Microsatellites can be misleading: an empirical and simulation study.

It has been long recognized that highly polymorphic genetic markers can lead to underestimation of divergence between populations when migration is low. Microsatellite loci, which are characterized by extremely high mutation rates, are particularly likely to be affected. Here, we report genetic differentiation estimates in a contact zone between two chromosome races of the common shrew (Sorex araneus), based on 10 autosomal microsatellites, a newly developed Y-chromosome microsatellite, and mitochondrial DNA. These results are compared to previous data on proteins and karyotypes. Estimates of genetic differentiation based on F- and R-statistics are much lower for autosomal microsatellites than for all other genetic markers. We show by simulations that this discrepancy stems mainly from the high mutation rate of microsatellite markers for F-statistics and from deviations from a single-step mutation model for R-statistics. The sex-linked genetic markers show that all gene exchange between races is mediated by females. The absence of male-mediated gene flow most likely results from male hybrid sterility.

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

Climate-based empirical models show biased predictions of butterfly communities along environmental gradients

A better understanding of the factors that mould ecological community structure is required to accurately predict community composition and to anticipate threats to ecosystems due to global changes. We tested how well stacked climate-based species distribution models (S-SDMs) could predict butterfly communities in a mountain region. It has been suggested that climate is the main force driving butterfly distribution and community structure in mountain environments, and that, as a consequence, climate-based S-SDMs should yield unbiased predictions. In contrast to this expectation, at lower altitudes, climate-based S-SDMs overpredicted butterfly species richness at sites with low plant species richness and underpredicted species richness at sites with high plant species richness. According to two indices of composition accuracy, the Sorensen index and a matching coefficient considering both absences and presences, S-SDMs were more accurate in plant-rich grasslands. Butterflies display strong and often specialised trophic interactions with plants. At lower altitudes, where land use is more intense, considering climate alone without accounting for land use influences on grassland plant richness leads to erroneous predictions of butterfly presences and absences. In contrast, at higher altitudes, where climate is the main force filtering communities, there were fewer differences between observed and predicted butterfly richness. At high altitudes, even if stochastic processes decrease the accuracy of predictions of presence, climate-based S-SDMs are able to better filter out butterfly species that are unable to cope with severe climatic conditions, providing more accurate predictions of absences. Our results suggest that predictions should account for plants in disturbed habitats at lower altitudes but that stochastic processes and heterogeneity at high altitudes may limit prediction success of climate-based S-SDMs.

Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.

Policy Credibility and Delegation to Independent Regulatory Agencies: A Comparative Empirical Analysis

Independent regulatory agencies are one of the main institutional features of the 'rising regulatory state' in Western Europe. Governments are increasingly willing to abandon their regulatory competencies and to delegate them to specialized institutions that are at least partially beyond their control. This article examines the empirical consistency of one particular explanation of this phenomenon, namely the credibility hypothesis, claiming that governments delegate powers so as to enhance the credibility of their policies. Three observable implications are derived from the general hypothesis, linking credibility and delegation to veto players, complexity and interdependence. An independence index is developed to measure agency independence, which is then used in a multivariate analysis where the impact of credibility concerns on delegation is tested. The analysis relies on an original data set comprising independence scores for thirty-three regulators. Results show that the credibility hypothesis can explain a good deal of the variation in delegation. The economic nature of regulation is a strong determinant of agency independence, but is mediated by national institutions in the form of veto players.

Cutting edge: influence of the TCR Vbeta domain on the selection of semi-invariant NKT cells by endogenous ligands.

Invariant Valpha14 (Valpha14i) NKT cells are a murine CD1d-dependent regulatory T cell subset characterized by a Valpha14-Jalpha18 rearrangement and expression of mostly Vbeta8.2 and Vbeta7. Whereas the TCR Vbeta domain influences the binding avidity of the Valpha14i TCR for CD1d-alpha-galactosylceramide complexes, with Vbeta8.2 conferring higher avidity binding than Vbeta7, a possible impact of the TCR Vbeta domain on Valpha14i NKT cell selection by endogenous ligands has not been studied. In this study, we show that thymic selection of Vbeta7(+), but not Vbeta8.2(+), Valpha14i NKT cells is favored in situations where endogenous ligand concentration or TCRalpha-chain avidity are suboptimal. Furthermore, thymic Vbeta7(+) Valpha14i NKT cells were preferentially selected in vitro in response to CD1d-dependent presentation of endogenous ligands or exogenously added self ligand isoglobotrihexosylceramide. Collectively, our data demonstrate that the TCR Vbeta domain influences the selection of Valpha14i NKT cells by endogenous ligands, presumably because Vbeta7 confers higher avidity binding.

Authors' response to 'Limitless longevity': the contribution of rectangularization to the secular increase in life expectancy: an empirical study.

