled with the Butterworth filter (fc=0.8). Indeed, we observed a global 20% RC improvement over other OSEM settings and a 50% increase as compared to the best FBP reconstruction. In any case, both attenuation and diffusion corrections must be applied, thus improving RC while preserving good image noise (COV<10%). Both 2D-ROI and 3D-VOI analysis lead to similar results. Nevertheless, we recommend using 3D-VOI since tumor uptake regions are intrinsically 3D. RC-corrected TNR values lie within 17% around the true value, substantially improving the evaluation of small volume (<15 mL) regions. Conclusions: This study reports the multi-parameter optimization of 99mTc MAA SPECT/CT images reconstruction in planning 90Y dosimetry for SIRT. In phantoms, accurate quantification of TNR was obtained using OSEM coupled with Butterworth and RC correction.

Endoleak and in-stent thrombus detection with CT angiography in a thoracic aortic aneurysm phantom at different tube energies using filtered back projection and iterative algorithms.

PURPOSE: To determine the lower limit of dose reduction with hybrid and fully iterative reconstruction algorithms in detection of endoleaks and in-stent thrombus of thoracic aorta with computed tomographic (CT) angiography by applying protocols with different tube energies and automated tube current modulation. MATERIALS AND METHODS: The calcification insert of an anthropomorphic cardiac phantom was replaced with an aortic aneurysm model containing a stent, simulated endoleaks, and an intraluminal thrombus. CT was performed at tube energies of 120, 100, and 80 kVp with incrementally increasing noise indexes (NIs) of 16, 25, 34, 43, 52, 61, and 70 and a 2.5-mm section thickness. NI directly controls radiation exposure; a higher NI allows for greater image noise and decreases radiation. Images were reconstructed with filtered back projection (FBP) and hybrid and fully iterative algorithms. Five radiologists independently analyzed lesion conspicuity to assess sensitivity and specificity. Mean attenuation (in Hounsfield units) and standard deviation were measured in the aorta to calculate signal-to-noise ratio (SNR). Attenuation and SNR of different protocols and algorithms were analyzed with analysis of variance or Welch test depending on data distribution. RESULTS: Both sensitivity and specificity were 100% for simulated lesions on images with 2.5-mm section thickness and an NI of 25 (3.45 mGy), 34 (1.83 mGy), or 43 (1.16 mGy) at 120 kVp; an NI of 34 (1.98 mGy), 43 (1.23 mGy), or 61 (0.61 mGy) at 100 kVp; and an NI of 43 (1.46 mGy) or 70 (0.54 mGy) at 80 kVp. SNR values showed similar results. With the fully iterative algorithm, mean attenuation of the aorta decreased significantly in reduced-dose protocols in comparison with control protocols at 100 kVp (311 HU at 16 NI vs 290 HU at 70 NI, P ≤ .0011) and 80 kVp (400 HU at 16 NI vs 369 HU at 70 NI, P ≤ .0007). CONCLUSION: Endoleaks and in-stent thrombus of thoracic aorta were detectable to 1.46 mGy (80 kVp) with FBP, 1.23 mGy (100 kVp) with the hybrid algorithm, and 0.54 mGy (80 kVp) with the fully iterative algorithm.

Evolving team compositions by agent swapping

Optimizing collective behavior in multiagent systems requires algorithms to find not only appropriate individual behaviors but also a suitable composition of agents within a team. Over the last two decades, evolutionary methods have emerged as a promising approach for the design of agents and their compositions into teams. The choice of a crossover operator that facilitates the evolution of optimal team composition is recognized to be crucial, but so far, it has never been thoroughly quantified. Here, we highlight the limitations of two different crossover operators that exchange entire agents between teams: restricted agent swapping (RAS) that exchanges only corresponding agents between teams and free agent swapping (FAS) that allows an arbitrary exchange of agents. Our results show that RAS suffers from premature convergence, whereas FAS entails insufficient convergence. Consequently, in both cases, the exploration and exploitation aspects of the evolutionary algorithm are not well balanced resulting in the evolution of suboptimal team compositions. To overcome this problem, we propose combining the two methods. Our approach first applies FAS to explore the search space and then RAS to exploit it. This mixed approach is a much more efficient strategy for the evolution of team compositions compared to either strategy on its own. Our results suggest that such a mixed agent-swapping algorithm should always be preferred whenever the optimal composition of individuals in a multiagent system is unknown.

Optimization of preservation conditions of As (III) bioreporter bacteria.

