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In epidemiology science, the importance to explore innovative modeling tools for acutely analyzing epidemic diffusion is turning into a big challenge considering the myriad of real-world aspects to capture. Typically, equation-based models, such as SIS and SIR, are used to study the propagation of diseases over a population. Improved approaches also include human-mobility patterns as network information to describe contacts among individuals. However, there still is the need to incorporate in these models information about different types of contagion, geographical information, humans habits, and environmental properties. In this paper, we propose a novel approach that takes into account: 1. direct and indirect epidemic contagion pathways to explore the dynamics of the epidemic, 2. the times of possible contagions, and 3. human-mobility patterns. We combine these three features exploiting time-varying hypergraphs, and we embed this model into a design-methodology for agent-based models (ABMs), able to improve the correctness in the epidemic estimations of classical contact-network approaches. We further describe a diffusion algorithm suitable for our design-methodology and adaptable to the peculiarities of any disease spreading policies and/or models. Finally, we tested our methodology by developing an ABM, realizing the SIS epidemic compartmental model, for simulating an epidemic propagation over a population of individuals. We experimented the model using real user-mobility data from the location-based social network Foursquare, and we demonstrated the high-impact of temporal direct and indirect contagion pathways.

Breakfast cereals are present on the market as different types and, in general, are one of the food categories in which voluntary information, such as nutrition or health claims (NHC) or gluten free (GF) declarations, have the largest distribution. The aims of the present study were to compare (i) the nutritional declaration among different types of breakfast cereals, as well as among products with and without NHC or GF declarations, and (ii) the salt and sugar contents with the &ldquo, Italian shared objectives for the improvement of the nutritional characteristics of food&rdquo, To this aim, the nutrition declarations of 371 different breakfast cereal items, available in 13 retailers present on the Italian market, were analysed. Data showed an elevated inter-product variability, with cereal bars and muesli having the highest energy, total fat, and saturate contents per 100 g. Limited differences were found comparing products with and without NHC, as well as those with GF declaration. Most of the breakfast cereals were compliant to the shared objectives, although some items with NHC or GF declaration still have sugar or salt contents higher than these objectives. In conclusion, these data suggest that the different characteristics and the regulated information reported on the food label should not be considered as a marker of the overall nutritional quality. Thus, this study supports the importance of reading and understanding the information made on food label.

Today, coding skills are among the most required competencies worldwide, often also for non-computer scientists. Because of this trend, community contribution-based, question-and-answer (Q&A) platforms became prominent for finding the proper solution to all programming issues. Stack Overflow has been the most popular platform for technical-related questions for years. Still, recently, some programming-related subreddits of Reddit have become a standing stone for questions and discussions. This work investigates the developers' behavior and community formation around the twenty most popular programming languages. We examined two consecutive years of programming-related questions from Stack Overflow and Reddit, performing a longitudinal study on users' posting activity and their high-order interaction patterns abstracted via hypergraphs. Our analysis highlighted crucial differences in how these Q&A platforms are utilized by their users. In line with previous literature, it emphasized the constant decline of Stack Overflow in favor of more community-friendly platforms, such as Reddit, which has been growing rapidly lately.

Agent-based simulations represent an effective scientific tool, with numerous applications from social sciences to biology, which aims to emulate or predict complex phenomena through a set of simple rules performed by multiple agents. To simulate a large number of agents with complex models, practitioners have developed high-performance parallel implementations, often specialized for particular scenarios and target hardware. It is, however, difficult to obtain portable simulations, which achieve high performance and at the same time are easy to write and to reproduce on different hardware. This article gives a complete presentation of OpenABL , a domain-specific language and a compiler for agent-based simulations that enable users to achieve high-performance parallel and distributed agent simulations with a simple and portable programming environment. OpenABL is comprised of (1) an easy-to-program language, which relies on domain abstractions and explicitly exposes agent parallelism, synchronization and locality, (2) a source-to-source compiler, and (3) a set of pluggable compiler backends, which generate target code for multi-core CPUs, GPUs, and cloud-based systems. We evaluate OpenABL on simulations from different fields. In particular, our analysis includes predator–prey and keratinocyte, two complex simulations with multiple step functions, heterogeneous agent types, and dynamic creation and removal of agents. The results show that OpenABL -generated codes are portable to different platforms, perform similarly to manual target-specific implementations, and require significantly fewer lines of codes.

