Abstract

Abstract
Phenotypic plasticity is important in adaptation and shapes the evolution of organisms. However, we understand little about what aspects of the genome are important in facilitating plasticity. Eusocial insect societies produce plastic phenotypes from the same genome, as reproductives (queens) and nonreproductives (workers). The greatest plasticity is found in the simple eusocial insect societies in which individuals retain the ability to switch between reproductive and nonreproductive phenotypes as adults. We lack comprehensive data on the molecular basis of plastic phenotypes. Here, we sequenced genomes, microRNAs (miRNAs), and multiple transcriptomes and methylomes from individual brains in a wasp (Polistes canadensis) and an ant (Dinoponera quadriceps) that live in simple eusocial societies. In both species, we found few differences between phenotypes at the transcriptional level, with little functional specialization, and no evidence that phenotype-specific gene expression is driven by DNA methylation or miRNAs. Instead, phenotypic differentiation was defined more subtly by nonrandom transcriptional network organization, with roles in these networks for both conserved and taxon-restricted genes. The general lack of highly methylated regions or methylome patterning in both species may be an important mechanism for achieving plasticity among phenotypes during adulthood. These findings define previously unidentified hypotheses on the genomic processes that facilitate plasticity and suggest that the molecular hallmarks of social behavior are likely to differ with the level of social complexity.

Abstract
Outlying observations are commonly encountered in the analysis of time series. In this paper a Bayesian approach is employed to detect additive outliers in order one Poisson integer-valued autoregressive time series. The methodology is informative and allows the identification of the observations which require further inspection. The procedure is illustrated with simulated and observed data sets.

Abstract
Encontrar pessoas com interesses semelhantes dentro de um domínio pode fornecer um importante auxílio na gestão de centros de investigação. Como a produção académica é facilmente obtida em bases de dados bibliográficas e académicas, estas podem ser usadas para descobrir as afinidades entre os investigadores que não estejam já evidenciadas pela co-autoria. Este processo de descoberta dá-se com a ajuda de técnicas de análise de texto, na base dos termos utilizados nos respectivos documentos. A afinidade pode ser representada em forma de rede, em que os nós representam os artigos de cada investigador e as ligações representam similaridade entre os diferentes investigadores. Cada nó pode ser caracterizado através de diversas medidas de centralidade na rede e algoritmos de detecção de comunidades permitem identificar grupos com interesses semelhantes. Cada nó é ainda caracterizado por um conjunto de palavras-chave e resumos descobertos automaticamente com a ajuda de técnicas avançadas. Este artigo fornece mais detalhes sobre os métodos adoptados e/ou desenvolvidos, alguns dos quais foram implementados no nosso protótipo. Os métodos descritos são gerais e aplicáveis a muitos domínios diferentes, incluindo documentos que descrevem projetos de I&D, documentos associados a legislação, processos judiciais ou procedimentos médicos. Acreditamos deste modo que este trabalho pode ser útil para um público relativamente amplo.

Abstract
As the amount of content and the number of users in social relationships is continually growing in the Internet, resource sharing and access policy management is difficult, time-consuming and error-prone. Cross-domain recommendation of private or protected resources managed and secured by each domain’s specific access rules is impracticable due to private security policies and poor sharing mechanisms. This work focus on exploiting resource’s content, user’s preferences, users’ social networks and semantic information to cross-relate different resources through their meta information using recommendation techniques that combine collaborative-filtering techniques with semantics annotations, by generating associations between resources. The semantic similarities established between resources are used on a hybrid recommendation engine that interprets user and resources’ semantic information. The recommendation engine allows the promotion and discovery of unknownunknown resources to users that could not even know about the existence of those resources thus providing means to solve the cross-domain recommendation of private or protected resources. © Springer International Publishing AG 2016.

Abstract
This work presents a recommender system of economic news articles. Its objectives are threefold: (i) managing the vocabulary of the economic news domain to improve the system based on the seamlessly intervention of the documentalist (ii) automatically multi-classify the economic new articles and users profiles based on the domain vocabulary, and (iii) recommend the articles by comparing the multiclassification of the articles and profiles of the users. While several solutions exist to recommend news, multi-classify document and compare representations of items and profiles. They are not automatically adaptable to provide a mutual answer to previous points. Even more, existing approaches lacks substantial correlation with the human and in particular with the documentalist perspective. © Springer International Publishing Switzerland 2015.