Validation of and comparison between a semidistributed rainfall-runoff hydrological model (PREVAH) and a spatially distributed snow-evolution model (SnowModel) for snow cover prediction in mountain ecosystems

Snow cover is an important control in mountain environments and a shift of the snow-free period triggered by climate warming can strongly impact ecosystem dynamics. Changing snow patterns can have severe effects on alpine plant distribution and diversity. It thus becomes urgent to provide spatially explicit assessments of snow cover changes that can be incorporated into correlative or empirical species distribution models (SDMs). Here, we provide for the first time a with a lower overestimation comparison of two physically based snow distribution models (PREVAH and SnowModel) to produce snow cover maps (SCMs) at a fine spatial resolution in a mountain landscape in Austria. SCMs have been evaluated with SPOT-HRVIR images and predictions of snow water equivalent from the two models with ground measurements. Finally, SCMs of the two models have been compared under a climate warming scenario for the end of the century. The predictive performances of PREVAH and SnowModel were similar when validated with the SPOT images. However, the tendency to overestimate snow cover was slightly lower with SnowModel during the accumulation period, whereas it was lower with PREVAH during the melting period. The rate of true positives during the melting period was two times higher on average with SnowModel with a lower overestimation of snow water equivalent. Our results allow for recommending the use of SnowModel in SDMs because it better captures persisting snow patches at the end of the snow season, which is important when modelling the response of species to long-lasting snow cover and evaluating whether they might survive under climate change.

Cerebriforms cells in cerebrum: an unusual finding

Background. Mycosis Fungoides (MF) is the most common cutaneous T-cell lymphoma, and large cell trasformation (tMF) is an adverse prognostic event. Extra-cutaneous dissemination can occur in the course of the disease, but dissemination to the central nervous system (CNS) is uncommon. Moreover, CNS lymphomas are overall rare and most often of B-cell phenotype. We report a case of CNS large T-cell lymphoma presenting as multiple cerebral lesions in a patient with a history of MF. Methods. We report a case of a 33-year-old woman, known since the age of 16 for erythematous plaques thought to be atopic dermatitis, who developed, end 2012, multiple nodular skin lesions and peripheral adenopathies. Two skin lesions were biopsied simultaneously, and diagnosed as MF and tMF. A lymph node biopsy showed dermatopathic changes without lymphoma (Stage IIB). She received local treatment (UVB, PUVA and radiation therapy) and interferon therapy, and experienced almost complete remission. In 2015 neurological symptoms lead to evidence multiple cerebral lesions, suspicious for lymphoma, evaluated by stereotaxic biopsies. We compared histopathological and molecular features of these with previous skin specimens. After negative bone marrow staging biopsy, she was recently started on chemotherapy (MATRIX). Short follow-up shows rapidly worsening clinical conditions. Results. One of the initial skin biopsies showed atypical lymphoid cells with epidermotropism, Pautrier abcesses and CD4+ CD30- phenotype; the other revealed diffuse dermal infiltration by predominantly large cerebriform tumor cells with high proliferative fraction, and CD2−CD3 −CD4+/−CD7−CD30+ALK- EMA- non-cytotoxic immunophenotype. Altogether, these results led us to diagnose MF and tMF, respectively. The brain was infiltrated by large atypical lymphoid cells with cerebriform nuclei, somewhat anaplastic features and perivascular distribution. By immunohistochemistry, tumor cells were highly proliferative, with a CD2−CD3+CD5−CD7+CD30+ activated cytotoxic immunophenotype. A diagnosis of CD30+ cytotoxic peripheral T-cell lymphoma was retained. TRG and TRB clonality analyses revealed clonal rearrangements in skin and CNS biopsies, with identical patterns in both skin specimens but only minimally overlapping profiles when compared to the CNS sample. Der Pathologe 6 ? 2015 | 633 Conclusions. The reported case illustrates an uncommon finding of a CNS T-cell lymphoma in a patient with previous MF, questioning the clonal relationship between the two diseases and challenging the adequate classification of this CNS lymphoma as either a progression or a de novo lymphoma. Despite differences in immunophenotype and clonality patterns, this CNS lymphoma could possibly represent an aggressive divergent evolution of a primary cutaneous T-cell lymphoma. Additional sequencing is ongoing to try to solve the question.

