Introduction: Connections among neurons form one of the most amazing and effective network in nature. At higher level, also the functional structures of the brain is organized as a network. It is therefore natural to use modern techniques of network analysis to describe the structures of networks in the brain. Many studies have been conducted in this area, showing that the structure of the neuronal network is complex, with a small-world topology, modularity and the presence of hubs. Other studies have been conducted to investigate the dynamical processes occurring in brain networks, analyzing local and large-scale network dynamics. Recently, network diffusion dynamics have been proposed as a model for the progression of brain degenerative diseases and for traumatic brain injuries. Methods: In this paper, the dynamics of network diffusion is re-examined and reaction-diffusion models on networks is introduced in order to better describe the degenerative dynamics in the brain. Results: Numerical simulations of the dynamics of injuries in the brain connectome are presented. Different choices of reaction term and initial condition provide very different phenomenologies, showing how network propagation models are highly flexible. Discussion: The uniqueness of this research lies in the fact that it is the first time that reaction-diffusion dynamics have been applied to the connectome to model the evolution of neurodegenerative diseases or traumatic brain injury. In addition, the generality of these models allows the introduction of non-constant diffusion and different reaction terms with non-constant parameters, allowing a more precise definition of the pathology to be studied.

Motivation: Gene-disease associations are fundamental for understanding disease etiology and developing effective interventions and treatments. Identifying genes not yet associated with a disease due to a lack of studies is a challenging task in which prioritization based on prior knowledge is an important element. The computational search for new candidate disease genes may be eased by positive-unlabeled learning, the machine learning setting in which only a subset of instances are labeled as positive while the rest of the data set is unlabeled. In this work, we propose a set of effective network-based features to be used in a novel Markov diffusion-based multi-class labeling strategy for putative disease gene discovery. Results: The performances of the new labeling algorithm and the effectiveness of the proposed features have been tested on ten different disease data sets using three machine learning algorithms. The new features have been compared against classical topological and functional/ontological features and a set of network- and biological-derived features already used in gene discovery tasks. The predictive power of the integrated methodology in searching for new disease genes has been found to be competitive against state-of-the-art algorithms.Availability and implementation: The source code of NIAPU can be accessed at https://github. com/AndMastro/NIAPU. The source data used in this study are available online on the respective websites.

Introduction: Mathematical modeling has emerged as a crucial component in understanding the epidemiology of infectious diseases. In fact, contemporary surveillance efforts for epidemic or endemic infections heavily rely on mathematical and computational methods. This study presents a novel agent-based multi-level model that depicts the transmission dynamics of gonorrhea, a sexually transmitted infection (STI) caused by the bacterium Neisseria gonorrhoeae. This infection poses a significant public health challenge as it is endemic in numerous countries, and each year sees millions of new cases, including a concerning number of drug-resistant cases commonly referred to as gonorrhea superbugs or super gonorrhea. These drug-resistant strains exhibit a high level of resistance to recommended antibiotic treatments.MethodsThe proposed model incorporates a multi-layer network of agents' interaction representing the dynamics of sexual partnerships. It also encompasses a transmission model, which quantifies the probability of infection during sexual intercourse, and a within-host model, which captures the immune activation following gonorrhea infection in an individual. It is a combination of agent-based modeling, which effectively captures interactions among various risk groups, and probabilistic modeling, which enables a theoretical exploration of sexual network characteristics and contagion dynamics.ResultsNumerical simulations of the dynamics of gonorrhea infection using the complete agent-based model are carried out. In particular, some examples of possible epidemic evolution are presented together with an application to a real case study. The goal was to construct a virtual population that closely resembles the target population of interest.DiscussionThe uniqueness of this research lies in its objective to accurately depict the influence of distinct sexual risk groups and their interaction on the prevalence of gonorrhea. The proposed model, having interpretable and measurable parameters from epidemiological data, facilitates a more comprehensive understanding of the disease evolution.

