Chronic cholesterol administration to the brain supports complete and long-lasting cognitive and motor amelioration in Huntington's disease

: Evidence that Huntington's disease (HD) is characterized by impaired cholesterol biosynthesis in the brain has led to strategies to increase its level in the brain of the rapidly progressing R6/2 mouse model, with a positive therapeutic outcome. Here we tested the long-term efficacy of chronic administration of cholesterol to the brain of the slowly progressing zQ175DN knock-in HD mice in preventing ("early treatment") or reversing ("late treatment") HD symptoms. To do this we used the most advanced formulation of cholesterol loaded brain-permeable nanoparticles (NPs), termed hybrid-g7-NPs-chol, which were injected intraperitoneally. We show that one cycle of treatment with hybrid-g7-NPs-chol, administered in the presymptomatic ("early treatment") or symptomatic ("late treatment") stages is sufficient to normalize cognitive defects up to 5 months, as well as to improve other behavioral and neuropathological parameters. A multiple cycle treatment combining both early and late treatments ("2 cycle treatment") lasting 6 months generates therapeutic effects for more than 11 months, without severe adverse reactions. Sustained cholesterol delivery to the brain of zQ175DN mice also reduces mutant Huntingtin aggregates in both the striatum and cortex and completely normalizes synaptic communication in the striatal medium spiny neurons compared to saline-treated HD mice. Furthermore, through a meta-analysis of published and current data, we demonstrated the power of hybrid-g7-NPs-chol and other strategies able to increase brain cholesterol biosynthesis, to reverse cognitive decline and counteract the formation of mutant Huntingtin aggregates. These results demonstrate that cholesterol delivery via brain-permeable NPs is a therapeutic option to sustainably reverse HD-related behavioral decline and neuropathological signs over time, highlighting the therapeutic potential of cholesterol-based strategies in HD patients. DATA AVAILABILITY: This study does not include data deposited in public repositories. Data are available on request to the corresponding authors

Study and production of front perception systems for vehicles using computer vision

In questa tesi saranno presentati i sistemi di percezione frontale a 3 telecamere in posizione asimmetrica dei veicoli autonomi TerraMax e TerraMax2 che hanno partecipato alle edizioni 2004 e 2005 della DARPA Grand Challenge e 2007 della DARPA Urban Challenge, con la descrizione degli algoritmi utilizzati per la calibrazione di telecamere, la stabilizzazione di immagini, l'estrazione di corsie stradali, la rilevazione di pendenza e l'individuazione di ostacoli in ambiente off-road. Saranno inoltre descritti due algoritmi per la percezione di cartelli di fine prescrizione, caso particolare nel panorama dei segnali stradali perché privo di colore, e di pannelli integrativi.This thesis presents the front perception system based on 3 cameras placed in asymmetric position mounted on the autonomous vehicles TerraMax and TerraMax2, which participated to the 2004 and 2005 editions of the DARPA Grand Challenge and to the DARPA Urban Challenge in 2007. The algorithms used for camera calibration, images stabilization, lane localization, slope perception and obstacle detection in off-road environment will be described. In the second part of the thesis, two algorithms for de-rescrition sign (which are particularly hard to be detected because of color absence) and additional panel detection

The Single Frame Stereo Vision System for Reliable Obstacle Detection used during the 2005 DARPA Grand Challenge on TerraMax

Autonomous driving in off-road environments requires an exceptionally capable sensor system, especially given that the unstructured environment does not provide many of the cues available in on-road environments. This paper presents a variable-width-baseline (up to 1.5 meters) single-frame stereo vision system for obstacle detection that can meet the needs of autonomous navigation in extreme environments. Efforts to maximize computational speed ---both in the attention given to accurate and stable calibration and the exploitation of the processors MMX and SSE instruction sets--- allow a guaranteed 15 fps rate. Along with the assured speed, the system proves very robust against false positives. The system has been field tested on the TerraMax vehicle, one of only five vehicles to complete the 2005 DARPA Grand Challenge course and the only one to do so using a vision system for obstacle detection

Obstacle Detection with Stereo Vision for Off-Road Vehicle Navigation

In this paper we present an artificial vision algorithm for real-time obstacle detection in unstructured environments. The images have been taken using a stereoscopical vision system. The system uses a new approach, of low computational load, to calculate a V-disparity image between left and right corresponding images, in order to estimate the cameras pitch oscillation caused by the vehicle movement. Then, the obstacles are localized by stereo matching and mapped in real world coordinates. Experimental results on sequences taken from a moving vehicle (which partecipated to the DARPA Grand Challenge 2004) in different unstructured scenarios are then presented, to demonstrate the validity of the approach

The Single Frame Stereo Vision System for Reliable Obstacle Detection used during

Abstract — Autonomous driving in off-road environments requires an exceptionally capable sensor system, especially given that the unstructured environment does not provide many of the cues available in on-road environments. This paper presents a variable-width-baseline (up to 1.5 meters) single-frame stereo vision system for obstacle detection that can meet the needs of autonomous navigation in extreme environments. Efforts to maximize computational speed —both in the attention given to accurate and stable calibration and the exploitation of the processors MMX and SSE instruction sets — allow a guaranteed 15 fps rate. Along with the assured speed, the system proves very robust against false positives. The system has been field tested on the TerraMax TM vehicle, one of only five vehicles to complete the 2005 DARPA Grand Challenge course and the only one to do so using a vision system for obstacle detection. I

Fedriga, “A Decision Network Based Frame-work for Visual Off-Road Path Detection Problem

Abstract — This paper describes a Decision Network based frame-work used for path-detection algorithm development in autonomous vehicle applications. Lane marker detection algorithms do not work in off-road environments. Off-road trails have too much complexity, with widely varying textures and many differing natural boundaries. The authors have developed a general approach. Images are segmented into regions, based on the homogeneity of some pixel properties and the resulting regions are classified as road or not-road by a Decision Network Process. Combinations of contiguous clusters form the path surface, allowing any arbitrary path to be represented. I

Obstacle detection with stereo vision for off-road vehicle navigation. Paper presented at the

In this paper we present an artificial vision algorithm for real-time obstacle detection in unstructured environments. The images have been taken using a stereoscopical vision system. The system uses a new approach, of low computational load, to calculate a V-disparity image between left and right corresponding images, in order to estimate the cameras pitch oscillation caused by the vehicle movement. Then, the obstacles are localized by stereo matching and mapped in real world coordinates. Experimental results on sequences taken from a moving vehicle (which partecipated to the DARPA Grand Challenge 2004) in different unstructured scenarios are then presented, to demonstrate the validity of the approach. 1