This work deals with a generalization of the minimum Target Set Selection (TSS) problem, a key algorithmic question in information diffusion research due to its potential commercial value. Firstly proposed by Kempe et al., the TSS problem is based on a linear threshold diffusion model defined on an input graph with node thresholds, quantifying the hardness to influence each node. The goal is to find the smaller set of items that can influence the whole network according to the diffusion model defined. This study generalizes the TSS problem on networks characterized by many-to-many relationships modeled via hypergraphs. Specifically, we introduce a linear threshold diffusion process on such structures, which evolves as follows. Let H=(V,E) be a hypergraph. At the beginning of the process, the nodes in a given set S⊆V are influenced. Then, at each iteration, (i) the influenced hyperedges set is augmented by all edges having a sufficiently large number of influenced nodes, (ii) consequently, the set of influenced nodes is enlarged by all the nodes having a sufficiently large number of already influenced hyperedges. The process ends when no new nodes can be influenced. Exploiting this diffusion model, we define the minimum Target Set Selection problem on hypergraphs (TSSH). Being the problem NP-hard (as it generalizes the TSS problem), we introduce four heuristics and provide an extensive evaluation on real-world networks.

Experimental and clinical studies evidenced the health effects of moderate consumption of beer, mainly due to the presence of bioactive compounds, such as polyphenols, vitamins, or fibers. To exploit the potential beneficial effect on health and in disease prevention of these compounds, a new beverage based on barley malts and hops named Aliophen&reg, has been designed, through a patented production process, with a high total polyphenolic amount compared to alcohol-free beer and similar to the one present in light and dark beers. In the present study, the antioxidant activity of Aliophen&reg, against low-density lipoprotein (LDL) oxidation and its ability to protect erythrocytes from hemolysis have been characterized. Moreover, the chemopreventive effect of Aliophen&reg, against colon cancer has been assessed, employing a mouse model of chemically induced carcinogenesis using azoxymethane (AOM). Data obtained showed that Aliophen at a low dose (3 mg/kg) inhibited the formation of preneoplastic lesions, polyps, and tumors. At higher doses (300 mg/kg) the protective effect was measured in the first phase of the onset of cancer. The antioxidant properties of Aliophen&reg, were also observed in AOM-treated mice where it increased the serum antioxidant capacity. Based on the data presented, Aliophen&reg, can exert promising health effects, including an anticancer capacity presumably associated with its antioxidant properties.

Real-world complex networks are usually being modeled as graphs. The concept of graphs assumes that the relations within the network are binary (for instance, between pairs of nodes); however, this is not always true for many real-life scenarios, such as peer-to-peer communication schemes, paper co-authorship, or social network interactions. For such scenarios, it is often the case that the underlying network is better and more naturally modeled by hypergraphs. A hypergraph is a generalization of a graph in which a single (hyper)edge can connect any number of vertices. Hypergraphs allow modelers to have a complete representation of multi-relational (many-to-many) networks; hence, they are extremely suitable for analyzing and discovering more subtle dependencies in such data structures. Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms (such as searching or traversing the network) to computing significant hypergraph measures, to including more challenging algorithms (such as community detection). In this paper, we present a new software library, SimpleHypergraphs.jl, written in the Julia language and designed for high-performance computing on hypergraphs and propose two new algorithms for analyzing their properties: s-betweenness and modified label propagation. We also present various approaches for hypergraph visualization integrated into our tool. In order to demonstrate how to exploit the library in practice, we discuss two case studies based on the 2019 Yelp Challenge dataset and the collaboration network built upon the Game of Thrones TV series. The results are promising and they confirm the ability of hypergraphs to provide more insight than standard graph-based approaches., 32 pages, 10 figures, 7 tables, submitted to Internet Mathematics Journal