Most financial signals show time dependency that, combined with noisy and extreme events, poses serious problems in the parameter estimations of statistical models. Moreover, when addressing asset pricing, portfolio selection, and investment strategies, accurate estimates of the relationship among assets are as necessary as are delicate in a time-dependent context. In this regard, fundamental tools that increasingly attract research interests are precision matrix and graphical models, which are able to obtain insights into the joint evolution of financial quantities. In this paper, we present a robust divergence estimator for a time-varying precision matrix that can manage both the extreme events and time-dependency that affect financial time series. Furthermore, we provide an algorithm to handle parameter estimations that uses the "maximization-minimization" approach. We apply the methodology to synthetic data to test its performances. Then, we consider the cryptocurrency market as a real data application, given its remarkable suitability for the proposed method because of its volatile and unregulated nature.

The SARS-CoV-2 non-structural protein 13 (nsp13) helicase is an essential enzyme for viral replication and has been identified as an attractive target for the development of new antiviral drugs. In detail, the helicase catalyzes the unwinding of double-stranded DNA or RNA in a 5? to 3? direction and acts in concert with the replication-transcription complex (nsp7/nsp8/nsp12). In this work, bioinformatics and computational tools allowed us to perform a detailed conservation analysis of the SARS-CoV-2 helicase genome and to further predict the druggable enzyme's binding pockets. Thus, a structure-based virtual screening was used to identify valuable compounds that are capable of recognizing multiple nsp13 pockets. Starting from a database of around 4000 drugs already approved by the Food and Drug Administration (FDA), we chose 14 shared compounds capable of recognizing three out of four sites. Finally, by means of visual inspection analysis and based on their commercial availability, five promising compounds were submitted to in vitro assays. Among them, PF-03715455 was able to block both the unwinding and NTPase activities of nsp13 in a micromolar range.

Primary biliary cholangitis (PBC) is a chronic, cholestatic, immune-mediated, and progressive liver disorder. Treatment to preventing the disease from advancing into later and irreversible stages is still an unmet clinical need. Accordingly, we set up a drug repurposing framework to find potential therapeutic agents targeting relevant pathways derived from an expanded pool of genes involved in different stages of PBC. Starting with updated human protein–protein interaction data and genes specifically involved in the early and late stages of PBC, a network medicine approach was used to provide a PBC “proximity” or “involvement” gene ranking using network diffusion algorithms and machine learning models. The top genes in the proximity ranking, when combined with the original PBC-related genes, resulted in a final dataset of the genes most involved in PBC disease. Finally, a drug repurposing strategy was implemented by mining and utilizing dedicated drug–gene interaction and druggable genome information knowledge bases (e.g., the DrugBank repository). We identified several potential drug candidates interacting with PBC pathways after performing an over-representation analysis on our initial 1121-seed gene list and the resulting disease-associated (algorithm-obtained) genes. The mechanism and potential therapeutic applications of such drugs were then thoroughly discussed, with a particular emphasis on different stages of PBC disease. We found that interleukin/EGFR/TNF-alpha inhibitors, branched-chain amino acids, geldanamycin, tauroursodeoxycholic acid, genistein, antioestrogens, curcumin, antineovascularisation agents, enzyme/protease inhibitors, and antirheumatic agents are promising drugs targeting distinct stages of PBC. We developed robust and transparent selection mechanisms for prioritizing already approved medicinal products or investigational products for repurposing based on recognized unmet medical needs in PBC, as well as solid preliminary data to achieve this goal.

This study aimed at updating previous data on HIV-1 integrase variability, by using effective bioinformatics methods combining different statistical instruments from simple entropy and mutation rate to more specific approaches such as Hellinger distance. A total of 2133 HIV-1 integrase sequences were analyzed in: i) 1460 samples from drug-naïve [DN] individuals; ii) 386 samples from drug-experienced but INI-naïve [IN] individuals; iii) 287 samples from INI-experienced [IE] individuals. Within the three groups, 76 amino acid positions were highly conserved (<=0.2% variation, Hellinger distance: <0.25%), with 35 fully invariant positions; while, 80 positions were conserved (>0.2% to <1% variation, Hellinger distance: <1%). The H12-H16-C40-C43 and D64-D116-E152 motifs were all well conserved. Some residues were affected by dramatic changes in their mutation distributions, especially between DN and IE samples (Hellinger distance >=1%). In particular, 15 positions (D6, S24, V31, S39, L74, A91, S119, T122, T124, T125, V126, K160, N222, S230, C280) showed a significant decrease of mutation rate in IN and/or IE samples compared to DN samples. Conversely, 8 positions showed significantly higher mutation rate in samples from treated individuals (IN and/or IE) compared to DN. Some of these positions, such as E92, T97, G140, Y143, Q148 and N155, were already known to be associated with resistance to integrase inhibitors; other positions including S24, M154, V165 and D270 are not yet documented to be associated with resistance. Our study confirms the high conservation of HIV-1 integrase and identified highly invariant positions using robust and innovative methods. The role of novel mutations located in the critical region of HIV-1 integrase deserves further investigation.

