Background: The investigation of mindfulness meditation practice, classically divided into focused attention meditation (FAM), and open monitoring meditation (OMM) styles, has seen a long tradition of theoretical, affective, neurophysiological and clinical studies. In particular, the high temporal resolution of magnetoencephalography (MEG) or electroencephalography (EEG) has been exploited to fill the gap between the personal experience of meditation practice and its neural correlates. Mounting evidence, in fact, shows that human brain activity is highly dynamic, transiting between different brain states (microstates). In this study, we aimed at exploring MEG microstates at source-level during FAM, OMM and in the resting state, as well as the complexity and criticality of dynamic transitions between microstates. Methods: Ten right-handed Theravada Buddhist monks with a meditative expertise of minimum 2,265 h participated in the experiment. MEG data were acquired during a randomized block design task (6 min FAM, 6 min OMM, with each meditative block preceded and followed by 3 min resting state). Source reconstruction was performed using eLORETA on individual cortical space, and then parcellated according to the Human Connect Project atlas. Microstate analysis was then applied to parcel level signals in order to derive microstate topographies and indices. In addition, from microstate sequences, the Hurst exponent and the Lempel-Ziv complexity (LZC) were computed. Results: Our results show that the coverage and occurrence of specific microstates are modulated either by being in a meditative state or by performing a specific meditation style. Hurst exponent values in both meditation conditions are reduced with respect to the value observed during rest, LZC shows significant differences between OMM, FAM, and REST, with a progressive increase from REST to FAM to OMM. Discussion: Importantly, we report changes in brain criticality indices during meditation and between meditation styles, in line with a state-like effect of meditation on cognitive performance. In line with previous reports, we suggest that the change in cognitive state experienced in meditation is paralleled by a shift with respect to critical points in brain dynamics.

Despite its high importance for crop yield prediction and monitoring, early-season crop mapping is severely hampered by the absence of timely ground truth. To cope with this issue, this study aims at evaluating the capability of PRISMA hyperspectral satellite images compared with Sentinel-2 multispectral imagery to produce early- and in-season crop maps using consolidated machine and deep learning algorithms. Results show that the accuracy of crop type classification using Sentinel-2 images is meaningfully poor compared with PRISMA (14% in overall accuracy (OA)). The 1D-CNN algorithm, with 89%, 91%, and 92% OA for winter, summer, and perennial cultivations, respectively, shows for the PRISMA images the highest accuracy in the in-season crop mapping and the fastest algorithm that achieves acceptable accuracy (OA 80%) for the winter, summer, and perennial cultivations early-season mapping using PRISMA images. Moreover, the 1D-CNN algorithm shows a limited reduction (6%) in performance, appearing to be the best algorithm for crop mapping within operational use in cross-farm applications. Machine/deep learning classification algorithms applied on the test fields cross-scene demonstrate that PRISMA hyperspectral time series images can provide good results for early- and in-season crop mapping.

Purpose: Common Variable Immunodeficiency (CVID) is characterized by hypogammaglobulinemia and failure of specific antibody production due to B-cell defects. However, studies have documented various T-cell abnormalities, potentially linked to viral complications. The frequency of Cytomegalovirus (CMV) replication in CVID cohorts is poorly studied. To address this gap in knowledge, we set up an observational study with the objectives of identifying CVID patients with active viraemia (CMV, Epstein-Barr virus (EBV)), evaluating potential correlations with immunophenotypic characteristics, clinical outcome, and the dynamic progression of clinical phenotypes over time. Methods: 31 CVID patients were retrospectively analysed according to viraemia, clinical and immunologic characteristics. 21 patients with non CVID humoral immunodeficiency were also evaluated as control. Results: Active viral replication of CMV and/or EBV was observed in 25% of all patients. CMV replication was detected only in CVID patients (16%). CVID patients with active viral replication showed reduced HLA-DR+ NK counts when compared with CMV-DNA negative CVID patients. Viraemic patients had lower counts of LIN−DNAMbright and LIN−CD16+ inflammatory lymphoid precursors which correlated with NK-cell subsets. Analysis of the dynamic progression of CVID clinical phenotypes over time, showed that the initial infectious phenotype progressed to complicated phenotypes with time. All CMV viraemic patients had complicated disease. Conclusion: Taken together, an impaired production of inflammatory precursors and NK activation is present in CVID patients with active viraemia. Since “Complicated” CVID occurs as a function of disease duration, there is need for an accurate evaluation of this aspect to improve classification and clinical management of CVID patients.

