Trevor J.

Li-Fraumeni syndrome leads to an increased predisposition to tumour development. Here, the authors develop a support vector machine model to predict early cancer risk in individuals using peripheral blood DNA methylation profiles.

Background: Polybasic peptides are being developed as components of reagents for diagnosing and treating patients with systemic amyloidosis. In addition to fibrils, amyloid deposits ubiquitously contain heparan sulfate proteoglycans. We have hypothesized that pan amyloid-targeting peptides can specifically engage, in addition to fibrils, a subset of glycosaminoglycans (GAGs) with high negative charge density. In this study, we characterized the binding of peptides p5+14 (a PET imaging agent for amyloid [124I-evuzamitide]) and p5R (a fusion protein used in the therapeutic AT-02) to GAGs. Methods: The peptide structure was evaluated in the presence of low molecular weight heparin using circular dichroism, and their interaction with synthetic GAGs of varying length and charge was interrogated. The binding patterns of p5+14 and p5R were compared using correlation analyses. Results: The peptides exist as mixed structural-fractions in solution but adopt an α-helical structure in the presence of heparin. Both peptides preferentially recognize heparin and heparan sulfate GAGs with a linear positive correlation between binding and the total charge and charge density. Conclusions: These peptides have previously been shown to specifically target amyloid deposits in vivo. A component of this specificity is their preferential interaction with a subset of heparan sulfate GAGs that have high charge density, potentially related to the degree of 6-O-sulfation. These data support the hypotheses that amyloid-associated GAGs have unique sulfation patterns, thereby explaining why these peptides do not bind GAGs found on the plasma membrane and extracellular matrix of healthy tissues.

Department of Animal Science, University of Minnesota, Saint Paul, MN 55108, USA Minnesota Supercomputing Institute, Minneapolis, MN 55455, USA

COVID-19 is associated with long-term neurological complications, but its impact on new-onset dementia (NOD), particularly vascular dementia (VaD) and Alzheimer’s disease (AD), remains unclear. We observed adults aged 50 years and older from the UK Biobank over a median observational period exceeding two years following COVID-19 infection. Incidences of various types of dementia (including all-cause dementia, AD, and VaD) in these individuals were compared with those in propensity-score-matched controls without COVID-19 and in individuals with non-COVID respiratory illnesses (including both non-communicable respiratory conditions and non-COVID respiratory tract infections). We found that COVID-19 survivors had a higher likelihood of developing NOD compared to uninfected controls. This increased risk was primarily driven by VaD rather than AD; however, the risk did not surpass that observed among individuals with non-COVID respiratory illnesses. Notably, individuals with pre-existing mental health conditions were particularly vulnerable, exhibiting significantly higher risks of VaD following COVID-19 infection.

Accurate prediction of reinforced concrete shear strength is essential for structural safety, yet datasets often contain a mix of raw geometric and material properties alongside physics-informed engineered features, making optimal feature selection challenging. This study introduces a statistically principled framework that advances feature reduction for neural networks in three novel ways. First, it extends the artificial neural network-based signal-to-noise ratio (ANN-SNR) method, previously limited to classification, into regression tasks for the first time. Second, it couples ANN-SNR with a confidence-interval (CI)-based stopping rule, using the lower bound of the baseline ANN’s R2 confidence interval as a rigorous statistical threshold for determining when feature elimination should cease. Third, it systematically evaluates both raw experimental variables and physics-informed engineered features, showing how their combination enhances both robustness and interpretability. Applied to experimental concrete shear strength data, the framework revealed that many low-SNR features in conventional formulations contribute little to predictive performance and can be safely removed. In contrast, hybrid models that combined key raw and engineered features consistently yielded the strongest performance. Overall, the proposed method reduced the input feature set by approximately 45% while maintaining results statistically indistinguishable from baseline and fully optimized models (R2 ≈ 0.85). These findings demonstrate that ANN-SNR with CI-based stopping provides a defensible and interpretable pathway for reducing model complexity in reinforced concrete shear strength prediction, offering practical benefits for design efficiency without compromising reliability.

Limb loss remains a significant clinical challenge, but regenerative medicine approaches such as gene therapy offer a promising strategy to trigger...

The tumor suppressor p53 is mutationally inactivated in ≈50% of human cancers. Approximately one-third of the mutations lower the melting temperatu...

Active systems of self-propelled agents, e.g., birds, fish, and bacteria, can organize their collective motion into myriad autonomous behaviors. Ub...

Context Connecting animal movement across scales—both among spatial scales and between individual and population level processes—is a central theme in ecology, including the sub-fields of movement and landscape ecology. However, most modeling frameworks operate at either the individual (Lagrangian) or population (Eulerian) level. Objectives We aimed to review how a partial differential equation (PDE) known as the ecological diffusion equation (EDE) offers connections among the scales of animal movement and how it can be embedded in commonly used statistical frameworks (e.g., hierarchical models) and fit to multiple types of data, such as count, presence-only, presence-absence, and telemetry data. In doing so, we also highlight emerging and promising avenues of research related to statistical EDE models. Methods We present how the EDE is derived from a simple set of first principles, can be used to represent both individual animal movement and spatiotemporal population dynamics, and can be fit to data by being applied in a hierarchical statistical framework. Finally, we review literature on recent applications and advances related to EDE models. Results In presenting the derivation of the EDE and reviewing recent advances and applications of EDE models, we established that the EDE can enable a mechanistic understanding of animal movement across scales, as well as the integration of multiple types of data. We also identified a number of topics where EDE models can be advanced to gain additional ecological insight. Conclusions Ecological diffusion is a process that emerges naturally from first principles governing animal movement and can be used to model both individual and population-level spatiotemporal dynamics. Statistical EDE models are related to other modeling frameworks, such as species distribution models, occupancy models, and step-selection analysis. The flexibility of hierarchical statistical modeling allows combining different types of data collected on movement and population dynamics, and the theoretical underpinnings of the EDE allow it to connect ecological processes occurring at fine and coarse spatial scales, as well as individual animal movement and population dynamics.