Background: DNA methylation is the main epigenetic mechanism driving changes in phenotype without altering genotype. Since the end of the seventies the role of methylation in cancer has become increasingly clear. Objective: The aim of this work is to shed light on the impact of methylation events on cancer cells, providing evidence that differential methylation in small regions, mostly characterized by hypermethylation, affects gene regulation while differential methylation in large genomic regions, mostly characterized by hypomethylation, affects chromosomal organization. Methods: By exploiting a solid computational and statistical analysis, methylation maps of cancer and normal samples in six different cancer types were studied, looking for those genomic regions showing differentially methylated patterns between the two conditions. Results: Through a chromosome sliding windows approach, a set of differentially methylated genomic micro regions of size 2 K bp and macro regions of size 1 M bp, were identified. Micro regions are mostly linked to functional elements while macro regions are mostly linked to nuclear chromosome organization. Results discussed in previous works were confirmed, providing clear evidence that hypermethylation mainly occurs in significant micro regions while hypomethylation mainly occurs in significant macro regions. Interestingly the presence of differentially methylated regions common for six different cancers were identified and some unexpected and previously unexplored peculiar methylation patterns were also found. Conclusions: The effective and robust computational and statistical methodology presented in this work can be used to shed light on the role that DNA methylation plays in cancer and in other non malignant diseases and can be customized to study differentially methylated patterns in specific areas of interest of the genome both at a small scale and at a large scale.

This book offers an informal, easy-to-understand account of topics in modern physics and mathematics. The focus is, in particular, on statistical mechanics, soft matter, probability, chaos, complexity, and models, as well as their interplay. The book features 28 key entries and it is carefully structured so as to allow readers to pursue different paths that reflect their interests and priorities, thereby avoiding an excessively systematic presentation that might stifle interest. While the majority of the entries concern specific topics and arguments, some relate to important protagonists of science, highlighting and explaining their contributions. Advanced mathematics is avoided, and formulas are introduced in only a few cases. The book is a user-friendly tool that nevertheless avoids scientific compromise. It is of interest to all who seek a better grasp of the world that surrounds us and of the ideas that have changed our perceptions. Il libro prova a colmare almeno in parte quel vuoto lasciato dalla divulgazione scientifica mainstream, interessata principalmente a dare risalto agli aspetti più sensazionalistici e bizzarri delle scoperte scientifiche, svelando il fascino presente in argomenti che non vengono solitamente discussi nei libri di divulgazione e nelle vite di scienziati poco conosciuti al grande pubblico, ma che hanno posto le basi per la scienza come la conosciamo ora.

Alarms periodically emerge for viral pneumonia infections due to coronavirus. In all cases, these are zoonoses passing the barrier between species and infect humans. The legitimate concern of the international community is due to the fact that the new identified coronavirus, named SARS-CoV-2 (previously called 2019-nCoV), has a quite high mortality rate, around 2%, and a strong ability to spread, with an estimated reproduction number higher than 2. Even though all countries are doing their utmost to stop the pandemic, the only reliable solution to tackle the infection is the rapid development of a vaccine. For this purpose, the means of bioinformatics, applied in the context of reverse-vaccinology paradigm, can be of fundamental help to select the most promising peptides able to trigger an effective immune response. In this short report, using the concept of nullomer and introducing a distance from human self, we provide a list of peptides that could deserve experimental investigation in the view of a potential vaccine for SARS-CoV-2.

