The aim of the work is to propose a methodology for the stimulation of a 3D in vitro skin model to activate wound healing. The presented work is in the frame of the national research project, CronXCov, "Checking the CHRONIC to prevent COVid-19", devoted to understand how physiologic and inflamed skin on chip 3D models evolve upon a range of physical (e.g., electrical, mechanical, optical) stimulations, over time. Thanks to the 3D modelling, using Next Generation Sequencing and the network medicine frame of analysis to process the data, we will systematically characterize the effects of the applied stimuli, offering new insight for the exploitation of wound healing.

Thanks to innovative sample-preparation and sequencing technologies, gene expression in individual cells can now be measured for thousands of cells in a single experiment. Since its introduction, single-cell RNA sequencing (scRNA-seq) approaches have revolutionized the genomics field as they created unprecedented opportunities for resolving cell heterogeneity by exploring gene expression profiles at a single-cell resolution. However, the rapidly evolving field of scRNA-seq invoked the emergence of various analytics approaches aimed to maximize the full potential of this novel strategy. Unlike population-based RNA sequencing approaches, scRNA seq necessitates comprehensive computational tools to address high data complexity and keep up with the emerging single-cell associated challenges. Despite the vast number of analytical methods, a universal standardization is lacking. While this reflects the fields' immaturity, it may also encumber a newcomer to blend in. In this review, we aim to bridge over the abovementioned hurdle and propose four ready-to-use pipelines for scRNA-seq analysis easily accessible by a newcomer, that could fit various biological data types. Here we provide an overview of the currently available single-cell technologies for cell isolation and library preparation and a step by step guide that covers the entire canonical analytic workflow to analyse scRNA-seq data including read mapping, quality controls, gene expression quantification, normalization, feature selection, dimensionality reduction, and cell clustering useful for trajectory inference and differential expression. Such workflow guidelines will escort novices as well as expert users in the analysis of complex scRNA-seq datasets, thus further expanding the research potential of single-cell approaches in basic science, and envisaging its future implementation as best practice in the field.

?1-Antitrypsin (AAT) deficiency is a common genetic disease presenting with lung and liver diseases. AAT deficiency results from pathogenic variants in the SERPINA1 gene encoding AAT and the common mutant Z allele of SERPINA1 encodes for Z ?1-antitrypsin (ATZ), a protein forming hepatotoxic polymers retained in the endoplasmic reticulum of hepatocytes. PiZ mice express the human ATZ and are a valuable model to investigate the human liver disease of AAT deficiency. In this study, we investigated differential expression of microRNAs (miRNAs) between PiZ and control mice and found that miR-34b/c was up-regulated and its levels correlated with intrahepatic ATZ. Furthermore, in PiZ mouse livers, we found that Forkhead Box O3 (FOXO3) driving microRNA-34b/c (miR-34b/c) expression was activated and miR-34b/c expression was dependent upon c-Jun N-terminal kinase (JNK) phosphorylation on Ser. Deletion of miR-34b/c in PiZ mice resulted in early development of liver fibrosis and increased signaling of platelet-derived growth factor (PDGF), a target of miR-34b/c. Activation of FOXO3 and increased miR-34c were confirmed in livers of humans with AAT deficiency. In addition, JNK-activated FOXO3 and miR-34b/c up-regulation were detected in several mouse models of liver fibrosis. This study reveals a pathway involved in liver fibrosis and potentially implicated in both genetic and acquired causes of hepatic fibrosis.

We present a new method for assessing homophily in networks whose vertices have categorical attributes, namely when the vertices of networks come partitioned into classes. We apply this method to Protein- Protein Interaction networks, where vertices correspond to proteins, partitioned according to they func- tional role, and edges represent potential interactions between proteins. Similarly to other classical and well consolidated approaches, our method compares the relative edge density of the subgraphs induced by each class with the corresponding expected relative edge density under a null model. The novelty of our approach consists in prescribing an endogenous null model, namely, the sample space of the null model is built on the input network itself. This allows us to give exact explicit expression for the z-score of the relative edge density of each class as well as other related statistics. The z-scores directly quantify the statistical significance of the observed homophily via ?Ceby?s ?ev inequality. The expression of each z-score is entered by the network structure through basic combinatorial invariant such as the number of subgraphs with two spanning edges. Each z-score is computed in O(n3) worst-case time for a network with n vertices. This leads to an overall effective computational method for assesing homophily. Theoretical results are then exploited to prove that Protein-Protein Interaction networks networks are significantly homophillous.

