Background: Splenomegaly represents a frequent non-infectious manifestation in Common Variable Immunodeficiency (CVID) and associates with specific clinical and immunophenotypic characteristics. Objective: To investigate the association between splenomegaly, infections, and immunophenotype in CVID patients. Methods: A cohort of 32 CVID patients (13 with splenomegaly) was enrolled. Infectious workup encompassed a detailed medical history and data derived from routine diagnostic assessments including specific virological analysis of blood and stool samples, and QuantiFERON assay for tuberculosis. Immunophenotype was assessed by multiparametric flow cytometry. Statistical analyses were performed using Prism and Jamovi software. Results: CMV viraemia was detected in 40 % of splenomegalic CVID (sCVID) and was absent in non-sCVID patients. Of all infectious agents, CMV was the only one associated with splenomegaly (p = 0.009). The inclusion of CMV replication as a causative factor for splenomegaly in CVID is in line with the knowledge that splenomegaly is a hallmark of acute CMV infection and could help explain in the present CVID cohort 75 % of otherwise unexplained splenomegalies. Flow cytometric analysis in sCVID vs. non-sCVID confirmed decreases in NK cell numbers and activation, in circulating inflammatory precursors (Lin−CD16+), and increased T cell activation as defined by HLA-DR/CD69/CD38 expression. Conclusion: Splenomegaly in CVID patients may associate also with CMV replication. The combined identification in CMV+ sCVID of NK cell, inflammatory precursor and T cell imbalances suggests a possible combined cellular defect at precursor level in a subset of sCVID patients. When integrated into everyday clinical management, CMV viraemia could become a useful additional parameter for patient characterization and stratification.

TBX1, a T-box transcription factor, is essential for pharyngeal apparatus development and marks cardiopharyngeal mesoderm (CPM) in various species. However, in mammals, we have an incomplete knowledge of the molecular pathways driving CPM diversification and of the role of TBX1 in this context. Using CPM-relevant in vitro differentiation of wild-type and Tbx1−/− mouse embryonic stem cells, we performed simultaneous single-nucleus RNA-seq and ATAC-seq at two stages, validated findings in embryos, and found that TBX1 loss affects gene expression and chromatin remodeling in a cell subpopulationspecific manner. TBX1 regulates chromatin accessibility and gene expression of distinct and evolutionarily conserved transcriptional modules for branchiomeric and cardiac development, and for tissue morphogenesis. Computational analyses predicted a feed-forward regulatory relationship between TBX1 and SIX factors. Notably, selected Tbx1 mutant CPM cell populations showed an altered differentiation trajectory, exhibiting activation of a mesothelial-like transcriptional program. We also observed cell death later in development. Thus, TBX1 is crucial for maintaining CPM transcriptional identity.

Electrical stimulation (ES) is widely employed in both clinical therapies and research settings where it has shown promise in promoting tissue regeneration, wound healing, and inflammation control. Research has also highlighted ES as a regulator of DNA demethylation, which plays a critical role in nerve regeneration and cellular repair mechanisms. While the impact of ES on epigenetic processes is recognized, its broader effects on cellular functions, particularly in inflammation and wound healing, are less understood. We recently showed how ES impacts inflammatory states by modulating transcriptomic and metabolomic profiles in a 3Din vitromodel where human fibroblasts and keratinocytes are included in a collagen matrix, i.e., even in the absence of the nervous system. Here, we propose to deepen our exploration on the differential effects on DNA methylation, including an investigation of the correlation with age acceleration using a mitotic clock. These results confirm and caution on the differential effect of DC on inflamed and non-inflamed samples and suggest an involvement of direct current stimuli at 1 V ((Formula presented.)) in the control of senescent processes associated with mitosis and inflammation; the mechanistic details of these will have to be clarified with additional experiments.

The paper focuses on the improvement of the existing nsparse Nagasaka et al. algorithm and its extension to the multi-GPU setting for the application of real engineering problems. In this work, we propose a distributed multi-GPU framework for SpGEMM that is designed specifically for the nsparse like algorithms. The results show similar to 2 times speed-up for nsparse and close to ideal scalability of the multi-GPU extension with the number of GPUs. Finally, we test the proposed algorithm in the AMG setting by computing the double SpGEMM product.

