Automatic Mapping and Classification of Spatial Environmental Data

The paper deals with the development and application of the generic methodology for automatic processing (mapping and classification) of environmental data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve the problem of spatial data mapping (regression). The Probabilistic Neural Network (PNN) is considered as an automatic tool for spatial classifications. The automatic tuning of isotropic and anisotropic GRNN/PNN models using cross-validation procedure is presented. Results are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm using independent validation data set. Real case studies are based on decision-oriented mapping and classification of radioactively contaminated territories.

Species distribution models for conservation-oriented studies in Switzerland : filling data and tools gaps

SUMMARYSpecies distribution models (SDMs) represent nowadays an essential tool in the research fields of ecology and conservation biology. By combining observations of species occurrence or abundance with information on the environmental characteristic of the observation sites, they can provide information on the ecology of species, predict their distributions across the landscape or extrapolate them to other spatial or time frames. The advent of SDMs, supported by geographic information systems (GIS), new developments in statistical models and constantly increasing computational capacities, has revolutionized the way ecologists can comprehend species distributions in their environment. SDMs have brought the tool that allows describing species realized niches across a multivariate environmental space and predict their spatial distribution. Predictions, in the form of probabilistic maps showing the potential distribution of the species, are an irreplaceable mean to inform every single unit of a territory about its biodiversity potential. SDMs and the corresponding spatial predictions can be used to plan conservation actions for particular species, to design field surveys, to assess the risks related to the spread of invasive species, to select reserve locations and design reserve networks, and ultimately, to forecast distributional changes according to scenarios of climate and/or land use change.By assessing the effect of several factors on model performance and on the accuracy of spatial predictions, this thesis aims at improving techniques and data available for distribution modelling and at providing the best possible information to conservation managers to support their decisions and action plans for the conservation of biodiversity in Switzerland and beyond. Several monitoring programs have been put in place from the national to the global scale, and different sources of data now exist and start to be available to researchers who want to model species distribution. However, because of the lack of means, data are often not gathered at an appropriate resolution, are sampled only over limited areas, are not spatially explicit or do not provide a sound biological information. A typical example of this is data on 'habitat' (sensu biota). Even though this is essential information for an effective conservation planning, it often has to be approximated from land use, the closest available information. Moreover, data are often not sampled according to an established sampling design, which can lead to biased samples and consequently to spurious modelling results. Understanding the sources of variability linked to the different phases of the modelling process and their importance is crucial in order to evaluate the final distribution maps that are to be used for conservation purposes.The research presented in this thesis was essentially conducted within the framework of the Landspot Project, a project supported by the Swiss National Science Foundation. The main goal of the project was to assess the possible contribution of pre-modelled 'habitat' units to model the distribution of animal species, in particular butterfly species, across Switzerland. While pursuing this goal, different aspects of data quality, sampling design and modelling process were addressed and improved, and implications for conservation discussed. The main 'habitat' units considered in this thesis are grassland and forest communities of natural and anthropogenic origin as defined in the typology of habitats for Switzerland. These communities are mainly defined at the phytosociological level of the alliance. For the time being, no comprehensive map of such communities is available at the national scale and at fine resolution. As a first step, it was therefore necessary to create distribution models and maps for these communities across Switzerland and thus to gather and collect the necessary data. In order to reach this first objective, several new developments were necessary such as the definition of expert models, the classification of the Swiss territory in environmental domains, the design of an environmentally stratified sampling of the target vegetation units across Switzerland, the development of a database integrating a decision-support system assisting in the classification of the relevés, and the downscaling of the land use/cover data from 100 m to 25 m resolution.The main contributions of this thesis to the discipline of species distribution modelling (SDM) are assembled in four main scientific papers. In the first, published in Journal of Riogeography different issues related to the modelling process itself are investigated. First is assessed the effect of five different stepwise selection methods on model performance, stability and parsimony, using data of the forest inventory of State of Vaud. In the same paper are also assessed: the effect of weighting absences to ensure a prevalence of 0.5 prior to model