The paper deals with a special filtered approximation method, which originates interpolation polynomials at Chebyshev zeros by using de la Vallée Poussin filters. In order to get an optimal approximation in spaces of locally continuous functions equipped with weighted uniform norms, the related Lebesgue constants have to be uniformly bounded. In previous works this has already been proved under different sufficient conditions. Here, we complete the study by stating also the necessary conditions to get it. Several numerical experiments are also given to test the theoretical results and make comparisons to Lagrange interpolation at the same nodes.

In this paper, some recent applications of the so-called Generalized Bernstein polynomials are collected. This polynomial sequence is constructed by means of the samples of a continuous function f on equispaced points of [0; 1] and depends on an additional parameter which can be suitable chosen in order to improve the rate of convergence to the function f, as the smoothness of f increases, overcoming the well-known low degree of approximation achieved by the classical Bernstein polynomials or by the piecewise polynomial approximation. The applications considered here deal with the numerical integration and the simultaneous approximation. Quadrature rules on equidistant nodes of [0; 1] are studied for the numerical computation of ordinary integrals in one or two dimensions, and usefully employed in Nyström methods for solving Fredholm integral equations. Moreover, the simultaneous approximation of the Hilbert transform and its derivative (the Hadamard transform) is illustrated. For all the applications, some numerical details are given in addition to the error estimates, and the proposed approximation methods have been implemented providing numerical tests which confirm the theoretical estimates. Some open problems are also introduced.

The present paper concerns filtered de la Vallée Poussin (VP) interpolation at the Chebyshev nodes of the four kinds. This approximation model is interesting for applications because it combines the advantages of the classical Lagrange polynomial approximation (interpolation and polynomial preserving) with the ones of filtered approximation (uniform boundedness of the Lebesgue constants and reduction of the Gibbs phenomenon). Here we focus on some additional features that are useful in the applications of filtered VP interpolation. In particular, we analyze the simultaneous approximation provided by the derivatives of the VP interpolation polynomials. Moreover, we state the uniform boundedness of VP approximation operators in some Sobolev and Hölder-Zygmund spaces where several integro-differential models are uniquely and stably solvable.

A methodology to generate calibrated maps of soil moisture from C-band synthetic aperture radar (SAR) images processed by SAR interferometry (InSAR) technique is presented. The proposed methodology uses atmospheric phase delay (APD) maps obtained from a time series of Sentinel-1 interferograms, to disentangle the APD and soil moisture contributions to Sentinel-1 interferograms. We show how the high spatial resolution and short temporal baseline of Sentinel-1 image can help to estimate soil moisture using a daisy chain InSAR processing. The estimated soil moisture maps are compared with in situ data collected by five soil moisture sensors installed in an experimental field, characterized by bare soil, located close to Lisbon, Portugal. Results show that after removing the APD effects in SAR interferogram, there is a correction of the bias in the soil moisture estimation and an improvement in the correlation coefficient with the soil moisture measurements, from 0.38 to 0.78. Soil moisture changes were measured during a sequence of rain events in the winter season. A root-mean-square (rms) error less than 0.04 m3/m3 was found over a variety of meteorological conditions.

Starting from recent experimental observations of starlings and jackdaws, we propose a minimal agent-based mathematical model for bird flocks based on a system of second-order delayed stochastic differential equations with discontinuous (both in space and time) right-hand side. The model is specifically designed to reproduce self-organized spontaneous sudden changes of direction, not caused by external stimuli like predator's attacks. The main novelty of the model is that every bird is a potential turn initiator, thus leadership is formed in a group of indistinguishable agents. We investigate some theoretical properties of the model and we show the numerical results. Biological insights are also discussed.

