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Panayotis K Vlachakis, Panagiotis Theofilis, Stergios Soulaidopoulos, Emilia Lazarou, Konstantinos Tsioufis, George Lazaros First Cardiology Department, School of Medicine, Hippokration General Hospital, National and Kapodistrian University of Athens, Athens, 11527, GreeceCorrespondence: George Lazaros, First Cardiology Department, School of Medicine, Hippokration General Hospital, National and Kapodistrian University of Athens, Vas. Sofias 114, Athens, 11527, Greece, Email glaz35@hotmail.comAbstract: Recurrent pericarditis (RP) has been traditionally regarded as a “nightmare” for both clinicians and patients. Until approximately a decade ago, available treatments were thin on the ground with non-steroidal anti-inflammatory medications, glucocorticoids, colchicine, and classical immunosuppressants being the only options. The first important step in the tale of RP was the advent of colchicine in clinical practice, which has been shown to halve the rate of first and subsequent pericarditis recurrences. The second major breakthrough advance in this setting was the introduction of interleukin-1 inhibitors based on the recently unveiled autoinflammatory nature of pericarditis. At present, anti-interleukin-1 inhibitors available for clinical use in patients with refractory RP include anakinra and rilonacept, with the latter having obtained FDA approval for this indication. Apart from the remarkable efficacy and good safety profile which is a common feature of all anti-interleukin-1 compounds, rilonacept has the advantage of weekly administration (instead of daily compared to anakinra) which is important in terms of adherence to treatment and improved quality of life albeit at the expense of a higher cost. This review aims to summarize the available evidence on the role of rilonacept in the treatment of RP and the reduction of the recurrences risk.Keywords: rilonacept, interleukin-1 blockers, NLRP3 inflammasome, recurrent pericarditis

This work examines the daily bidding problem of a grid-connected microgrid with locally deployed resources for electricity generation, storage and its own electricity demand. Trading electricity in energy markets may offer economic incentives but exposes the microgrid to financial risk caused by market commitments. Hence, a multi-objective, two-stage stochastic mixed integer linear programming (MILP) model is formulated, extending prior work of a risk-neutral microgrid bidding approach. The multi-objective model minimises both expected total cost of day-ahead microgrid operations and financial risk from bidding measured by conditional value-at-risk (CVaR). Bidding curves derived as first stage decisions are always feasible under present market rules – including a limitation on the number of break points per submitted curve – while being near optimal for the microgrid’s day-ahead recourse schedule. The proposed optimisation model is embedded in a variant of the ɛ-constrained method to generate bidding curve candidates with different trade-offs between the two conflicting objectives. Moreover, scenario reduction is used to compromise accuracy of the uncertainty set for better computational performance. Particularly, the marginal relative probability distance between initial and reduced scenario set is suggested to make a decision on the extent of scenario reduction. The proposed solution procedure is tested in a computational study to demonstrate its applicability to generate optimal microgrid bidding curve candidates with different emphasis between total cost and CVaR in reasonable computational time.

Background: With advances in high-throughput technologies, there has been an enormous increase in data related to profiling the activity of molecules in disease. While such data provide more comprehensive information on cellular actions, their large volume and complexity pose difficulty in accurate classification of disease phenotypes. Therefore, novel modelling methods that can improve accuracy while offering interpretable means of analysis are required. Biological pathways can be used to incorporate a priori knowledge of biological interactions to decrease data dimensionality and increase the biological interpretability of machine learning models. Methodology: A mathematical optimisation model is proposed for pathway activity inference towards precise disease phenotype prediction and is applied to RNA-Seq datasets. The model is based on mixed-integer linear programming (MILP) mathematical optimisation principles and infers pathway activity as the linear combination of pathway member gene expression, multiplying expression values with model-determined gene weights that are optimised to maximise discrimination of phenotype classes and minimise incorrect sample allocation. Results: The model is evaluated on the transcriptome of breast and colorectal cancer, and exhibits solution results of good optimality as well as good prediction performance on related cancer subtypes. Two baseline pathway activity inference methods and three advanced methods are used for comparison. Sample prediction accuracy, robustness against noise expression data, and survival analysis suggest competitive prediction performance of our model while providing interpretability and insight on key pathways and genes. Overall, our work demonstrates that the flexible nature of mathematical programming lends itself well to developing efficient computational strategies for pathway activity inference and disease subtype prediction.

