Giulio Iannello

Università Campus Bio-Medico

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Paolo Soda

Università Campus Bio-Medico

Luca Vollero

Università Campus Bio-Medico

Ermanno Cordelli

Università Campus Bio-Medico

Mario Merone

Università Campus Bio-Medico

Rosa Sicilia

Università Campus Bio-Medico

Leonardo Onofri

Università Campus Bio-Medico

Alessandro Bria

Università degli studi di Cassino e del Lazio Meridionale

Francesco S. Pavone

University of Florence

Massimo Bernaschi

Istituto per le Applicazioni del Calcolo Mauro Picone

Filippo Cacace

Università Campus Bio-Medico

Cooperative Institutions

University of Cyprus

197

Chinese University of Hong Kong

189

Applied Multilayers (United Kingdom)

189

Università Campus Bio-Medico

116

Istituti di Ricovero e Cura a Carattere Scientifico

Sapienza University of Rome

Campus Bio Medico University Hospital

University of Naples Federico II

University of Florence

National Research Council

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Micron-scale Resolution Optical Tomography of Entire Mouse Brains with Confocal Light Sheet Microscopy

Journal of Visualized Experiments (2013)

Ludovico Silvestri Alessandro Bria Irene Costantini Leonardo Sacconi Hanchuan Peng

Understanding the architecture of mammalian brain at single-cell resolution is one of the key issues of neuroscience. However, mapping neuronal soma and projections throughout the whole brain is still challenging for imaging and data management technologies. Indeed, macroscopic volumes need to be reconstructed with high resolution and contrast in a reasonable time, producing datasets in the TeraByte range. We recently demonstrated an optical method (confocal light sheet microscopy, CLSM) capable of obtaining micron-scale reconstruction of entire mouse brains labeled with enhanced green fluorescent protein (EGFP). Combining light sheet illumination and confocal detection, CLSM allows deep imaging inside macroscopic cleared specimens with high contrast and speed. Here we describe the complete experimental pipeline to obtain comprehensive and human-readable images of entire mouse brains labeled with fluorescent proteins. The clearing and the mounting procedures are described, together with the steps to perform an optical tomography on its whole volume by acquiring many parallel adjacent stacks. We showed the usage of open-source custom-made software tools enabling stitching of the multiple stacks and multi-resolution data navigation. Finally, we illustrated some example of brain maps: the cerebellum from an L7-GFP transgenic mouse, in which all Purkinje cells are selectively labeled, and the whole brain from a thy1-GFP-M mouse, characterized by a random sparse neuronal labeling.

Optical sectioning

10.3791/50696-v

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Citations (3)

Quantitative neuroanatomy of all Purkinje cells with light sheet microscopy and high-throughput image analysis

Frontiers in Neuroanatomy (2015)

Ludovico Silvestri Marco Paciscopi Paolo Soda Filippo Biamonte Giulio Iannello

Characterizing the cytoarchitecture of mammalian central nervous system on a brain-wide scale is becoming a compelling need in neuroscience. For example, realistic modeling of brain activity requires the definition of quantitative features of large neuronal populations in the whole brain. Quantitative anatomical maps will also be crucial to classify the cytoarchtitectonic abnormalities associated with neuronal pathologies in a high reproducible and reliable manner. In this paper, we apply recent advances in optical microscopy and image analysis to characterize the spatial distribution of Purkinje cells (PCs) across the whole cerebellum. Light sheet microscopy was used to image with micron-scale resolution a fixed and cleared cerebellum of an L7-GFP transgenic mouse, in which all PCs are fluorescently labeled. A fast and scalable algorithm for fully automated cell identification was applied on the image to extract the position of all the fluorescent PCs. This vectorized representation of the cell population allows a thorough characterization of the complex three-dimensional distribution of the neurons, highlighting the presence of gaps inside the lamellar organization of PCs, whose density is believed to play a significant role in autism spectrum disorders. Furthermore, clustering analysis of the localized somata permits dividing the whole cerebellum in groups of PCs with high spatial correlation, suggesting new possibilities of anatomical partition. The quantitative approach presented here can be extended to study the distribution of different types of cell in many brain regions and across the whole encephalon, providing a robust base for building realistic computational models of the brain, and for unbiased morphological tissue screening in presence of pathologies and/or drug treatments.