calibration; the effect of limiting absences beyond the environmental envelope defined by presences; four different methods for incorporating spatial autocorrelation; and finally, the effect of integrating predictor interactions. Results allowed to specifically enhance the GRASP tool (Generalized Regression Analysis and Spatial Predictions) that now incorporates new selection methods and the possibility of dealing with interactions among predictors as well as spatial autocorrelation. The contribution of different sources of remotely sensed information to species distribution models was also assessed. The second paper (to be submitted) explores the combined effects of sample size and data post-stratification on the accuracy of models using data on grassland distribution across Switzerland collected within the framework of the Landspot project and supplemented with other important vegetation databases. For the stratification of the data, different spatial frameworks were compared. In particular, environmental stratification by Swiss Environmental Domains was compared to geographical stratification either by biogeographic regions or political states (cantons). The third paper (to be submitted) assesses the contribution of pre- modelled vegetation communities to the modelling of fauna. It is a two-steps approach that combines the disciplines of community ecology and spatial ecology and integrates their corresponding concepts of habitat. First are modelled vegetation communities per se and then these 'habitat' units are used in order to model animal species habitat. A case study is presented with grassland communities and butterfly species. Different ways of integrating vegetation information in the models of butterfly distribution were also evaluated. Finally, a glimpse to climate change is given in the fourth paper, recently published in Ecological Modelling. This paper proposes a conceptual framework for analysing range shifts, namely a catalogue of the possible patterns of change in the distribution of a species along elevational or other environmental gradients and an improved quantitative methodology to identify and objectively describe these patterns. The methodology was developed using data from the Swiss national common breeding bird survey and the article presents results concerning the observed shifts in the elevational distribution of breeding birds in Switzerland.The overall objective of this thesis is to improve species distribution models as potential inputs for different conservation tools (e.g. red lists, ecological networks, risk assessment of the spread of invasive species, vulnerability assessment in the context of climate change). While no conservation issues or tools are directly tested in this thesis, the importance of the proposed improvements made in species distribution modelling is discussed in the context of the selection of reserve networks.RESUMELes modèles de distribution d'espèces (SDMs) représentent aujourd'hui un outil essentiel dans les domaines de recherche de l'écologie et de la biologie de la conservation. En combinant les observations de la présence des espèces ou de leur abondance avec des informations sur les caractéristiques environnementales des sites d'observation, ces modèles peuvent fournir des informations sur l'écologie des espèces, prédire leur distribution à travers le paysage ou l'extrapoler dans l'espace et le temps. Le déploiement des SDMs, soutenu par les systèmes d'information géographique (SIG), les nouveaux développements dans les modèles statistiques, ainsi que la constante augmentation des capacités de calcul, a révolutionné la façon dont les écologistes peuvent comprendre la distribution des espèces dans leur environnement. Les SDMs ont apporté l'outil qui permet de décrire la niche réalisée des espèces dans un espace environnemental multivarié et prédire leur distribution spatiale. Les prédictions, sous forme de carte probabilistes montrant la distribution potentielle de l'espèce, sont un moyen irremplaçable d'informer chaque unité du territoire de sa biodiversité potentielle. Les SDMs et les prédictions spatiales correspondantes peuvent être utilisés pour planifier des mesures de conservation pour des espèces particulières, pour concevoir des plans d'échantillonnage, pour évaluer les risques liés à la propagation d'espèces envahissantes, pour choisir l'emplacement de réserves et les mettre en réseau, et finalement, pour prévoir les changements de répartition en fonction de scénarios de changement climatique et/ou d'utilisation du sol. En évaluant l'effet de plusieurs facteurs sur la performance des modèles et sur la précision des prédictions spatiales, cette thèse vise à améliorer les techniques et les données disponibles pour la modélisation de la distribution des espèces et à fournir la meilleure information possible aux gestionnaires pour appuyer leurs décisions et leurs plans d'action pour la conservation de la biodiversité en Suisse et au-delà. Plusieurs programmes de surveillance ont été mis en place de l'échelle nationale à l'échelle globale, et différentes sources de données sont désormais disponibles pour les chercheurs qui veulent modéliser la distribution des espèces. Toutefois, en raison du manque de moyens, les données sont souvent collectées à une résolution inappropriée, sont échantillonnées sur des zones limitées, ne sont pas spatialement explicites ou ne fournissent pas une information écologique suffisante. Un exemple typique est fourni par les données sur 'l'habitat' (sensu biota). Même s'il s'agit d'une information essentielle pour des mesures de conservation efficaces, elle est souvent approximée par l'utilisation du sol, l'information qui s'en approche le plus. En outre, les données ne sont souvent pas échantillonnées selon un plan d'échantillonnage établi, ce qui biaise