Bayesian Networks for the Age Classification of Living Individuals: a Study on Transition Analysis

Over the past few decades, age estimation of living persons has represented a challenging task for many forensic services worldwide. In general, the process for age estimation includes the observation of the degree of maturity reached by some physical attributes, such as dentition or several ossification centers. The estimated chronological age or the probability that an individual belongs to a meaningful class of ages is then obtained from the observed degree of maturity by means of various statistical methods. Among these methods, those developed in a Bayesian framework offer to users the possibility of coherently dealing with the uncertainty associated with age estimation and of assessing in a transparent and logical way the probability that an examined individual is younger or older than a given age threshold. Recently, a Bayesian network for age estimation has been presented in scientific literature; this kind of probabilistic graphical tool may facilitate the use of the probabilistic approach. Probabilities of interest in the network are assigned by means of transition analysis, a statistical parametric model, which links the chronological age and the degree of maturity by means of specific regression models, such as logit or probit models. Since different regression models can be employed in transition analysis, the aim of this paper is to study the influence of the model in the classification of individuals. The analysis was performed using a dataset related to the ossifications status of the medial clavicular epiphysis and results support that the classification of individuals is not dependent on the choice of the regression model.

Probabilistic age classification with Bayesian networks: a study on the ossification status of the medial clavicular epiphysis

In the past few decades, the rise of criminal, civil and asylum cases involving young people lacking valid identification documents has generated an increase in the demand of age estimation. The chronological age or the probability that an individual is older or younger than a given age threshold are generally estimated by means of some statistical methods based on observations performed on specific physical attributes. Among these statistical methods, those developed in the Bayesian framework allow users to provide coherent and transparent assignments which fulfill forensic and medico-legal purposes. The application of the Bayesian approach is facilitated by using probabilistic graphical tools, such as Bayesian networks. The aim of this work is to test the performances of the Bayesian network for age estimation recently presented in scientific literature in classifying individuals as older or younger than 18 years of age. For these exploratory analyses, a sample related to the ossification status of the medial clavicular epiphysis available in scientific literature was used. Results obtained in the classification are promising: in the criminal context, the Bayesian network achieved, on the average, a rate of correct classifications of approximatively 97%, whilst in the civil context, the rate is, on the average, close to the 88%. These results encourage the continuation of the development and the testing of the method in order to support its practical application in casework.

Classification supervisée multi-étiquette en actes de dialogue: analyse discriminante et transformations de Schoenberg

Abstract This work studies the multi-label classification of turns in simple English Wikipedia talk pages into dialog acts. The treated dataset was created and multi-labeled by (Ferschke et al., 2012). The first part analyses dependences between labels, in order to examine the annotation coherence and to determine a classification method. Then, a multi-label classification is computed, after transforming the problem into binary relevance. Regarding features, whereas (Ferschke et al., 2012) use features such as uni-, bi-, and trigrams, time distance between turns or the indentation level of the turn, other features are considered here: lemmas, part-of-speech tags and the meaning of verbs (according to WordNet). The dataset authors applied approaches such as Naive Bayes or Support Vector Machines. The present paper proposes, as an alternative, to use Schoenberg transformations which, following the example of kernel methods, transform original Euclidean distances into other Euclidean distances, in a space of high dimensionality. Résumé Ce travail étudie la classification supervisée multi-étiquette en actes de dialogue des tours de parole des contributeurs aux pages de discussion de Simple English Wikipedia (Wikipédia en anglais simple). Le jeu de données considéré a été créé et multi-étiqueté par (Ferschke et al., 2012). Une première partie analyse les relations entre les étiquettes pour examiner la cohérence des annotations et pour déterminer une méthode de classification. Ensuite, une classification supervisée multi-étiquette est effectuée, après recodage binaire des étiquettes. Concernant les variables, alors que (Ferschke et al., 2012) utilisent des caractéristiques telles que les uni-, bi- et trigrammes, le temps entre les tours de parole ou l'indentation d'un tour de parole, d'autres descripteurs sont considérés ici : les lemmes, les catégories morphosyntaxiques et le sens des verbes (selon WordNet). Les auteurs du jeu de données ont employé des approches telles que le Naive Bayes ou les Séparateurs à Vastes Marges (SVM) pour la classification. Cet article propose, de façon alternative, d'utiliser et d'étendre l'analyse discriminante linéaire aux transformations de Schoenberg qui, à l'instar des méthodes à noyau, transforment les distances euclidiennes originales en d'autres distances euclidiennes, dans un espace de haute dimensionnalité.

Le projet RDoC : la classification psychiatrique de demain ?

Nous présentons dans cet article l'histoire, les grands principes méthodologiques ainsi que la réception scientifique et médiatique du projet Research domain criteria (RDoC) lancé en 2009 aux États-Unis par le National institute of mental health (NIMH). Le projet RDoC, dévolu à la recherche, s'oppose au Manuel diagnostique et statistique des troubles mentaux (DSM) en mettant l'accent sur les dimensions du fonctionnement normal du cerveau, au croisement des recherches génétiques, des neurosciences cognitives et des sciences comportementales. Ce projet représente un pari sur le futur et son succès est tributaire de l'adhésion des chercheurs américains au nouveau cadre de référence qu'il propose, cadre qui reste encore largement à construire.