This work introduces the problem of social influence diffusion in complex networks, where vertices are linked not only through simple pairwise relationships to other nodes but with groups of nodes of arbitrary size. A challenging problem that arises in this domain is to determine a small subset of nodes S (a target-set) able to spread their influence in the whole network. This problem has been formalized and studied in different ways, and many viable solutions have been found for graphs. These have been applied to study several phenomena in research fields such as social, economic, biological, and physical sciences. In this contribution, we investigated the social influence problem on hypergraphs. As hypergraphs are mathematical structures generalization of graphs, they can naturally model the many-to-many relationships characterizing a complex network. Given a network represented by a hypergraph H=(V, E), we consider a dynamic influence diffusion process on H, evolving as follows. At the beginning of the process, the nodes in a given set S (Formula Presented) V are influenced. Then, at each iteration, the influenced hyperedges set is augmented by all hyperedges having a sufficiently large number of influenced nodes. Consequently, the set of influenced nodes is extended by all the nodes contained in a sufficiently large number of already influenced hyperedges. The process terminates when no new nodes can be influenced. The so defined problem is an inherent chicken-and-egg question as nodes are influenced by groups of other nodes (or hyperedges), while hyperedges (or group of nodes) are influenced by the nodes they contain. In this paper, we provide a formal definition of the influence diffusion problem on hypergraphs. We propose a set of greedy-based heuristic strategies for finding the minimum influence target set, and we present an in-depth analysis of their performance on several classes of random hypergraphs. Furthermore, we describe an experiment on a real use-case, based on the character co-occurrences network of the Game-of-Thrones TV Series.

Hypergraphs are natural generalization of graphs in which a single (hyper)edge can connect any number of vertices. As a result, hypergraphs are suitable and useful to model many important networks and processes. Typical applications are related to social data analysis and include situations such as exchanging emails with several recipients, reviewing products on social platforms, or analyzing security vulnerabilities of information networks. In many situations, using hypergraphs instead of classical graphs allows us to better capture and analyze dependencies within the network. In this paper, we propose a new library, named SimpleHypergraphs.jl, designed for efficient hypegraph analysis. The library exploits the Julia language flexibility and direct support for distributed computing in order to bring a new quality for simulating and analyzing processes represented as hypergraphs. In order to show how the library can be used we study two case studies based on the Yelp dataset. Results are promising and confirm the ability of hypergraphs to provide more insight than standard graph-based approaches.

Under a Creative Commons license.-- Review.-- et al., Targeted therapies and the consequent adoption of >personalized> oncology have achieved notablesuccesses in some cancers; however, significant problems remain with this approach. Many targetedtherapies are highly toxic, costs are extremely high, and most patients experience relapse after a fewdisease-free months. Relapses arise from genetic heterogeneity in tumors, which harbor therapy-resistantimmortalized cells that have adopted alternate and compensatory pathways (i.e., pathways that are notreliant upon the same mechanisms as those which have been targeted). To address these limitations, aninternational task force of 180 scientists was