The paper proposes an approach to deal with the day by day dynamic behaviour of Oil & Gas assets, providing support for optimized decisions on wells and facilities. The approach is based on: o A set of software agents, trained with a machine learning approach to understand the health status of the components of the reservoir/well/plant system and capable of proposing optimization actions for the corresponding subsystem; o An inter-agent negotiation approach, capable of evaluating the optimization actions of the single agents in the wider picture of the overall optimization of the producing asset. The paper will describe how this approach has been implemented, as well as an example application.

More than twenty years ago the reverse vaccinology paradigm came to light trying todesign new vaccines based on the analysis of genomic information in order to selectthose pathogen peptides able to trigger an immune response. In this context, focusingon the proteome of Trypanosoma cruzi, we investigated the link between theprobabilities for pathogen peptides to be presented on a cell surface and their distancefrom human self. We found a reasonable but, as far as we know, undiscoveredproperty: the farther the distance between a peptide and the human-self the higherthe probability for that peptide to be presented on a cell surface. We also found thatthe most distant peptides from human self bind, on average, a broader collection ofHLAs than expected, implying a potential immunological role in a large portion ofindividuals. Finally, introducing a novel quantitative indicator for a peptide tomeasure its potential immunological role, we proposed a pool of peptides that could bepotential epitopes and that can be suitable for experimental testing. The software tocompute peptide classes according to the distance from human self is free available athttp://www.iasi.cnr.it/~dsantoni/nullomers.

The ongoing COVID-19 pandemic still requires fast and effective efforts from all fronts, including epidemiology, clinical practice, molecular medicine, and pharmacology. A comprehensive molecular framework of the disease is needed to better understand its pathological mechanisms, and to design successful treatments able to slow down and stop the impressive pace of the outbreak and harsh clinical symptomatology, possibly via the use of readily available, off-the-shelf drugs. This work engages in providing a wider picture of the human molecular landscape of the SARS-CoV-2 infection via a network medicine approach as the ground for a drug repurposing strategy. Grounding on prior knowledge such as experimentally validated host proteins known to be viral interactors, tissue-specific gene expression data, and using network analysis techniques such as network propagation and connectivity significance, the host molecular reaction network to the viral invasion is explored and exploited to infer and prioritize candidate target genes, and finally to propose drugs to be repurposed for the treatment of COVID-19. Ranks of potential target genes have been obtained for coherent groups of tissues/organs, potential and distinct sites of interaction between the virus and the organism. The normalization and the aggregation of the different scores allowed to define a preliminary, restricted list of genes candidates as pharmacological targets for drug repurposing, with the aim of contrasting different phases of the virus infection and viral replication cycle.

Standard reaction-diffusion systems are characterized by infinite velocities and no persistence in the movement of individuals, two conditions that are violated when considering living organisms. Here we consider a discrete particle model in which individuals move following a persistent random walk with finite speed and grow with logistic dynamics. We show that, when the number of individuals is very large, the individual-based model is well described by the continuous reactive Cattaneo equation (RCE), but for smaller values of the carrying capacity important finite-population effects arise. The effects of fluctuations on the propagation speed are investigated both considering the RCE with a cutoff in the reaction term and by means of numerical simulations of the individual-based model. Finally, a more general Lévy walk process for the transport of individuals is examined and an expression for the front speed of the resulting traveling wave is proposed.

