The fiber of persistent homology for trees.

Consider the space of continuous functions on a geometric tree  whose persistent homology gives rise to a finite generic barcode . We show that there are exactly as many path connected components in this space as there are merge trees whose barcode is . We find that each component is homotopy equivalent to a configuration space on  with specialised constraints encoded by the merge tree.

Deriving Optimal Treatment Timing for Adaptive Therapy: Matching the Model to the Tumor Dynamics.

Adaptive therapy (AT) protocols have been introduced to combat drug resistance in cancer, and are characterized by breaks from maximum tolerated dose treatment (the current standard of care in most clinical settings). These breaks are scheduled to maintain tolerably high levels of tumor burden, employing competitive suppression of treatment-resistant sub-populations by treatment-sensitive sub-populations. AT has been integrated into several ongoing or planned clinical trials, including treatment of metastatic castrate-resistant prostate cancer, ovarian cancer, and BRAF-mutant melanoma, with initial clinical results suggesting that it can offer significant extensions in the time to progression over the standard of care.

CAA-related enlarged perivascular spaces are associated with abnormal angioarchitecture in human brain tissue: A key role for white matter atrophy?

Cerebral Amyloid Angiopathy, a common age-related small vessel disease leading to hemorrhagic stroke, shares many characteristics with Alzheimer's disease: toxic amyloid deposits, microvascular alterations and enlarged perivascular spaces (EPVS). Together, PVS enlargement, reduced amyloid-β clearance and further accumulation form a vicious cycle underlying disease progression. Yet, the neuropathological correlates of EPVS, including the associated angioarchitecture, are poorly understood.

Computational model of cardiac cycle driven brain pulsation: a novel tool for brain disease exploration.

Effective non-invasive detection and tracking of disease progression remains a challenge in medical research. This is particularly critical for brain-related conditions such as Chiari malformations, hydrocephalus or neurodegenerative disorders, as an early detection can significantly improve patient outcomes. One promising approach looks to identify changes in the patterns of brain movement during a cardiac cycle, using aMRI or ultrasound, as a diagnostic proxy for a given condition.

Correction: Kalisvaart et al. Relative Wash-In Rate in Dynamic Contrast-Enhanced Magnetic Resonance Imaging as a New Prognostic Biomarker for Event-Free Survival in 82 Patients with Osteosarcoma: A Multicenter Study. 2024, , 1954.

In the original publication [...

Effect of Cardiac Arrest Center Protocol Implementation on Survival After Nontraumatic Out-of-Hospital Cardiac Arrest.

Background: Evidence on the effect of treatment at a cardiac arrest center (CAC) on clinical outcomes is incomplete and conflicting. The aim was to evaluate the effect of treatment at a CAC on survival after out-of-hospital cardiac arrest.

Methods: For this retrospective cohort study, data from all 790 consecutive patients admitted to our center from January 2018 to May 2023 after nontraumatic out-of hospital cardiac arrest were analyzed.

Mechanical confinement governs phenotypic plasticity in melanoma.

Phenotype switching is a form of cellular plasticity in which cancer cells reversibly move between two opposite extremes: proliferative versus invasive states. Although it has long been hypothesized that such switching is triggered by external cues, the identity of these cues remains unclear. Here we demonstrate that mechanical confinement mediates phenotype switching through chromatin remodelling.

Development of an Artificial Neural Network-Based Tool for Predicting Failures in Composite Laminate Structures.

Composite materials are widely used in aerospace, automotive, biomedical, and renewable energy sectors due to their high strength-to-weight ratio and design flexibility. However, their anisotropic and layered nature makes structural analysis and failure prediction challenging. Traditional methods require solving complex interlaminar stress-strain equations, demanding significant computational resources.

An Asymptotic Analysis of Bivalent Monoclonal Antibody-Antigen Binding.

Ligand-receptor interactions are fundamental to many biological processes. For example in antibody-based immunotherapies, the dynamics of an antibody binding with its target antigen directly influence the potency and efficacy of monoclonal antibody (mAb) therapies. In this paper, we present an asymptotic analysis of an ordinary differential equation (ODE) model of bivalent antibody-antigen binding in the context of mAb cancer therapies, highlighting the complexity associated with bivalency of the antibody.

Chaotic dynamics for the two-body problem on a sphere.

We prove the existence of chaotic trajectories for the two body problem on a sphere. The trajectories we construct encounter near collisions and are similar to the second species solutions of Poincaré of the classical three body problem. The construction uses a general result on Lagrangian systems with Newtonian singularities of the potential, which is based on the method of an anti-integrable limit of Serge Aubry.

Recurrence affects the geometry of visual representations across the ventral visual stream in the human brain.

The human brain orchestrates object vision through an interplay of feedforward processing in concert with recurrent processing. However, where, when, and how recurrent processing contributes to visual processing is incompletely understood due to the difficulties in teasing apart feedforward and recurrent processing. We combined a backward masking paradigm with multivariate analysis on EEG and fMRI data to isolate and characterize the nature of recurrent processing.

Narratives from GPT-derived networks of news and a link to financial markets dislocations.

We introduce a novel framework to study the dynamics of news narratives, by leveraging GPT3.5 advanced text analysis capabilities and graph theory. In particular, we focus on a corpus of economic articles from The Wall Street Journal and dynamically extract the main topics of discussion over time, in a completely systematic and scalable fashion.

Nephrotoxicity and kidney outcomes in pediatric oncology patients.

Background: Acute kidney injury (AKI) is a serious complication during pediatric cancer treatment. Nephrotoxic medication may increase the risk of developing AKI, which may necessitate modifications to standard treatment and may also increase the risk of chronic kidney disease (CKD). This study investigates the incidence of AKI, the impact of nephrotoxic medications and the association between AKI and the development of CKD.

