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 In order to enhance drug effectiveness and safety, this review examines withdrawn drugs and presents a novel paradigm for their redesign.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.

During March 2025, three intrusions of Saharan dust affected southern Italy, with observable effects on atmospheric composition and, in particular, on greenhouse gases. A recent study conducted by the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (CNR-IMAA) documented these events through integrated in situ and remote sensing observations. Significant variations in CH4 and CO2 concentrations were detected in correspondence with the dust transport episodes. In this work, we propose an approach based on Physics-Informed Neural Networks (PINNs) to retrieve the vertical profile of CH4. The results are evaluated against high-precision ground-based measurements from CNR-IMAA, in order to assess the model’s predictive accuracy and its sensitivity to atmospheric variations associated with the presence of mineral aerosols.

In this paper we propose a mathematical model of the capillary and permeability properties of lime-based mortars from the historic built heritage of Catania (Sicily, Italy) produced by using two different types of volcanic aggregate, i.e. ghiara and azolo. In order to find a formulation for the capillary pressure and the permeability as functions of the saturation level inside the porous medium we calibrate the numerical algorithm against imbibition data. The validation of the mathematical model was done by comparing the experimental retention curve with the one obtained by the simulation algorithm. Indeed, with the proposed approach it was possible to reproduce the main features of the experimentally observed phenomenon for both materials.

This study introduces an explainable Artificial Intelligence (XAI) framework that couples legal-domain NLP with Structural Topic Modeling (STM) and WordNet semantic graphs to rigorously analyze over 1,900 GDPR enforcement decision summaries from a public dataset. Our methodology focuses on demonstrating the pipeline's validity respect to manual analyses by inspecting the results of four well-know research questions: (1) cross-country fine distribution disparities (automated metadata extraction); (2) the violation severity-fine amount relationship (keyness and semantic analysis); (3) structural text patterns (network analysis and STM); and (4) prevalent enforcement triggers (topic prevalence modeling) The pipeline's validity is underscored by its ability to replicate key findings from previous manual analyses while enabling a more nuanced exploration of GDPR enforcement trends. Our results confirm significant disparities in enforcement across EU member states and reveal that monetary penalties do not consistently correlate with violation severity. Specifically, serious infringements, particularly those involving video surveillance, frequently result in low-value fines, especially when committed by individuals or smaller entities. This highlights that a substantial proportion of severe violations are attributed to smaller actors. Methodologically, the framework's ability to quickly replicate such well-known patterns, alongside its transparency and reproducibility, establishes its potential as a scalable tool for transparent and explainable GDPR enforcement analytics.

This study investigates the optical properties and variability of the mass absorption coefficient (MAC) of carbonaceous aerosols produced by the combustion of different fuels. Emissions were also characterized in terms of particle size distribution and concentrations of elemental carbon (EC) and organic carbon (OC). Experiments were conducted in an atmospheric simulation chamber with a soot generator fueled with propane and a commercial diesel engine running on regular diesel and hydrotreated vegetable oil (HVO). Different methods of sampling and analyzing carbonaceous aerosols were evaluated, focusing on workplace environments. The EC : TC (total carbon) ratios were found to be 0.7 ± 0.1 for propane, 0.15 ± 0.05 for diesel, and 0.4 ± 0.2 for HVO, indicating a higher proportion of OC in the diesel and HVO samples. Fresh soot particles showed monomodal log-normal distributions with peaks varying based on the fuel type and combustion process, with propane particles exhibiting a peak at larger particle sizes compared to HVO and diesel. The optical properties revealed that the MAC values varied across different fuel exhausts. Diesel combustion produced more light-absorbing particles compared to propane and HVO, with MAC values measured between 870 and 635 nm ranging from 6.2 ± 0.5 to 9.4 ± 0.4 m2g-1 for commercial diesel, 5.2 ± 0.5 to 7.8 ± 1.1 m2g-1 for propane, and 5.8 ± 0.2 to 8.4 ± 0.6 m2g-1 for HVO.

While the beneficial impacts of meditation are increasingly acknowledged, its underlying neural mechanisms remain poorly understood. We examined the electrophysiological brain signals of expert Buddhist monks during two established meditation methods known as Samatha and Vipassana, which employ focused attention and open-monitoring technique. By combining source-space magnetoencephalography with advanced signal processing and machine learning tools, we provide an unprecedented assessment of the role of brain oscillations, complexity, and criticality in meditation. In addition to power spectral density, we computed long-range temporal correlations (LRTC), deviation from criticality coefficient (DCC), Lempel-Ziv complexity, 1/f slope, Higuchi fractal dimension, and spectral entropy. Our findings indicate increased levels of neural signal complexity during both meditation practices compared to the resting state, alongside widespread reductions in gamma-band LRTC and 1/f slope. Importantly, the DCC analysis revealed a separation between Samatha and Vipassana, suggesting that their distinct phenomenological properties are mediated by specific computational characteristics of their dynamic states. Furthermore, in contrast to most previous reports, we observed a decrease in oscillatory gamma power during meditation, a divergence likely due to the correction of the power spectrum by the 1/f slope, which could reduce potential confounds from broadband 1/f activity. We discuss how these results advance our comprehension of the neural processes associated with focused attention and open-monitoring meditation practices.