Neuroanatomy

10.3389/fnana.2015.00068

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An automated algorithmic recognition technique to support parallel software development

Beniamino Di Martino Giulio Iannello Hans Zima

Techniques for automatic program recognition, at the algorithmic level, could be of great interest in the area of program parallelization, because the selection of suitable parallelization strategies is driven by algorithmic features of the code. A technique for the specification and automatic recognition of algorithmic concepts is presented. Its flexibility and expressivity power for specifying the hierarchy, the constraints and the relationships among concepts allow it to deal with recognition of algorithmic concepts within optimized code, irregular computations, and in the presence of code sharing, delocalization, implementation variations and other problems related to program recognition in the context of the imperative languages typically used for scientific computation.

Code (set theory)

10.1109/pdse.1997.596832

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Day-ahead Forecast of PV Systems and End-Users in the Contest of Renewable Energy Communities

Tommaso Capotosto Anna Rita Di Fazio Sara Perna Francesco Conte Giulio Iannello

Efficient forecasting algorithms represent a key issue for monitoring and control of energy communities (ECs). After recalling the basic steps to perform a forecasting analysis, this paper reports the preliminary results obtained within the framework of the Italian ComER project, that aims to develop methods and tools for management and control of renewable ECs. Day-ahead forecast is applied to photovoltaic (PV) systems, residential end-users, and a public building. For each user, firstly an accurate preliminary analysis of public and private datasets of the target variables is performed. Then, forecasting methods based on persistence, multiple linear regression (MLR) and autoregressive integrated moving average (ARIMA) models have been implemented. Finally, results are compared to identify the more accurate forecasting method for each user belonging to the EC.

CONTEST

10.23919/aeit56783.2022.9951849

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The classification of Crithidia luciliae immunofluorescence test (CLIFT) using a novel automated system

Arthritis Research & Therapy (2014)

Francesca Buzzulini Amelia Rigon Paolo Soda Leonardo Onofri María Infantino

Abstract Introduction In recent years, there has been an increased demand for computer-aided diagnosis (CAD) tools to support clinicians in the field of indirect immunofluorescence. To this aim, academic and industrial research is focusing on detecting antinuclear, anti-neutrophil, and anti-double-stranded (anti-dsDNA) antibodies. Within this framework, we present a CAD system for automatic analysis of dsDNA antibody images using a multi-step classification approach. The final classification of a well is based on the classification of all its images, and each image is classified on the basis of the labeling of its cells. Methods We populated a database of 342 images—74 positive (21.6%) and 268 negative (78.4%)— belonging to 63 consecutive sera: 15 positive (23.8%) and 48 negative (76.2%). We assessed system performance by using k-fold cross-validation. Furthermore, we successfully validated the recognition system on 83 consecutive sera, collected by using different equipment in a referral center, counting 279 images: 92 positive (33.0%) and 187 negative (67.0%). Results With respect to well classification, the system correctly classified 98.4% of wells (62 out of 63). Integrating information from multiple images of the same wells recovers the possible misclassifications that occurred at the previous steps (cell and image classification). This system, validated in a clinical routine fashion, provides recognition accuracy equal to 100%. Conclusion The data obtained show that automation is a viable alternative for Crithidia luciliae immunofluorescence test analysis.

