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Glioblastoma multiforme is a highly aggressive form of brain cancer, with a median survival time for diagnosed patients of 15 months. Treatment of this cancer is typically a combination of radiation, chemotherapy and surgical removal of the tumour. However, the highly invasive and diffuse nature of glioblastoma makes surgical intrusions difficult, and the diffusive properties of glioblastoma are poorly understood. In this paper, we introduce a stochastic interacting particle system as a model of in vitro glioblastoma migration, along with a maximum likelihood-algorithm designed for inference using microscopy imaging data. The inference method is evaluated on in silico simulation of cancer cell migration, and then applied to a real data set. We find that the inference method performs with a high degree of accuracy on the in silico data, and achieve promising results given the in vitro data set., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.)

Saving and investment behaviour is crucial for all individuals to guarantee their welfare during work-life and retirement. We introduce a deep reinforcement learning model in which agents learn optimal portfolio allocation and saving strategies suitable for their heterogeneous profiles. The environment is calibrated with occupation- and age-dependent income dynamics. The research focuses on heterogeneous income trajectories dependent on agents' profiles and incorporates the parameterisation of agents' behaviours. The model provides a new flexible methodology to estimate lifetime consumption and investment choices for individuals with heterogeneous profiles.

Particle systems made up of interacting agents is a popular model used in a vast array of applications, not the least in biology where the agents can represent everything from single cells to animals in a herd. Usually, the particles are assumed to undergo some type of random movements, and a popular way to model this is by using Brownian motion. The magnitude of random motion is often quantified using mean squared displacement, which provides a simple estimate of the diffusion coefficient. However, this method often fails when data is sparse or interactions between agents frequent. In order to address this, we derive a conjugate relationship in the diffusion term for large interacting particle systems undergoing isotropic diffusion, giving us an efficient inference method. The method accurately accounts for emerging effects such as anomalous diffusion stemming from mechanical interactions. We apply our method to an agent-based model with a large number of interacting particles, and the results are contrasted with a naive mean square displacement-based approach. We find a significant improvement in performance when using the higher-order method over the naive approach. This method can be applied to any system where agents undergo Brownian motion and will lead to improved estimates of diffusion coefficients compared to existing methods., (© 2023. The Author(s).)

This article introduces the methodology of agent-based modelling (ABM), explains how it contributes to understanding the dynamics of climate-relevant behaviour and discusses the challenges to implementing behavioural theory in ABMs. Next, an overview will be given on recent advances in environmentally relevant ABMs. The conclusions address the future of the ABM tool in the context of environmentally relevant behaviour in research and education., Competing Interests: Conflict of interest statement Nothing declared., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The present study uses agent-based modeling (ABM) to examine the effectiveness of a nudge policy for improving recycling behavior. In our simulation, agents' recycling behavior is computed by components of the Theory of Planned Behaviour (i.e., attitudes, perceived behavioral control, social norms) and influenced by other agents as well as their surrounding (i.e., amount of waste in the area). The simulation, based on real data from a Taiwan community district, confirms realistic recycling trends and demonstrates the usefulness and reliability of ABM as a method to examine the effectiveness of waste management policies. An additional step in our simulation was to manipulate the amount of waste in the community to test the effect of a nudge policy based on social norms. Results showed that the policy increases recycling activity, but predominantly in low waste scenarios. This suggests that nudges, in the form of norm-based policies, can be an effective solution to enhancing people's recycling behavior under specific circumstances., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The re-emerging outbreak of COVID-19 in Beijing, China, in the summer of 2020 originated from a SARS-CoV-2-infested wholesale food supermarket. We postulated that the Xinfadi market outbreak has links with food-trade activities. Our Susceptible to the disease, Infectious, and Recovered coupled Agent Based Modelling (SIR-ABM) analysis for studying the diffusion of SARS-CoV-2 particles suggested that the trade-distancing strategy effectively reduces the reproduction number (R0). The retail shop closure strategy reduced the number of visitors to the market by nearly half. In addition, the buy-local policy option reduced the infection by more than 70% in total. Therefore, retail closures and buy-local policies could serve as significantly effective strategies that have the potential to reduce the size of the outbreak and prevent probable outbreaks in the future., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper., (© 2021 Published by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.)

