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Functional graphs (FGs) model the graph structures used to analyse the behaviour of functions from a discrete set to itself. In turn, such functions are used to study real complex phenomena evolving in time. As the systems involved can be quite large, it is interesting to decompose and factorise them into several subgraphs acting together. Polynomial equations over functional graphs provide a formal way to represent this decomposition and factorisation mechanism, and solving them validates or invalidates hypotheses on their decomposability. The current solution method breaks down a single equation into a series of basic equations of the form A×X=B (with A, X, and B being FGs) to identify the possible solutions. However, it is able to consider just FGs made of cycles only. This work proposes an algorithm for solving these basic equations for general connected FGs. By exploiting a connection with the cancellation problem, we prove that the upper bound to the number of solutions is closely related to the size of the cycle in the coefficient A of the equation. The cancellation problem is also involved in the main algorithms provided by the paper. We introduce a polynomial-time semi-decision algorithm able to provide constraints that a potential solution will have to satisfy if it exists. Then, exploiting the ideas introduced in the first algorithm, we introduce a second exponential-time algorithm capable of finding all solutions by integrating several ‘hacks’ that try to keep the exponential as tight as possible.

Il capitolo descrive il framework pedagogico che ha orientato la ricerca sul campo sulla base del Manifesto Una scuola

Nineteen elementary cellular automata rules (and their conjugation, reflection, reflected-conjugation) have been proven not maximum sensitive to synchronism, i.e. they do not have a different dynamics for each (non-equivalent) block-sequential update schedule (defined as ordered partitions of cell positions). In this work we present exact measurements of the sensitivity to synchronism for these rules, as functions of the size. These exhibit a surprising variety of values and associated proof methods, such as the isomer pairs of rule 128, and the connection to the bisection of Lucas numbers of rule 8.

Several software tools for the simulation and analysis of biochemical reaction networks have been developed in the last decades; however, assessing and comparing their computational performance in executing the typical tasks of computational systems biology can be limited by the lack of a standardized benchmarking approach. To overcome these limitations, we propose here a novel tool, named SMGen, designed to automatically generate synthetic models of reaction networks that, by construction, are characterized by relevant features (e.g., system connectivity and reaction discreteness) and non-trivial emergent dynamics of real biochemical networks. The generation of synthetic models in SMGen is based on the definition of an undirected graph consisting of a single connected component that, generally, results in a computationally demanding task; to speed up the overall process, SMGen exploits a main–worker paradigm. SMGen is also provided with a user-friendly graphical user interface, which allows the user to easily set up all the parameters required to generate a set of synthetic models with any number of reactions and species. We analysed the computational performance of SMGen by generating batches of symmetric and asymmetric reaction-based models (RBMs) of increasing size, showing how a different number of reactions and/or species affects the generation time. Our results show that when the number of reactions is higher than the number of species, SMGen has to identify and correct a large number of errors during the creation process of the RBMs, a circumstance that increases the running time. Still, SMGen can generate synthetic models with hundreds of species and reactions in less than 7 s.

The authors present a case of fatal amyloid cardiomyopathy, which was diagnosed only upon autopsy. A 57-year-old man was admitted to the hospital for scheduled percutaneous cardiac procedure of transcatheter radiofrequency ablation due to persistent atrial fibrillation and atrial flutter. Ventricular fibrillation was recorded in the monitor 2 h after the surgical procedure. Therefore, he was defibrillated and intubated, but he died for nosocomial pneumonia 26 days after being admitted. A judicial autopsy was ordered by the prosecutor due to an alleged medical malpractice. The autopsy confirmed the cause of death being pneumonia, but also revealed an occult restrictive cardiomyopathy with a thick and firm myocardium. Viscera samples were then collected for microscopic examination. Histopathologic analysis showed diffuse amyloid deposits in the myocardium, especially in the perivascular and subendocardial spaces. Amyloid deposits were also detected in all the other organs, except for the brain. Furthermore, immunohistochemistry for light chains was performed on the heart tissue sample, resulting to be positive. In the case presented herein, autopsy and histopathologic examination were crucial to diagnose an occult systemic amyloidosis (AL-type). In fact, it has been observed that the rarity of systematic amyloidosis and its unusual clinical onset were at first mistakenly perceived as a medical malpractice due to a technical error within the catheter ablation for atrial fibrillation. As a consequence, upon discussing the clinical and medicolegal implications concerning the case, the focus was placed on the undiagnosed systemic amyloidosis and on the causality between surgical procedure and the patient's death.

This paper provides an algorithmic pipeline for studying the intrinsic structure of a finite discrete dynamical system (DDS) modelling an evolving phenomenon. Here, by intrinsic structure we mean, regarding the dynamics of the DDS under observation, the feature of resulting from the 'cooperation' of the dynamics of two or more smaller DDS. The intrinsic structure is described by an equation over DDS which represents a hypothesis over the phenomenon under observation. The pipeline allows solving such an equation, i.e., validating the hypothesis over the phenomenon, as far the asymptotic behaviour and the number of states of the DDS under observation are concerned. The results are about the soundness and completeness of the pipeline and they are obtained by exploiting the algebraic setting for DDS introduced in Dennunzio et al. (2018).

Lead halide perovskites have been revolutionary in the last decade in many optoelectronic sectors. Their bismuth-based counterparts have been considered a good alternative thanks to their composition of earth-abundant elements, good chemical stability, and low toxicity. Moreover, their electronic structure is in a quasi-zero-dimensional (0D) configuration, and they have recently been explored for use beyond optoelectronics. A significant limitation in applying thin-film technology is represented by the difficulty of synthesizing compact layers with easily scalable methods. Here, the engineering of a two-step synthesis in an air of methylammonium bismuth iodide compact thin films is reported. The critical steps of the process have been highlighted so that the procedure can be adapted to different substrates and application areas.

