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    Identification of Brain Structures and Functional Cortico - Muscular Networks: Machine Learning Object Recognition and Network Physiology Approach
    (Università della Calabria, 2020-03-05) Rizzo, Rossella; Critelli, Salvatore; Pantano, Pietro; Ivanov, Plamen
    The brain is the most complex part in the human body. This organ is responsible for our intelligence, interpreting sensation, initiating body movement, and controlling all of our behaviors. Over hundreds of years, scientists have learned much about the brain, from a microscopic and macroscopic point of view. We now know the general rules under which information is transferred from neuron to neuron and we can differentiate between various brain structures and brain areas, each of them responsible of a particular function in the human organism. However, due to the vast complexity of the brain, much remains to be discovered. Researchers continue to explore the mechanics regulating a healthy brain that functions quickly and automatically, but we are still at the point where much work remains to identify the key differences between a physiological and a pathological situation in anatomic brain structures and functionality of the brain. The lack of information in this sense affects the diagnostic process of many neurodegenerative disorders, that can be discovered only from the symptoms shown by the subject and that, therefore, can be treated to reduce the pain and to give better conditions of life. The present research aims to better understand anatomic brain structures and functional interactions networks in the brain in order to early diagnose the most common neurodegenerative diseases. In the framework of the investigation of the anatomic brain structures the Neuroimaging is the most powerful tool used in basic research and clinical field. The Magnetic Resonance Imaging (MRI) is one of the most recent techniques of brain imaging and largely used for its low degree of invasion in the human body. It can provide valuable information in the detection of morphological markers that can highlight on the healthy status of the subject. A fundamental step in the pre-processing and analysis of magnetic resonance images is the individuation of the Mid-Sagittal Plane (MSP), where the mid brain is located, in order to set a coordinate reference system for the MRI scan images, and to precisely measure small changes in the surfaces, volumes and distances between different brain areas, which are used as biomarkers in the diagnostic process of certain diseases, such as Parkinson, Alzheimer, Progressive Supra-Nuclear Palsy. In this regard, part of the present research involves the improvement of brain MRIs analysis, with the use of machine learning techniques applied for the automatic identification of the MSP. In particular, the proposed method, Image Pixel Intensity (IPI) algorithm, is implemented in MatLab and is based on the k-mean, which allow to automatically segment the 2D MRIs in different brain tissues, and automatically identifies the slice where the brain tissues are most distinct from each other exploiting the intensity of the resonance signal expressed in the MRI by the color of the grayscale pixels. The results of this algorithm have been compared with the evaluation of four medical experts who manually identified the Mid-Sagittal, providing an average percentage error of 1.84%, and demonstrating that the proposed algorithm is promising and could be directly incorporated into larger diagnostic support systems. Following the main aim of the present research, the early diagnosis of neurodegenerative diseases, another machine learning technique, elastic net, has been implemented in Matlab in order to automatically predict the brain age, exploiting relationships involving the amount of gray matter present in the brain of the subjects analyzed, through a structural MRI study. The outcome of this work is the identification of profound correlations between the expected brain age and the general cognitive state: semantic verbal fluidity, processing speed, visual attention and cognitive flexibility, and visual attention and cognitive flexibility. Among the neurodegenerative diseases Parkinson lately acquired particular interest, due to its growing diffusion even within forty years old patients. This led to the study of functional interactions networks between the brain and the locomotor system during different sleep stages. Electroencephalography (EEG) and electromyography (EMG) data of healthy subjects and Parkinson's patients have been analyzed highlighting the correlations between different frequency bands present in the electrical signals emitted in the different brain areas and in the muscles of the chin and leg. Synchronous bursts in electrical activity signals in the brain and muscles have been analyzed, using the innovative method of Time Delay Stability (TDS), based on the cross-correlation function in consecutive time windows between two different signals. Links between the different frequency bands of different brain areas and the muscles with a long stable delay of the peak in the cross-correlation function are considered more stable, then stronger. The same analysis has been conducted on healthy and Parkinson's subjects, showing substantial differences in the networks of cortico-muscular interactions involving different frequencies between a physiological situation and a pathological one. Each sleep stage is uniquely identified by a particular pattern in the brain-muscle interactions. For Parkinson’s subjects these functional patterns change during each sleep stage; moreover, in general the strength of the links decreases during wake and light sleep but increases or remains the same during REM and deep sleep, especially for the brain-leg interactions, showing that during the waking phase the brain is not able to adequately control the muscles of the lower limbs. Analyzing in details the behavior of muscles the electric activity of different muscle fibers has been studied, considering subjects of different age groups (children, young adults and elderly subjects) in situations of stress or rest. In particular, EMG signals from the muscles of the leg and the back have been taken into account. The analysis shows that rest and stress have very different patterns, due to the different types of muscle fibers involved and how they behave during muscle relaxation and contraction; these relationships also change with age, identifying patterns that uniquely identify the age of the subjects analyzed and also vary during the same exercise by marking the precise point at which the subject reaches fatigue first and exhaustion afterwards.