Annually, the EU produces more than 100 million tonnes of urban biowaste, which is largely under-valorized and in some cases even still landfilled without any energy or material recovery. If Europe wants to be ready for the future, it will need to make better use of this large biomass potential within a circular economy approach. The research project funded by the European Commission under the Horizon 2020 programme entitled 'VOLATILE-Biowaste derived volatile fatty acid platform for biopolymers, bioactive compounds and chemical building blocks' aimed to produce volatile fatty acids (VFAs) from biowaste for reprocessing into products, materials or substances to close the material loop. During the project, the partners were able to obtain average volatile fatty acid yields of 627 g COD/kg organic matter (OM) for food waste, 448 g COD/kg OM for separately collected vegetable, garden and fruit waste (VGF) and 384 g COD/kg OM for the organic fraction of municipal solid waste (OF-MSW) at concentrations ranging from 12 to 48 g/L, 6 to 40 g/L and 13 to 26 g/L, respectively. A membrane filtration cascade consisting of micro-, ultra- and nano-filtration followed by reverse osmosis was identified as a feasible way to purify and concentrate the VFA effluent, making them a suitable carbon source for further fermentation processes. Besides technical optimization, socio-economic and legal aspects associated with this platform technology were also studied and show that although this technology is still in development, it is providing an answer to changing societal and market expectations both regarding organic waste treatment and bio-based production strategies. Based on the current technological, economic and market evolutions, it is expected that the VFAP will play an important role in organic waste treatment in the coming years., (© The Author(s) 2021. Published by Oxford University Press on behalf of FEMS. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Technology innovations create possibilities to capture exposure-related data at a great depth and breadth. Considering, though, the substantial hurdles involved in collecting individual data for whole populations, this study introduces a first approach of simulating human movement and interaction behaviour, using Agent Based Modelling (ABM). A city scale ABM was developed for urban Thessaloniki, Greece that feeds into population-based exposure assessment without imposing prior bias, basing its estimations onto emerging properties of the behaviour of the computerised autonomous decision makers (agents) that compose the city-system. Population statistics, road and buildings networks data were transformed into human, road and building agents, respectively. Survey outputs with time-use patterns were associated with human agent rules, aiming to model representative to real-world behaviours. Moreover, time-geography of exposure data, derived from a local sensors campaign, was used to inform and enhance the model. As a prevalence of an agent-specific decision-making, virtual individuals of different sociodemographic backgrounds express different spatiotemporal behaviours and their trajectories are coupled with spatially resolved pollution levels. Personal exposure was evaluated by assigning PM concentrations to human agents based on coordinates, type of location and intensity of encountered activities. Study results indicated that PM2.5 inhalation adjusted exposure between housemates can differ by 56.5% whereas exposure between two neighbours can vary by as much as 87%, due to the prevalence of different behaviours. This study provides details of a new methodology that permits the cost-effective construction of refined time-activity diaries and daily exposure profiles, taking into account different microenvironments and sociodemographic characteristics. The proposed method leads to a refined exposure assessment model, addressing effectively vulnerable subgroups of population. It can be used for evaluating the probable impacts of different public health policies prior to implementation reducing, therefore, the time and expense required to identify efficient measures., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Oscillating protein signals control the branching and expansion of blood vessels.

Formation of a regularly branched blood vessel network is crucial in development and physiology. Here we show that the expression of the Notch ligand Dll4 fluctuates in individual endothelial cells within sprouting vessels in the mouse retina in vivo and in correlation with dynamic cell movement in mouse embryonic stem cell-derived sprouting assays. We also find that sprout elongation and branching associates with a highly differential phase pattern of Dll4 between endothelial cells. Stimulation with pathologically high levels of Vegf, or overexpression of Dll4, leads to Notch dependent synchronization of Dll4 fluctuations within clusters, both in vitro and in vivo. Our results demonstrate that the Vegf-Dll4/Notch feedback system normally operates to generate heterogeneity between endothelial cells driving branching, whilst synchronization drives vessel expansion. We propose that this sensitive phase transition in the behaviour of the Vegf-Dll4/Notch feedback loop underlies the morphogen function of Vegfa in vascular patterning.

Objective: Design task assignment algorithms based on the patterns of disease spread among the population., Scope: Epidemiology studies spatiotemporal patterns of illness in populations and the factors affecting it. An epidemic emerges out of the population activities and environment. Task assignment is a common activity in many realms where sub-tasks are created, delegated, and collectively carried out to achieve the original task. Due to its complexity and context, task assignment can be a challenging activity that can result in limited outcomes. This research studies task assignment as an epidemic assigned to a distributed system. We have developed computational models to understand the outbreak of aerosol-borne diseases by using the agent-based modelling approach. Experiments are carried out to observe the patterns of emergence during the spread of disease among the individuals and get insights of their mechanisms. These mechanisms are used to design algorithms for task assignment on distributed systems., Results: Understanding the emergent behaviour of diseases can provide the platform for the development of distributed algorithms that can be helpful in overcoming some of the challenges of task assignment in a distributed system., (© 2016 The Author(s). Published by Elsevier B.V.)

This work uses agent-based modelling (ABM) to simulate sexually transmitted infection (STIs) spread within a population of 1000 agents over a 10-year period, as a preliminary investigation of the suitability of ABM methodology to simulate STI spread. The work contrasts compartmentalized mathematical models that fail to account for individual agents, and ABMs commonly applied to simulate the spread of respiratory infections. The model was developed in C++ using the Boost 1.47.0 libraries for the normal distribution and OpenGL for visualization. Sixteen agent parameters interact individually and in combination to govern agent profiles and behaviours relative to infection probabilities. The simulation results provide qualitative comparisons of STI mitigation strategies, including the impact of condom use, promiscuity, the form of the friend network, and mandatory STI testing. Individual and population-wide impacts were explored, with individual risk being impacted much more dramatically by population-level behaviour changes as compared to individual behaviour changes.