Decay and Fission of Magnetic Quivers.

In exploring supersymmetric theories with eight supercharges, the Higgs branches present an intriguing window into strong coupling dynamics. Magnetic quivers serve as crucial tools for understanding these branches. Here, we introduce the decay and fission algorithm for unitary magnetic quivers.

Drop impact onto immiscible liquid films floating on pools.

The interface dynamics of a droplet impacting onto a liquid pool has been well studied, and the common interfacial velocity quantified for the cases when the pool is both the same and a different fluid to the impacting droplet. In this work we investigate, experimentally and computationally, the scenario of a droplet impacting onto a pool of the same fluid coated by a layer of another fluid with various thicknesses. The effect of the film thickness on the penetration velocity of the upper droplet-film interface is measured for experiments and simulations, and carefully compared to theoretical predictions for early-to-moderate timescales in the limiting cases of: (i) zero film thickness, in which the film has no effect and thus behaves like a fluid on same fluid impact, and (ii) infinite film thickness, in which the underlying pool has no effect.

FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious diseases.

Through our respiratory system, many viruses and diseases frequently spread and pass from one person to another. Covid-19 served as an example of how crucial it is to track down and cut back on contacts to stop its spread. There is a clear gap in finding automatic methods that can detect hand-to-face contact in complex urban scenes or indoors.

Interpersonal trust: Asymptotic analysis of a stochastic coordination game with multi-agent learning.

We study the interpersonal trust of a population of agents, asking whether chance may decide if a population ends up with high trust or low trust. We model this by a discrete time, stochastic coordination game with pairwise interactions occurring at random in a finite population. Agents learn about the behavior of the population using a weighted average of what they have observed in past interactions.

Representations of imaginary scenes and their properties in cortical alpha activity.

Imagining natural scenes enables us to engage with a myriad of simulated environments. How do our brains generate such complex mental images? Recent research suggests that cortical alpha activity carries information about individual objects during visual imagery. However, it remains unclear if more complex imagined contents such as natural scenes are similarly represented in alpha activity.

Rupture point is associated with divergent hemodynamics in intracranial aneurysms.

Background: Understanding the risk factors leading to intracranial aneurysm (IA) rupture have still not been fully clarified. They are vital for proper medical guidance of patients harboring unruptured IAs. Clarifying the hemodynamics associated with the point of rupture could help could provide useful information about some of the risk factors.

Two biases in incubation time estimation related to exposure.

Background: Estimation of the SARS-CoV-2 incubation time distribution is hampered by incomplete data about infection. We discuss two biases that may result from incorrect handling of such data. Notified cases may recall recent exposures more precisely (differential recall).

EEG connectivity and network analyses predict outcome in patients with disorders of consciousness - A systematic review and meta-analysis.

Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis.

Sensitivity of cartilage mechanical behaviour to spatial variations in material properties.

Articular cartilage tissue exhibits a spatial dependence in material properties that govern mechanical behaviour. A mathematical model of cartilage tissue under one dimensional confined compression testing is developed for normal tissue that takes account of these variations in material properties. Modifications to the model representative of a selection of mechanisms driving osteoarthritic cartilage are proposed, allowing application of the model to both physiological and pathophysiological, osteoarthritic tissue.

Balancing versus modelling in weighted analysis of non-randomised studies with survival outcomes: A simulation study.

Weighting methods are widely used for causal effect estimation in non-randomised studies. In general, these methods use the propensity score (PS), the probability of receiving the treatment given the covariates, to arrive at the respective weights. All of these "modelling" methods actually optimize prediction of the respective outcome, which is, in the PS model, treatment assignment.

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin.

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data.

A nonparametric proportional risk model to assess a treatment effect in time-to-event data.

Time-to-event analysis often relies on prior parametric assumptions, or, if a semiparametric approach is chosen, Cox's model. This is inherently tied to the assumption of proportional hazards, with the analysis potentially invalidated if this assumption is not fulfilled. In addition, most interpretations focus on the hazard ratio, that is often misinterpreted as the relative risk (RR), the ratio of the cumulative distribution functions.

A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data.

Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily).

A Mean Field to Capture Asynchronous Irregular Dynamics of Conductance-Based Networks of Adaptive Quadratic Integrate-and-Fire Neuron Models.

Mean-field models are a class of models used in computational neuroscience to study the behavior of large populations of neurons. These models are based on the idea of representing the activity of a large number of neurons as the average behavior of mean-field variables. This abstraction allows the study of large-scale neural dynamics in a computationally efficient and mathematically tractable manner.

A reactive neural network framework for water-loaded acidic zeolites.

Under operating conditions, the dynamics of water and ions confined within protonic aluminosilicate zeolite micropores are responsible for many of their properties, including hydrothermal stability, acidity and catalytic activity. However, due to high computational cost, operando studies of acidic zeolites are currently rare and limited to specific cases and simplified models. In this work, we have developed a reactive neural network potential (NNP) attempting to cover the entire class of acidic zeolites, including the full range of experimentally relevant water concentrations and Si/Al ratios.

Classification of anisotropic Triebel-Lizorkin spaces.

This paper provides a characterization of expansive matrices generating the same anisotropic homogeneous Triebel-Lizorkin space for and . It is shown that if and only if the homogeneous quasi-norms associated to the matrices ,  are equivalent, except for the case with . The obtained results complement and extend the classification of anisotropic Hardy spaces , , in Bownik (Mem Am Math Soc 164(781):vi+122, 2003).

Gap junctions in Turing-type periodic feather pattern formation.

Periodic patterning requires coordinated cell-cell interactions at the tissue level. Turing showed, using mathematical modeling, how spatial patterns could arise from the reactions of a diffusive activator-inhibitor pair in an initially homogeneous 2D field. Most activators and inhibitors studied in biological systems are proteins, and the roles of cell-cell interaction, ions, bioelectricity, etc.

Neuronal activity induces symmetry breaking in neurodegenerative disease spreading.

Dynamical systems on networks typically involve several dynamical processes evolving at different timescales. For instance, in Alzheimer's disease, the spread of toxic protein throughout the brain not only disrupts neuronal activity but is also influenced by neuronal activity itself, establishing a feedback loop between the fast neuronal activity and the slow protein spreading. Motivated by the case of Alzheimer's disease, we study the multiple-timescale dynamics of a heterodimer spreading process on an adaptive network of Kuramoto oscillators.

Exploiting in silico modelling to enhance translation of liver cell therapies from bench to bedside.

Cell therapies are emerging as promising treatments for a range of liver diseases but translational bottlenecks still remain including: securing and assessing the safe and effective delivery of cells to the disease site; ensuring successful cell engraftment and function; and preventing immunogenic responses. Here we highlight three therapies, each utilising a different cell type, at different stages in their clinical translation journey: transplantation of multipotent mesenchymal stromal/signalling cells, hepatocytes and macrophages. To overcome bottlenecks impeding clinical progression, we advocate for wider use of mechanistic in silico modelling approaches.

Personalized Timing for Allogeneic Stem-Cell Transplantation in Hematologic Neoplasms: A Target Trial Emulation Approach Using Multistate Modeling and Microsimulation.

Purpose: Decision about the optimal timing of a treatment procedure in patients with hematologic neoplasms is critical, especially for cellular therapies (most including allogeneic hematopoietic stem-cell transplantation [HSCT]). In the absence of evidence from randomized trials, real-world observational data become beneficial to study the effect of the treatment timing. In this study, a framework to estimate the expected outcome after an intervention in a time-to-event scenario is developed, with the aim of optimizing the timing in a personalized manner.