Objectives: Sofosbuvir/Ledipasvir (SOF/LDV) has been approved by the European Medicine Agency (EMA)for the treatment of children and adolescents (at least 3 years of age) with chronic hepatitis C (CHC) genotype 1, 3, and 4 infection. The aim of this study was to evaluate the efficacy and safety of SOF/LDV in adolescents (12 to <18 years old) with CHC in the real-world setting. Methods: Prospective, open-label, multicentre study involving 12 Italian centres. Patients received the fixed-dose combination of SOF/LDV (400/90 mg) once daily-ribavirin as per EMA approval and recommendations. The key efficacy endpoint was sustained virological response 12 weeks after the end of treatment (SVR12) as per intention-to-treat analysis. Safety was assessed by adverse events and clinical/laboratory data. Results: Seventy-eight consecutive adolescents (median age 15.2 years, range 12-17.9; girls 53.8%) were enrolled and treated between June 2018 and December 2019. Genotype distribution was as follows: Genotype 1 (82.1%), 3 (2.5%), and 4 (15.4%). Seventy-six (97.4%) patients completed treatment and follow-up. Overall, SVR12 was 98.7%. One patient was lost to follow-up after 4 weeks of treatment; 1 patient completed treatment and missed the follow-up visit. No virological breakthrough or relapse were observed. No patient experienced grade 3 to 4 adverse event or serious adverse event. Conclusions: The results of this real-world study confirmed the high efficacy and the optimal safety profile of SOF/LDV for treatment of CHC in adolescents.

Discrete dynamical systems (DDS) are a useful tool for modelling the dynamical behavior of many phenomena occurring in a huge variety of scientific domains. Boolean automata networks, genetic regulation networks, and metabolic networks are just a few examples of DDS used in Bioinformatics. Equations over DDS have been introduced as a formal tool to check the model against experimental data. Solving generic equations over DDS has been proved undecidable. In this paper we propose to solve a decidable abstraction which consists in equations having a constant part. The abstraction we focus on consists in restricting the solutions to equations involving only the periodic behavior of DDS. We provide a fast and scalable method to solve such abstractions.

Un Sistema Dinamico finito a tempo Discreto (SDD) è costituito da un insieme finito X, chiamato insieme degli stati, e da una funzione f che associa a uno stato v lo stato f(v). I SDD sono uno modello formale per rappresentare fenomeni che compaiono in Fisica, Matematica, Biologia e, naturalmente, in Informatica. Sebbene la formalizzazione matematica e i risultati ottenuti fino ad oggi siano eleganti e significativi, spesso non sono molto adatti nella pratica a causa del loro elevato costo computazionale. In letteratura è noto che i SDD, dotati di opportune operazioni di somma e prodotto, formano un semianello commutativo. Questa struttura algebrica permette di scrivere equazioni polinomiali in cui i coefficienti e le incognite sono SDD. In particolare, se siamo interessati a una dinamica derivata da dati sperimentali, possiamo scrivere un'equazione con la dinamica come termine destro costante e le ipotesi sulla funzione f (o sulle sue proprietà) in un termine polinomiale a sinistra. La ricerca di soluzioni a questa equazione permette di comprendere meglio il fenomeno e le sue proprietà. Questo approccio è interessante, ma presenta importanti limitazioni dal punto di vista computazionale. La soluzione di un'equazione polinomiale (con diverse variabili) è, in generale, indecidibile e, anche se ci concentriamo sul caso della validazione delle ipotesi, il costo computazionale rimane elevato. L'idea è quindi quella di cercare approssimazioni che diano informazioni rilevanti sulle soluzioni dell'equazione originale. È possibile introdurre tre astrazioni (equazioni più semplici) per identificare: il numero di stati delle variabili, il comportamento asintotico o il comportamento transitorio (ciò che accade prima che il sistema si stabilizzi). Ognuna di esse è costruita da un punto di vista teorico e algoritmico per introdurre un metodo per eseguire la validazione delle ipotesi sui SDD. In questa tesi si dimostra che, attraverso trasformazioni algebriche, è poss, A Finite Discrete-time Dynamical System (DDS) consists of a finite set X, called state space, and a function f, called next-state map (which associates to a state v the state f(v)). DDS are a formal tool for modelling phenomena that appear in Physics, Mathematics, Biology, and, of course, in Computer Science. While the mathematical formalisation and the results that found up to nowadays are elegant and meaningful, often they are not very suitable in practice because of their high computational cost. In the literature, it is known that DDS equipped with appropriate sum and product operations form a commutative semiring. This algebraic structure allows us to write polynomial equations in which the coefficients and unknowns are DDS. In particular, if we are interested in some dynamics derived from experimental data, we can write an equation with this as a constant right-hand term and model assumptions about the function f (or its properties) in a polynomial left-hand term. Finding solutions to this equation allow us to better understand the phenomenon and its properties. This approach is interesting but it has important limitations from a computational point of view. Solving a polynomial equation (with several variables) is, in general, undecidable, and even if we focus on the case of hypothesis validation, the computational cost remains high. The idea is then to look for approximations that give relevant information about the solutions of the original equation. It is possible to introduce three abstractions (simpler equations) to identify: the number of states of the variables, the asymptotic behaviour, or the transient behaviour (what happens before the system stabilises). Each one is built from a theoretical and algorithmic point of view to introduce a method to perform hypothesis validation on DDS. In this thesis, it is shown that through algebraic transformations, it is possible to enumerate the solutions of a polynomial equation with a constant term by enumeratin