les échantillons et par conséquent les résultats de la modélisation. Comprendre les sources de variabilité liées aux différentes phases du processus de modélisation s'avère crucial afin d'évaluer l'utilisation des cartes de distribution prédites à des fins de conservation.La recherche présentée dans cette thèse a été essentiellement menée dans le cadre du projet Landspot, un projet soutenu par le Fond National Suisse pour la Recherche. L'objectif principal de ce projet était d'évaluer la contribution d'unités 'd'habitat' pré-modélisées pour modéliser la répartition des espèces animales, notamment de papillons, à travers la Suisse. Tout en poursuivant cet objectif, différents aspects touchant à la qualité des données, au plan d'échantillonnage et au processus de modélisation sont abordés et améliorés, et leurs implications pour la conservation des espèces discutées. Les principaux 'habitats' considérés dans cette thèse sont des communautés de prairie et de forêt d'origine naturelle et anthropique telles que définies dans la typologie des habitats de Suisse. Ces communautés sont principalement définies au niveau phytosociologique de l'alliance. Pour l'instant aucune carte de la distribution de ces communautés n'est disponible à l'échelle nationale et à résolution fine. Dans un premier temps, il a donc été nécessaire de créer des modèles de distribution de ces communautés à travers la Suisse et par conséquent de recueillir les données nécessaires. Afin d'atteindre ce premier objectif, plusieurs nouveaux développements ont été nécessaires, tels que la définition de modèles experts, la classification du territoire suisse en domaines environnementaux, la conception d'un échantillonnage environnementalement stratifié des unités de végétation cibles dans toute la Suisse, la création d'une base de données intégrant un système d'aide à la décision pour la classification des relevés, et le « downscaling » des données de couverture du sol de 100 m à 25 m de résolution. Les principales contributions de cette thèse à la discipline de la modélisation de la distribution d'espèces (SDM) sont rassemblées dans quatre articles scientifiques. Dans le premier article, publié dans le Journal of Biogeography, différentes questions liées au processus de modélisation sont étudiées en utilisant les données de l'inventaire forestier de l'Etat de Vaud. Tout d'abord sont évalués les effets de cinq méthodes de sélection pas-à-pas sur la performance, la stabilité et la parcimonie des modèles. Dans le même article sont également évalués: l'effet de la pondération des absences afin d'assurer une prévalence de 0.5 lors de la calibration du modèle; l'effet de limiter les absences au-delà de l'enveloppe définie par les présences; quatre méthodes différentes pour l'intégration de l'autocorrélation spatiale; et enfin, l'effet de l'intégration d'interactions entre facteurs. Les résultats présentés dans cet article ont permis d'améliorer l'outil GRASP qui intègre désonnais de nouvelles méthodes de sélection et la possibilité de traiter les interactions entre variables explicatives, ainsi que l'autocorrélation spatiale. La contribution de différentes sources de données issues de la télédétection a également été évaluée. Le deuxième article (en voie de soumission) explore les effets combinés de la taille de l'échantillon et de la post-stratification sur le la précision des modèles. Les données utilisées ici sont celles concernant la répartition des prairies de Suisse recueillies dans le cadre du projet Landspot et complétées par d'autres sources. Pour la stratification des données, différents cadres spatiaux ont été comparés. En particulier, la stratification environnementale par les domaines environnementaux de Suisse a été comparée à la stratification géographique par les régions biogéographiques ou par les cantons. Le troisième article (en voie de soumission) évalue la contribution de communautés végétales pré-modélisées à la modélisation de la faune. C'est une approche en deux étapes qui combine les disciplines de l'écologie des communautés et de l'écologie spatiale en intégrant leurs concepts de 'habitat' respectifs. Les communautés végétales sont modélisées d'abord, puis ces unités de 'habitat' sont utilisées pour modéliser les espèces animales. Une étude de cas est présentée avec des communautés prairiales et des espèces de papillons. Différentes façons d'intégrer l'information sur la végétation dans les modèles de répartition des papillons sont évaluées. Enfin, un clin d'oeil aux changements climatiques dans le dernier article, publié dans Ecological Modelling. Cet article propose un cadre conceptuel pour l'analyse des changements dans la distribution des espèces qui comprend notamment un catalogue des différentes formes possibles de changement le long d'un gradient d'élévation ou autre gradient environnemental, et une méthode quantitative améliorée pour identifier et décrire ces déplacements. Cette méthodologie a été développée en utilisant des données issues du monitoring des oiseaux nicheurs répandus et l'article présente les résultats concernant les déplacements observés dans la distribution altitudinale des oiseaux nicheurs en Suisse.L'objectif général de cette thèse est d'améliorer les modèles de distribution des espèces en tant que source d'information possible pour les différents outils de conservation (par exemple, listes rouges, réseaux écologiques, évaluation des risques de propagation d'espèces envahissantes, évaluation de la vulnérabilité des espèces dans le contexte de changement climatique). Bien que ces questions de conservation ne soient pas directement testées dans cette thèse, l'importance des améliorations proposées pour la modélisation de la distribution des espèces est discutée à la fin de ce travail dans le contexte de la sélection de réseaux de réserves.