Immunofluorescence

10.1186/ar4510

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Exploratory Radiomics for Predicting Adaptive Radiotherapy in Non-Small Cell Lung Cancer

Rosa Sicilia Ermanno Cordelli Sara Ramella Michele Fiore Carlo Greco

The possibility of planning a therapy minimizing side effects and optimizing efficacy of cancer treatments is one of the main challenges tackled by precision medicine research in oncology. In this context, radiomics is revealing itself as a promising path for better understanding the correct approach to personalized cures. Its primary aim is to go beyond basic medical images analysis, which only leverages on direct measurements on the tumor mass, i.e. dimension and shape. On the contrary, radiomics approach is oriented to the extraction of heterogeneous and quantitative data from the images to characterize the disease from a wider perspective, in order to provide the physician a valid support for the therapy decision and survival prediction. This manuscript presents an application of radiomics to Non-Small Cell Lung Cancer, dealing with the novel task of predicting the possibility to carry out an adaptive therapy. We achieved promising performance, reporting a radiomics signature for predicting tumor reduction during therapy.

Personalized Medicine

10.1109/cbms.2018.00051

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Image sensors and VPU acceleration for data analysis and classification

Lorenzo Petrosino Giulio Iannello Mario Merone Luca Vollero

The term IoT (Internet of Things) refers to the set of technologies and protocols that enable the connection of physical devices with the Internet allowing the exchange and processing of information. To support and enable data processing by these systems, in recent years, there are more and more AI (Artificial Intelligence) algorithms within IoT solutions. The latter with AI integration, however, require both the ability to collect large amounts of data and the significant computing resources needed to process them. The current Cloud paradigm does not best support applications with stringent requirements in terms of response time or security. To overcome these limitations, research is focusing on the development of Edge Computing systems, i.e., based on a distributed computing model in which data processing occurs as close as possible to where the data is collected. In this work we do not aspire to the invention of a method or model of Edge Computing, but rather we want to analyze and verify the possibilities of the technology on the market. Developing an image classification system of subjects with or without face masks, we evaluate the performance increases obtained on a Raspberry Pi device, through the use of VPU (Vision Processing Unit) Movidius™ Myriad 2 and Myriad X accelerators. Developing an image classification system of subjects with or without face masks.

Data Processing

10.1109/metroind4.0iot51437.2021.9488432

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Solving biomedical classification tasks by softmax reconstruction in ECOC framework

Roberto D’Ambrosio Giulio Iannello Paolo Soda

Several medical and biological applications face with multiclass recognition problems. Such polychotomies can be addressed by decomposition techniques, which reduce the polychotomy into a series of dichotomies and then provide the final multiclass label using a reconstruction rule. Within this framework, we present a reconstruction rule based on softmax regression, where the features of the new classification task are the crisp labels and the reliabilities of dichotomizers' classifications. The approach has been tested on six medical and biological datasets, decomposing the polychotomies via the Error-Correcting Output Code. Its performances favorably compare with those provided by other two well-known reconstruction rules both in terms of global accuracy and accuracy per class.

Softmax function

Multiclass classification

10.1109/cbms.2013.6627834

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Citations (2)

Advances in Computer-Based Autoantibodies Analysis

Communications in computer and information science (2010)

Paolo Soda Giulio Iannello

IIf

10.1007/978-3-642-11721-3_26

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Definition of the DISC concurrent language

ACM SIGPLAN Notices (1989)

Giulio Iannello Antonino Mazzeo Giorgio Ventre

Although commercial distributed computers are now available, developing software for these systems is still a hard task. This prevents the users from getting full advantage out of the use of distributed computers. Among the techiniques proposed to make it easier to program the new architectures, concurrent languages have proved to be an effective approach both in system programming and in application development.The concurrent programming language DISC has been designed to promote software engineering techniques in the development of distributed programs. The language extends C with CSP-based concurrent mechanisms and it is especially suited for system programming of loosely coupled distributed architectures. A simple programming environment, running under UNIX System V operating system, allows the user to develop programs in a modular fashion. A first implementation of DISC has been developed on a network of UNIX machines. DISC programs are portable on a wide class of distributed computers, because of both the choice of UNIX as the host environment and the substantial independence of the language run time support from the hardware.In this article we report the definition of DISC and an example to show informally the practical use of the language.

10.1145/71052.71061

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Citations (7)