RankMerging: A supervised learning-to-rank framework to predict links in large social network

Uncovering unknown or missing links in social networks is a difficult task because of their sparsity and because links may represent different types of relationships, characterized by different structural patterns. In this paper, we define a simple yet efficient supervised learning-to-rank framework, called RankMerging, which aims at combining information provided by various unsupervised rankings. We illustrate our method on three different kinds of social networks and show that it substantially improves the performances of unsupervised metrics of ranking. We also compare it to other combination strategies based on standard methods. Finally, we explore various aspects of RankMerging, such as feature selection and parameter estimation and discuss its area of relevance: the prediction of an adjustable number of links on large networks.Comment: 43 pages, published in Machine Learning Journa

PREVALÊNCIA DE SINTOMAS DEPRESSIVOS EM ESTUDANTES DE MEDICINA: UM ARTIGO DE REVISÃO

O ingresso na universidade representa uma mudança de vida em que novas pressões, novas experiências e novas vivências acontecerão. O acadêmico de medicina é altamente vulnerável a apresentar sintomas depressivos devido ao conjunto de fatores que desencadeiam o aumento progressivo do grau de estresse ao decorrer do curso como auto-cobrança excessiva, intensa carga horária, responsabilidade profissional, entre outros. Os objetivos foram identificar a prevalência de sintomas depressivos em estudantes de medicina em universidades brasileiras, avaliando-os de acordo com idade, sexo e período e curso, relacionando a prevalência às possíveis causas desses sintomas. Foi realizada uma revisão bibliográfica de quinze (15) artigos dos vinte e cinco (25) encontrados durante uma semana de busca no segundo semestre de 2014, nos dos bancos de dados MEDLINE, LILCAS e SciELo. Os sintomas depressivos prevalecem nos acadêmicos do sexo feminino. Alunos que cursam o primeiro ou o terceiro ano. A idade não se mostrou um critério influente nos sintomas depressivos. O estudo mostrou uma situação alarmante. Sendo necessário maior cuidado com as mulheres e os estudantes da transição entre curso básico e clínica médica (grupos com maiores índices desses sintomas depressivos no geral). Assim, nota-se uma demanda de programas para a redução da sintomatologia de depressão nesses estudantes, com estratégias voltadas principalmente para os grupos com maior prevalência

Allosteric Antagonist Modulation of TRPV2 by Piperlongumine Impairs Glioblastoma Progression.

The use of computational tools to identify biological targets of natural products with anticancer properties and unknown modes of action is gaining momentum. We employed self-organizing maps to deconvolute the phenotypic effects of piperlongumine (PL) and establish a link to modulation of the human transient receptor potential vanilloid 2 (hTRPV2) channel. The structure of the PL-bound full-length rat TRPV2 channel was determined by cryo-EM. PL binds to a transient allosteric pocket responsible for a new mode of anticancer activity against glioblastoma (GBM) in which hTRPV2 is overexpressed. Calcium imaging experiments revealed the importance of Arg539 and Thr522 residues on the antagonistic effect of PL and calcium influx modulation of the TRPV2 channel. Downregulation of hTRPV2 reduces sensitivity to PL and decreases ROS production. Analysis of GBM patient samples associates hTRPV2 overexpression with tumor grade, disease progression, and poor prognosis. Extensive tumor abrogation and long term survival was achieved in two murine models of orthotopic GBM by formulating PL in an implantable scaffold/hydrogel for sustained local therapy. Furthermore, in primary tumor samples derived from GBM patients, we observed a selective reduction of malignant cells in response to PL ex vivo. Our results establish a broadly applicable strategy, leveraging data-motivated research hypotheses for the discovery of novel means tackling cancer

Portuguese recommendations for the use of methotrexate in rheumatic diseases – 2016 update

info:eu-repo/semantics/publishedVersio

Phénomènes de diffusion sur les grands réseaux : mesure et analyse pour la modélisation