We present an all-around study of the visitors flow in crowded museums: a combination of Lagrangian field measurements and statistical analyses enable us to create stochastic digital-twins of the guest dynamics, unlocking comfort- and safety-driven optimizations. Our case study is the Galleria Borghese museum in Rome (Italy), in which we performed a real-life data acquisition campaign. We specifically employ a Lagrangian IoT-based visitor tracking system based on Raspberry Pi receivers, displaced in fixed positions throughout the museum rooms, and on portable Bluetooth Low Energy beacons handed over to the visitors. Thanks to two algorithms: a sliding window-based statistical analysis and an MLP neural network, we filter the beacons RSSI and accurately reconstruct visitor trajectories at room-scale. Via a clustering analysis, hinged on an original Wasserstein-like trajectory-space metric, we analyze the visitors paths to get behavioral insights, including the most common flow patterns. On these bases, we build the transition matrix describing, in probability, the room-scale visitor flows. Such a matrix is the cornerstone of a stochastic model capable of generating visitor trajectories in silico. We conclude by employing the simulator to enhance the museum fruition while respecting numerous logistic and safety constraints. This is possible thanks to optimized ticketing and new entrance/exit management.

This book offers an informal, easy-to-understand account of topics in modern physics and mathematics. The focus is, in particular, on statistical mechanics, soft matter, probability, chaos, complexity, and models, as well as their interplay. The book features 28 key entries and it is carefully structured so as to allow readers to pursue different paths that reflect their interests and priorities, thereby avoiding an excessively systematic presentation that might stifle interest. While the majority of the entries concern specific topics and arguments, some relate to important protagonists of science, highlighting and explaining their contributions. Advanced mathematics is avoided, and formulas are introduced in only a few cases. The book is a user-friendly tool that nevertheless avoids scientific compromise. It is of interest to all who seek a better grasp of the world that surrounds us and of the ideas that have changed our perceptions. Il libro prova a colmare almeno in parte quel vuoto lasciato dalla divulgazione scientifica mainstream, interessata principalmente a dare risalto agli aspetti più sensazionalistici e bizzarri delle scoperte scientifiche, svelando il fascino presente in argomenti che non vengono solitamente discussi nei libri di divulgazione e nelle vite di scienziati poco conosciuti al grande pubblico, ma che hanno posto le basi per la scienza come la conosciamo ora.

This work aims at studying the crystallization process of Hydroxyapatite samples in three different chemical environments (Cit, Glr, CitOH), as a function of time and temperature (25°C, 37°C or biomimetic temperature, 60°C and 80°C) . In particular non-crystalline and/or precursor states (SAXS) are expected to play a key-role in this analysis. Due to the huge amount of data collected at Synchrotron facilities, a preliminary correlation evaluation is needed in order to extract the most representative curves showing significant modification in shape and/or in the regions of interest. An algorithm based on Hierarchical Non-Negative Matrix Factorization (intensity SAXS profiles are positive) has been developed and applied in order to select 2^n profiles (n==number of bisections of the original data set). The comparison of the algorithm findings to the known particle morphologies (SAXS fitting) has spotted the HA crystallization dynamics (time resolved) beneath, both at different temperatures and chemical environments.

