In this study, the results of 46170 simulations corresponding to the same number of virtual subjects, experiencing different lifestyle conditions, are analysed for the construction of a statistical model able to recapitulate the simulated dynamics. Investigation about the mechanisms involved in the onset of type 2 diabetes in absence of familiarity is the focus of a research project which has led to the development of a computational model that recapitulates the aetiology of the disease. The model simulates the metabolic and immunological alterations related to type-2 diabetes associated to several clinical, physiological and behavioural characteristics of representative virtual patients.

Report del convegno "MACH2019", svolto a Roma in data 25-29 marzo 2019, co-organizzato da Università degli Studi di Milano, Istituto per le Applicazioni del Calcolo M. Picone, Università degli Studi di Sassari, finanziato da Università degli Studi di Milano, Istituto per le Applicazioni del Calcolo M. Picone e INdAM (Istituto Nazionale di Alta Matematica). Obiettivo: costruire un ponte permanente tra esperti del patrimonio culturale e la comunità matematica. Il report raccoglie info sul convegno (focus, topics, data e location, organizzatori, speaker e partecipanti sponsors e attività extra), dati sui partecipanti, agenda ed abstract e prossimi passi (proceedings). Anno: 2019 (8 aprile)

Recently, structural monitoring by radar remote sensing has become more necessary for both economic and security reasons. Infrastructure monitoring with no incorporated deformation sensors (e.g., old generation water dams for which regulations did not impose monitoring capabilities) is usually performed by regular in situ topographic surveys. However, these surveys cannot be performed very often, and alternative methods are desirable. A feasible nonintrusive way to monitor such a structure is with interferometric synthetic aperture radar (SAR) data that can be acquired with monostatic/bistatic sensors.

We designed a ground-based radar system with a C-band 2-D cross multiple input multiple output (MIMO) array for 3-D imaging and displacement estimation purposes. For this system, we developed a far-field pseudo-polar image format algorithm using pseudo-polar spherical coordinate. The use of a tensor compressive sensing technique allows to focus under-sampled raw data and to optimize the data acquisition time and memory usage. A novel algorithm, named as tensor-based iterative adaptive approach, is proposed for the effective and efficient reconstruction of sparse targets with a reduced level of sidelobes. Experimental results validate the designed radar system and the proposed algorithms.

Plain Language Summary Weather forecasts will never be perfect because our models are simplified representations of nature and our observations of the atmosphere are inaccurate. In this study we show, nevertheless, that it is possible to improve such forecasts by interpreting the atmospheric signals in spaceborne radar observations of the Earth surface, indicative of the distribution of water vapor. Better and more detailed maps of water vapor are found to lead to better forecasts not just of water vapor but also of precipitation. A two and a half years assessment covering a wide range of weather conditions in a very well monitored region near the Appalachian Mountains, USA, suggests that the proposed methodology has a significant impact in the quality of the forecasts and could easily be implemented. The present study assesses the added value of high-resolution maps of precipitable water vapor, computed from synthetic aperture radar interferograms , in short-range atmospheric predictability. A large set of images, in different weather conditions, produced by Sentinel-1A in a very well monitored region near the Appalachian Mountains, are assimilated by the Weather Research and Forecast (WRF) model. Results covering more than 2 years of operation indicate a consistent improvement of the water vapor predictability up to a range comparable with the transit time of the air mass in the synthetic aperture radar interferograms footprint, an overall improvement in the forecast of different precipitation events, and better representation of the spatial distribution of precipitation. This result highlights the significant potential for increasing short-range atmospheric predictability from improved high-resolution precipitable water vapor initial data, which can be obtained from new high-resolution all-weather microwave sensors.

A passive bistatic ground-based synthetic aperture radar (PB-GB-SAR) system without a dedicated transmitter has been developed by using commercial-off-the-shelf (COTS) hardware for local-area high-resolution imaging and displacement measurement purposes. Different from the frequency-modulated or frequency-stepped continuous wave signal commonly used by GB-SAR, the continuous digital TV signal broadcast by a geostationary satellite has been adopted by PB-GB-SAR. In order to increase the coherence between the reference and surveillance channels, frequency and phase synchronization of multiple low noise blocks (LNBs) has been conducted. Then, the back-projection algorithm (BPA) and the range migration algorithm (RMA) have been modified for PB-GB-SAR to get the focused SAR image. Field experiments have been carried out to validate the designed PB-GB-SAR system and the proposed methods. It has been found that different targets within 100 m (like the fence, light pole, tree, and car) can be imaged by the PB-GB-SAR system. With a metallic plate moved on a positioner, it has been observed that the displacement of the target can be estimated by PB-GB-SAR with submillimeter accuracy.