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    Applicazione dei big data nel turismo, marketing ed education
    (Università della Calabria, 2020-03-18) Giglio, Simona; Critelli, Salvatore; Pantano, Pietro
    Il mondo è attualmente inondato da dati e l’avanzare delle tecnologie digitali amplifica questo fenomeno in modo esponenziale. Tale fenomeno viene etichettato con il concetto di Big Data ovvero le tracce digitali che le nostre attività quotidiane lasciano per effetto dell’uso massiccio dei sistemi ICT (Information Communication Technologies). I Big Data sono diventati il nuovo microscopio che rende “misurabile” la società. Per tali ragioni, la ricerca è incentrata sull’analisi dei Big data, estratti dai social media, da indagini online, da piattaforme di recensioni e da database, attraverso l’applicazione di tecniche e strumenti sviluppati nell’ambito dell’Intelligenza Artificiale. Algoritmi di machine learning, analisi semantica ed analisi statistica sono stati utilizzati per estrarre, dai Big Data, informazione sotto forma di “conoscenza” e “valore”, dimostrando come dati di grandi dimensioni possano fungere da ricca fonte di informazione, da un lato, per comprendere il comportamento dell’utente, parte integrante di una società complessa (conoscenza), e dall’altro, per sostenere i processi decisionali e i servizi forniti agli utenti/consumatori (valore). Il lavoro si caratterizza per un approccio multidisciplinare tra settori differenti quali le scienze sociali, le scienze statistiche e l’informatica. Questo ha permesso di fondare la ricerca sui Big Data nella teoria, e fornire un efficace recupero e analisi dei dati nella pratica. Le tecniche di machine learning sono state applicate per (i) il riconoscimento delle immagini, (ii) per la creazione di cluster, (iii) per l’analisi del testo (sentiment analysis) e (iv) per la profilazione di classi di utenti. Per il riconoscimento delle immagini l’approccio ha richiamato le reti neurali artificiali (deep artificial neural networks), algoritmi e sistemi computazionali ispirati al cervello umano utilizzando le potenzialità del programma Wolfram Mathematica e la disponibilità di dati estratti da social network quali Flickr, Twitter, Instagram ed altre piattaforme come TripAdvisor. Gli strumenti utilizzati nella ricerca hanno permesso di indagare e di rilevare in modo oggettivo dall’analisi di immagini e di testi condivisi sul web, alcuni comportamenti cognitivi degli utenti/consumatori alla base delle loro scelte nonché l’attrattività di una destinazione turistica e la qualità dell’esperienza dell’utente. Lo studio del significato delle parole nel testo ha aperto la strada al web semantico che permette ad un utente di acquisire informazioni approfondite durante una ricerca attraverso un sistema formato da una rete di relazioni e connessioni tra documenti. Partendo dalle ricerche di Ogden e Richards sullo studio del significato e di Jakobson che studiò i processi comunicativi, si è cercato di strutturare e sistematizzare un processo che riflette un atto comunicativo ed informativo tale che un simbolo (immagine) attraverso l’applicazione di un significante (machine learning che si sostituisce al processo mentale proprio dell’uomo) permettesse l’esplicitazione di un referente (oggetto\etichetta) che opportunatamente porta alla trasmissione di un messaggio sotto forma di conoscenza. Il tutto coordinato da un sistema in grado di coniugare fattori differenti in un’ottica interdisciplinare dove l’analisi dei dati combacia perfettamente con la linguistica. Attingendo da studi precedenti, i risultati raggiunti dimostrano che gli algoritmi di analisi dei Big Data quali l’apprendimento automatico contribuiscono da un lato alla comprensione sull’esperienza dell’utente verso un luogo, una destinazione; d’altra parte, la loro analisi fornisce una conoscenza sistematica delle valutazioni dei consumatori su un determinato prodotto o servizio e verso lo sviluppo di una sorta di “intelligenza sociale”. Inoltre i risultati della ricerca propongono come, un approccio più sofisticato al monitoraggio dei social media nel contesto turistico e nel marketing, nonché nel settore dell’education, possa contribuire a migliorare le decisioni strategiche e le politiche operative degli stakeholder nonché ad avere una visione psicologica sugli atteggiamenti e sul comportamento di un ampio spettro di utenti.
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    <> costruzione di possibili profili di sostenibilità applicati a scala di quartiere.