Linear solvers are key components in any software platform for scientific and engineering computing. The solution of large and sparse linear systems lies at the core of physics-driven numerical simulations relying on partial differential equations (PDEs) and often represents a significant bottleneck in data-driven procedures, such as scientific machine learning. In this paper, we present an efficient implementation of the preconditioned s-step Conjugate Gradient (CG) method, originally proposed by Chronopoulos and Gear in 1989, for large clusters of Nvidia GPU-accelerated computing nodes. The method, often referred to as communication-reduced or communication-avoiding CG, reduces global synchronizations and data communication steps compared to the standard approach, enhancing strong and weak scalability on parallel computers. Our main contribution is the design of a parallel solver that fully exploits the aggregation of low-granularity operations inherent to the s-step CG method to leverage the high throughput of GPU accelerators. Additionally, it applies overlap between data communication and computation in the multi-GPU sparse matrix-vector product. Experiments on classic benchmark datasets, derived from the discretization of the Poisson PDE, demonstrate the potential of the method.

Many paintings from the 19th century have exhibited signs of fading and discoloration, often linked to cadmium yellow, a pigment widely used by artists during that time. In this work, we develop a mathematical model of the cadmium sulfide photo catalytic reaction responsible for these damages. By employing nonlo cal integral operators, we capture the interplay between chemical processes and environmental factors, offering a detailed representation of the degradation mechanisms. Furthermore, we present a second order positivity-preserving numerical method designed to accurately simulate the phenomenon and ensure reliable predictions across different scenarios, along with a comprehensive sensitivity analysis of the model.

In this work, we focus on the computation of the zeros of a monic Laguerre–Sobolev orthogonal polynomial of degree n. Taking into account the associated four–term recurrence relation, this problem can be formulated as a generalized eigenvalue problem, involving a lower bidiagonal matrix and a 2–banded lower Hessenberg matrix of order n. Unfortunately, the considered generalized eigenvalue problem is very ill–conditioned, and classical balancing procedures do not improve it. Therefore, customary techniques for solving the generalized eigenvalue problem, like the QZ method, yield unreliable results. Here, we propose a novel balancing procedure that drastically reduces the ill–conditioning of the eigenvalues of the involved matrix pencil. Moreover, we propose a fast and reliable algorithm, with O(n2) computational complexity and O(n) memory, exploiting the structure of the considered matrix pencil.

A fast and weakly stable method for computing the zeros of a particular class of hypergeometric polynomials is presented. The studied hypergeometric polynomials satisfy a higher order differential equation and generalize Laguerre polynomials. The theoretical study of the asymptotic distribution of the spectrum of these polynomials is an active research topic. In this article we do not contribute to the theory, but provide a practical method to contribute to further and better understanding of the asymptotic behavior. The polynomials under consideration fit into the class of Sobolev orthogonal polynomials, satisfying a four–term recurrence relation. This allows computing the roots via a generalized eigenvalue problem. After condition enhancing similarity transformations, the problem is transformed into the computation of the eigenvalues of a comrade matrix, which is a symmetric tridiagonal modified by a rank–one matrix. The eigenvalues are then retrieved by relying on an existing structured rank based fast algorithm. Numerical examples are reported studying the accuracy, stability and conforming the efficiency for various parameter settings of the proposed approach.

We consider equations of the type: (Formula presented) , for general linear operators R in any spatial dimension. We prove that such equations almost always exhibit finite-time singularities for smooth and localised solutions. Singularities can even form in settings where solutions dissipate an energy. Such equations arise naturally as models in various physical settings such as inviscid and complex fluids.

Drug toxicity and market withdrawals are two issues that often obstruct the lengthy and intricate drug discovery process. In order to enhance drug effectiveness and safety, this review examines withdrawn drugs and presents a novel paradigm for their redesign. In addition to addressing methodological issues with toxicity datasets, this study highlights important shortcomings in in silico drug toxicity prediction models and suggests solutions. High-throughput screening (HTS) has greatly progressed with the advent of 3D organoid and organ-on-chip (OoC) technologies, which provide physiologically appropriate systems that replicate the structure and function of human tissue. These systems provide accurate, human-relevant data for drug development, toxicity evaluation, and disease modeling, overcoming the limitations of traditional 2D cell cultures and animal models. Their integration into HTS pipelines has shown to have a major influence, promoting drug redesign efforts and enabling improved accuracy in preclinical research. The potential of fragment-based drug discovery to enhance pharmacokinetics (PK) and pharmacodynamics (PD) when combined with conventional techniques is highlighted in this study. The limits of animal models are discussed, with a focus on the need of bioengineered humanized systems such OoC technologies and 3D organoids. To improve drug candidate screening and simulate real illnesses, advanced models are crucial. This leads to improved target affinity and fewer adverse effects.