Long-term preservation of bioreporter bacteria is essential for the functioning of cell-based detection devices, particularly when field application, e.g., in developing countries, is intended. We varied the culture conditions (i.e., the NaCl content of the medium), storage protection media, and preservation methods (vacuum drying vs. encapsulation gels remaining hydrated) in order to achieve optimal preservation of the activity of As (III) bioreporter bacteria during up to 12 weeks of storage at 4 degrees C. The presence of 2% sodium chloride during the cultivation improved the response intensity of some bioreporters upon reconstitution, particularly of those that had been dried and stored in the presence of sucrose or trehalose and 10% gelatin. The most satisfying, stable response to arsenite after 12 weeks storage was obtained with cells that had been dried in the presence of 34% trehalose and 1.5% polyvinylpyrrolidone. Amendments of peptone, meat extract, sodium ascorbate, and sodium glutamate preserved the bioreporter activity only for the first 2 weeks, but not during long-term storage. Only short-term stability was also achieved when bioreporter bacteria were encapsulated in gels remaining hydrated during storage.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Comparative performance of supertree algorithms in large data sets using the soapberry family (Sapindaceae) as a case study.

For the last 2 decades, supertree reconstruction has been an active field of research and has seen the development of a large number of major algorithms. Because of the growing popularity of the supertree methods, it has become necessary to evaluate the performance of these algorithms to determine which are the best options (especially with regard to the supermatrix approach that is widely used). In this study, seven of the most commonly used supertree methods are investigated by using a large empirical data set (in terms of number of taxa and molecular markers) from the worldwide flowering plant family Sapindaceae. Supertree methods were evaluated using several criteria: similarity of the supertrees with the input trees, similarity between the supertrees and the total evidence tree, level of resolution of the supertree and computational time required by the algorithm. Additional analyses were also conducted on a reduced data set to test if the performance levels were affected by the heuristic searches rather than the algorithms themselves. Based on our results, two main groups of supertree methods were identified: on one hand, the matrix representation with parsimony (MRP), MinFlip, and MinCut methods performed well according to our criteria, whereas the average consensus, split fit, and most similar supertree methods showed a poorer performance or at least did not behave the same way as the total evidence tree. Results for the super distance matrix, that is, the most recent approach tested here, were promising with at least one derived method performing as well as MRP, MinFlip, and MinCut. The output of each method was only slightly improved when applied to the reduced data set, suggesting a correct behavior of the heuristic searches and a relatively low sensitivity of the algorithms to data set sizes and missing data. Results also showed that the MRP analyses could reach a high level of quality even when using a simple heuristic search strategy, with the exception of MRP with Purvis coding scheme and reversible parsimony. The future of supertrees lies in the implementation of a standardized heuristic search for all methods and the increase in computing power to handle large data sets. The latter would prove to be particularly useful for promising approaches such as the maximum quartet fit method that yet requires substantial computing power.

CT dose optimization when changing to CT multi-detector row technology

The purpose of this article was to review the strategies to control patient dose in adult and pediatric computed tomography (CT), taking into account the change of technology from single-detector row CT to multi-detector row CT. First the relationships between computed tomography dose index, dose length product, and effective dose in adult and pediatric CT are revised, along with the diagnostic reference level concept. Then the effect of image noise as a function of volume computed tomography dose index, reconstructed slice thickness, and the size of the patient are described. Finally, the potential of tube current modulation CT is discussed.

Optimization of snowmaking in high mountains ski resort

Ski resorts are deploying more and more systems of artificial snow. These tools are necessary to ensure an important economic activity for the high alpine valleys. However, artificial snow raises important environmental issues that can be reduced by an optimization of its production. This paper presents a software prototype based on artificial intelligence to help ski resorts better manage their snowpack. It combines on one hand a General Neural Network for the analysis of the snow cover and the spatial prediction, with on the other hand a multiagent simulation of skiers for the analysis of the spatial impact of ski practice. The prototype has been tested on the ski resort of Verbier (Switzerland).

How Much Does It Cost? Optimization of Costs in Sequence Analysis of Social Science Data

One major methodological problem in analysis of sequence data is the determination of costs from which distances between sequences are derived. Although this problem is currently not optimally dealt with in the social sciences, it has some similarity with problems that have been solved in bioinformatics for three decades. In this article, the authors propose an optimization of substitution and deletion/insertion costs based on computational methods. The authors provide an empirical way of determining costs for cases, frequent in the social sciences, in which theory does not clearly promote one cost scheme over another. Using three distinct data sets, the authors tested the distances and cluster solutions produced by the new cost scheme in comparison with solutions based on cost schemes associated with other research strategies. The proposed method performs well compared with other cost-setting strategies, while it alleviates the justification problem of cost schemes.