Related article : Letter to the Editor: Karin Modig, Sven Drefahl, and Anders Ahlbon.Limitless longevity: Comment on the Contribution of rectangularization to the secular increase of life expectancy

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Characterization of tumor antigen specific CD8+ T cells and semi-invariant Vα24/Vβ11 NKT cells in tumor invaded liver

Summary: Detailed knowledge on tumor antigen expression and specific immune cells is required for a rational design of immunotherapy for patients with tumor invaded liver. In this study, we confirmed that Cancer/Testis (CT) tumor-associated antigens are frequently expressed in hepatocellular carcinoma (HCC) and searched for the presence of CD8+ T cells specific for these antigens. In 2/10 HLA-A2+ patients with HCC, we found that MAGE-A10 and/or SSX-2 specific CD8+ T cells naturally responded to the disease, since they were enriched in tumor lesions but not in non-tumoral liver. Isolated T cells specifically and strongly killed tumor cells in vitro, suggesting that these CTL were selected in vivo for high avidity antigen recognition, providing the rational for specific immunotherapy of HCC, based on immunization with CT antigens such as MAGE-Al 0 and SSX-2. Type 1 NKT cells express an invariant TCR α chain (Vα24.1α18, paired with Vβ11 in human) and share a specific reactivity to αGalactosylceramide (αGC) presented by CD1d. These cells can display paradoxical immuno-regulatory properties including strong anti-tumor effects upon αGC administration in murine models. To understand why NKT cells were not sufficiently protective against tumor development in patients with tumor invaded liver, we characterized the diversity of Vα24/Vβ11 NKT cells in healthy donors (HD) and cancer patients: NKT cells from HD and patients were generally diverse in terms of TCR β chain (Vβ11) variability and NKT cells from HD showed a variable recognition of αGC loaded CD 1 d multimers. Vα24/ Vβ11 NKT cells can be divided in 3 populations, the CD4, DN (CD4-/CD8-) and CD8 NKT cell subsets that show distinct ability of cytokine production. In addition, our functional analysis revealed that DN and CD8 subsets displayed a higher cytolytic potential and a weaker IFNγ release than the CD4 NKT cell subset. NKT cell subsets were variably represented in the blood of HD and cancer patients. However, HD with high NKT cell frequencies displayed an enrichment of the DN and CD8 subsets, and few of them were suggestive of an oligoclonal expansion in vivo. Comparable NKT cell frequencies were found between blood, non-tumoral liver and tumor of patients. In contrast, we identified a gradual enrichment of CD4 NKT cells from blood to the liver and to the tumor, together with a decrease of DN and CD8 NKT cell subsets. Most patient derived NKT cells were unresponsive upon αGalactosylceramide stimulation ex vivo; NKT cells from few patients displayed a weak responsiveness with different cytokine polarization. The NKT cell repertoire was thus different in tumor tissue, suggesting that CD4 NKT cells infiltrating tumors may be detrimental for protection against tumors and instead may favour the tumor growth/recurrence as recently reported in mice. Résumé en français scientifique : Afin de développer le traitement des patients porteurs d'une tumeur dans le foie par immunothérapie, de nouvelles connaissances sont requises concernant l'expression d'antigènes par les tumeurs et les cellules immunitaires spécifiques de ces antigènes. Nous avons vérifié que des antigènes associés aux tumeurs, tels que les antigènes « Cancer-Testis » (CT), sont fréquemment exprimés par le carcinome hepatocéllulaire (CHC). La recherche de lymphocytes T CD8+ spécifiques (CTL) de ces antigènes a révélé que des CTL spécifiques de MAGE-A10 et/ou SSX-2 ont répondu naturellement à la tumeur chez 2/10 patients étudiés. Ces cellules étaient présentes dans les lésions tumorales mais pas dans le foie adjacent. De plus, ces CTL ont démontré une activité cytolytique forte et spécifique contre les cellules tumorales in vitro, ce qui suggère que ces CTL ont été sélectionnés pour une haute avidité de reconnaissance de l'antigène in vivo. Ces données fournissent une base pour l'immunothérapie spécifique du CHC, en proposant de cibler les antigènes CT tels que MAGE-A10 ou SSX-2. Les cellules NKT de type 1 ont une chaîne α de TCR qui est invariante (chez l'homme, Vα24Jα18, apparié avec Vβ11) et reconnaissent spécifiquement l'αGalactosylceramide (αGC) présenté par CD1d. Ces cellules ont des propriétés immuno¬régulatrices qui peuvent être parfois contradictoires et leur activation par l'αGC induit une forte protection anti-tumorale chez la souris: Afin de comprendre pourquoi ces cellules ne sont pas assez protectrices contre le développement des tumeurs dans le foie chez