Mathematical Modeling to Investigate Antiarrhythmic Drug Side Effects: Rate-Dependence Role in Ionic Currents and Action Potentials Shape in the O’Hara Model

Sudden cardiac death due to ventricular arrhythmia is one of the leading causes of mortality in the world. In the last decades, it has proven that anti-arrhythmic drugs, which prolong the refractory period by means of prolongation of the cardiac action potential duration (APD), play a good role in preventing of relevant human arrhythmias. However, it has long been observed that the “class III antiarrhythmic effect” diminish at faster heart rates and that this phenomenon represent a big weakness, since it is the precise situation when arrhythmias are most prone to occur. It is well known that mathematical modeling is a useful tool for investigating cardiac cell behavior. In the last 60 years, a multitude of cardiac models has been created; from the pioneering work of Hodgkin and Huxley (1952), who first described the ionic currents of the squid giant axon quantitatively, mathematical modeling has made great strides. The O’Hara model, that I employed in this research work, is one of the modern computational models of ventricular myocyte, a new generation began in 1991 with ventricular cell model by Noble et al. Successful of these models is that you can generate novel predictions, suggest experiments and provide a quantitative understanding of underlying mechanism. Obviously, the drawback is that they remain simple models, they don’t represent the real system. The overall goal of this research is to give an additional tool, through mathematical modeling, to understand the behavior of the main ionic currents involved during the action potential (AP), especially underlining the differences between slower and faster heart rates. In particular to evaluate the rate-dependence role on the action potential duration, to implement a new method for interpreting ionic currents behavior after a perturbation effect and to verify the validity of the work proposed by Antonio Zaza using an injected current as a perturbing effect.

Simulating event related potentials in tasks involving conflict resolution: a neural network

In this thesis, the main Executive Control theories are exposed. Methods typical of Cognitive and Computational Neuroscience are introduced and the role of behavioural tasks involving conflict resolution in the response elaboration, after the presentation of a stimulus to the subject, are highlighted. In particular, the Eriksen Flanker Task and its variants are discussed. Behavioural data, from scientific literature, are illustrated in terms of response times and error rates. During experimental behavioural tasks, EEG is registered simultaneously. Thanks to this, event related potential, related with the current task, can be studied. Different theories regarding relevant event related potential in this field - such as N2, fERN (feedback Error Related Negativity) and ERN (Error Related Negativity) – are introduced. The aim of this thesis is to understand and simulate processes regarding Executive Control, including performance improvement, error detection mechanisms, post error adjustments and the role of selective attention, with the help of an original neural network model. The network described here has been built with the purpose to simulate behavioural results of a four choice Eriksen Flanker Task. Model results show that the neural network can simulate response times, error rates and event related potentials quite well. Finally, results are compared with behavioural data and discussed in light of the mentioned Executive Control theories. Future perspective for this new model are outlined.

Classical electrodynamics: retarded potentials and power emission by accelerated charges

L'obiettivo di questo lavoro è quello di analizzare la potenza emessa da una carica elettrica accelerata. Saranno studiati due casi speciali: accelerazione lineare e accelerazione circolare. Queste sono le configurazioni più frequenti e semplici da realizzare. Il primo passo consiste nel trovare un'espressione per il campo elettrico e il campo magnetico generati dalla carica. Questo sarà reso possibile dallo studio della distribuzione di carica di una sorgente puntiforme e dei potenziali che la descrivono. Nel passo successivo verrà calcolato il vettore di Poynting per una tale carica. Useremo questo risultato per trovare la potenza elettromagnetica irradiata totale integrando su tutte le direzioni di emissione. Nell'ultimo capitolo, infine, faremo uso di tutto ciò che è stato precedentemente trovato per studiare la potenza emessa da cariche negli acceleratori.

Analisi di log files prodotti dal sistema StoRM usato dall’esperimento ATLAS attraverso algoritmi di clustering

L'esperimento ATLAS, come gli altri esperimenti che operano al Large Hadron Collider, produce Petabytes di dati ogni anno, che devono poi essere archiviati ed elaborati. Inoltre gli esperimenti si sono proposti di rendere accessibili questi dati in tutto il mondo. In risposta a questi bisogni è stato progettato il Worldwide LHC Computing Grid che combina la potenza di calcolo e le capacità di archiviazione di più di 170 siti sparsi in tutto il mondo. Nella maggior parte dei siti del WLCG sono state sviluppate tecnologie per la gestione dello storage, che si occupano anche della gestione delle richieste da parte degli utenti e del trasferimento dei dati. Questi sistemi registrano le proprie attività in logfiles, ricchi di informazioni utili agli operatori per individuare un problema in caso di malfunzionamento del sistema. In previsione di un maggiore flusso di dati nei prossimi anni si sta lavorando per rendere questi siti ancora più affidabili e uno dei possibili modi per farlo è lo sviluppo di un sistema in grado di analizzare i file di log autonomamente e individuare le anomalie che preannunciano un malfunzionamento. Per arrivare a realizzare questo sistema si deve prima individuare il metodo più adatto per l'analisi dei file di log. In questa tesi viene studiato un approccio al problema che utilizza l'intelligenza artificiale per analizzare i logfiles, più nello specifico viene studiato l'approccio che utilizza dell'algoritmo di clustering K-means.

Automatic classification of stellar orbits in axisymmetric potentials

Within the classification of orbits in axisymmetric stellar systems, we present a new algorithm able to automatically classify the orbits according to their nature. The algorithm involves the application of the correlation integral method to the surface of section of the orbit; fitting the cumulative distribution function built with the consequents in the surface of section of the orbit, we can obtain the value of its logarithmic slope m which is directly related to the orbit’s nature: for slopes m ≈ 1 we expect the orbit to be regular, for slopes m ≈ 2 we expect it to be chaotic. With this method we have a fast and reliable way to classify orbits and, furthermore, we provide an analytical expression of the probability that an orbit is regular or chaotic given the logarithmic slope m of its correlation integral. Although this method works statistically well, the underlying algorithm can fail in some cases, misclassifying individual orbits under some peculiar circumstances. The performance of the algorithm benefits from a rich sampling of the traces of the SoS, which can be obtained with long numerical integration of orbits. Finally we note that the algorithm does not differentiate between the subtypes of regular orbits: resonantly trapped and untrapped orbits. Such distinction would be a useful feature, which we leave for future work. Since the result of the analysis is a probability linked to a Gaussian distribution, for the very definition of distribution, some orbits even if they have a certain nature are classified as belonging to the opposite class and create the probabilistic tails of the distribution. So while the method produces fair statistical results, it lacks in absolute classification precision.