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.

Evaluating spine micro-architectural texture (via TBS) discriminates major osteoporotic fractures from controls both as well as and independent of site matched BMD: the Eastern European TBS study.

The aim of the study was to assess the clinical performance of the model combining areal bone mineral density (aBMD) at spine and microarchitecural texture (TBS) for the detection of the osteoporotic fracture. The Eastern European Study is a multicenter study (Serbia, Bulgaria, Romania and Ukraine) evaluating the role of TBS in routine clinical practice as a complement to aBMD. All scans were acquired on Hologic Discovery and GE Prodigy densitometers in a routine clinical manner. The additional clinical values of aBMD and TBS were analyzed using a two steps classification tree approach (aBMD followed by TBS tertiles) for all type of osteoporotic fracture (All-OP Fx). Sensitivity, specificity and accuracy of fracture detection as well as the Net Reclassification Index (NRI) were calculated. This study involves 1031 women subjects aged 45 and older recruited in east European countries. Clinical centers were cross-calibrated in terms of BMD and TBS. As expected, areal BMD (aBMD) at spine and TBS were only moderately correlated (r (2) = 0.19). Prevalence rate for All-OP Fx was 26 %. Subjects with fracture have significant lower TBS and aBMD than subjects without fracture (p < 0.01). TBS remains associated with the fracture even after adjustment for age and aBMD with an OR of 1.27 [1.07-1.51]. When using aBMD T-score of -2.5 and the lowest TBS tertile thresholds, both BMD and TBS were similar in terms of sensitivity (35 vs. 39 %), specificity (78 vs. 80 %) and accuracy (64 vs. 66 %). aBMD and TBS combination, induced a significant improvement in sensitivity (+28 %) and accuracy (+17 %) compared to aBMD alone whereas a moderate improvement was observed in terms of specificity (+9 %). The overall combination gain was 36 % as expressed using the NRI. aBMD and TBS combination decrease significantly the number of subjects needed to diagnose from 7 for aBMD alone to 2. In a multi-centre Eastern European cohort, we have shown that the use of TBS in addition to the aBMD permit to reclassified correctly more than one-third of the overall subjects. Furthermore, the number of subjects needed to diagnose fell to 2 subjects. Economical studies have to be performed to evaluate the gain induced by the use of TBS for the healthcare system.