This work presents a methodology for the short-term forecast of intense rainfall based on a neural network and the integration of Global Navigation and Positioning System (GNSS) and meteorological data. Precipitable water vapor (PWV) derived from GNSS is combined with surface pressure, surface temperature and relative humidity obtained continuously from a ground-based meteorological station. Five years of GNSS data from one station in Lisbon, Portugal, are processed. Data for precipitation forecast are also collected from the meteorological station. Spaceborne Spinning Enhanced Visible and Infrared Imager (SEVIRI) data of cloud top measurements are also gathered, providing collocated information on an hourly basis. In previous studies it was found that the time-varying PWV is correlated with rainfall and can be used to detected heavy rain. However, a significant number of false positives were found, meaning that the evolution of PWV does not contain enough information to infer future rain. In this work, a nonlinear autoregressive exogenous neural network model (NARX) is used to process the GNSS and meteorological data to forecast the hourly precipitation. The proposed methodology improves the detection of intense rainfall events and reduces the number of false positives, with a good classification score varying from 63% up to 72% and a false positive rate of 36% down to 21%, for the tested years in the dataset. A score of 64% for intense rain events classification with 22% false positive rate is obtained for the most recent years. The method also achieves an almost 100% hit rate for the rain vs no rain detection, with close to no false alarms.

Operated by the H2020 SOMA Project, the recently established Social Observatory for Disinformation and Social Media Analysis supports researchers, journalists and fact-checkers in their quest for quality information. At the core of the Observatory lies the DisInfoNet Toolbox, designed to help a wide spectrum of users understand the dynamics of (fake) news dissemination in social networks. DisInfoNet combines text mining and classification with graph analysis and visualization to offer a comprehensive and user-friendly suite. To demonstrate the potential of our Toolbox, we consider a Twitter dataset of more than 1.3M tweets focused on the Italian 2016 constitutional referendum and use DisInfoNet to: (i) track relevant news stories and reconstruct their prevalence over time and space; (ii) detect central debating communities and capture their distinctive polarization/narrative; (iii) identify influencers both globally and in specific "disinformation networks".

In this work we introduce and study the strong generalized minimum label spanning tree (GMLST), a novel optimization problem defined on edge-labeled graphs. Given a label set associated to each edge of the input graph, the aim is to look for the spanning tree using the minimum number of labels. Differently from the previously introduced GMLST problem, including a given edge in the solution means that all its labels are used. We present a mathematical formulation, as well as three heuristic approaches to solve the problem. Computational results compare the performances of the proposed algorithms.

Questa presentazione descrive le conclusioni finali del progetto ECOPOTENTIAL riguardo al coinvolgimento degli stakeholder delle aree protette nel definire e condurre le attività di ricerca a fianco dei ricercatori, illustrando punti di forza e di debolezza, come risultati da un questionario sottoposto ai gestori delle Aree Protette.

Pubblicazione divulgativa della collana Comics&Science dedicato all'astrofisica

volume divulgativo dedicato alla tavola periodica della serie Comics&Science

his report forms the basis for the selection of the ninth Earth Explorer mission within ESA's Earth Observation Programme. Two competing 'Fast Track' candidates, the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission and the Surface ocean KInematics Multiscale (SKIM) mission. Each have each undergone a rapid and compressed Phase A feasibility study. This report covers the FORUM mission.

We tackle the issue of measuring and analyzing the visitors’ dynamics in crowded museums. We propose an IoT-based system – supported by artificial intelligence models – to reconstruct the visitors’ trajectories throughout the museum spaces. Thanks to this tool, we are able to gather wide ensembles of visitors’ trajectories, allowing useful insights for the facility management and the preservation of the art pieces. Our contribution comes with one successful use case: the Galleria Borghese in Rome, Italy.

We study the coarsening dynamics of a two-dimensional system via numerical simulations. The system under consideration is a biphasic system consisting of domains of a dispersed phase closely packed together in a continuous phase and separated by thin interfaces. Such a system is elastic and typically out of equilibrium. The equilibrium state is attained via the coarsening dynamics, wherein the dispersed phase slowly diffuses through the interfaces, causing the domains to change in size and eventually rearrange abruptly. The effect of rearrangements is propagated throughout the system via the intrinsic elastic interactions and may cause rearrangements elsewhere, resulting in intermittent bursts of activity and avalanche behaviour. Here we aim at quantitatively characterizing the corresponding avalanche statistics (i.e. size, duration, and inter-avalanche time). Despite the coarsening dynamics is triggered by an internal driving mechanism, we find quantitative indications that such avalanche statistics displays scaling-laws very similar to those observed in the response of disordered materials to external loads.

