Cost of Illness and Contingent Valuation : Controlling for the Motivations of Expressed Preferences in an Attempt to Avoid Double-Counting

This paper addresses the issue of double counting of health impacts in the context of cost of illness valuation. Double counting occurs when estimates are jointly used, which rely on valuation techniques that overlap. As a solution, we propose to limit the scope of each of the valuation method to a specific range of impacts. In order to limit the contingentvaluation method to the exclusive valuation of intangible costs, we propose a three steps approach : (1) leave the respondents free to valuate the consequences which matter to them, (2) elicit respondent's motivations, (3) control for the influence motivations have on elicited values. This procedure was applied in a Swiss contingent-valuation. An econometric treatment was applied in order to limit the scope of the estimates of the contingent valuation method to intangibles,therefore the possibility to a combination of methods with the risk of double-counting and underestimating costs being kept to a minimum.

Efforts to standardise Ki-67 counting in breast cancer. A pilot study of the Swiss Working Group of Gyneco- and Breast Pathologists

Goals: Adjuvant chemotherapy decisions in breast cancer are increasing based on the pathologist's assessment of the proliferation fraction in the tumor. Yet, how good and how reproducible are we pathologists at providing reliable Ki-67 readings on breast carcinomas. Exactly how to count and in which areas to count within a tumor remains inadequately standardized. The Swiss Working Group of Gyneco- and Breast Pathologists has tried to appreciate this dilemma and to propose ways to obtain more reproducible results.Methods: In a first phase, 5 pathologists evaluated Ki67 counts in 10 breast cancers by exact counting (500 cells) and by eyeballing. Pathologists were free to select the region in which Ki67 was evaluated. In a second phase 16 pathologists evaluated Ki-67 counts in 3 breast cancers also by exact counting and eyeballing, but in predefined fields of interest. In both phases, Ki67 was assessed in centrally immunostained slides (ZH) and on slides immunostained in the 11 participating laboratories. In a third phase, these same 16 pathologists were once again asked to read the 3 cases from phase 2, plus three new cases, and this time exact guidelines were provided as to what exactly is considered a Ki-67 positive nucleus.Results: Discordance of Ki67 assessment was due to each of the following 4 factors: (i) pathologists' divergent definitions of what counts as a positive nucleus (ii) the mode of assessment (counting vs. eyeballing), (iii) immunostaining technique/protocol/antibody, and (iv) the selection of the area in which to count.Conclusion: Providing guidelines as to where to count (representative field in the tumor periphery and omitting hot spots) and what nuclei to count (even faintly immunostained nuclei count as positive) reduces the discordance rates of Ki67 readings between pathologists. Laboratory technique is only of minor importance (even over a large antibody dilution range), and counting nuclei does not improve accuracy, but rather aggravates deviations from the group mean values.Disclosure of Interest: None Declared

Classification of very high spatial resolution imagery using mathematical morphology and support vector machines

We investigate the relevance of morphological operators for the classification of land use in urban scenes using submetric panchromatic imagery. A support vector machine is used for the classification. Six types of filters have been employed: opening and closing, opening and closing by reconstruction, and opening and closing top hat. The type and scale of the filters are discussed, and a feature selection algorithm called recursive feature elimination is applied to decrease the dimensionality of the input data. The analysis performed on two QuickBird panchromatic images showed that simple opening and closing operators are the most relevant for classification at such a high spatial resolution. Moreover, mixed sets combining simple and reconstruction filters provided the best performance. Tests performed on both images, having areas characterized by different architectural styles, yielded similar results for both feature selection and classification accuracy, suggesting the generalization of the feature sets highlighted.

A survey of transposable element classification systems--a call for a fundamental update to meet the challenge of their diversity and complexity.

The increase of publicly available sequencing data has allowed for rapid progress in our understanding of genome composition. As new information becomes available we should constantly be updating and reanalyzing existing and newly acquired data. In this report we focus on transposable elements (TEs) which make up a significant portion of nearly all sequenced genomes. Our ability to accurately identify and classify these sequences is critical to understanding their impact on host genomes. At the same time, as we demonstrate in this report, problems with existing classification schemes have led to significant misunderstandings of the evolution of both TE sequences and their host genomes. In a pioneering publication Finnegan (1989) proposed classifying all TE sequences into two classes based on transposition mechanisms and structural features: the retrotransposons (class I) and the DNA transposons (class II). We have retraced how ideas regarding TE classification and annotation in both prokaryotic and eukaryotic scientific communities have changed over time. This has led us to observe that: (1) a number of TEs have convergent structural features and/or transposition mechanisms that have led to misleading conclusions regarding their classification, (2) the evolution of TEs is similar to that of viruses by having several unrelated origins, (3) there might be at least 8 classes and 12 orders of TEs including 10 novel orders. In an effort to address these classification issues we propose: (1) the outline of a universal TE classification, (2) a set of methods and classification rules that could be used by all scientific communities involved in the study of TEs, and (3) a 5-year schedule for the establishment of an International Committee for Taxonomy of Transposable Elements (ICTTE).