The impact of protein-coding genes on cancer onset and progression is a well-establishedparadigm in molecular oncology. Nevertheless, unveiling the contribution of the noncoding genes--including long noncoding RNAs (lncRNAs)--to tumorigenesis represents a great challenge forpersonalized medicine, since they (i) constitute the majority of the human genome, (ii) are essentialand flexible regulators of gene expression and (iii) present all types of genomic alterations describedfor protein-coding genes. LncRNAs have been increasingly associated with cancer, their highlytissue- and cancer type-specific expression making them attractive candidates as both biomarkersand therapeutic targets. Medulloblastoma is one of the most common malignant pediatric braintumors. Group 3 is the most aggressive subgroup, showing the highest rate of metastasis at diagnosis.Transcriptomics and reverse genetics approaches were combined to identify lncRNAs implicatedin Group 3 Medulloblastoma biology. Here we present the first collection of lncRNAs dependenton the activity of the MYC oncogene, the major driver gene of Group 3 Medulloblastoma. Weassessed the expression profile of selected lncRNAs in Group 3 primary tumors and functionallycharacterized these species. Overall, our data demonstrate the direct involvement of three lncRNAsin Medulloblastoma cancer cell phenotypes The impact of protein-coding genes on cancer onset and progression is a well-established paradigm in molecular oncology. Nevertheless, unveiling the contribution of the noncoding genes--including long noncoding RNAs (lncRNAs)--to tumorigenesis represents a great challenge for personalized medicine, since they (i) constitute the majority of the human genome, (ii) are essential and flexible regulators of gene expression and (iii) present all types of genomic alterations described for protein-coding genes. LncRNAs have been increasingly associated with cancer, their highly tissue- and cancer type-specific expression making them attractive candidates as both bi-omarkers and therapeutic targets. Medulloblastoma is one of the most common malignant pediatric brain tumors. Group 3 is the most aggressive subgroup, showing the highest rate of metastasis at diagnosis. Transcriptomics and reverse genetics approaches were combined to identify lncRNAs implicated in Group 3 Medulloblastoma biology. Here we present the first collection of lncRNAs dependent on the activity of the MYC oncogene, the major driver gene of Group 3 Medulloblastoma. We assessed the expression profile of selected lncRNAs in Group 3 primary tumors and functionally characterized these species. Overall, our data demonstrate the direct involvement of three lncRNAs in Medulloblastoma cancer cell phenotypes.

The ionospheric D-region affects propagation of electromagnetic waves including ground-based signals and satellite signals during its intensive disturbances. Consequently, the modeling of electromagnetic propagation in the D-region is important in many technological domains. One of sources of uncertainty in the modeling of the disturbed D-region is the poor knowledge of its parameters in the quiet state at the considered location and time period. We present the Quiet Ionospheric D-Region (QIonDR) model based on data collected in the ionospheric D-region remote sensing by very low/low frequency (VLF/LF) signals and the Long-Wave Propagation Capability (LWPC) numerical model. The QIonDR model provides both Wait's parameters and the electron density in the D-region area of interest at a given daytime interval. The proposed model consists of two steps. In the first step, Wait's parameters are modeled during the quiet midday periods as a function of the daily sunspot number, related to the long-term variations during solar cycle, and the seasonal parameter, providing the seasonal variations. In the second step, the output of the first step is used to model Wait's parameters during the whole daytime. The proposed model is applied to VLF data acquired in Serbia and related to the DHO and ICV signals emitted in Germany and Italy, respectively. As a result, the proposed methodology provides a numerical tool to model the daytime Wait's parameters over the middle and low latitudes and an analytical expression valid over a part of Europe for midday parameters.

In this work, we study the impact of high-energy radiation induced by solar X-ray flares on the determination of the temporal change in precipitable water vapor (?PWV) as estimated using the synthetic aperture radar (SAR) meteorology technique. As recent research shows, this radiation can significantly affect the ionospheric D-region and induces errors in the estimation of the total electron content (TEC) by the applied models. Consequently, these errors are reflected in the determination of the phase delay and in many different types of measurements and models, including calculations of meteorological parameters based on SAR observations. The goal of this study is to quantify the impact of solar X-ray flares on the estimation of ?PWV and provide an estimate of errors induced if the vertical total electron content (VTEC) is obtained by single layer models (SLM) or multiple layer models (MLM) (these models do not include ionosphere properties below the altitude of 90 km as input parameters and cannot provide information about local disturbances in the D-region). The performed analysis is based on a known procedure for the determination of the D-region electron density (and, consequently, the vertical total electron content in the D-region (VTEC)) using ionospheric observations of very low frequency (VLF) radio waves. The main result indicates that if the D-region, perturbed by medium-sized and intense X-ray flares, is not modeled, errors occur in the determination of ?PWV. This study emphasizes the need for improved MLMs for the estimation of the TEC, including observational data at D-region altitudes during medium-sized and intense X-ray flare events.

