University of Modena and Reggio Emilia

About: University of Modena and Reggio Emilia is a education organization based out in Modena, Italy. It is known for research contribution in the topics: Population & Medicine. The organization has 8179 authors who have published 22418 publications receiving 671337 citations. The organization is also known as: Università degli Studi di Modena e Reggio Emilia & Universita degli Studi di Modena e Reggio Emilia.

Fluoxetine effects on molecular, cellular and behavioral endophenotypes of depression are driven by the living environment

Abstract: Selective serotonin reuptake inhibitors (SSRIs) represent the most common treatment for major depression. However, their efficacy is variable and incomplete. In order to elucidate the cause of such incomplete efficacy, we explored the hypothesis positing that SSRIs may not affect mood per se but, by enhancing neural plasticity, render the individual more susceptible to the influence of the environment. Consequently, SSRI administration in a favorable environment promotes a reduction of symptoms, whereas in a stressful environment leads to a worse prognosis. To test such hypothesis, we exposed C57BL/6 mice to chronic stress in order to induce a depression-like phenotype and, subsequently, to fluoxetine treatment (21 days), while being exposed to either an enriched or a stressful condition. We measured the most commonly investigated molecular, cellular and behavioral endophenotypes of depression and SSRI outcome, including depression-like behavior, neurogenesis, brain-derived neurotrophic factor levels, hypothalamic-pituitary-adrenal axis activity and long-term potentiation. Results showed that, in line with our hypothesis, the endophenotypes investigated were affected by the treatment according to the quality of the living environment. In particular, mice treated with fluoxetine in an enriched condition overall improved their depression-like phenotype compared with controls, whereas those treated in a stressful condition showed a distinct worsening. Our findings suggest that the effects of SSRI on the depression- like phenotype is not determined by the drug per se but is induced by the drug and driven by the environment. These findings may be helpful to explain variable effects of SSRI found in clinical practice and to device strategies aimed at enhancing their efficacy by means of controlling environmental conditions.

POSEidon: Face-from-Depth for Driver Pose Estimation

Abstract: Fast and accurate upper-body and head pose estimation is a key task for automatic monitoring of driver attention, a challenging context characterized by severe illumination changes, occlusions and extreme poses. In this work, we present a new deep learning framework for head localization and pose estimation on depth images. The core of the proposal is a regressive neural network, called POSEidon, which is composed of three independent convolutional nets followed by a fusion layer, specially conceived for understanding the pose by depth. In addition, to recover the intrinsic value of face appearance for understanding head position and orientation, we propose a new Face-from-Depth model for learning image faces from depth. Results in face reconstruction are qualitatively impressive. We test the proposed framework on two public datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new challenging dataset mainly inspired by the automotive setup. Results show that our method overcomes all recent state-of-art works, running in real time at more than 30 frames per second.