Bayesian classification criterion for forensic multivariate data

This study presents a classification criteria for two-class Cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland, law enforcement authorities regularly ask laboratories to determine cannabis plant's chemotype from seized material in order to ascertain that the plantation is legal or not. In this study, the classification analysis is based on data obtained from the relative proportion of three major leaf compounds measured by gas-chromatography interfaced with mass spectrometry (GC-MS). The aim is to discriminate between drug type (illegal) and fiber type (legal) cannabis at an early stage of the growth. A Bayesian procedure is proposed: a Bayes factor is computed and classification is performed on the basis of the decision maker specifications (i.e. prior probability distributions on cannabis type and consequences of classification measured by losses). Classification rates are computed with two statistical models and results are compared. Sensitivity analysis is then performed to analyze the robustness of classification criteria.

Cytostatic lung perfusion results in heterogeneous spatial regional blood flow and drug distribution: evaluation of different cytostatic lung perfusion techniques in a porcine model.

OBJECTIVES: Comparison of doxorubicin uptake, leakage and spatial regional blood flow, and drug distribution was made for antegrade, retrograde, combined antegrade and retrograde isolated lung perfusion, and pulmonary artery infusion by endovascular inflow occlusion (blood flow occlusion), as opposed to intravenous administration in a porcine model. METHODS: White pigs underwent single-pass lung perfusion with doxorubicin (320 mug/mL), labeled 99mTc-microspheres, and Indian ink. Visual assessment of the ink distribution and perfusion scintigraphy of the perfused lung was performed. 99mTc activity and doxorubicin levels were measured by gamma counting and high-performance liquid chromatography on 15 tissue samples from each perfused lung at predetermined localizations. RESULTS: Overall doxorubicin uptake in the perfused lung was significantly higher (P = .001) and the plasma concentration was significantly lower (P < .0001) after all isolated lung perfusion techniques, compared with intravenous administration, without differences between them. Pulmonary artery infusion (blood flow occlusion) showed an equally high doxorubicin uptake in the perfused lung but a higher systemic leakage than surgical isolated lung perfusion (P < .0001). The geometric coefficients of variation of the doxorubicin lung tissue levels were 175%, 279%, 226%, and 151% for antegrade, retrograde, combined antegrade and retrograde isolated lung perfusion, and pulmonary artery infusion by endovascular inflow occlusion (blood flow occlusion), respectively, compared with 51% for intravenous administration (P = .09). 99mTc activity measurements of the samples paralleled the doxorubicin level measurements, indicating a trend to a more heterogeneous spatial regional blood flow and drug distribution after isolated lung perfusion and blood flow occlusion compared with intravenous administration. CONCLUSIONS: Cytostatic lung perfusion results in a high overall doxorubicin uptake, which is, however, heterogeneously distributed within the perfused lung.

Clinical implications of molecular neuropathology and biomarkers for malignant glioma.

Malignant gliomas are currently diagnosed based on morphological criteria and graded according to the World Health Organization classification of primary brain tumors. This algorithm of diagnosis and classification provides clinicians with an estimated prognosis of the natural course of the disease. It does not reflect the expected response to specific treatments beyond surgery (eg, radiotherapy or alkylating chemotherapy). Clinical experience has revealed that gliomas sharing similar histomorphological criteria might indeed have different clinical courses and exhibit highly heterogenous responses to treatments. This was very impressively demonstrated first for oligodendrogliomas. The presence or lack of combined deletions of the chromosomal segments 1p/19q was associated with different benefit from radiotherapy and chemotherapy. We review current molecular markers for malignant gliomas and discuss their current and future impact on clinical neuro-oncology.

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.

Defining and searching for structural motifs using DeepView/Swiss-PdbViewer.

BACKGROUND: Today, recognition and classification of sequence motifs and protein folds is a mature field, thanks to the availability of numerous comprehensive and easy to use software packages and web-based services. Recognition of structural motifs, by comparison, is less well developed and much less frequently used, possibly due to a lack of easily accessible and easy to use software. RESULTS: In this paper, we describe an extension of DeepView/Swiss-PdbViewer through which structural motifs may be defined and searched for in large protein structure databases, and we show that common structural motifs involved in stabilizing protein folds are present in evolutionarily and structurally unrelated proteins, also in deeply buried locations which are not obviously related to protein function. CONCLUSIONS: The possibility to define custom motifs and search for their occurrence in other proteins permits the identification of recurrent arrangements of residues that could have structural implications. The possibility to do so without having to maintain a complex software/hardware installation on site brings this technology to experts and non-experts alike.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Pathology and biology of peripheral T-cell lymphomas.