assembled to explore the concept of a low-toxicity >broad-spectrum> therapeutic approach that could simultaneously target many key pathways and mechanisms. Using cancer hallmark phenotypes and the tumor microenvironment to account for the various aspectsof relevant cancer biology, interdisciplinary teams reviewed each hallmark area and nominated a widerange of high-priority targets (74 in total) that could be modified to improve patient outcomes. For thesetargets, corresponding low-toxicity therapeutic approaches were then suggested, many of which werephytochemicals. Proposed actions on each target and all of the approaches were further reviewed forknown effects on other hallmark areas and the tumor microenvironment. Potential contrary or procar-cinogenic effects were found for 3.9% of the relationships between targets and hallmarks, and mixedevidence of complementary and contrary relationships was found for 7.1%. Approximately 67% of therelationships revealed potentially complementary effects, and the remainder had no known relationship. Among the approaches, 1.1% had contrary, 2.8% had mixed and 62.1% had complementary relationships. These results suggest that a broad-spectrum approach should be feasible from a safety standpoint. Thisnovel approach has potential to be relatively inexpensive, it should help us address stages and types ofcancer that lack conventional treatment, and it may reduce relapse risks. A proposed agenda for futureresearch is offered., Amr Amin was funded by Terry Fox Foundation Grant # TF-13-20 and UAEU Program for Advanced Research (UPAR) #31S118; Jack Arbiser was funded by NIHAR47901; Alexandra Arreola was funded by NIH NRSA Grant F31CA154080; Alla Arzumanyan was funded by NIH (NIAID) R01: Combination therapies for chronic HBV, liver disease, and cancer (AI076535); Work in the lab of Asfar S. Azmi is supported by NIH R21CA188818 as well as from Sky Foundation Inc. Michigan; Fabian Benencia was supported by NIH Grant R15 CA137499-01; Alan Bilsland was supported by the University of Glasgow, Beatson Oncology Centre Fund, CRUK (www.cancerresearchuk.org) Grant C301/A14762; Amancio Carnero was supported by grants from the Spanish Ministry of Economy and Competitivity, ISCIII (Fis: PI12/00137, RTICC: RD12/0036/0028) co-funded by FEDER from Regional Development European Funds (European Union), Consejeria de Ciencia e Innovacion (CTS-6844 and CTS-1848) and Consejeria de Salud of the Junta de Andalucia (PI-0135-2010 and PI-0306-2012). His work on this project has also been made possible thanks to the Grant PIE13/0004 co-funded by the ISCIII and FEDER funds; Stephanie C. Casey was supported by NIH Grant F32CA177139; Mrinmay Chakrabarti was supported by the United Soybean Board; Rupesh Chaturvedi was supported by an NIH NCCAM Grant (K01AT007324); Georgia Zhuo Chen was supported by an NIH NCI Grant (R33 CA161873-02); Helen Chen acknowledges financial support from the Michael Cuccione Childhood Cancer Foundation Graduate Studentship; Sophie Chen acknowledges financial support from the Ovarian and Prostate Cancer Research Trust, UK; Yi Charlie Chen acknowledges financial support from the West Virginia Higher Education Policy Commission/Division of Science Research, his research was also supported by NIH grants (P20RR016477 and P20GM103434) from the National Institutes of Health awarded to the West Virginia IDeA Network of Biomedical Research Excellence; Maria Rosa Ciriolo was partially supported by the Italian Association for Cancer Research (AIRC) Grants #IG10636 and #15403; Helen M. Coley acknowledges financial support from the GRACE Charity, UK and the Breast Cancer Campaign, UK; Marisa Connell was supported by a Michael Cuccione Childhood Cancer Foundation Postdoctoral Fellowship; Sarah Crawford was supported by a research grant from Connecticut State University; Charlotta Dabrosin acknowledges financial support from the Swedish Research Council and the Swedish Research Society; Giovanna Damia gratefully acknowledges the generous contributions of The Italian Association for Cancer Research (IG14536 to G.D.), Santanu Dasgupta gratefully acknowledges the support of the University of Texas Health Science Centre at Tyler, Elsa U. Pardee Foundation; William K. Decker was supported in part by CPRIT, the