Background: Immune dysregulation and SARS-CoV-2 plasma viremia have been implicated in fatal COVID-19 disease. However, how these two factors interact to shape disease outcomes is unclear. Methods: We carried out viral and immunological phenotyping on a prospective cohort of 280 patients with COVID-19 presenting to acute care hospitals in Boston, Massachusetts and Genoa, Italy between June 1, 2020 and February 8, 2022. Disease severity, mortality, plasma viremia, and immune dysregulation were assessed. A mouse model of lethal H1N1 influenza infection was used to analyze the therapeutic potential of Notch4 and pyroptosis inhibition in disease outcome. Results: Stratifying patients based on %Notch4+ Treg cells and/or the presence of plasma viremia identified four subgroups with different clinical trajectories and immune phenotypes. Patients with both high %Notch4+ Treg cells and viremia suffered the most disease severity and 90-day mortality compared to the other groups even after adjusting for baseline comorbidities. Increased Notch4 and plasma viremia impacted different arms of the immune response in SARS-CoV-2 infection. Increased Notch4 was associated with decreased Treg cell amphiregulin expression and suppressive function whereas plasma viremia was associated with increased monocyte cell pyroptosis. Combinatorial therapies using Notch4 blockade and pyroptosis inhibition induced stepwise protection against mortality in a mouse model of lethal H1N1 influenza infection. Conclusions: The clinical trajectory and survival outcome in hospitalized patients with COVID-19 is predicated on two cardinal factors in disease pathogenesis: viremia and Notch4+ Treg cells. Intervention strategies aimed at resetting the immune dysregulation in COVID-19 by antagonizing Notch4 and pyroptosis may be effective in severe cases of viral lung infection.

Background: Aggressive malignancies, such as pancreatic cancer, are increasingly impacting young, female populations. Our investigation centered on whether the observed trends in cancer incidence were unique to pancreatic cancer or indicative of a broader trend across various cancer types. To delve deeper into this phenomenon, we analyzed cancer incidence trends across different age and sex groups. Furthermore, we explored differences in cancer incidence within specific young subgroups aged 18 to 26 and 27 to 34, to better understand the emerging incidence trend among young individuals. Methods: This study collected cancer incidence data from one of the Surveillance, Epidemiology, and End Results cancer registry databases (SEER22), with 10,183,928 total cases from 2000 to 2020. Data were analyzed through Joinpoint trend analysis approach to evaluate sex- and age-specific trends in cancer incidence. Exposure rates were reported as Average Annual Percentage Changes (AAPCs). Results: The analysis revealed significant age and sex-specific disparities, particularly among individuals aged 18–26 and 27–34. Pancreatic cancer incidence rates increased more in females aged 18–26 (AAPC, 9.37% [95% CI, 7.36–11.41%]; p <.0001) than in males (4.43% [95% CI, 2.36–6.53%]; p <.0001). Notably, among gender, age, and other malignancies, young females had the highest AAPCs for pancreatic cancer. Additionally, the incidence of gastric cancer, myeloma, and colorectal malignancies also showed higher AAPCs in young females compared to males. Conclusions: Recognizing emerging risk populations for highly lethal malignancies is crucial for early detection and effective disease management.

: The T-BOX transcription factor TBX1 is essential for the development of the pharyngeal apparatus and it is haploinsufficient in DiGeorge syndrome (DGS), a developmental anomaly associated with congenital heart disease and other abnormalities. The murine model recapitulates the heart phenotype and showed collagen accumulation. We first used a cellular model to study gene expression during cardiogenic differentiation of WT and Tbx1-/- mouse embryonic stem cells. Then we used a mouse model of DGS to test whether interfering with collagen accumulation using an inhibitor of lysyl hydroxylase would modify the cardiac phenotype of the mutant. We found that loss of Tbx1 in a precardiac differentiation model was associated with up regulation of a subset of ECM-related genes, including several collagen genes. In the in vivo model, early prenatal treatment with Minoxidil, a lysyl hydroxylase inhibitor, ameliorated the cardiac outflow tract septation phenotype in Tbx1 mutant fetuses, but it had no effect on septation in WT fetuses. We conclude that TBX1 suppresses a defined subset of ECM-related genes. This function is critical for OFT septation because the inhibition of collagen cross-linking in the mutant reduces significantly the penetrance of septation defects.

