Real-world facility planning problems often require to tackle simultaneously network connectivity and zonal requirements, in order to guarantee an equitable provision of services and an efficient flow of goods, people and information among the facilities. Nonetheless, such challenges have not been addressed jointly so far. In this paper we explore the introduction of advanced network connectivity features and spatial-related requirements within Covering Location Problems. We adopt a broad modelling perspective, accounting for structural and economic aspects of connectivity features, while allowing the choice for one or more facilities to serve the facility networks as depots, and containing the maximal distance between any active facility and such depot(s). A novel class of Multi-objective Covering Location problems are proposed, utilising Mixed Integer Linear Programming as a modelling tool. Aiming at obtaining efficiently the arising Pareto Sets and providing actionable decision-making support throughout real planning processes, we adapt to our problem the robust variant of the AUGMEnted ?-CONstraint method (AUGMECON-R). Furthermore, we exploit the mathematical properties of the proposed problems to design tailored Matheuristic algorithms which boost the scalability of the solution method, with particular reference to the case of multiple depots. By conducting a comprehensive computational study on benchmark instances, we provide a thorough proof of concept for the novel problems, highlighting the challenging nature of the advanced connectivity features and the scalability of the proposed Matheuristics. From a managerial standpoint, the suitability of the proposed work in responding effectively to the motivating needs is showcased.

Dissolution of drug from its solid form to a dissolved form is an important consideration in the design and optimization of drug delivery devices, particularly owing to the abundance of emerging compounds that are extremely poorly soluble. When the solid dosage form is encapsulated, for example by the porous walls of an implant, the impact of the encapsulant drug transport properties is a further confounding issue. In such a case, dissolution and diffusion work in tandem to control the release of drug. However, the interplay between these two competing processes in the context of drug delivery is not as well understood as it is for other mass transfer problems, particularly for practical controlled-release considerations such as an encapsulant layer around the drug delivery device. To address this gap, this work presents a mathematical model that describes controlled release from a drug-loaded device surrounded by a passive porous layer. A solution for the drug concentration distribution is derived using the method of eigenfunction expansion. The model is able to track the dissolution front propagation, and predict the drug release curve during the dissolution process. The utility of the model is demonstrated through comparison against experimental data representing drug release from a cylindrical drugloaded orthopedic fixation pin, where the model is shown to capture the data very well. Analysis presented here reveals how the various geometrical and physicochemical parameters influence drug dissolution and, ultimately, the drug release profile. It is found that the non-dimensional initial concentration plays a key role in determining whether the problem is diffusion-limited or dissolution-limited, whereas the nature of the problem is largely independent of other parameters including diffusion coefficient and encapsulant thickness. We expect the model will prove to be a useful tool for those designing encapsulated drug delivery devices, in terms of optimizing the design of the device to achieve a desired drug release profile.

Modellistica e calcolo di polimeri semiflessibili in campi esterni

This paper investigates the model for pedestrian flow firstly proposed in [Cristiani, Priuli, and Tosin, SIAM J. Appl. Math., 75:605-629, 2015]. The model assumes that each individual in the crowd moves in a known domain, aiming at minimizing a given cost functional. Both the pedestrian dynamics and the cost functional itself depend on the position of the whole crowd. In addition, pedestrians are assumed to have predictive abilities, but limited in time.

In this paper we deal with pedestrian modeling, aiming at simulating crowd behavior in normal and emergency scenarios, including highly congested mass events. We are specifically concerned with a new agent-based, continuous-in-space, discrete-in-time, nondifferential model, where pedestrians have finite size and are compressible to a certain extent. The model also takes into account the pushing behavior appearing at extremely high densities. The main novelty is that pedestrians are not assumed to generate any kind of "field" which governs the dynamics of the others in the space around them. Instead, the behavior of each pedestrian solely relies on its knowledge of the environment and the evaluation of interpersonal distances between it and the others. The model is able to reproduce the concave/concave fundamental diagram with a "double hump" (i.e. with a second peak) which shows up when body forces come into play. We present several numerical tests (some of them being inspired by the recent ISO 20414 standard), which show how the model can reproduce classical self-organizing patterns.

Computer Vision and 3D printing have rapidly evolved in the last 10 years but interactions among them have been very limited so far, despite the fact that they share several mathematical techniques. We try to fill the gap presenting an overview of some techniques for Shape-from-Shading problems as well as for 3D printing with an emphasis on the approaches based on nonlinear partial differential equations and optimization. We also sketch possible couplings to complete the process of object manufacturing starting from one or more images of the object and ending with its final 3D print. We will give some practical examples of this procedure.

Presents several mathematical problems for 3D and 3D printing plus a survey that gives the links between the two areas Contains contributions from highly reputed academic and industrial researchers with a long experience Shows several techniques, applications and benchmarks that can be useful for young researchers approaching the field