Generalized Wasserstein distances allow us to quantitatively compare two continuous or atomic mass distributions with equal or different total masses. In this paper, we propose four numerical methods for the approximation of three different generalized Wasserstein distances introduced in the past few years, giving some insights into their physical meaning. After that, we explore their usage in the context of a sensitivity analysis of differential models for traffic flow. The quantification of the models’ sensitivity is obtained by computing the generalized Wasserstein distances between two (numerical) solutions corresponding to different inputs, including different boundary conditions.

We prove the non-existence and strong ill-posedness of the Incompressible Porous Media (IPM) equation for initial data that are small H2(R2) perturbations of the linearly stable profile −x2. A remarkable novelty of the proof is the construction of an H2 perturbation, which solves the IPM equation and neutralizes the stabilizing effect of the background profile near the origin, where a strong deformation leading to non-existence in H2 is created. This strong deformation is achieved through an iterative procedure inspired by the work of Córdoba and Martínez-Zoroa (2022) [7]. However, several differences - beyond purely technical aspects - arise due to the anisotropic and, more importantly, to the partially dissipative nature of the equation, adding further challenges to the analysis.

The problem of protein structure determination is usually solved by X-ray crystallography. Several in silico deep learning methods have been developed to overcome the high attrition rate, cost of experiments and extensive trial-and-error settings, for predicting the crystallization propensities of proteins based on their sequences. In this work, we benchmark the power of open protein language models (PLMs) through the TRILL platform, a be-spoke framework democratizing the usage of PLMs for the task of predicting crystallization propensities of proteins. By comparing LightGBM / XGBoost classifiers built on the average embedding representations of proteins learned by different PLMs, such as ESM2, Ankh, ProtT5-XL, ProstT5, xTrimoPGLM, SaProt with the performance of state-of-the-art sequence-based methods like DeepCrystal, ATTCrys and CLPred, we identify the most effective methods for predicting crystallization outcomes. The LightGBM classifiers utilizing embeddings from ESM2 model with 30 and 36 transformer layers and 150 and 3000 million parameters respectively have performance gains by 3-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5\%$$\end{document} than all compared models for various evaluation metrics, including AUPR (Area Under Precision-Recall Curve), AUC (Area Under the Receiver Operating Characteristic Curve), and F1 on independent test sets. Furthermore, we fine-tune the ProtGPT2 model available via TRILL to generate crystallizable proteins. Starting with 3000 generated proteins and through a step of filtration processes including consensus of all open PLM-based classifiers, sequence identity through CD-HIT, secondary structure compatibility, aggregation screening, homology search and foldability evaluation, we identified a set of 5 novel proteins as potentially crystallizable.

In this paper, we present an extension of the Generic Second Order Models (GSOM) for traffic flow on road networks. We define a Riemann solver at the junction based on a priority rule and provide an iterative algorithm to construct solutions at junctions with n incoming and m outgoing roads. The logic underlying our solver is as follows: the flow is maximized while respecting the priority rule, which can be adjusted if the supply of an outgoing road exceeds the demand of a higher-priority incoming road. Approximate solutions for Cauchy problems are constructed using wave-front tracking. We establish bounds on the total variation of waves interacting with the junction and present explicit calculations for junctions with two incoming and two outgoing roads. A key novelty of this work is the detailed analysis of returning waves - waves generated at the junction that return to the junction after interacting along the roads - which, in contrast to first-order models such as LWR, can increase flux variation.

Background and Objective: Hepatoblastoma is the most common pediatric liver cancer and represents a serious clinical challenge as no effective therapies have yet been found for advanced states and relapses of the disease. Methods: In this work, we use a well-established agent-based model of the immune response now equipped with anti-cancer therapy response to study the evolution of the disease and the role of the immune system in its containment. Results: We simulate the course of hepatoblastoma over three years in a population of virtual patients, successfully mimicking clinical mortality and symptom onset rates, as well as observations on the main tumor transcriptomic subtypes. Conclusions: The capacity of the introduced framework to reproduce clinical data and the heterogeneity of hepatoblastoma, combined with the possibility of observing the dynamics of cellular entities at the microscopic scale and the key chemical signals involved in disease progression, makes the model a promising resource for future research on in silico trials.