Cancer Prevention and Research Institute of Texas; Anna Mae E. Diehl was supported by NIH National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), the NIH National Institute on Alcohol Abuse and Alcoholism (NIAAA), Gilead and Shire Pharmaceuticals; Q. Ping Dou was partially supported by NIH/NCI (1R01CA20009, 5R01CA127258-05 and R21CA184788), and NIH P30 CA22453 (to Karmanos Cancer Institute); Janice E. Drew was supported by the Scottish Government's Rural and Environment Science and Analytical Services Division; Eyad Elkord thanks the National Research Foundation, United Arab Emirates University and the Terry Fox Foundation for supporting research projects in his lab; Bassel El-Rayes was supported by Novartis Pharmaceutical, Aveo Pharmaceutical, Roche, Bristol Myers Squibb, Bayer Pharmaceutical, Pfizer, and Kyowa Kirin; Mark A. Feitelson was supported by NIH/NIAID Grant AI076535, Dean W. Felsher was supported by NIH grants (R01CA170378, U54CA149145, and U54CA143907); Lynnette R Ferguson was financially supported by the Auckland Cancer Society and the Cancer Society of New Zealand; Gary L. Firestone was supported by NIH Public Service Grant CA164095 awarded from the National Cancer Institute; Christian Frezza “would like to acknowledge funding from a Medical Research Council CCU-Program Grant on cancer metabolism, and a unique applicant AICR project grant”; Mark M. Fuster was supported by NIH Grant R01-HL107652; Alexandros G. Georgakilas was supported by an EU Marie Curie Reintegration Grant MC-CIG-303514, Greek National funds through the Operational Program ‘Educational and Lifelong Learning of the National Strategic Reference Framework (NSRF)-Research Funding Program THALES (Grant number MIS 379346) and COST Action CM1201 ‘Biomimetic Radical Chemistry’; Michelle F. Green was supported by a Duke University Molecular Cancer Biology T32 Training Grant; Brendan Grue was supported by a National Sciences Engineering and Research Council Undergraduate Student Research Award in Canada; Dorota Halicka was supported by by NIH NCI grant NCI RO1 28704; Petr Heneberg was supported by the Charles University in Prague projects UNCE 204015 and PRVOUK P31/2012, by the Czech Science Foundation projects 15-03834Y and P301/12/1686, by the Czech Health Research Council AZV project 15-32432A, and by the Internal Grant Agency of the Ministry of Health of the Czech Republic project NT13663-3/2012; Matthew D. Hirschey wishes to acknowledge Duke University Institutional Support, the Duke Pepper Older Americans Independence Center (OAIC) Program in Aging Research supported by the National Institute of Aging (P30AG028716-01) and NIH/NCI training grants to Duke University (T32-CA059365-19 and 5T32-CA059365), Lorne J. Hofseth was supported by NIH grants (1R01CA151304, 1R03CA1711326, and 1P01AT003961); Kanya Honoki was supported in part by the grant from the Ministry of Education, Culture, Sports, Science and Technology, Japan (No. 24590493); Hsue-Yin Hsu was supported in part by grants from the Ministry of Health and Welfare (CCMP101-RD-031 and CCMP102-RD-112) and Tzu-Chi University (61040055-10) of Taiwan; Lasse D. Jensen was supported by Svenska Sallskapet for Medicinsk Forskning, Gosta Fraenkels Stiftelse, Ak.e Wibergs Stiftelse, Ollie och Elof Ericssons Stiftelse, Linkopings Universitet and the Karolinska Institute, Sweden; Wen G. Jiang wishes to acknowledge the support by Cancer Research Wales, the Albert Hung Foundation, the Fong Family Foundation, and Welsh Government A4B scheme; Lee W. Jones was supported in part by grants from the NIH NCI; W Nicol Keith was supported by the University of Glasgow, Beatson Oncology Centre Fund, CRUK (www.cancerresearchuk.org) Grant C301/A14762; Sid P. Kerkar was supported by the NIH Intramural Research Program; Rob J. Kulathinal was supported by the National Science Foundation, and the American Cancer Society; Byoung S. Kwon was supported in part by National Cancer Center (NCC-1310430-2) and National Research Foundation (NRF-2005-0093837); Anne Le was supported by Sol Goldman Pancreatic Cancer Research Fund Grant 80028595, a Lustgarten Fund Grant 90049125 and Grant NIHR21CA169757 (to Anne Le); Michael A. Lea was funded by the The Alma Toorock Memorial for Cancer Research; Ho-Young Lee., This work