Endothelial cells (EC) differentiate from multiple sources, including the cardiopharyngeal mesoderm, which gives rise also to cardiac and branchiomeric muscles. The enhancers activated during endothelial differentiation within the cardiopharyngeal mesoderm are not completely known. Here, we use a cardiogenic mesoderm differentiation model that activates an endothelial transcription program to identify endothelial regulatory elements activated in early cardiogenic mesoderm. Integrating chromatin remodeling and gene expression data with available single-cell RNA-seq data from mouse embryos, we identify 101 putative regulatory elements of EC genes. We then apply a machine-learning strategy, trained on validated enhancers, to predict enhancers. Using this computational assay, we determine that 50% of these sequences are likely enhancers, some of which are already reported. We also identify a smaller set of regulatory elements of well-known EC genes and validate them using genetic and epigenetic perturbation. Finally, we integrate multiple data sources and computational tools to search for transcriptional factor binding motifs. In conclusion, we show EC regulatory sequences with a high likelihood to be enhancers, and we validate a subset of them using computational and cell culture models. Motif analyses show that the core EC transcription factors GATA/ETS/FOS is a likely driver of EC regulation in cardiopharyngeal mesoderm.

Advances in material design have led to the rapid development of novel materials with increasing complexity and functions in bioengineering. In particular, functionally graded materials (FGMs) offer important advantages in various fields of application. In this work, we consider a heterogeneous reaction-diffusion model for an FGM spherical drug release system that generalizes the multi-layer configuration to arbitrary spatially-variable coefficients. Our model proposes a possible form for the drug diffusivity and reaction rate functions exhibiting fixed average material properties and a drug release profile that can be continuously varied between the limiting cases of a homogeneous system (constant coefficients) and two-layer system (stepwise coefficients). A semi-analytical solution is then used to solve the model, which provides closed-form expressions for the drug concentration and drug release profiles in terms of generalized Fourier series. Our results show how the release rate of the proposed FGM drug release system can be controlled and continuously varied between a fast (homogeneous) and slow (two-layer) release while maintaining the same averaged values for the diffusivity and reaction rate.

Objective: Drug delivery from a drug-loaded device into an adjacent tissue is a complicated process involving drug transport through diffusion and advection, coupled with drug binding kinetics responsible for drug uptake in the tissue. This work presents a theoretical model to predict drug delivery from a device into a multilayer tissue, assuming linear reversible drug binding in the tissue layers. Methods: The governing mass conservation equations based on diffusion, advection and drug binding in a multilayer cylindrical geometry are written, and solved using Laplace transformation. The model is used to understand the impact of various non-dimensional parameters on the amounts of bound and unbound drug concentrations as functions of time. Results: Good agreement for special cases considered in past work is demonstrated. The effect of forward and reverse binding reaction rates on the multilayer drug binding process is studied in detail. The effect of the nature of the external boundary condition on drug binding and drug loss is also studied. For typical parameter values, results indicate that only a small fraction of drug delivered binds in the tissue. Additionally, the amount of bound drug rises rapidly with time due to early dominance of the forward reaction, reaches a maxima and then decays due to the reverse reaction. Conclusions: The general model presented here can account for other possible effects such as drug absorption within the device. Besides generalizing past work on drug delivery modeling, this work also offers analytical tools to understand and optimize practical drug delivery devices.

