Universidade Federal de Minas Gerais

About: Universidade Federal de Minas Gerais is a education organization based out in Belo Horizonte, Minas Gerais, Brazil. It is known for research contribution in the topics: Population & Immune system. The organization has 41631 authors who have published 75688 publications receiving 1249905 citations.

Protozoan encounters with Toll-like receptor signalling pathways: implications for host parasitism

Abstract: Toll-like receptors (TLRs) have emerged as a major receptor family involved in non-self recognition. They have a vital role in triggering innate immunity and orchestrate the acquired immune response during bacterial and viral infection. However, the role of TLRs during infection with protozoan pathogens is less clear. Nevertheless, our understanding of how these parasitic microorganisms engage the host TLR signalling system has now entered a phase of rapid expansion. This Review describes recent insights into how parasitic protozoans are sensed by TLR molecules, and how the TLR system itself can be targeted by these microbial pathogens for their own survival.

Conceptual Change or Conceptual Profile Change

Abstract: In this paper I draw an overview of a new model to analyse conceptual evolution in the classroom, based on the notion of Conceptual Profile. This model differs from conceptual change models in suggesting that it is possible to use different ways of thinking in different domains and that a new concept does not necessarily replace previous and alternative ideas. According to this model, learning science is to change a conceptual profile and become conscious of the different zones of the profile, which includes commonsense and scientific ideas.

Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

Abstract: Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or "spoofed") and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, while the second approach focuses on learning the weights of the network via back-propagation. We consider nine biometric spoofing benchmarks --- each one containing real and fake samples of a given biometric modality and attack type --- and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come.

Supervised Learning for Fake News Detection

Abstract: A large body of recent works has focused on understanding and detecting fake news stories that are disseminated on social media. To accomplish this goal, these works explore several types of features extracted from news stories, including source and posts from social media. In addition to exploring the main features proposed in the literature for fake news detection, we present a new set of features and measure the prediction performance of current approaches and features for automatic detection of fake news. Our results reveal interesting findings on the usefulness and importance of features for detecting false news. Finally, we discuss how fake news detection approaches can be used in the practice, highlighting challenges and opportunities.