For a graph G and a positive integer c, let Mc(G) be the size of a subgraph of G induced by a randomly sampled subset of c vertices. Second-order moments of Mc(G) encode part of the structure of G. We use this fact, coupled to classical moment inequalities, to prove graph theoretical results, to give combinatorial identities, to bound the size of the c-densest subgraph from below and the size of the c-sparsest subgraph from above, and to provide bounds for approximate enumeration of trivial subgraphs.

We propose AFTNet, a novel network-constraint survival analysis method based on the Weibull accelerated failure time (AFT) model solved by a penalized likelihood approach for variable selection and estimation. When using the log-linear representation, the inference problem becomes a structured sparse regression problem for which we explicitly incorporate the correlation patterns among predictors using a double penalty that promotes both sparsity and grouping effect. Moreover, we establish the theoretical consistency for the AFTNet estimator and present an efficient iterative computational algorithm based on the proximal gradient descent method. Finally, we evaluate AFTNet performance both on synthetic and real data examples.

Land cover, or the biophysical cover of the earth's surface, plays an essential role in climate and environmental dynamics. Processes involving land cover change, are among the factors that most threaten the ecosystems sustainability and services. The objective of the work is to explore the potential of the PRISMA multi-temporal hyperspectral imagery in generating new EO products to complement/improve the products provided by Copernicus' Land Monitoring Service for the analysis and monitoring of complex and fragile ecosystems such as the coastal Metaponto (Southern Italy) by estimating of the land biological and economic productivity loss and land degradation vulnerability. Preliminary results showed that an improvement in ecosystem mapping is supported by the use of Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN) and Support Vector Machines (SVM) and a hybrid approach to define the vegetation trait, leads to significant improvement in the damage assessment and land degradation assessment

Cyber insurance is a crucial tool for managing risks associated with cyber threats. A challenging task for insurance companies lies in pricing cyber risk. Our study is motivated by the reasonable assumption that firms entering into cyber insurance contracts face diverse cyber threats in terms of types and magnitude. Considering these differences ensures that premiums align with the actual risk exposure of the insured. The study discusses this approach proposing a case study based on the Chronology of Data Breaches provided by the Privacy Rights Clearinghouse.

Background: Osteoarthritis (OA) is a multifactorial, hypertrophic, and degenerative condition involving the whole joint and affecting a high percentage of middle-aged people. It is due to a combination of factors, although the pivotal mechanisms underlying the disease are still obscure. Moreover, current treatments are still poorly effective, and patients experience a painful and degenerative disease course. Methods: We used an integrative approach that led us to extract a consensus signature from a meta-analysis of three different OA cohorts. We performed a network-based drug prioritization to detect the most relevant drugs targeting these genes and validated in vitro the most promising candidates. We also proposed a risk score based on a minimal set of genes to predict the OA clinical stage from RNA-Seq data. Results: We derived a consensus signature of 44 genes that we validated on an independent dataset. Using network analysis, we identified Resveratrol, Tenoxicam, Benzbromarone, Pirinixic Acid, and Mesalazine as putative drugs of interest for therapeutics in OA for anti-inflammatory properties. We also derived a list of seven gene-targets validated with functional RT-qPCR assays, confirming the in silico predictions. Finally, we identified a predictive subset of genes composed of DNER, TNFSF11, THBS3, LOXL3, TSPAN2, DYSF, ASPN and HTRA1 to compute the patient's risk score. We validated this risk score on an independent dataset with a high AUC (0.875) and compared it with the same approach computed using the entire consensus signature (AUC 0.922). Conclusions: The consensus signature highlights crucial mechanisms for disease progression. Moreover, these genes were associated with several candidate drugs that could represent potential innovative therapeutics. Furthermore, the patient's risk scores can be used in clinical settings.

Claude Shannon, eclettico matematico e ingegnere del Novecento, è considerato il padre della teoria dell’informazione, perché offrì una definizione formale, quantitativamente misurabile, di questo concetto, assimilandolo a quello di altre grandezze fisiche che possono essere descritte e calcolate matematicamente. Dimostrò poi fino a che punto l’informazione contenuta in un messaggio possa essere compressa, in modo da aumentare la velocità di trasmissione. Il secondo e fondamentale risultato di Shannon riguarda invece il canale di trasmissione, un qualunque mezzo attraverso il quale il messaggio viaggia e che può degradare parte dei contenuti trasmessi se il tasso di trasmissione supera la capacità del canale. Entrano in gioco quindi grandezze come l’errore, che si può ridurre inserendo nel messaggio strumenti matematici di correzione, e la stessa entropia, concetto sviluppato nella termodinamica ma che può riguardare anche la trasmissione delle informazioni, quale misura dell’incertezza di un risultato (la probabilità che sia quello giusto). Per esempio, in un testo italiano, la «e» è più probabile di una «z» e la stringa «le banche hanno un anno di tempo» è più probabile di «le banche anno un hanno di tempo». La teoria dell’informazione di Shannon è alla base di tutta la comunicazione digitale, che utilizza strumenti matematici per la compressione dei segnali, oggi indispensabile, per la riduzione degli errori di tramissione e per la gestione delle reti.

FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) has been approved to be the ninth Earth Explorer mission of the European Space Agency and is scheduled for launch in 2027. The core FORUM instrument is a Fourier transform spectrometer, which will, for the first time, measure the upwelling spectral radiance in the far-infrared (FIR) and mid-infrared (MIR) portions of the Earth’s spectrum. These radiances will be processed up to level 2, to determine mainly the vertical profile of water vapor, surface spectral emissivity, and cloud parameters. In this paper, we assess the performance of the FORUM surface spectral emissivity product based on all-sky sensitivity study. In the FIR, we find that the retrieval error is mainly driven by the precipitable water vapor (PWV) in clear-sky conditions. In dry atmospheres, FIR emissivity can be retrieved with an error less than 0.01. In cloudy conditions, small errors can be achieved for optically thin clouds, especially for small values of the PWV. In the MIR, we observe that a large thermal contrast between the surface and the lowest atmospheric layers increases the sensitivity of the measurements to the surface emissivity in clear-sky conditions and an emissivity retrieval error less than 0.01 can usually be achieved. In cloudy conditions, small errors can be achieved for optically thin clouds, especially for large values of the surface temperature. Applying a coarser retrieval grid further reduces retrieval error, at the expense of an increased emissivity smoothing error.

Summary: We present tidysbml, an R package able to perform compartments, species, and reactions data extraction from Systems Biology Markup Language (SBML) documents (up to Level 3) in tabular data structures (i.e. R dataframes) to easily access and handle the richness of the biological information. Thanks to its output format, the package facilitates data manipulation, enabling manageable construction, and therefore analysis, of custom networks, as well as data retrieval, by means of R packages such as igraph, RCy3, and biomaRt. Exemplar data (i.e. SBML files) are extracted from Reactome.

Motivation: methyLImp, a method we recently introduced for the missing value estimation of DNA methylation data, has demonstrated competitive performance in data imputation compared to the existing, general-purpose, approaches. However, imputation running time was considerably long and unfeasible in case of large datasets with numerous missing values. Results: methyLImp2 made possible computations that were previously unfeasible. We achieved this by introducing two important modifications that have significantly reduced the original running time without sacrificing prediction performance. First, we implemented a chromosome-wise parallel version of methyLImp. This parallelization reduced the runtime by several 10-fold in our experiments. Then, to handle large datasets, we also introduced a mini-batch approach that uses only a subset of the samples for the imputation. Thus, it further reduces the running time from days to hours or even minutes in large datasets.

Food allergy (FA) affects approximately 6–8% of children worldwide causing a significant impact on the quality of life of children and their families. In past years, the possible role of weaning in the development of FA has been studied. According to recent studies, this is still controversial and influenced by several factors, such as the type of food, the age at food introduction and family history. In this narrative review, we aimed to collect the most recent evidence about weaning and its role in FA development, organizing the gathered data based on both the type of study and the food. As shown in most of the studies included in this review, early food introduction did not show a potential protective role against FA development, and we conclude that further evidence is needed from future clinical trials.

In this talk we provide explicit upper bounds on some distances between the (law of the) output of a random Gaussian neural network and (the law of) a random Gaussian vector. Our main results concern deep random Gaussian neural networks, with a rather general activation function. The upper bounds show how the widths of the layers, the activation function and other architecture parameters affect the Gaussian approximation of the output. Our techniques, relying on Stein's method and integration by parts formulas for the Gaussian law, yield estimates on distances which are indeed integral probability metrics, and include the convex distance. This latter metric is defined by testing against indicator functions of measurable convex sets, and so allows for accurate estimates of the probability that the output is localized in some region of the space. Such estimates have a significant interest both from a practitioner's and a theorist's perspective.

We introduce a weak notion of $2\times 2$-minors of gradients for a suitable subclass of $BV$ functions. In the case of maps in $BV(\mathbb{R}^2; \mathbb{R}^2)$ such a notion extends the standard definition of Jacobian determinant to non-Sobolev maps. We use this distributional Jacobian to prove a compactness and $\Gamma$-convergence result for a new model describing the emergence of topological singularities in two dimensions, in the spirit of Ginzburg-Landau and core-radius approaches. Within our framework, the order parameter is an $SBV$ map $u$ taking values in the unit sphere in $\mathbb{R}^2$ and the energy is given by the sum of the squared $L^2$ norm of the approximate gradient $\nabla u$ and of the length of (the closure of) the jump set of $u$ multiplied by $\frac 1 \varepsilon$. Here, $\varepsilon$ is a length-scale parameter. We show that, in the $|\log\varepsilon|$ regime, the distributional Jacobians converge, as $\varepsilon \to 0^+$, to a finite sum $\mu$ of Dirac deltas with weights multiple of $\pi$, and that the corresponding effective energy is given by the total variation of $\mu$.

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.