A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems.

In this work, we propose and analyze a new local time-decoupled squared Wasserstein-2 method for reconstructing the distribution of unknown parameters in dynamical systems from a finite number of observed temporal trajectories. Specifically, we show that a stochastic neural network model, which can be effectively trained by minimizing our proposed local time-decoupled squared Wasserstein-2 loss function, is an effective model for approximating the distribution of uncertain model parameters in dynamical systems. Through several numerical examples, we showcase the effectiveness of our proposed method in reconstructing the distribution of parameters in different dynamical systems.

Applying population mechanistic modelling to find determinants of chimeric antigen receptor T-cells dynamics in month-one lymphoma patients.

Background: Chimeric antigen receptor (CAR) T-cells have been utilized for the treatment of several malignancies, including Non-Hodgkin lymphomas. A myriad of product- and patient-specific factors determines the extent of patient response, and determining which are most impactful requires analysis of clinical data.

Methods: We used population-level ordinary differential equation models to fit clinical flow cytometry and tumour biopsy data from the TRANSCEND-NHL-001 (NCT02631044) study [1].

Decoding how higher-order network interactions shape contagion dynamics.

Complex contagion models that involve contagion along higher-order structures, such as simplicial complexes and hypergraphs, yield new classes of mean-field models. Interestingly, the differential equations arising from many such models often exhibit a similar form, resulting in qualitatively comparable global bifurcation patterns. Motivated by this observation, we investigate a generalised mean-field-type model that provides a unified framework for analysing a range of different models.

Parameter estimation in a dynamic Chung-Lu random graph.

In this paper, we consider a dynamic version of the Chung-Lu random graph in which the edges alternate between being present and absent. The main contribution concerns a technique by which one can estimate the underlying dynamics from partial information, in particular from snapshots of the total number of edges present. The efficacy of our inference method is demonstrated through a series of numerical experiments.

Climate variability amplifies the need for vector-borne disease outbreak preparedness.

In locations that do not currently experience vector-borne disease (VBD) outbreaks but may be at risk under climate change, modeling future climate suitability for transmission is important for outbreak preparedness. Uncertainty in the future climate arises from three sources-differences in emissions scenarios, structural uncertainty across climate models, and internal climate variability (ICV)-but ICV is rarely considered in climate-VBD studies. Here, we demonstrate that ICV is a key source of uncertainty in climate suitability for VBD transmission, even decades into the future.

Nonparametric Estimation of Transition Intensities in Interval-Censored Markov Multistate Models Without Loops.

Interval-censored multistate data is collected when the state of a subject is observed periodically. The analysis of such data using nonparametric multistate models was not possible until recently but is very desirable as it allows for more flexibility than its parametric counterparts. The single available result to date has some unique drawbacks.

Decomposed Linear Dynamical Systems (dLDS) models reveal instantaneous, context-dependent dynamic connectivity in C. elegans.

Mounting evidence indicates that neural "tuning" can be highly variable within an individual across time and across individuals. Furthermore, modulatory effects can change the relationship between neurons as a function of behavioral or other conditions, meaning that the changes in activity (the derivative) may be as important as the activity itself. Current computational models cannot capture the nonstationarity and variability of neural coding, preventing the quantitative evaluation of these effects.

Distinguishing Short-Term Versus Long-Term Responses in Cover-Class Structured Community Dynamics: A Test With Grassland Drought Response.

Climate change is increasing the magnitude and frequency of precipitation extremes. Consequently, grassland community dynamics are destabilising and becoming harder to predict since models typically simulate long-term (asymptotic) behaviour, potentially neglecting short-term (transient) behaviour. Here, we use cover data from an experiment performed over 8 years to model short- and long-term responses of three functional groups (grasses, legumes, and non-leguminous forbs) to precipitation extremes.

Controlling treatment toxicity in ovarian cancer to prime the patient for tumor extinction therapy.

High-grade serous ovarian cancer (HGSOC) remains a major clinical challenge. In particular among those patients with homologous recombination (HR)-proficient tumors (>50%), most eventually succumb to their disease due to high recurrence rates, acquired resistance, and cumulative toxicity. This report summarizes work from the 12 IMO Workshop in which we explored an alternative "extinction therapy" strategy for frontline treatment of HGSOC.

Optimal Control of Immune Checkpoint Inhibitor Therapy in a Heart-Tumour Model.

Autoimmune myocarditis, or cardiac muscle inflammation, is a rare but frequently fatal side-effect of immune checkpoint inhibitors (ICIs), a class of cancer therapies. Despite the dangers that side-effects such as these pose to patients, they are rarely, if ever, included explicitly when mechanistic mathematical modelling of cancer therapy is used for optimization of treatment. In this paper, we develop a two-compartment mathematical model which incorporates the impact of ICIs on both the heart and the tumour.

Modeling cell differentiation in neuroblastoma: Insights into development, malignancy, and treatment relapse.

Neuroblastoma is a paediatric extracranial solid cancer that arises from the developing sympathetic nervous system and is characterised by an abnormal distribution of cell types in tumours compared to healthy infant tissues. In this paper, we propose a new mathematical model of cell differentiation during sympathoadrenal development. By performing Bayesian inference of the model parameters using clinical data from patient samples, we show that the model successfully accounts for the observed differences in cell type heterogeneity among healthy adrenal tissues and four common types of neuroblastomas.

Nowcasting cases and trends during the measles 2023/24 outbreak in England.

Objectives: In 2023/24, England had its largest measles outbreak in a decade. Lags from symptom onset to test results made laboratory-confirmed case data inherently retrospective rather than real-time. Reporting lags varied by measles prevalence and testing purpose.