Understanding information diffusion on complex networks is a key issue from a theoretical and applied perspective. Epidemiology-inspired SIR models have been proposed to model information diffusion. Recent papers have analyzed this question from a data-driven perspective. We complement these findings investigating if epidemic models calibrate with a systematic procedure are capable of reproducing key spreading cascade properties. We first identify a large-scale, rich dataset from which we can reconstruct the diffusion trail and the underlying network. Secondly, we examine the simple SIR model as a baseline model and conclude that it was unable to generate structurally realistic spreading cascades. We found the same result examining model extensions to which take into account heterogeneities observed in the data. In contrast, other models which take into account time patterns available in the data generate qualitatively more similar cascades. Although one key property was not reproduced in any model, this result highlights the importance of taking time patterns into account. We have also analyzed the impact of the underlying network structure on the models examined. In our data the observed cascades were constrained in time, so we could not rely on the theoretical results relating the asymptotic behavior of the epidemic and network topological features. Performing simulations we assessed the impact of these common topological properties in time-bounded epidemic and identified that the distribution of neighbors of seed nodes had the most impact among the investigated properties in our context. We conclude discussing identifying perspectives opened by this work.Dans cette thèse nous avons étudié la diffusion de l'information dans les grands graphes de terrain, en se focalisant sur les patterns structurels de la propagation. Sur le plan empirique, il s'est avéré difficile de capturer la structure des cascades de diffusion en termes de mesures simples. Sur le plan théorique, l'approche classique consiste à étudier des modèles stochastiques de contagion. Néanmoins, l'analyse formelle de ces modèles reste limité, car les graphes de terrain ont généralement une topologie complexe et le processus de diffusion se produit dans une fenêtre de temps limitée. Par conséquent, une meilleure compréhension des données empiriques, des modèles théoriques et du lien entre les deux est également cruciale pour la caractérisation de la diffusion dans les grands graphes de terrain. Après un état de l'art sur les graphes de terrain et la diffusion dans ce contexte au premier chapitre, nous décrivons notre jeu de données et discutons sa pertinence au chapitre 2. Ensuite, dans le chapitre 3, nous évaluons la pertinence du modèle SIR simple et de deux extensions qui prennent en compte des hétérogénéités de notre jeu de données. Dans le chapitre 4, nous explorons la prise en compte du temps dans l'évolution du réseau sous-jacent et dans le modèle de diffusion. Dans le chapitre 5, nous évaluons l'impacte de la structure du graphe sous-jacent sur la structure des cascades de diffusion générées avec les modèles étudiés dans les chapitres précédents. Nous terminons la thèse par un bilan des résultats et des perspectives ouvertes par les travaux menés dans cette thèse

Phénomènes de diffusion sur les grands réseaux (mesure et analyse pour la modélisation)

Dans cette thèse nous avons étudié la diffusion de l'information dans les grands graphes de terrain, en se focalisant sur les patterns structurels de la propagation. Sur le plan empirique, il s'est avéré difficile de capturer la structure des cascades de diffusion en termes de mesures simples. Sur le plan théorique, l'approche classique consiste à étudier des modèles stochastiques de contagion. Néanmoins, l'analyse formelle de ces modèles reste limité, car les graphes de terrain ont généralement une topologie complexe et le processus de diffusion se produit dans une fenêtre de temps limitée. Par conséquent, une meilleure compréhension des données empiriques, des modèles théoriques et du lien entre les deux est également cruciale pour la caractérisation de la diffusion dans les grands graphes de terrain. Après un état de l'art sur les graphes de terrain et la diffusion dans ce contexte au premier chapitre, nous décrivons notre jeu de données et discutons sa pertinence au chapitre 2. Ensuite, dans le chapitre 3, nous évaluons la pertinence du modèle SIR simple et de deux extensions qui prennent en compte des hétérogénéités de notre jeu de données. Dans le chapitre 4, nous explorons la prise en compte du temps dans l'évolution du réseau sous-jacent et dans le modèle de diffusion. Dans le chapitre 5, nous évaluons l'impacte de la structure du graphe sous-jacent sur la structure des cascades de diffusion générées avec les modèles étudiés dans les chapitres précédents. Nous terminons la thèse par un bilan des résultats et des perspectives ouvertes par les travaux menés dans cette thèse.Understanding information diffusion on complex networks is a key issue from a theoretical and applied perspective. Epidemiology-inspired SIR models have been proposed to model information diffusion. Recent papers have analyzed this question from a data-driven perspective. We complement these findings investigating if epidemic models calibrate with a systematic procedure are capable of reproducing key spreading cascade properties. We first identify a large-scale, rich dataset from which we can reconstruct the diffusion trail and the underlying network. Secondly, we examine the simple SIR model as a baseline model and conclude that it was unable to generate structurally realistic spreading cascades. We found the same result examining model extensions to which take into account heterogeneities observed in the data. In contrast, other models which take into account time patterns available in the data generate qualitatively more similar cascades. Although one key property was not reproduced in any model, this result highlights the importance of taking time patterns into account. We have also analyzed the impact of the underlying network structure on the models examined. In our data the observed cascades were constrained in time, so we could not rely on the theoretical results relating the asymptotic behavior of the epidemic and network topological features. Performing simulations we assessed the impact of these common topological properties in time-bounded epidemic and identified that the distribution of neighbors of seed nodes had the most impact among the investigated properties in our context. We conclude discussing identifying perspectives opened by this work.PARIS-JUSSIEU-Bib.électronique (751059901) / SudocSudocFranceF