Conferences are invaluable for career progression, offering unique opportunities for networking, collaboration, and learning. However, there are challenges associated with the traditional in-person conference format. For example, there is a significant ecological impact from attendees’ travel behaviour, and there are social inequities in conference attendance, with historically marginalised groups commonly facing barriers to participation. Innovative practices that enable academic conferences to be ‘done differently’ are crucial for addressing these ecological and social sustainability challenges. However, while some such practices have emerged in recent years, largely due to the COVID-19 pandemic, little research has been done on their effectiveness. Our study addresses this gap using a mixed methods approach to analyse a real-world decentralised multi-hub conference held in 2023, comparing it to traditional in-person conference and fully online conference scenarios. The decentralised multi-hub format consists of local in-person hubs in different locations around the world, each with a unique local programme developed around a shared core global programme; there is no single centralised point of control. We calculated the CO2 emissions from transport for each scenario and found the decentralised multi-hub conference had significantly lower emissions than a traditional in-person conference, but higher emissions than a fully online conference. We also interviewed 14 local hub organisers and attendees to gain their perspectives about the ecological and social sustainability benefits of the decentralised multi-hub format. We found that the more accessible and inclusive format attracted a more diverse range of attendees, meaning that the benefits attributed to conference attendance were able to be shared more equitably. These findings demonstrate the ecological and social sustainability benefits of doing conferences differently, and can be used as further evidence in the argument to help transition conferences to a more desirable state in terms of ecological and social sustainability.

Questo rapporto tecnico documenta in modo sistematico lo stato attuale della Biblioteca “Mauro Piconeˮ dellʼIstituto per le Applicazioni del Calcolo (IAC-CNR), con particolare attenzione allʼorganizzazione fisica del patrimonio (circa 26.000 unità), alla stratificazione dei sistemi di catalogazione (MSC/AMS, ordinamento alfabetico, inventario SIGLA/U‐GOV) e alle ricadute operative sulla ricerca e sulla fruizione scientifica. Viene presentato un audit quantitativo su un campione del 10% del posseduto, volto a misurare tempi medi di ricerca, tassi di errore nella localizzazione e livello di digitalizzazione dei dati bibliografici, evidenziando criticità di standardizzazione, interoperabilità e visibilità rispetto al Servizio Bibliotecario Nazionale (SBN) e alle normative ICCU. Il documento propone una roadmap di migrazione verso un sistema integrato di gestione bibliotecaria (ILS, con particolare riferimento a Koha), basato su UNIMARC/MARC21, REICAT e sui modelli FRBR/RDA, delineando linee guida tecniche e organizzative per lʼallineamento alla cooperazione SBN e per la valorizzazione del patrimonio nel periodo 2025‐2028

Increasing use of new digital services offers tremendous opportunities for modern society, but also entails new risks. One tool for managing cyber risk is cyber insurance. While cyber insurance has attracted much attention and optimism, interdependent cyber risks and lack of actuarial data have prompted some insurers to adopt a more proactive role, not only insuring losses but also assisting clients with preventive work such as managed detection and response solutions, i.e., investments in their own cybersecurity. The purpose of this paper is to propose and theoretically investigate yet a further extension of this role, where insurers facilitate security investments between interdependent firms, which get the opportunity to invest a share of their insurance premiums to improve the security of each other. It is demonstrated that if insurers can facilitate such investments, then under common theoretical assumptions this can make a positive contribution to overall welfare. The paper is concluded by a discussion of the relevance and applicability of this theoretical contribution in practice.

Topological stars are solutions of Einstein-Maxwell theory in D=5. For specific choices of the parameters, the solution is capped and thus smooth and horizonless and can be reduced to D=4 along a circle. We study the energy and angular momentum radiated by a scalar particle moving on a circular orbit in the D=4 noncompact directions, extending a previous study [Phys. Rev. D 110, 084077 (2024)PRVDAQ2470-001010.1103/PhysRevD.110.084077]. We also discuss self-force effects on the motion of a spinless probe.