This work addresses the multiscale optimization of the purification processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational flow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, flow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody purification process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models. Keywords: Antibody purification, Multiscale optimization, Antigen-binding fragment, Mixed-integer programming, Data-driven model, Piecewise linear regression

Energy generation and platform chemicals production from biomass are a potential route towards an oil-free economy. Pyrolysis is one of the key technologies for transforming biomass into both fuels and chemicals. However, pyrolysis is a complex and energy-intensive process, and optimizing the operation for reducing its energy requirements is critical for the design of competitive biorefineries. This work presents a model to describe cellulose pyrolysis based on mass, energy and momentum conservation of solid and gaseous species. Lumped and detailed kinetic models are used to investigate how heating conditions impact pyrolysis product distribution. The resulting complex system was solved using gPROMS. Results suggest that pyrolysis mainly occurs in the boundary of the modelled particles. The developed model presents flexibility to use lumped and detailed kinetic models and provided both a general perspective of the pyrolysis process and detailed information on product distribution. Using this model, the results show that an initial high heating rate, followed by a lower heating rate, could reduce energy requirements by 10 % without changing the product distribution. There is also a trade-off between the yield of high added-value products, such as levoglucosan, and the overall energy requirement.

In the present work, two new, (multi-)parametric programming (mp-P)-inspired algorithms for the solution of mixed-integer nonlinear programming (MINLP) problems are developed, with their main focus being on process synthesis problems. The algorithms are developed for the special case in which the nonlinearities arise because of logarithmic terms, with the first one being developed for the deterministic case, and the second for the parametric case (p-MINLP). The key idea is to formulate and solve the square system of the first-order Karush-Kuhn-Tucker (KKT) conditions in an analytical way, by treating the binary variables and/or uncertain parameters as symbolic parameters. To this effect, symbolic manipulation and solution techniques are employed. In order to demonstrate the applicability and validity of the proposed algorithms, two process synthesis case studies are examined. The corresponding solutions are then validated using state-of-the-art numerical MINLP solvers. For p-MINLP, the solution is given by an optimal solution as an explicit function of the uncertain parameters.

This paper presents a novel prototype platform that uses the same LaTeX mark-up language, commonly used to typeset mathematical content, as an input language for modeling optimization problems of various classes. The platform converts the LaTeX model into a formal Algebraic Modeling Language (AML) representation based on Pyomo through a parsing engine written in Python and solves by either via NEOS server or locally installed solvers, using a friendly Graphical User Interface (GUI). The distinct advantages of our approach can be summarized in (i) simplification and speed-up of the model design and development process (ii) non-commercial character (iii) cross-platform support (iv) easier typo and logic error detection in the description of the models and (v) minimization of working knowledge of programming and AMLs to perform mathematical programming modeling. Overall, this is a presentation of a complete workable scheme on using LaTeX for mathematical programming modeling which assists in furthering our ability to reproduce and replicate scientific work.

The design of heating systems for dwellings using new technologies, or new versions of old technologies, requires the ability to predict the temperatures in a dwelling. The temperature behaviour can be modelled, typically by differential equations which incorporate thermal driving forces and the thermal inertia of a dwelling. The development and characterisation of these models is usually based on fitting data accumulated over sufficient time to capture the behaviour of the dwelling under different conditions (summer, winter, etc.). Model fitting relies on assumptions about the behaviour of the system. Optimisation can be used to examine these assumptions and gain insight into this behaviour. This paper describes the application of a nature inspired algorithm, known as the Plant Propagation Algorithm , a variant of a Variable Neighbourhood Search algorithm, to the problem of modelling a dwelling heated by an air source heat pump. The algorithm is evaluated using different population evolution strategies and implemented using a simple parallel computing paradigm on a multi-core desktop system. The results are used to identify potential sources of missing data which could explain the observed behaviour of the dwelling.

Community structure detection has proven to be important in revealing the underlying properties of complex networks. The standard problem, where a partition of disjoint communities is sought, has been continually adapted to offer more realistic models of interactions in these systems. Here, a two-step procedure is outlined for exploring the concept of overlapping communities. First, a hard partition is detected by employing existing methodologies. We then propose a novel mixed integer non linear programming (MINLP) model, known as OverMod, which transforms disjoint communities to overlapping. The procedure is evaluated through its application to protein-protein interaction (PPI) networks of the rat, E. coli, yeast and human organisms. Connector nodes of hard partitions exhibit topological and functional properties indicative of their suitability as candidates for multiple module membership. OverMod identifies two types of connector nodes, inter and intra-connector, each with their own particular characteristics pertaining to their topological and functional role in the organisation of the network. Inter-connector proteins are shown to be highly conserved proteins participating in pathways that control essential cellular processes, such as proliferation, differentiation and apoptosis and their differences with intra-connectors is highlighted. Many of these proteins are shown to possess multiple roles of distinct nature through their participation in different network modules, setting them apart from proteins that are simply 'hubs', i.e. proteins with many interaction partners but with a more specific biochemical role.

Networks constitute powerful means of representing various types of complex systems, where nodes denote the system entities and edges express the interactions between the entities. An important topological property in complex networks is community structure, where the density of edges within subgraphs is much higher than across different subgraphs. Each of these subgraphs forms a community (or module). In literature, a metric called modularity is defined that measures the quality of a partition of nodes into different mutually exclusive communities. One means of deriving community structure is modularity maximisation. In this paper, a novel mathematical programming-based model, DiMod, is proposed that tackles the problem of maximising modularity for directed networks.