    (2013-11-29) Manfredi, Emilia; Pantano, Pietro; Rossi, Francesco
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    Nano materials and innovative laser-based accelerators for cultural heritage
    (2017-07-12) Veltri, Simona; Pantano, Pietro; Bonanno, Assunta; Antici, Patrizio
    Università della Calabria, Dipartimento di Fisica
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    Modellazione di linguaggi naturali e artificiali attraverso la scienza delle reti
    (2012-12-17) Bertacchini, Francesca; Bilotta, Eleonora; Pantano, Pietro
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    La comunicazione scientifica nell'arte tra realtà e realtà aumentata
    (2014-06-05) Rinaudo, Daniela; Pantano, Pietro
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    Experimental and theoretical study of polyhedral carbon Nano-Onions
    (2018-02-23) Basantes Valverde, Marlon Danilo; Pantano, Pietro; Caputi, Lorenzo; De Luca, Giorgio
    Carbon nano-onions (CNOs), in their spherical or polyhedral forms, represent an important class of nanomaterials due to their peculiar physical and electrochemical properties. Among the different methods of production, arc discharge between graphite electrodes sustained by deionized water is one of the most promising to obtain good quality CNOs. The arc discharge method is applied to optimize the production of CNOs, and the synthesized nanomaterials by TEM was studied. An innovative experimental arrangement is used to obtain CNOs dispersed in water together with other carbon nanomaterials, and a black hard cathodic deposit. A simple mechanical grinding of the deposit it allowed to obtain turbostratic polyhedral CNOs with different aspect ratios, which exhibited higher stability towards burning in air, compared to CNOs found in water. A mechanism for the formation of the CNOs contained in the deposit, different from the generally accepted mechanism responsible for the synthesis of CNOs dispersed in water, is hypothesized. These spherical or polyhedral multi-shell fullerenes are widely studied owing to their interesting electronic and mechanical proprieties; nevertheless, comparative studies on these nanoparticles remain scarce. Herein, some key electronic proprieties of single and double walled icosahedral fullerenes as function of their sizes were calculated in the frame of the Density Functional Theory. In particular, structures of icosahedral polyhedral fullerenes, previously validated, were used to get the gap between the Highest Occupied Molecular Orbital and the Lowest Unoccupied Molecular Orbital levels (H-L gap), electron affinity, first ionization potential, electronegativity as well as the Density of the electronic States. This work shows that the H-L gap of the single-wall fullerenes decreases as the nanoparticles size increases, whereas an opposite trend was obtained for the double walled fullerenes. Going from single to double wall nanoparticles; a systematic and marked decrease of the H-L gap was found although, this difference reduces increasing the size of the double walled up obtaining an inversion. The DOS structures of SW nanoparticles changes radically adding a second shell, and the extent of these changes depends on the sizes of the analyzed fullerenes
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    Gene expression as a digital communication system
    (2018-02-02) Cevallos Vilacrés, Yesenia Elizabeth; Pantano, Pietro; Marano, Salvatore
    This PhD thesis extends upon the information theories of digital communication systems to analyse biological communications (nanocommunications) in order to accurately model biological communication as digital communication by providing an essential analysis of the analogies between both systems. As such, this work analysed gene expression from two perspectives: digital communication systems as a general perspective and internetwork systems as a specific perspective (keeping in mind that digital communication networks are a subarea of digital communication systems). First, this work presents a novel layered network model that represents gene expression and the role of the Golgi apparatus as an internetwork router to transmit proteins to a target organ. Second, supported by the aforementioned layered network model, this work presents a digital communication system end-to-end model that represents gene expression with regard to the production of proteinaceous hormones in the endocrine system by using Shannon’s theorem. In addition, each molecular process encoding biological information, from the transcription and translation of deoxyribonucleic acid (DNA) to hormone signalling, is represented by a layered network model. These models apply the general advantages of digital internetworks and systems (i.e., performance and efficiency) to the transmission of biological information in gene expression systems. The proposed models and analysis define the duality between digital and biological communication systems, and the results herein can be used to overcome the disadvantages of both systems. One of the most important applications of the current study is the potential use of the characteristics of both communication systems in the nano/bio-hybrid medical field (i.e., for the treatment of diseases such as cancer). Hence, the analysis presented in this study may prevent side effects by specifically enhancing the transmission of information to a suitable destination (i.e., to specific target organs), thereby facilitating the development of optimal and less expensive treatments.
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    Aspetti genetici dell'evoluzione linguistica nelle popolazioni umane: un'analisi comparativa e interdisciplinare
    (2017-07-12) Vigna, Mara; Pantano, Pietro; Passarino, Giuseppe; Dato, Serena