Experimental and computational studies have revealed that T-cell cross-reactivity is a widespread phenomenon that can either be advantageous or detrimental to the host. In particular, detrimental effects can occur whenever the clonal dominance of memory cells is not justified by their infection-clearing capacity. Using an agent-based model of the immune system, we recently predicted the “memory anti-naïve” phenomenon, which occurs when the secondary challenge is similar but not identical to the primary stimulation. In this case, the pre-existing memory cells formed during the primary infection may be rapidly deployed in spite of their low affinity and can actually prevent a potentially higher affinity naïve response from emerging, resulting in impaired viral clearance. This finding allowed us to propose a mechanistic explanation for the concept of “antigenic sin” originally described in the context of the humoral response. However, the fact that antigenic sin is a relatively rare occurrence suggests the existence of evolutionary mechanisms that can mitigate the effect of the memory anti-naïve phenomenon. In this study we use computer modeling to further elucidate clonal dominance and the memory anti-naïve phenomenon, and to investigate a possible mitigating factor called attrition. Attrition has been described in the experimental and computational literature as a combination of competition for space and apoptosis of lymphocytes via type-I interferon in the early stages of a viral infection. This study systematically explores the relationship between clonal dominance and the mechanism of attrition. Our results suggest that attrition can indeed mitigate the memory anti-naïve effect by enabling the emergence of a diverse, higher affinity naïve response against the secondary challenge. In conclusion, modeling attrition allows us to shed light on the nature of clonal interaction and dominance.

Background: Radiotherapy (RT) plays a fundamental role in the treatment of pediatric central nervous system (CNS) malignancies, but its late sequelae are still a challenging question. Despite developments in modern high-conformal photon techniques and proton beam therapy (PBT) are improving the normal tissues dose-sparing while maintaining satisfactory target coverage, clinical advantages supporting the optimal treatment strategy have to be better evaluated in long-term clinical studies and assessed in further radiobiological analyses. Our analysis aimed to systematically review current knowledge on the dosimetric advantages of PBT in the considered setting, which should be the basis for future specific studies. Materials and methods: A PubMed and Google Scholar search was conducted in June 2019 to select dosimetric studies comparing photon versus proton RT for pediatric patients affected by CNS tumors. Then, a systematic review and meta-analysis according to the PRISMA statement was performed. Average and standard deviation values of Conformity Index, Homogeneity Index, and mean and maximum doses to intracranial and extracranial organs at risk (OARs) were specifically evaluated for secondary dosimetric comparisons. The standardized mean differences (SMDs) for target parameters and the mean differences (MDs) for OARs were summarized in forest plots (P < 0.05 was considered statistically significant). Publication bias was also assessed by the funnel plot and Egger's regression test. Results: Among the 88 identified papers, a total of twelve studies were included in the meta-analysis. PBT showed dosimetric advantages in target homogeneity (significant especially in the subgroup comparing PBT and 3D conformal RT), as well as in the dose sparing of almost all analyzed OARs (significantly superior results for brainstem, normal brain, and hippocampal dose constraints and for extracranial OARs parameters, excluding the kidneys). Publication bias was observed for Conformity Index. Conclusion: Our analysis supports the evidence of dosimetric advantages of PBT over photon RT, especially in the dose sparing of normal growing tissues. Confirmations from wider well-designed studies are required.

The highly conserved zinc finger CCCTC-binding factor (CTCF) regulates genomic imprinting and gene expression by acting as a transcriptional activator or repressor of promoters and insulator of enhancers. The multiple functions of CTCF are accomplished by co-association with other protein partners and are dependent on genomic context and tissue specificity. Despite the critical role of CTCF in the organization of genome structure, to date, only a subset of CTCF interaction partners have been identified. Here we present a large-scale identification of CTCF binding partners using affinity purification and high-resolution LC-MS/MS analysis. In addition to functional enrichment of specific protein families such as the ribosomal proteins and the DEAD box helicases, we identified novel high-confidence CTCF interactors that provide a still unexplored biochemical context for CTCF's multiple functions. One of the newly validated CTCF interactors is BRG1, the major ATPase subunit of the chromatin remodeling complex SWI/SNF, establishing a relationship between two master regulators of genome organization. This work significantly expands the current knowledge of the human CTCF interactome and represents an important resource to direct future studies aimed at uncovering molecular mechanisms modulating CTCF pleiotropic functions throughout the genome.

A recently proposed iterated alternating sequential (IAS) MEG inverse solver algorithm, based on the coupling of a hierarchical Bayesian model with computationally efficient Krylov subspace linear solver, has been shown to perform well for both superficial and deep brain sources. However, a systematic study of its ability to correctly identify active brain regions is still missing. We propose novel statistical protocols to quantify the performance of MEG inverse solvers, focusing in particular on how their accuracy and precision at identifying active brain regions. We use these protocols for a systematic study of the performance of the IAS MEG inverse solver, comparing it with three standard inversion methods, wMNE, dSPM, and sLORETA. To avoid the bias of anecdotal tests towards a particular algorithm, the proposed protocols are Monte Carlo sampling based, generating an ensemble of activity patches in each brain region identified in a given atlas. The performance in correctly identifying the active areas is measured by how much, on average, the reconstructed activity is concentrated in the brain region of the simulated active patch. The analysis is based on Bayes factors, interpreting the estimated current activity as data for testing the hypothesis that the active brain region is correctly identified, versus the hypothesis of any erroneous attribution. The methodology allows the presence of a single or several simultaneous activity regions, without assuming that the number of active regions is known. The testing protocols suggest that the IAS solver performs well with both with cortical and subcortical activity estimation.