Effects of motive-oriented therapeutic relationship in a ten-session general psychiatric treatment of borderline personality disorder: a randomized controlled trial.

Background: Motive-oriented therapeutic relationship (MOTR) was postulated to be a particularly helpful therapeutic ingredient in the early treatment phase of patients with personality disorders, in particular with borderline personality disorder (BPD). The present randomized controlled study using an add-on design is the first study to test this assumption in a 10-session general psychiatric treatment with patients presenting with BPD on symptom reduction and therapeutic alliance. Methods: A total of 85 patients were randomized. They were either allocated to a manual-based short variant of the general psychiatric management (GPM) treatment (in 10 sessions) or to the same treatment where MOTR was deliberately added to the treatment. Treatment attrition and integrity analyses yielded satisfactory results. Results: The results of the intent-to-treat analyses suggested a global efficacy of MOTR, in the sense of an additional reduction of general problems, i.e. symptoms, interpersonal and social problems (F1, 73 = 7.25, p < 0.05). However, they also showed that MOTR did not yield an additional reduction of specific borderline symptoms. It was also shown that a stronger therapeutic alliance, as assessed by the therapist, developed in MOTR treatments compared to GPM (Z55 = 0.99, p < 0.04). Conclusions: These results suggest that adding MOTR to psychiatric and psychotherapeutic treatments of BPD is promising. Moreover, the findings shed additional light on the perspective of shortening treatments for patients presenting with BPD. © 2014 S. Karger AG, Basel.

Urban image classification with semisupervised multiscale cluster kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

Garnet growth in the Nufenen Pass area (Swiss Alps)