Osteosarcoma (OS) is a rare form of primary bone cancer, impacting approximately 3.4 × 106 individuals worldwide each year, primarily afflicting children. Given the limitations of existing cancer therapies, the emergence of nanotheranostic platforms has generated considerable research interest in recent decades. These platforms seamlessly integrate therapeutic potential of drug compounds with the diagnostic capabilities of imaging probes within a single construct. This innovation has opened avenues for enhanced drug delivery to targeted sites while concurrently enabling real-time monitoring of the vehicle's trajectory. In this study, we developed a nanotheranostic system employing the layer-by-layer (LbL) technique on a core containing doxorubicin (DOXO) and in-house synthesized carbon quantum dots. By utilizing chitosan and chondroitin sulfate as polyelectrolytes, we constructed a multilayered coating to encapsulate DOXO and docetaxel, achieving a coordinated co-delivery of both drugs. The LbL-functionalized nanoparticles exhibited an approximate size of 150 nm, manifesting a predominantly uniform and spherical morphology, with an encapsulation efficiency of 48% for both drugs. The presence of seven layers in these systems facilitated controlled drug release over time, as evidenced by in vitro release tests. Finally, the impact of the LbL-functionalized nanoparticles was evaluated on U2OS and Saos-2 osteosarcoma cells. The synergistic effect of the two drugs was found to be crucial in inducing cell death, particularly in Saos-2 cells treated with nanoparticles at concentrations higher than 10 μg/ml. Transmission electron microscopy analysis confirmed the internalization of the nanoparticles into both cell types through endocytic mechanisms, revealing an underlying mechanism of necrosis-induced cell death.

The aim of this paper is to describe a Matlab package for computing the simultaneous Gaussian quadrature rules associated with a variety of multiple orthogonal polynomials. Multiple orthogonal polynomials can be considered as a generalization of classical orthogonal polynomials, satisfying orthogonality constraints with respect to different measures, with Moreover, they satisfy -term recurrence relations. In this manuscript, without loss of generality, is considered equal to The so-called simultaneous Gaussian quadrature rules associated with multiple orthogonal polynomials can be computed by solving a banded lower Hessenberg eigenvalue problem. Unfortunately, computing the eigendecomposition of such a matrix turns out to be strongly ill-conditioned and the Matlab function balance.m does not improve the condition of the eigenvalue problem. Therefore, most procedures for computing simultaneous Gaussian quadrature rules are implemented with variable precision arithmetic. Here, we propose a Matlab package that allows to reliably compute the simultaneous Gaussian quadrature rules in floating point arithmetic. It makes use of a variant of a new balancing procedure, recently developed by the authors of the present manuscript, that drastically reduces the condition of the Hessenberg eigenvalue problem.

The present work focuses on a non-local integro-differential model reproducing Cancer-on-chip experiments where tumor cells, treated with chemotherapy drugs, secrete chemical signals stimulating the immune response. The reliability of the model in reproducing the phenomenon of interest is investigated through a global sensitivity analysis, rather than a local one, to have global information about the role of parameters, and by examining potential non-linear effects in greater detail. Focusing on a region in the parameter space, the effect of 13 model parameters on the in silico outcome is investigated by considering 11 different target outputs, properly selected to monitor the spatial distribution and the dynamics of immune cells along the period of observation. In order to cope with the large number of model parameters to be investigated and the computational cost of each numerical simulation, a two-step global sensitivity analysis is performed. First, the screening Morris method is applied to rank the effect of the 13 model parameters on each target output and it emerges that all the output targets are mainly affected by the same 6 parameters. The extended Fourier Amplitude Sensitivity Test (eFAST) method is then used to quantify the role of these 6 parameters. As a result, the proposed analysis highlights the feasibility of the considered space of parameters, and indicates that the most relevant parameters are those related to the chemical field and cell-substrate adhesion. In turn, it suggests how to possibly improve the model description as well as the calibration procedure, in order to better capture the observed phenomena and, at the same time, reduce the complexity of the simulation algorithm. On one hand, the model could be simplified by neglecting cell–cell alignment effects unless clear empirical evidences of their importance emerge. On the other hand, the best way to increase the accuracy and reliability of our model predictions would be to have experimental data/information to reduce the uncertainty of the more relevant parameters.

We address the problem of user fast revocation in the lattice-based Ciphertext Policy Attribute-Based Encryption (CP-ABE) by extending the scheme originally introduced by Zhang and Zhang [Zhang J, Zhang Z. A ciphertext policy attribute-based encryption scheme without pairings. In: International Conference on Information Security and Cryptology. Springer; 2011. p. 324–40. doi: https://doi.org/10.1007/978-3-642-34704-7_23.]. While a lot of work exists on the construction of revocable schemes for CP-ABE based on pairings, works based on lattices are not so common, and – to the best of our knowledge – we introduce the first server-aided revocation scheme in a lattice-based CP-ABE scheme, hence being embedded in a post-quantum secure environment. In particular, we rely on semi-trusted “mediators” to provide a multi-step decryption capable of handling mediation without re-encryption. We comment on the scheme and its application, and we provide performance experiments on a prototype implementation in the Attribute-Based Encryption spin-off library of Palisade to evaluate the overhead compared with the original scheme.

