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Francesco Orsini   Dr.  Institute, Department or Faculty Head 
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Francesco Orsini published an article in March 2018.
Top co-authors See all
Claudia Riccardi

240 shared publications

Dipartimento di Fisica G. Occhialini, Università degli Studi di Milano‐Bicocca and INFM, piazza della Scienza 3, I‐20126 Milano, Italy

Francesco S. Pavone

229 shared publications

European Laboratory for Non Linear Spectroscopy (LENS), Italy

Ludovico Silvestri

146 shared publications

LENS - Lab. Europeo di Spettroscopie Non-Lineari (Italy)

Annibale Biggeri

136 shared publications

Francesco Cilurzo

104 shared publications

27
Publications
6
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84
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Publication Record
Distribution of Articles published per year 
(2005 - 2018)
Total number of journals
published in
 
20
 
Publications See all
PROCEEDINGS-ARTICLE 0 Reads 0 Citations Mapping the quantitative cytoarchitecture of the whole mouse brain by light-sheet microscopy and digital brain atlasing ... Ludovico Silvestri, Antonino Paolo Di Giovanna, Giacomo Mazz... Published: 14 March 2018
Neural Imaging and Sensing 2018, doi: 10.1117/12.2286616
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Quantitative and scalable whole-brain neuroanatomical mapping, with cellular resolution and molecular specificity, poses significant technological challenges. Indeed, a high image quality must be preserved reliably across the entire specimen and not only in a few representative volumes. On the other hand, robust and automated image analysis methods must be appropriately scalable to teravoxel datasets. Here, we present an experimental pipeline, involving tissue clearing, high-resolution light-sheet microscopy, volume registration to atlas, and deep learning strategies for image analysis, allowing the reconstruction of 3D maps of selected cell types in the whole mouse brain. We employed RAPID autofocusing [Silvestri et al., submitted] to keep the system sharply in focus throughout the entire mouse brain, without reducing the microscope throughput. Images were spatially anchored to reference atlas using semi-automatic tools (xNII family, http://www.nesys.uio.no). Finally, we used novel high-throughput tools for image processing, including deep learning strategies [Frasconi et al., 2014] to localize single neurons with high accuracy. By applying our pipeline to transgenically-labeled samples, we can produce an atlas of spatial distribution of genetically-defined cell types. Besides being a valuable reference for neurobiologists, these datasets can be used to build realistic simulations of neuronal functioning, such as in the Human Brain Project.
Article 2 Reads 0 Citations Methadone Dose Adjustments, Plasma R-Methadone Levels and Therapeutic Outcome of Heroin Users: A Randomized Clinical Tri... Guido Mannaioni, Cecilia Lanzi, Michela Lotti, Valentina Gal... Published: 01 January 2018
European Addiction Research, doi: 10.1159/000485029
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Aims: We aimed to improve the retention in treatment and therapeutic outcome of methadone maintenance treatment (MMT) patients by adjusting the oral methadone dose in order to reach a “target” plasma R-methadone level (80–250 ng/mL). Methods: A multicenter randomized controlled trial was organized. Results: The intention-to-treat statistical analysis showed that repeated dose adjustments performed in order to obtain therapeutic plasma R-methadone levels did not improve retention in treatment of heroin-dependent patients. However, patients having plasma methadone levels in the “target range” at the beginning of the study had a better retention in treatment than controls. Furthermore, patients succeeding in keeping plasma R-methadone target levels (per protocol analysis) remained in treatment and improved their social scores better than controls. ­Conclusion: Although the primary endpoint of this study was not demonstrated, a post hoc and a per protocol analysis suggested that patients in MMT with plasma R-methadone concentrations in the target range have a better therapeutic outcome than controls.
PROCEEDINGS-ARTICLE 1 Read 0 Citations Software Tools for Efficient Processing of High-Resolution 3D Images of Macroscopic Brain Samples Giacomo Mazzamuto, Ludovico Silvestri, Irene Costantini, Fra... Published: 01 January 2018
Biophotonics Congress: Biomedical Optics Congress 2018 (Microscopy/Translational/Brain/OTS), doi: 10.1364/translational.2018.jth3a.64
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We present a software pipeline for high throughput stitching and processing of high-resolution tomographies of whole mouse brains. We then employ machine learning techniques for automatic segmentation and classification of neurons in the acquired datasets.
PROCEEDINGS-ARTICLE 2 Reads 0 Citations Towards a Full Volumetric Atlas of Cell-specific Neuronal Spatial Organization in the Entire Mouse Brain Ludovico Silvestri, Antonino Paolo Di Giovanna, Giacomo Mazz... Published: 01 January 2018
Biophotonics Congress: Biomedical Optics Congress 2018 (Microscopy/Translational/Brain/OTS), doi: 10.1364/translational.2018.jtu3a.62
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We present a pipeline, based on light-sheet microscopy and high-throughput image analysis, for full-volumetric 3D mapping of the spatial organization of selected neuronal types in the whole mouse brain.
BOOK-CHAPTER 0 Reads 0 Citations Toward the Creation of Urban Foodscapes: Case Studies of Successful Urban Agriculture Projects for Income Generation, Fo... Monique Centrone Stefani, Francesco Orsini, Francesca Magref... Published: 01 January 2018
Sustainable Development and Biodiversity, doi: 10.1007/978-3-319-67017-1_5
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Urban agriculture (UA) is a strategic means of achieving sustainable urban food security now and into the future. While a number of different models of agriculture will be required to provide food for the concentrated number of people in urban spaces, UA can be key tool in helping urban residents meet micronutrient requirements, whose deficiency is now recognized as the most important cause of “hidden hunger” globally. UA, or the cultivation of crops for food in small and irregular spaces within a city and its periphery, has been practiced for as long as cities have existed. However, it is gaining increased attention for both its limited scale, its effects on nutrition and community building, the associated possibilities for employment and social mobility, its relevance in a vibrant and diverse food system and for all of these reasons, its sustainability. UA projects are springing up in cities all over the world, always engaging the collaboration of multiple urban stakeholders and increasingly with a focus on the most vulnerable people in society: the poor, the elderly, children, and those suffering malnutrition and chronic disease. For all city dwellers, UA improves livelihoods within the city environment by reducing city ecological footprint, strengthening city resilience to climate change and environmental hazards, reducing noise, improving air quality and the city microclimate, minimizing drought by improving storm water management, and contributing to solid waste management. It increases human health and well-being through stress recovery, encouragement of exercise, reduction of urban violence, facilitating social contact, and providing sources of added employment and income. Using the concept of urban political ecology as a foundation for understanding food dynamics, this chapter will describe a range of different UA initiatives across the globe, giving special attention to their multifunctionality in the context of social vulnerabilities within urban spaces. In so doing, it will present UA as an important platform for community engagement and sustainability building toward increased food security. Turning concrete into green productive lands results in changes in physical, psychological, environmental, and community health. At the same time, the application of social leftovers (abandoned lands, unused rooftops, plastic and wood containers, etc.) onto productive means creates a new model for adding micronutrition to urban diets that integrates and takes seriously social, cultural, nutritional, and economic outcomes.
BOOK-CHAPTER 1 Read 0 Citations Erratum to: Rooftop Urban Agriculture Francesco Orsini, Marielle Dubbeling, Henk de Zeeuw, Giorgio... Published: 01 January 2017
Urban Agriculture, doi: 10.1007/978-3-319-57720-3_21
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