: Objectives: The quantitative analysis of tumor progression-monitored during and immediately after therapeutic interventions-can yield valuable insights into both long-term disease dynamics and treatment efficacy. Methods: We used a computational approach designed to support clinical decision-making, with a focus on personalized patient care, based on modeling therapy effects using effective parameters of the Gompertz law. Results: The method is applied to data from in vivo models undergoing neoadjuvant chemoradiotherapy, as well as conventional and FLASH radiation treatments. Conclusions: This user-friendly, phenomenological model captures distinct phases of treatment response and identifies a critical dose threshold distinguishing complete response from partial response or tumor regrowth. These findings lay the groundwork for real-time quantitative monitoring of disease progression during therapy and contribute to a more tailored and predictive clinical strategy.

Threshold Boolean Networks (TBNs) are constructed using threshold functions that evaluate whether the input values are strong enough for the function to be either "on" or "off." In this work, we explore the properties of the dynamics of TBNs. We propose a new approach to assess the robustness of these networks while addressing the issue of multiple attractors. This method suggests the existence of a set of dominant attractors in the dynamics of TBNs, a phenomenon not commonly observed in Kauffman's networks. We demonstrate this by conducting comparative experiments between the dynamics of TBNs and Random Boolean Networks (RBNs), focusing on variations in the number of inputs per variable. Our experiments also indicate that TBNs tend to exhibit a greater number of attractors per network, though these attractors are typically shorter in length. Finally, we conduct a sensitivity analysis to examine the stability of the dominant attractors in TBNs, which shows that the dominant fixed-point attractors do not always exhibit remarkable stability across the tested size and connectivity configurations.

In this paper we introduce the notion of parabolic α-Riesz flow, for α ∈ (0, d), extending the notion of s-fractional heat flows to negative values of the parameter s=−α2. Then, we determine the limit behaviour of these gradient flows as α → 0+ and α → d−. To this end we provide a preliminary Γ-convergence expansion for the Riesz interaction energy functionals. Then we apply abstract stability results for uniformly λ-convex functionals which guarantee that Γ-convergence commutes with the gradient flow structure.

The study of spin-glass dynamics, long considered the paradigmatic complex system, has reached important milestones. The availability of high-quality single crystals has allowed the experimental measurement of spin-glass coherence lengths of almost macroscopic dimensions, while the advent of special-purpose massive computers—by the Janus Collaboration—enables dynamical simulations that approach experimental timescales and length scales. This review provides an account of the quantitative convergence of these two avenues of research, with precise experimental measurements of the expected scaling laws and numerical reproduction of classic experimental results, such as memory and rejuvenation. The review opens with an examination of the defining spin-glass properties—randomness and frustration—and their experimental consequences. These apparently simple characteristics are shown to generate rich and complex physics. Models are introduced that enable quantitative dynamical descriptions, either analytically or through simulations. The many theoretical pictures of the low-temperature phase are reviewed. After a summary of the main numerical results in equilibrium, paying particular attention to the concept of temperature chaos, this review examines off-equilibrium dynamics in the absence of a magnetic field and shows how it can be related to the structure of the equilibrium spin-glass phase through the fluctuation-dissipation relations. The nonlinear response at a given temperature is then developed, including experiments and scaling in the vicinity of the spin-glass transition temperature Tg. The consequences of temperature change—including temperature chaos, rejuvenation, and memory—are reviewed. The interpretation of these phenomena requires several length scales relevant to dynamics to be identified, which, in turn, generates new insights. Finally, issues for future investigations are introduced, including what is to be “nailed down” theoretically, why the Ising Edwards-Anderson model is so successful at modeling spin-glass dynamics, and experiments yet to be undertaken. This review updates the field of spin glasses with broad application to a large variety of physical systems. In particular, this review tracks the progress of experiment, theory, and large-scale simulations. It highlights the importance of their synergy, from the inception of the field to the present day, and includes future opportunities for research.

The medical discourse entails the analysis of the modalities, which are far from unbiased, by which hypotheses and results are laid out in the dissemination of findings in scientific publications. This gives different emphases on the background, relevance, robustness, and assumptions that the audience takes for granted. This concept is extensively studied in socio-anthropology. However, it remains generally overlooked within the scientific community conducting the research. Yet, analyzing the discourse is crucial for several reasons: to frame policies that take into account an appropriately large screen of medical opportunities; to avoid overseeing promising but less walked paths; to grasp different types of representations of diseases, therapies, patients, and other stakeholders; to understand how these terms are conditioned by time and culture. While socio-anthropologists traditionally use manual curation methods–limited by the lengthy process–machine learning and AI may offer complementary tools to explore the vastness of an ever-growing body of medical literature. In this work, we propose a pipeline for the analysis of the medical discourse on the therapeutic approaches to rheumatoid arthritis using topic modeling and transformer-based emotion and sentiment analysis, overall offering complementary insights to previous curation.