The growth potential of a crypto project, typically sustained by an associated cryptocurrency, can be measured by the use cases spurred by the underlying technology. However, these projects are implemented through decentralized applications, with a weak (if any) feedback scheme. Hence, a metric that is widely used as a proxy for the healthiness of such projects is the number of transactions and related volumes. Nevertheless, such a metric can be subject to manipulation - the crypto market being an unregulated one, magnifies such a risk. To address the cited gap, in this paper, we design a comprehensive methodology to process large cryptocurrency transaction graphs that, after clustering user addresses of interest, derives a compact representation of the network that highlights interactions among clusters. The analysis of these interactions provides insights into/over/on the strength of the project.To show the quality and viability of our solution, we bring forward a use case centered on Polkadot. The Polkadot network, a cutting-edge cryptocurrency platform, has gained significant attention in the digital currency landscape due to its pioneering approach to interoperability and scalability. However, little is known about how many and to what extent its wide range of enabled use cases have been adopted by end-users so far. The answer to this type of question means mapping Polkadot (or any analyzed crypto project) on a palette that ranges from a thriving ecosystem to a speculative coin without compelling use cases.Our findings, rooted on extensive experimental results - we have parsed 12.5+ million blocks - , demonstrate that crypto exchanges exert considerable influence on the Polkadot network, owning nearly 40% of all addresses in the ledger and absorbing at least 80% of all transactions. In addition, the high volume of inter-exchange transactions (more than 20%) underscores the strong interconnections among just a couple of prominent exchanges, prompting further investigations into the behavior of these actors to uncover potential unethical activities, such as wash trading.These results are a testament to the quality and viability of the proposed solution that, while characterized by a high level of scalability and adaptability, is at the same time immune from the drawbacks of currently used metrics.

2023 has replaced 2016 as the warmest year on record since 1850, bringing us closer to the 1.5 oC limit set by the Paris Agreement. High temperatures increase the likelihood of extreme events, with heatwaves and drought being prominent among them. Climate change has led to a rise in the frequency of droughts, affecting countries that never experienced them. Assessing drought events is crucial and satellite data can provide significant assistance due to its large spatial coverage and continuous data supply. Based on the Infrared Atmospheric Sounder Interferometer (IASI), we designed a new Water Deficit Index (wdi) that we have already proven useful in detecting drought events. Unfortunately, infrared sensors such as IASI cannot penetrate thick cloud layers, so observations are blinded to surface emissions under cloudiness bringing sparse and not homogeneous distributed data over a given spatial region. To reconstruct a model of the field of interest for the entire surface on a regular grid mesh, interpolation techniques, and spatial statistics to deal with huge data sets are mandatory. In this paper, we exploited the capability of two machine learning algorithms, i.e. gradient boosting and random forest, in converting IASI L2 scattered data to a regular L3 grid. Specifically, we trained a model that can predict the wdi index over a 0.05o regular grid, using data from other sensors as a proxy together with vegetational products, soil indices, and territorial and geographic information as covariates. We applied the methodology over the Po Valley region, which experienced an intense drought in the last three years causing high vegetation and soil water stress. Overall, we found that these methods can yield good results and allow simultaneous regular grid conversion and downscaling.

Clustering univariate functional data is mostly based on projecting the curves onto an adequate basis and applying some distance or similarity models on the coefficients. The basis functions should be chosen depending on features of the function being estimated. Commonly used are Fourier, polynomial and splines, but these may not be well suited for curves that exhibit inhomogeneous behavior. Wavelets on the contrary are well suited for identifying highly discriminant local time and scale features, and are able to adapt to the data smoothness. In recent years, few methods, relying on wavelet-based similarity measures, have been proposed for clustering curves, observed on equidistant points. In this work, we present a non-equidistant design wavelet based method for non-parametrically estimating and clustering a large number of curves. The method consists of several crucial stages: fitting functional data by non-equispaced design wavelet regression, screening out nearly flat curves, denoising the remaining curves with wavelet thresholding, and finally clustering the denoised curves. Simulation studies compare our proposed method with some other functional clustering methods. The method is applied for clustering some real functional data profiles.