was supported by grants from the National Research Foundation of Korea (NRF), the Ministry of Science, ICT & Future Planning (MSIP), Republic of Korea (Nos. 2011-0017639 and 2011-0030001) and by a NIH Grant R01 CA100816; Liang-Tzung Lin was supported in part by a grant from the Ministry of Education of Taiwan (TMUTOP103005-4); Jason W. Locasale acknowledges support from NIH awards (CA168997 and AI110613) and the International Life Sciences Institute; Bal L. Lokeshwar was supported in part by United States’ Public Health Services Grants: NIH R01CA156776 and VA-BLR&D Merit Review Grant No. 5I01-BX001517-02; Valter D. Longo acknowledges support from NIH awards (P01AG034906 and R01AG020642) and from the V Foundation; Costas A. Lyssiotis was funded in part by the Pancreatic Cancer Action Network as a Pathway to Leadership Fellow and through a Dale F. Frey Breakthrough award from the Damon Runyon Cancer Research Foundation; Karen L. MacKenzie wishes to acknowledge the support from the Children's Cancer Institute Australia (affiliated with the University of New South Wales, Australia and the Sydney Children's Hospital Network); Maria Marino was supported by grant from University Roma Tre to M.M. (CLA 2013) and by the Italian Association for Cancer Research (AIRC-Grant #IG15221), Ander Matheu is funded by Carlos III Health Institute (AM: CP10/00539), Basque Foundation for Science (IKERBASQUE) and Marie Curie CIG Grant (AM: 2012/712404); Christopher Maxwell was supported by funding from the Canadian Institutes of Health Research, in partnership with the Avon Foundation for Women (OBC-134038) and the Canadian Institutes of Health Research New Investigator Salary Award (MSH-136647); Eoin McDonnell received Duke University Institutional Support; Kapil Mehta was supported by Bayer Healthcare System G4T (Grants4Targets); Gregory A. Michelotti received support from NIH NIDDK, NIH NIAAA, and Shire Pharmaceuticals; Vinayak Muralidhar was supported by the Harvard-MIT Health Sciences and Technology Research Assistantship Award; Elena Niccolai was supported by the Italian Ministry of University and the University of Italy; Virginia R. Parslow gratefully acknowledges the financial support of the Auckland Cancer Society Research Centre (ACSRC); Graham Pawelec was supported by the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF) Grant number 16SV5536K, and by the European Commission (FP7 259679 “IDEAL”); Peter L. Pedersen was supported by NIH Grant CA-10951; Brad Poore was supported by Sol Goldman Pancreatic Cancer Research Fund Grant 80028595, the Lustgarten Fund Grant 90049125, and Grant NIHR21CA169757 (to Anne Le); Satya Prakash was supported by a Canadian Institutes of Health Research Grant (MOP 64308); Lizzia Raffaghello was supported by an NIH Grant (P01AG034906-01A1) and Cinque per Mille dell’IRPEF–Finanziamento della Ricerca Sanitaria; Jeffrey C. Rathmell was supported by an NIH Grant (R01HL108006); Swapan K. Ray was supported by the United Soybean Board; Domenico Ribatti received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant agreement n°278570; Luigi Ricciardiello was supported by the AIRC Investigator Grants 10216 and 13837, and the European Community's Seventh Framework Program FP7/2007–2013 under Grant agreement 311876; Francis Rodier acknowledges the support of the Canadian Institute for Health Research (FR: MOP114962, MOP125857), Fonds de Recherche Québec Santé (FR: 22624), and the Terry Fox Research Institute (FR: 1030), Gian Luigi Russo contributed to this effort while participating in the Fulbright Research Scholar Program 2013–14; Isidro Sanchez-Garcia is partially supported by FEDER and by MICINN (SAF2012-32810), by NIH Grant (R01 CA109335-04A1), by Junta de Castilla y León (BIO/SA06/13) and by the ARIMMORA project (FP7-ENV-2011, European Union Seventh Framework Program). Isidro Sanchez-Garcia's lab is also a member of the EuroSyStem and the DECIDE Network funded by the European Union under the FP7 program; Andrew J. Sanders wishes to acknowledge the support by Cancer Research Wales, the Albert Hung Foundation, the Fong Family Foundation, and Welsh Government A4B scheme; Neeraj K. Saxena was supported by grant funding from NIH NIDDK (K01DK077137, R03DK089130); Dipali Sharma was partially funded by NIH