Abstract : Textural division of a mineral in pyramids, with their apices located at the centre of the mineral and their bases corresponding to the mineral faces is called textural sector zoning. Textural sector zoning is observed in many metamorphic minerals like andalousite and garnet. Garnets found in the graphite rich black shales of the Mesozoic cover of the Gotthard Massif display textural sector zoning. The morphology of this sector zoning is not the same in different types of black shales observed in the Nufenen pass area. Garnets in foliated black shales display a well developed sector zoning while garnets found in cm-scale layered black shales display well developed sectors in the direction of the schistosity plane. This sector zoning is always associated with up to 30μm sized birefringent lamellae emanating radial from the sector boundaries. They alternate with isotrope lamellae. The garnet forming reaction was determined using singular value decomposition approach and results compared to thermodynamic calculations. It is of the form chl + mu + cc + cld = bt + fds + ank + gt + czo and is similar in both layered and foliated black shales. The calculated X(O) is close to 0.36 and does not significantly vary during the metamorphic history of the rock. This corresponds to X CO2, X CH4, and X H2O BSE imaging of garnets on oriented-cuts revealed that the orientation of the lamellae found within the sectors is controlled by crystallography. BSE imaging and electron microprobe analysis revealed that these lamellae are calcium rich compared to the isotropic lamellae. The addition of Ca to an almandine rich garnet causes a small distortion of the X site and potentially, ordering. Ordered and disordered garnet might have very similar free energies for this composition. Hence, two garnets with different composition can be precipitated with minor overstepping of the reaction. It is enough that continued nucleation of a new garnet layer slightly prefers the same structure to assure a fiber-like growth of both garnet compositions side by side. This hypothesis is in agreement with the thermodynamic properties of the garnet solid solution described in the literature and could explain the textures observed in garnets with these compositions. To understand the differences in sector zoning morphology, and crystal growth kinetics, crystal size distribution were determined in several samples using 2D spatial analysis of slab surfaces. The same nucleation rate law was chosen for all cases. Different growth rate law for non-layered black shales and layered black shales were used. Garnet in layered black shales grew according to a growth rate law of the form R=kt ½. The transport of nutrient is the limiting factor. Transport will occur preferentially on the schistosity planes. The shapes of the garnets in such rocks are therefore ovoid with the longest axis parallel to the schistosity planes. Sector zoning is less developed with sectors present only parallel to the schistosity planes. Garnet in non-layered blackshales grew according to a growth rate law of the form R=kt. The limiting factor is the attachment at the surface of the garnet. Garnets in these rocks will display a well developed sector zoning in all directions. The growth rate law is thus influenced by the texture of the rock. It favours or hinders the transport of nutrient to the mineral surface. Résumé : La zonation sectorielle texturale consiste en la division d'un cristal en pyramides dont les sommets sont localisés au centre du minéral. La base de ces pyramides correspond aux faces du minéral. Ce type de zonation est fréquemment observé dans les minéraux métamorphiques tels que l'andalousite ou le grenat. Les grenats présents dans les marnes riches en graphites de la couverture Mésozoïque du Massif du Gotthard présent une zonation sectorielle texturale. La morphologie de cette zonation n'est pas la même dans les marnes litées et dans les marnes foliées. Les grenats des marnes foliées montrent des secteurs bien développés dans 3 directions. Les grenats des marnes litées montrent des secteurs développés uniquement dans la direction des plans de schistosité. Cette zonation sectorielle est toujours associée à des lamelles biréfringentes de quelques microns de large qui partent de la limite des secteurs et qui sont perpendiculaires aux faces du grenat. Ces lamelles alternent avec des lamelles isotropes. La réaction de formation du grenat a été déterminée par calcul matriciel et thermodynamique. La réaction est de la forme chl + mu + cc + cld= bt + fds + ank + gt + czo. Elle est similaire dans les roches litées et dans les roches foliées. L'évaluation des conditions fluides montrent que le X(O) est proche de 0.36 et ne change pas de façon significative durant l'histoire métamorphique de la roche. Des images BSE sur des coupes orientées ont révélé que l'orientation de lamelles biréfringentes est contrôlée parla crystallographie. La comparaison des analyses à la microsonde électronique et des images BSE révèle également que les lamelles biréfringentes sont plus riches en calcium que les lamelles isotropes. L'addition de calcium va déformer légèrement le site X et ainsi créer un ordre sur ce site. L'énergie interne d'un grenat ordré et d'un grenat désordonné sont suffisamment proches pour qu'un léger dépassement de l'énergie de la réaction de formation permette la coexistence des 2 types de grenat dans le même minéral. La formation de lamelles est expliquée par le fait qu'un grenat préférera la même structure. Ces observations sont en accord avec la thermodynamique des solutions solides du grenat et permet d'expliquer les structures similaires observées dans des grenats provenant de lithologies différentes. Une étude de la distribution des tailles des grenats et une modélisation de la croissance a permis de mettre en évidence 2 mécanismes de croissance différents suivant la texture de la roche. Dans les 2 cas, la loi de nucléation est la même. Dans les roches litées, la loi de croissance est de forme R=kt½. Le transport des nutriments est le facteur limitant. Ce transport a lieu préférentiellement dans la direction des niveaux de schistosité. Les grenats ont une forme légèrement allongée car la croissance des secteurs est facilitée sur les niveaux de schistosité. La croissance des grenats dans les roches foliées suit une loi de croissance de la forme R=kt. Les seuls facteurs limitant la croissance sont les processus d'attachement à la surface du grenat. La loi de croissance de ces grenats est donc contrainte par la texture de la roche. Cela se marque par des différences dans la morphologie de la zonation sectorielle.