The Mediterranean basin is one of those areas where the impact of climate change is showing its most alarming consequences. Many regions in this area, both woodlands and croplands, have been suffering from droughts and water deficits due to the intense summer heatwaves of the last decades. Monitoring these phenomena is key to understanding how they are evolving and what could be done to mitigate their effects. Emissivity is a useful parameter in identifying the presence (or absence) of water. Surface and dew point temperatures are extremely useful not only in measuring the intensity of the heatwave but also in accounting for how much water content the surface is losing as humidity to the atmosphere. This paper presents a climatological study of Southern Italy’s water loss for the period 2015-2023 based on daily observations acquired by the Infrared Atmospheric Sounding Interferometer (IASI), mounted on top of EUMETSAT’s MetOp satellites. The Water Deficit Index (WDI) and the Emissivity Contrast Index (ECI) were estimated: monthly averages of each quantity were produced for the period of interest. Moreover, a validation with in situ measurements was conducted to better understand how these heatwave-induced droughts have been impacting the surface on different types of land covers.

: The brain-related phenotypes observed in 22q11.2 deletion syndrome (DS) patients are highly variable, and their origin is poorly understood. Changes in brain metabolism might contribute to these phenotypes, as many of the deleted genes are involved in metabolic processes, but this is unknown. This study shows for the first time that Tbx1 haploinsufficiency causes brain metabolic imbalance. We studied two mouse models of 22q11.2DS using mass spectrometry, nuclear magnetic resonance spectroscopy, and transcriptomics. We found that Tbx1 +/- mice and Df1/+ mice, with a multigenic deletion that includes Tbx1, have elevated brain methylmalonic acid, which is highly brain-toxic. Focusing on Tbx1 mutants, we found that they also have a more general brain metabolomic imbalance that affects key metabolic pathways, such as glutamine-glutamate and fatty acid metabolism. We provide transcriptomic evidence of a genotype-vitamin B12 treatment interaction. In addition, vitamin B12 treatment rescued a behavioural anomaly in Tbx1 +/- mice. Further studies will be required to establish whether the specific metabolites affected by Tbx1 haploinsufficiency are potential biomarkers of brain disease status in 22q11.2DS patients.

Modern ICT infrastructures, i.e., cyber-physical systems and critical infrastructures relying on interconnected IT (Information Technology)- and OT (Operational Technology)-based components and (sub-)systems, raise complex challenges in tackling security and safety issues. Nowadays, many security controls and mechanisms have been made available and exploitable to solve specific security needs, but, when dealing with very complex and multifaceted heterogeneous systems, a methodology is needed on top of the selection of each security control that will allow the designer/maintainer to drive her/his choices to build and keep the system secure as a whole, leaving the choice of the security controls to the last step of the system design/development. This paper aims at providing a comprehensive methodological approach to design and preliminarily implement an Open Platform Architecture (OPA) to secure the cyber-physical systems of critical infrastructures. Here, the Open Platform Architecture (OPA) depicts how an already existing or under-design target system (TS) can be equipped with technologies that are modern or currently under development, to monitor and timely detect possibly dangerous situations and to react in an automatic way by putting in place suitable countermeasures. A multifaceted use case (UC) that is able to show the OPA, starting from the security and safety requirements to the fully designed system, will be developed step by step to show the feasibility and the effectiveness of the proposed methodology.

Current research directions indicate that vehicles with autonomous capabilities will increase in traffic contexts. Starting from data analyzed in R. E. Stern et al. (2018), this paper shows the benefits due to the traffic control exerted by a unique autonomous vehicle circulating on a ring track with more than 20 human-driven vehicles. Considering different traffic experiments with high stop-and-go waves and using a general microscopic model for emissions, it was first proved that emissions reduces by about 25%. Then, concentrations for pollutants at street level were found by solving numerically a system of differential equations with source terms derived from the emission model. The results outline that ozone and nitrogen oxides can decrease, depending on the analyzed experiment, by about 10% and 30%, respectively. Such findings suggest possible management strategies for traffic control, with emphasis on the environmental impact for vehicular flows.