Peripheral T-cell lymphomas (PTCLs) represent a heterogeneous group of more than 20 neoplastic entities derived from mature T cells and natural killer (NK) cells involved in innate and adaptive immunity. With few exceptions these malignancies, which may present as disseminated, predominantly extranodal or cutaneous, or predominantly nodal diseases, are clinically aggressive and have a dismal prognosis. Their diagnosis and classification is hampered by several difficulties, including a significant morphological and immunophenotypic overlap across different entities, and the lack of characteristic genetic alterations for most of them. Although there is increasing evidence that the cell of origin is a major determinant for the delineation of several PTCL entities, however, the cellular derivation of most entities remains poorly characterized and/or may be heterogeneous. The complexity of the biology and pathophysiology of PTCLs has been only partly deciphered. In recent years, novel insights have been gained from genome-wide profiling analyses. In this review, we will summarize the current knowledge on the pathobiological features of peripheral NK/T-cell neoplasms, with a focus on selected disease entities manifesting as tissue infiltrates primarily in extranodal sites and lymph nodes.

Phylogenetic analysis of plastid and nuclear DNA sequences reveals a rapid late Miocene radiation of the temperate bamboo tribe Arundinarieae (Poaceae, Bambusoideae)

Background: Arundinarieae are a large tribe of temperate woody bamboos for which phylogenetics are poorly understood because of limited taxon sampling and lack of informative characters. Aims: This study assessed phylogenetic relationships, origins and classification of Arundinarieae. Methods: DNA sequences (plastid trnL-F; nuclear ITS) were used for parsimony and Bayesian inference including 41 woody bamboo taxa. Divergence dates were estimated using a relaxed Bayesian clock. Results: Arundinarieae were monophyletic but their molecular divergence was low compared to the tropical Bambuseae. Ancestors of the Arundinarieae lineage were estimated to have diverged from the other bamboos 23 (15-30) million years ago (Mya). However, the Arundinarieae radiation occurred 10 (6-16) Mya compared to 18 (11-25) Mya for the tropical Bambuseae. Some groups could be defined within Arundinarieae, but they do not correspond to recognised subtribes such as Arundinariinae or Shibataeinae. Conclusions: Arundinarieae are a relatively ancient bambusoid lineage that underwent a rapid radiation in the late Miocene. The radiation coincides with the continental collision of the Indo-Australian and Eurasian Plates. Arundinarieae are distributed primarily in East Asia and the Himalayas to northern Southeast Asia. It is unknown whether they were present in Asia long before their radiation, but we believe recent dispersal is a more likely scenario. Keywords: Arundinarieae; Bambuseae; internal transcribed spacer (ITS); molecular clock; phylogenetics; radiation; temperate bamboos; Thamnocalaminae; trnL-F

How Reliable Is Ki-67 Immunohistochemistry in Grade 2 Breast Carcinomas? A QA Study of the Swiss Working Group of Breast- and Gynecopathologists.

Adjuvant chemotherapy decisions in breast cancer are increasingly based on the pathologist's assessment of tumor proliferation. The Swiss Working Group of Gyneco- and Breast Pathologists has surveyed inter- and intraobserver consistency of Ki-67-based proliferative fraction in breast carcinomas. METHODS: Five pathologists evaluated MIB-1-labeling index (LI) in ten breast carcinomas (G1, G2, G3) by counting and eyeballing. In the same way, 15 pathologists all over Switzerland then assessed MIB-1-LI on three G2 carcinomas, in self-selected or pre-defined areas of the tumors, comparing centrally immunostained slides with slides immunostained in the different laboratoires. To study intra-observer variability, the same tumors were re-examined 4 months later. RESULTS: The Kappa values for the first series of ten carcinomas of various degrees of differentiation showed good to very good agreement for MIB-1-LI (Kappa 0.56-0.72). However, we found very high inter-observer variabilities (Kappa 0.04-0.14) in the read-outs of the G2 carcinomas. It was not possible to explain the inconsistencies exclusively by any of the following factors: (i) pathologists' divergent definitions of what counts as a positive nucleus (ii) the mode of assessment (counting vs. eyeballing), (iii) immunostaining technique, and (iv) the selection of the tumor area in which to count. Despite intensive confrontation of all participating pathologists with the problem, inter-observer agreement did not improve when the same slides were re-examined 4 months later (Kappa 0.01-0.04) and intra-observer agreement was likewise poor (Kappa 0.00-0.35). CONCLUSION: Assessment of mid-range Ki-67-LI suffers from high inter- and intra-observer variability. Oncologists should be aware of this caveat when using Ki-67-LI as a basis for treatment decisions in moderately differentiated breast carcinomas.

Long noncoding RNAs in cardiac development and ageing.

A large part of the mammalian genome is transcribed into noncoding RNAs. Long noncoding RNAs (lncRNAs) have emerged as critical epigenetic regulators of gene expression. Distinct molecular mechanisms allow lncRNAs either to activate or to repress gene expression, thereby participating in the regulation of cellular and tissue function. LncRNAs, therefore, have important roles in healthy and diseased hearts, and might be targets for therapeutic intervention. In this Review, we summarize the current knowledge of the roles of lncRNAs in cardiac development and ageing. After describing the definition and classification of lncRNAs, we present an overview of the mechanisms by which lncRNAs regulate gene expression. We discuss the multiple roles of lncRNAs in the heart, and focus on the regulation of embryonic stem cell differentiation, cardiac cell fate and development, and cardiac ageing. We emphasize the importance of chromatin remodelling in this regulation. Finally, we discuss the therapeutic and biomarker potential of lncRNAs.