NCI grants (R01CA131294, R21 CA155686), the Avon Foundation and a Breast Cancer Research Foundation Grant (90047965); Markus David Siegelin received funding from National Institute of Health, NINDS Grant K08NS083732, and the 2013 AACR-National Brain Tumor Society Career Development Award for Translational Brain Tumor Research, Grant Number 13-20-23-SIEG; Neetu Singh was supported by funds from the Department of Science and Technology (SR/FT/LS-063/2008), New Delhi, India; Carl Smythe was supported by Yorkshire Cancer Research and The Wellcome Trust, UK; Carmela Spagnuolo was supported by funding from Project C.I.S.I.A., act n. 191/2009 from the Italian Ministry of Economy and Finance Project CAMPUS-QUARC, within program FESR Campania Region 2007/2013, objectives 2.1, 2.2; Diana M. Stafforini was supported by grants from the National Cancer Institute (5P01CA073992), IDEA Award W81XWH-12-1-0515 from the Department of Defense, and by the Huntsman Cancer Foundation; John Stagg was supported by the Canadian Institutes of Health Research; Pochi R. Subbarayan was supported by the University of Miami Clinical and Translational Science Institute (CTSI) Pilot Research Grant (CTSI-2013-P03) and SEEDS You Choose Awards; Phuoc T. Tran was funded by the DoD (W81XWH-11-1-0272 and W81XWH-13-1-0182), a Kimmel Translational Science Award (SKF-13-021), an ACS Scholar award (122688-RSG-12-196-01-TBG) and the NIH (R01CA166348); Kathryn E. Wellen receives funding from the National Cancer Institute, Pancreatic Cancer Action Network, Pew Charitable Trusts, American Diabetes Association, and Elsa U. Pardee Foundation; Huanjie Yang was partially supported by the Scientific Research Foundation for the Returned Oversea Scholars, State Education Ministry and Scientific and Technological Innovation Project, Harbin (2012RFLXS011), Paul Yaswen was supported by funding from the United States National Institutes of Health (ES019458) and the California Breast Cancer Research Program (17UB-8708); Clement Yedjou was supported by a grant from the National Institutes of Health (Grant # G1200MD007581), through the RCMI-Center for Environmental Health; Xin Yin was supported by NIH/National Heart, Lung, and Blood Institute Training Grant T32HL098062.; Jiyue Zhu was supported by NIH Grant R01GM071725; Massimo Zollo was supported by the European FP7-TuMIC HEALTH-F2-2008-201662, the Italian Association for Cancer research (AIRC) Grant IG # 11963 and the Regione Campania L.R:N.5, the European National Funds PON01-02388/1 2007-2013.

Agent-based simulation models are a powerful experimental tool for research and management in many scientific and technological fields. D-Mason is a parallel version of Mason, a library for writing and running Agent-based simulations. In this paper, we present a novel development of D-Mason, a decentralized communication strategy which realizes a Publish/Subscribe paradigm through a layer based on the MPI standard. We show that our communication mechanism is much more scalable and efficient than the previous centralized one.

Agent-Based Models (ABMs) are a class of models which, by simulating the behavior of multiple agents (i.e., ndependent actions, interactions and adaptation), aim to emulate and/or predict complex phenomena. One of the general features of ABM simulations is their experimental capacity, that requires a viable and reliable infrastructure to interact with a running simulation, monitoring its behaviour, as it proceeds, and applying changes to the configurations at run time, (the computational steering) in order to study "what if" scenarios. A common approach for improving the efficiency and the effectiveness of ABMs as a research tool is to distribute the overall computation on a number of machines, which makes the computational steering of the simulation particularly challenging. In this paper, we present the principles and the architecture design of the management and control infrastructure that is available in D-Mason, a framework for implementing distributed ABM simulations. Together with an efficient parallel distribution of the simulation tasks, D-Mason offers a number of facilities to support the computational steering of a simulation, i.e. monitoring and interacting with a running distributed simulation.