The present study assesses the viability of including water vapor data from Interferometry Synthetic Aperture Radar (InSAR) in the initialization of numerical weather prediction (NWP) models, using already available Sentinel-1 A and B products. Despite the limitations resulting from the 6-day return period of images produced by the 2-satellite system, it is found that for a sufficiently large domain designed to contain a set of images every 12 h (at varying locations), the impact on model performance is beneficial or at least neutral. The proposed methodology is tested in 24 consecutive 12 h forecasts, covering two cycles of the Sentinel-1 system and 214 images, for a domain containing Iberia. A statistical analysis of the forecast precipitable water vapor (PWV) against independent GNSS observations concluded for relevant improvements in the different scores, especially during a consecutive 3-day period where the standard initial data were less accurate. An analysis of the rain forecasts against gridded remote sensing observations further indicates an overall improvement in the grid-point distribution of different precipitation classes throughout the simulation, even when the mean impact of PWV assimilation was not significant. It is suggested that current InSAR data are already a useful source of NWP data and will only become more relevant as new systems are put into operation.

In this paper we analyse temporal variations of the phase of a very low frequency (VLF) signal, used for the lower ionosphere monitoring, in periods around four earthquakes (EQs) with magnitude greater than 4. We provide two analyses in time and frequency domains. First, we analyse time evolution of the phase noise. And second, we examine variations of the frequency spectrum using Fast Fourier Transform (FFT) in order to detect hydrodynamic wave excitations and attenuations. This study follows a previous investigation which indicated the noise amplitude reduction, and excitations and attenuations of the hydrodynamic waves less than one hour before the considered EQ events as a new potential ionospheric precursors of earthquakes. We analyse the phase of the ICV VLF transmitter signal emitted in Italy recorded in Serbia in time periods around four earthquakes occurred on 3, 4 and 9 November 2010 which are the most intensive earthquakes analysed in the previous study. The obtained results indicate very similar changes in the noise of phase and amplitude, and show an agreement in recorded acoustic wave excitations. However, properties in the obtained wave attenuation characteristics are different for these two signal parameters.

In multitemporal interferometric synthetic aperture radar (InSAR) applications, propagation delay in the troposphere introduces a major source of disturbance known as atmospheric phase screen (APS). This study proposes a novel framework to compensate for the APS from multitemporal ground-based InSAR data. The proposed framework first performs time-series clustering in accordance with the temporal APS behavior realized by the k-means clustering approach. In the second step, joint estimation of the APS and displacement velocity is performed. For this purpose, a novel interferometric signal model, including the APS modeled by the median profiles defined in each cluster, is proposed. The proposed framework is validated with the Ku-band ground-based synthetic aperture radar data sets measured over a mountainous area in Kumamoto, Japan. Tests on these data sets reveal that compared with the conventional approach, the presented approach improves displacement estimation accuracy under severe atmospheric conditions.

In this paper, we study linear parabolic equations on a finite oriented star-shaped network; the equations are coupled by transmission conditions set at the inner node, which do not impose continuity on the unknown. We consider this problem as a parabolic approximation of a set of the first-order linear transport equations on the network, and we prove that when the diffusion coefficient vanishes, the family of solutions converges to the unique solution to the first-order equations satisfying suitable transmission conditions at the inner node, which are determined by the parameters appearing in the parabolic transmission conditions.

The degradation of monumental stones resulting from the mutual interaction between mechanical actions and environment/pollution conditions is investigated here. In particular, the stone degradation is estimated as a function of the environmental conditions and the prediction of damaging phenomena, which can compromise permanently the fruition of monuments. This is done through a macroscopic phenomenological model which accounts for the main aspects of the problem: the chemical reaction and the mechanical behavior of stones. The sulphation reaction and the diffusion of the pollutant agents are described by suitable differential equations coupled with a variational formulation of fracture mechanics. The proposed model permits to evaluate how much aggressive atmospheric agents contribute to the decay of the mechanical properties of the stones as well as to establish the impact of the synergic chemical aggression and stress state. The latter is also influenced by the chemical reaction and by the evolving mechanical properties of the material. The main features of this approach are illustrated by specific numerical simulations.