L'idea imprenditoriale da cui prende origine la start-up ProNeuro, nasce come conseguenza del lavoro di ricerca svolto dai soci fondatori presso il Consiglio Nazionale delle Ricerche (CNR). Questo lavoro ha portato negli ultimi 3 anni al deposito di due domande di brevetto italiano, di cui una già estesa in PCT, che proteggono l'utilizzo della molecola ProNGF-A per scopi terapeutici mirati alla cura di patologie neurologiche e infiammatorie (domanda di brevetto Nr. 102018000003279 del 05/03/2018 e PCT/IB2019/051753 del 05/03/2019) e la produzione di una forma mutata di ProNGF-A e il suo utilizzo per terapia neurologica e di patologie cutanee (domanda di brevetto numero 102019000014646 del 12/08/2019). Tali brevetti sono di proprietà del CNR, mentre ProNeuro ha messo a punto un sistema di offerta finalizzato alla loro valorizzazione. Attraverso attività di Ricerca e Sviluppo, ProNeuro individua principi attivi farmacologici con attività protettiva e riparativa per il sistema nervoso, ne modifica la struttura per renderli maggiormente efficaci, sicuri e biocompatibili, mette a punto i metodi produttivi ed esegue le prime fasi di caratterizzazione dei loro effetti, prima di proporli ad aziende farmaceutiche per un successivo sviluppo come farmaci destinati al mercato. ProNeuro commercializza, quindi, i diritti di utilizzo della proprietà intellettuale e una serie di prodotti collegati alle attività di discovery, produzione (trasferimento tecnologico) e prima validazione sia predittiva che biologica di nuovi neurofarmaci. ProNeuro avrà la forma giuridica di Società a responsabilità limitata e si configura come spin-off CNR. Come tale, il rapporto tra la società ProNeuro e il CNR è regolato dal "Regolamento per la costituzione e la partecipazione del CNR alle Imprese Spin off, Del,18/2019". I brevetti sopracitati, attualmente di proprietà del CNR, verranno concessi in licenza a ProNeuro, con possibilità di sub-licenziare a terzi, sulla base del suddetto Regolamento. Questo prevede, infatti, la cessione a condizioni agevolate delle licenze sui brevetti di proprietà CNR, la messa a disposizione di risorse logistiche e strumentali in fase di start-up e l'autorizzazione al proprio personale a svolgere attività a favore delle spin-off, con copertura dei costi salariali per un terzo del tempo lavorativo per tre anni. La sede dell'impresa è stata individuata presso l'Istituto di Farmacologia Traslazionale del CNR, via del Fosso del Cavaliere 100, 00133 Roma

GOLIAT is an offshore production field that spans from the subsea wells up to a complete process plant installed on a FPSO. Due to the comprehensive instrumentation installed on the plant, it is the perfect case study to test an innovative agent based software architecture able to support production management. The modularity and the scalability provided by the agent based architecture guarantees the applicability of the method to any part of the plant. Each agent is in charge of supervising a specific or a group of equipment and is fed by the real-time data coming from the field. These data are then analysed through Machine Learning and Deep Learning algorithms which are incorporated within the agents. The machine learning algorithms estimate the current state of the equipment and provide a set of KPIs in order to understand both the production efficiency and the health status of the machines. Furthermore, learning from the observations of the state transition paths which happened in the past, the agents are capable of predicting the most likely future state. The latter capability is fundamental to prevent unplanned shutdowns and optimize the maintenance plans. On the basis of the estimated current state, each agent can also provide a list of actions targeted to maximize the efficiency from an "equipment" point of view. The actions coming from all the agents can then be collected and negotiated in order to maximize the production from a "plant" point of view. The negotiating algorithms are implemented in a super-agent that can support a human operator in the day-by-day management tasks of the plant. Even though the negotiating capabilities will be implemented in the future version of the application, the modular nature of the system guarantees an easy integration of the super-agent inside the agent's framework. The paper will present the results of the agent framework in terms of the robustness of state estimation and the correctness of the computed KPIs.

In the context of network medicine, disease genes, i.e. genes that have been experimentally associated to the onset or progression of a pathology, show a complex set of features that are not easily reduced to, and grasped by a simple network approach (e.g., studying centrality measures or clustering characteristics of the gene network). Here, to overcome such limitations and to exploit a larger set of informational attributes available, we analyze a sizeable integrated set of biological, ontological and topological features (including interaction data and GO categories, among others) related to different collections of disease genes (including, but not limited to sets related to several inflammatory and dysmetabolic diseases) via a comprehensive machine learning (ML) approach, in order to discover recurring patterns of attributes associated to families of disease genes. In this way the chances of revealing complex, hidden topological, ontological and statistical properties of the genes under scrutiny is wider and the derived "signature" can be heuristically used in a discovery process to find further yet unknown disease genes. We show hurdles, discriminating capabilities and main results in sorting out and in reconstructing the feature sets, in selecting the appropriate ML approach and in analyzing the datasets.