On the use of the symmetry-adapted Monte Carlo for an effective sampling of large configuration spaces. The test cases of calcite structured carbonates and melilites

International audienceThe symmetry-adapted Monte Carlo sampling scheme is applied for the ab initio study of two mineralsystems, namely the calcite structured compound Ca0.75Mg0.25CO3 and soda-melilite (Na,Ca)AlSi2O7. Itis shown how an extensive use of symmetry, from the sampling of atomic configurations up to thequantum-mechanical calculation, makes feasible the investigation of large configuration spaces. As forthe sampling, we describe an effective procedure to specifically target low-energy configurations onthe potential energy surface of supercells of virtually any size. It is based on the suggestion that a correlationbetween symmetry and energy of the configurations exists according to which atomic distributionsof minimum and maximum energy are likely to have some spatial symmetry. This hypothesis is verifiedempirically and leads to a significant alleviation of the original problem by virtue of the possibility of tailoringthe symmetry-adapted Monte Carlo to select only symmetric configurations. The latter are alsofound to display a probability distribution similar to that of the entire set of configurations, thus providing,eventually, a suitable ab initio reference for the parameterization of model Hamiltonians. The moststable configuration so identified is used as pivot for the selection of new configurations having the sameatomic distribution but for the exchange of a couple of atoms. These are called ‘‘neighbors” to highlightboth their structural and energetic proximity to the pivot. We illustrate how, by collecting neighbors ofconfigurations of increasing energy, the description of the system can be progressively and deterministicallyimproved up to convergence of the calculated average properties, whatever the temperature.The same scheme works when moving to a supercell larger than the initial one (but of equivalent symmetry)since it is shown that stable structures remain so at any volume

Impressive response to immunotherapy in a metastatic gastric cancer patient: could somatic copy number alterations help patient selection?

Abstract Background Metastatic gastric cancer (GC) is an incurable and aggressive disease with a poor prognosis. Immunotherapy is an attractive approach for treating patients with cancer, and studies using immunotherapy have shown promising results in melanoma, kidney and non-small cell lung cancers, among others. Case presentation We present a case of a 50-year-old woman with metastatic GC whose cancer had progressed after first-line chemotherapy and who received pembrolizumab as an experimental treatment. Molecular analyses showed that her tumor was negative for PD-L1 expression, contained microsatellite stability and several focal somatic copy number alterations. The patient experienced an almost complete response after eleven cycles of treatment. Her symptoms related to the disease disappeared, and the medication was well tolerated. Conclusions Despite reports of promising responses in some patients, immunotherapy is not suitable for all patients; therefore, we explored the molecular characteristics that could explain the exceptional response and clinical benefits observed in our patient

Portuguese recommendations for the use of methotrexate in rheumatic diseases - 2016 update

BACKGROUND: Methotrexate (MTX) is the first-line drug in the treatment of rheumatoid arthritis (RA) and the most commonly prescribed disease modifying anti-rheumatic drug. Moreover, it is also used as an adjuvant drug in patients under biologic therapies, enhancing the efficacy of biologic agents. OBJECTIVES: To review the literature and update the Portuguese recommendations for the use of MTX in rheumatic diseases first published in 2009. METHODS: The first Portuguese guidelines for the use of MTX in rheumatic diseases were published in 2009 and were integrated in the multinational 3E Initiative (Evidence Expertise Exchange) project. The Portuguese rheumatologists based on literature evidence and consensus opinion formulated 13 recommendations. At a national meeting, the recommendations included in this document were further discussed and updated. The document resulting from this meeting circulated to all Portuguese rheumatologists, who anonymously voted online on the level of agreement with the updated recommendations. RESULTS: Results presented in this article are mainly in accordance with previous guidelines, with some new information regarding hepatitis B infection during MTX treatment, pulmonary toxicity monitoring, hepatotoxicity management, association with hematologic neoplasms, combination therapy and tuberculosis screening during treatment. CONCLUSION: The present recommendations combine scientific evidence with expert opinion and attained desirable agreement among Portuguese rheumatologists. The regular update of these recommendations is essential in order to keep them a valid and useful tool in daily practice.publishersversionpublishe