Automatic Mallampati Classification Using Active Appearance Models

Difficult tracheal intubation assessment is an important research topic in anesthesia as failed intubations are important causes of mortality in anesthetic practice. The modified Mallampati score is widely used, alone or in conjunction with other criteria, to predict the difficulty of intubation. This work presents an automatic method to assess the modified Mallampati score from an image of a patient with the mouth wide open. For this purpose we propose an active appearance models (AAM) based method and use linear support vector machines (SVM) to select a subset of relevant features obtained using the AAM. This feature selection step proves to be essential as it improves drastically the performance of classification, which is obtained using SVM with RBF kernel and majority voting. We test our method on images of 100 patients undergoing elective surgery and achieve 97.9% accuracy in the leave-one-out crossvalidation test and provide a key element to an automatic difficult intubation assessment system.

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.

Anti-tumor necrosis factor drug survival in axial spondyloarthritis is independent of the classification criteria.

To compare the impact of meeting specific classification criteria [modified New York (mNY), European Spondyloarthropathy Study Group (ESSG), and Assessment of SpondyloArthritis international Society (ASAS) criteria] on anti-tumor necrosis factor (anti-TNF) drug retention, and to determine predictive factors of better drug survival. All patients fulfilling the ESSG criteria for axial spondyloarthritis (SpA) with available data on the axial ASAS and mNY criteria, and who had received at least one anti-TNF treatment were retrospectively retrieved in a single academic institution in Switzerland. Drug retention was computed using survival analysis (Kaplan-Meier), adjusted for potential confounders. Of the 137 patients classified as having axial SpA using the ESSG criteria, 112 also met the ASAS axial SpA criteria, and 77 fulfilled the mNY criteria. Drug retention rates at 12 and 24 months for the first biologic therapy were not significantly different between the diagnostic groups. Only the small ASAS non-classified axial SpA group (25 patients) showed a nonsignificant trend toward shorter drug survival. Elevated CRP level, but not the presence of bone marrow edema on magnetic resonance imaging (MRI) scans, was associated with significantly better drug retention (OR 7.9, ICR 4-14). In this cohort, anti-TNF drug survival was independent of the classification criteria. Elevated CRP level, but not positive MRI, was associated with better drug retention.

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.

The use of classification and regression trees to predict the likelihood of seasonal influenza.

Background Individual signs and symptoms are of limited value for the diagnosis of influenza. Objective To develop a decision tree for the diagnosis of influenza based on a classification and regression tree (CART) analysis. Methods Data from two previous similar cohort studies were assembled into a single dataset. The data were randomly divided into a development set (70%) and a validation set (30%). We used CART analysis to develop three models that maximize the number of patients who do not require diagnostic testing prior to treatment decisions. The validation set was used to evaluate overfitting of the model to the training set. Results Model 1 has seven terminal nodes based on temperature, the onset of symptoms and the presence of chills, cough and myalgia. Model 2 was a simpler tree with only two splits based on temperature and the presence of chills. Model 3 was developed with temperature as a dichotomous variable (≥38°C) and had only two splits based on the presence of fever and myalgia. The area under the receiver operating characteristic curves (AUROCC) for the development and validation sets, respectively, were 0.82 and 0.80 for Model 1, 0.75 and 0.76 for Model 2 and 0.76 and 0.77 for Model 3. Model 2 classified 67% of patients in the validation group into a high- or low-risk group compared with only 38% for Model 1 and 54% for Model 3. Conclusions A simple decision tree (Model 2) classified two-thirds of patients as low or high risk and had an AUROCC of 0.76. After further validation in an independent population, this CART model could support clinical decision making regarding influenza, with low-risk patients requiring no further evaluation for influenza and high-risk patients being candidates for empiric symptomatic or drug therapy.