Proteins are the core and the engine of every process in cells thus the study of mechanisms that drive the regulation of protein expression, is essential. Transcription factors play a central role in this extremely complex task and they synergically co-operate in order to provide a fine tuning of protein expressions. In the present study, we designed a mathematically well-founded procedure to investigate the mutual positioning of transcription factors binding sites related to a given couple of transcription factors in order to evaluate the possible association between them. We obtained a list of highly related transcription factors couples, whose binding site occurrences significantly group together for a given set of gene promoters, identifying the biological contexts in which the couples are involved in and the processes they should contribute to regulate. Studio delle sinergie tra fattori di trascrizione nei promotori

Diffusion and transport processes constitute a very important field of applied mathematics. They are useful in many different problems ranging from the diffusion of pollutants in the atmosphere and the sea, to the spreading of epidemics. Aside from their practical relevance, such processes have been very important in the history of physics and mathematics. We can recall Einstein's study of Brownian motion which was fundamental to give a definitive experimental evidence of the existence of atoms. Moreover diffusion processes have been the starting point for the building of the mathematical theory of stochastic processes (starting from the work of Langevin). Similarly the study of reaction and diffusion phenomena, starting from the seminal contribution of two 20th-century science giants (Ronald A. Fisher and Andrej N. Kolmogorov) has led to interesting developments both for applications and for the fruitful connections between stochastic processes and partial differential equation. In this article we discuss some general results developed in these areas, including few modern topics, as transport and reaction/diffusion on discrete structures (graphs). Such a theme has a great relevance, e.g. for the dissemination of information through the internet or the spreading of epidemics through the air transport network. I fenomeni di trasporto, e la loro generalizzazione ai casi con reazione, costituiscono un capitolo molto importante della matematica applicata e trovano utilizzo in ambiti molto vari, che vanno dalla diffusione di sostanze inquinanti in atmosfera e in mare, ai processi industriali, alla biomatematica, alla propagazione di epidemie. Oltre alla loro rilevanza pratica, lo studio di tali fenomeni ha portato contributi molto importanti nella storia della fisica e della matematica. Possiamo ricordare lo studio di Einstein sul moto browniano che è stato fondamentale per dare un'evidenza sperimentale definitiva della reale esistenza degli atomi. E, in generale, i processi di diffusione hanno costituito il punto di partenza per la costruzione della teoria matematica dei processi stocastici (a cominciare dal lavoro di Langevin). Analogamente lo studio dei fenomeni con diffusione e reazione, nati dal contributo di due giganti della scienza del 20-mo secolo (Ronald A. Fisher e Andrej N. Kolmogorov) nell'ambito della modellizzazione matematica di problemi biologici, ha poi portato a sviluppi interessanti sia nell'ambito applicativo che per le proficue connessioni tra processi stocastici ed equazioni alle derivate parziali. In questo articolo oltre a presentare alcuni tra i risultati generali sviluppati in questi ambiti, discuteremo anche aspetti più moderni legati al crescente interesse per i fenomeni di trasporto e reazione/diffusione su strutture discrete (grafi), una tematica questa di grande attualità (basti pensare alla diffusione delle informazioni su internet o alla propagazione delle epidemie per mezzo del network dei trasporti aerei) sviluppata attraverso una matematica raffinata.

Given a population of N elements with their geographical positions and the genetic (or lexical) distances between couples of elements (inferred, for example, from lexical differences between dialects which are spoken in different towns or from genetic differences between animal populations living in different faunal areas) a very interesting problem is to reconstruct the geographical positions of individuals using only genetic/lexical distances. From a technical point of view the program consists in extracting from the genetic/lexical distances a set of reconstructed geographical positions to be compared with the real ones. We show that geographical recovering is successful when the genetic/lexical distances are not a simple consequence of phylogenesis but also of horizontal transfers as, for example, vocabulary borrowings between different languages. Our results go well beyond the simple observation that geographical distances and genetic/lexical distances are correlated. The ascertainment of a correlation, in our perspective, merely is a prerequisite.