l'homme, nous avons étudié la diversité des cellules NKT Vα24/Vβ11 d'individus sains (IS) et de patients cancéreux. Les cellules NKT peuvent être sous-divisées en 3 populations : Les CD4, DN (CD4- /CD8-) ou CDS, qui ont la capacité de produire des cytokines différentes. Nos analyses fonctionnelles ont aussi révélé que les sous-populations DN et CD8 ont un potentiel cytolytique plus élevé et une production d'IFNγ plus faible que la sous-population CD4. Ces sous-populations sont représentées de manière variable dans le sang des IS ou des patients. Cependant, les IS avec un taux élevé de cellules NKT ont un enrichissement des sous- populations DN ou CDS, et certains suggèrent qu'il s'agit d'une expansion oligo-clonale in vivo. Les patients avaient des fréquences comparables de cellules NKT entre le sang, le foie et la tumeur. Par contre, la sous-population CD4 était progressivement enrichie du sang vers le foie et la tumeur, tandis que les sous-populations DN ou CD8 était perdues. La plupart des cellules NKT des patients ne réagissaient pas lors de stimulation avec l'αGC ex vivo et les cellules NKT de quelques patients répondaient faiblement et avec des polarisations de cytokines différentes. Ces données suggèrent que les cellules NKT CD4, prédominantes dans les tumeurs, sont inefficaces pour la lutte anti-tumorale et pourraient même favoriser la croissance ou la récurrence tumorale. Donc, une mobilisation spécifique des cellules NKT CD4 négatives par immunothérapie pourrait favoriser l'immunité contre des tumeurs chez l'homme. Résumé en français pour un large public Au sein des globules blancs, les lymphocytes T expriment un récepteur (le TCR), qui est propre à chacun d'entre eux et leur permet d'accrocher de manière très spécifique une molécule appelée antigène. Ce TCR est employé par les lymphocytes pour inspecter les antigènes associés avec des molécules présentatrices à la surface des autres cellules. Les lymphocytes T CD8 reconnaissent un fragment de protéine (ou peptide), qui est présenté par une des molécules du Complexe Majeur d'Histocompatibilité de classe I et tuent la cellule qui présente ce peptide. Ils sont ainsi bien adaptés pour éliminer les cellules qui présentent un peptide issu d'un virus quand la cellule est infectée. D'autres cellules T CD8 reconnaissent des peptides comme les antigènes CT, qui sont produits anormalement par les cellules cancéreuses. Nous avons confirmé que les antigènes CT sont fréquemment exprimés par le cancer du foie. Nous avons également identifié des cellules T CD8 spécifiques d'antigènes CT dans la tumeur, mais pas dans le foie normal de 2 patients sur 10. Cela signifie que ces lymphocytes peuvent être naturellement activés contre la tumeur et sont capables de la trouver. De plus les lymphocytes issus d'un patient ont démontré une forte sensibilité pour reconnaître l'antigène et tuent spécifiquement les cellules tumorales. Les antigènes CT représentent donc des cibles intéressantes qui pourront être intégrés dans des vaccins thérapeutiques du cancer du foie. De cette manière, les cellules T CD8 du patient lui-même pourront être induites à détruire de manière spécifique les cellules cancéreuses. Un nouveau type de lymphocytes T a été récemment découvert: les lymphocytes NKT. Quand ils reconnaissent un glycolipide présenté par la molécule CD1d, ils sont capables, de manière encore incomprise, d'initier, d'augmenter, ou à l'inverse d'inhiber la défense immunitaire. Ces cellules NKT ont démontré qu'elles jouent un rôle important dans la défense contre les tumeurs et particulièrement dans le foie des souris. Nous avons étudié les cellules NKT de patients atteints d'une tumeur dans le foie, afin de comprendre pourquoi elles ne sont pas assez protectrice chez l'homme. Les lymphocytes NKT peuvent être sous-divisés en 3 populations: Les CD4, les DN (CD4-/CD8-) et les CD8. Ces 3 classes de NKT peuvent produire différents signaux chimiques appelés cytokines. Contrairement aux cellules NKT DN ou CDS, seules les cellules NKT CD4 sont capables de produire des cytokines qui sont défavorables pour la défense anti-tumorale. Par ailleurs nous avons trouvé que les cellules NKT CD4 tuent moins bien les cellules cancéreuses que les cellules NKT DN ou CD8. L'analyse des cellules NKT, fraîchement extraites du sang, du foie et de la tumeur de patients a révélé que les cellules NKT CD4 sont progressivement enrichies du sang vers le foie et la tumeur. La large prédominance des NKT CD4 à l'intérieur des tumeurs suggère que, chez l'homme, ces cellules sont inappropriées pour la lutte anti-tumorale. Par ailleurs, la plupart des cellules NKT de patients n'étaient pas capables de produire des cytokines après stimulation avec un antigène. Cela explique également pourquoi ces cellules ne protègent pas contre les tumeurs dans le foie.