Bi-allelic hypomorphic mutations in DNMT3B disrupt DNA methyltransferase activity and lead to immunodeficiency, centromeric instability, facial anomalies syndrome, type 1 (ICF1). Although several ICF1 phenotypes have been linked to abnormally hypomethylated repetitive regions, the unique genomic regions responsible for the remaining disease phenotypes remain largely uncharacterized. Here we explored two ICF1 patient-derived induced pluripotent stem cells (iPSCs) and their CRISPR-Cas9-corrected clones to determine whether DNMT3B correction can globally overcome DNA methylation defects and related changes in the epigenome. Hypomethylated regions throughout the genome are highly comparable between ICF1 iPSCs carrying different DNMT3B variants, and significantly overlap with those in ICF1 patient peripheral blood and lymphoblastoid cell lines. These regions include large CpG island domains, as well as promoters and enhancers of several lineage-specific genes, in particular immune-related, suggesting that they are premarked during early development. CRISPR-corrected ICF1 iPSCs reveal that the majority of phenotype-related hypomethylated regions reacquire normal DNA methylation levels following editing. However, at the most severely hypomethylated regions in ICF1 iPSCs, which also display the highest increases in H3K4me3 levels and/or abnormal CTCF binding, the epigenetic memory persists, and hypomethylation remains uncorrected. Overall, we demonstrate that restoring the catalytic activity of DNMT3B can reverse the majority of the aberrant ICF1 epigenome. However, a small fraction of the genome is resilient to this rescue, highlighting the challenge of reverting disease states that are due to genome-wide epigenetic perturbations. Uncovering the basis for the persistent epigenetic memory will promote the development of strategies to overcome this obstacle.

CRC is an adult-onset carcinoma representing the third most common cancer and the second leading cause of cancer-related deaths in the world. EO-CRC (<45 years of age) accounts for 5% of the CRC cases and is associated with cancer-predisposing genetic factors in half of them. Here, we describe the case of a woman affected by BWSp who developed EO-CRC at age 27. To look for a possible molecular link between BWSp and EO-CRC, we analysed her whole-genome genetic and epigenetic profiles in blood, and peri-neoplastic and neoplastic colon tissues. The results revealed a general instability of the tumor genome, including copy number and methylation changes affecting genes of the WNT signaling pathway, CRC biomarkers and imprinted loci. At the germline level, two missense mutations predicted to be likely pathogenic were found in compound heterozygosity affecting the Cystic Fibrosis (CF) gene CFTR that has been recently classified as a tumor suppressor gene, whose dysregulation represents a severe risk factor for developing CRC. We also detected constitutional loss of methylation of the KCNQ1OT1:TSS-DMR that leads to bi-allelic expression of the lncRNA KCNQ1OT1 and BWSp. Our results support the hypothesis that the inherited CFTR mutations, together with constitutional loss of methylation of the KCNQ1OT1:TSS-DMR, initiate the tumorigenesis process. Further somatic genetic and epigenetic changes enhancing the activation of the WNT/beta-catenin pathway likely contributed to increase the growth advantage of cancer cells. Although this study does not provide any conclusive cause-effect relationship between BWSp and CRC, it is tempting to speculate that the imprinting defect of BWSp might accelerate tumorigenesis in adult cancer in the presence of predisposing genetic variants.

Epigenetic modifications are correlated to environmental factors. Exposure to ambient air pollution may contribute to the development of different diseases such as cancer, cardiovascular diseases, and neurological and metabolic disorders. Looking for the association between DNA methylation and exposure biomarkers may help in the prevention of adverse effects. Association analysis can be carried out through regression modeling. When dealing with the association between DNA methylation and pollutants, the response variable is beta-distributed, and linear regression models are not appropriate when the range is limited to (0, 1). Beta regression models are more suitable for this situation. Methylation levels can also be measured through the M-value statistic and association studies may be performed using classical linear regression models or robust linear regression models in the presence of outliers. An alternative to these models when the variable of interest does not behave linearly in all the predictors is given by a generalized linear model framework that incorporates non-linear terms and interactions. In this paper, we applied these models to a case study constituted of a cohort of healthy people living in regions exposed to different levels of pollution to investigate the association between DNA methylation and cadmium exposure.

Abstract. Electroencephalography (EEG) source imaging aims to reconstruct brainactivity maps from the neuroelectric potential difference measured on the skull. Toobtain the brain activity map, we need to solve an ill-posed and ill-conditionedinverse problem that requires regularization techniques to make the solution viable.When dealing with real-time applications, dimensionality reduction techniques can beused to reduce the computational load required to evaluate the numerical solutionof the EEG inverse problem. To this end, in this paper we use the random dipolesampling method, in which a Monte Carlo technique is used to reduce the numberof neural sources. This is equivalent to reducing the number of the unknownsin the inverse problem and can be seen as a first regularization step. Then, wesolve the reduced EEG inverse problem with two popular inversion methods, theweighted Minimum Norm Estimate (wMNE) and the standardized LOw Resolutionbrain Electromagnetic TomogrAphy (sLORETA). The main result of this paper is theerror estimates of the reconstructed activity map obtained with the randomized versionof wMNE and sLORETA. Numerical experiments on synthetic EEG data demonstratethe effectiveness of the random dipole sampling method.

Understanding brain function from magneto-electroencephalographic (M/EEG) measurements requires advanced mathematical and signal processing tools. Although the analysis of M/EEG data at sensors level sheds light on important brain mechanisms, full exploitation of the information contained in such brain data could be achieved by reconstructing the active neural sources from M/EEG measurements. This involves solving an ill-posed and ill-conditioned inverse problem in which not only the identification of the most suitable inversion method [1, 2] but also the calibration of the regularization parameters is of paramount importance. Once time series representing brain activity are available, a next step is to develop tools to extract meaningful information that characterizes brain activity [3, 4], for example, when the subject under study is affected by diseases that impair brain function. The mini-symposium brings together researchers from various disciplines who have developed methodologies that are being successfully used for the analysis of the M/EEG data, the solution of the underlying inverse problem and in the definition of brain fingerprint. The purpose is not only to present the latest research results in this area but also to create a fruitful environment for the development of new ideas.