Applied Mathematics (United States)

Long-lived wild rodents closely related to laboratory mice on the evolutionary scale may allow identification of dormant pathways that resist aging. Spiny mice (Acomys) are known for their exceptional regenerative capacity, but their resilience to aging is unknown. Here, we report that aged golden spiny mice (Acomys russatus), reared in a non-pathogen-free environment, resist functional decline, have a greater repair capacity with reduced senescence in immune-metabolic organs compared to their sister species, eastern spiny mice (Acomys dimidiatus). Aged A. russatus maintains transcriptional integrity akin to young mice, highlighting experimental checkpoints for inflammation and mortality. We identified that elevated levels of clusterin in A. russatus macrophages restrain inflammaging and enhance health span in aged mice. Thus, A. russatus biology reveals therapeutically actionable targets that may enhance or maintain function during aging.

Spatial omics technologies offer unprecedented insights into the cellular organization of tissues; however, they are not yet scalable for routine clinical use. In contrast, images of histological staining remain the foundation of pathological diagnosis despite lacking molecular information. Bridging this gap requires computational methods that can accurately infer spatial molecular data from histology alone. Here, we introduce TissueCraftAI, a generative artificial intelligence framework that predicts multi-modal spatial omics maps directly from standard histology images using natural language prompts. To train and validate our model, we created PRISM-12M, a large-scale dataset comprising over twelve million spatially registered histology and spatial omics image patches across fourteen tissue types from humans and mice. TissueCraftAI significantly outperforms existing methods in generating realistic histology images and predicting spatial proteomics and transcriptomics data with high fidelity. We demonstrated its utility in various downstream applications, including improving cell type annotation and enhancing the accuracy of patient survival predictions across multiple cancer types. By enabling flexible, query-driven in silico spatial molecular analysis using routine histology images, TissueCraftAI opens up new research avenues in computational pathology.

Chain relaxation in melts of moderately entangled macromolecules presents the first signs of topological constraints that are commonly manifested in the rheology of long commercial-grade polymers. In this study, we employ multiscale molecular dynamics simulations to examine the relaxation characteristics of model polyisoprene (PI) melts through the transition from the Rouse regime to entangled systems. We probed the impact of molecular weight and stereochemistry and evaluated the accuracy of theory in modeling the end-to-end relaxation. Our results support that trans-PI dynamics is distinct, with entanglements forming at a lower degree of polymerization than other compositions due to differences in the characteristic ratio and packing of this polymer. For all moderately entangled systems, modeling the end-to-end relaxation with the original Likhtman–McLeish theory with contour-length fluctuations presents deviations from recorded data in both the early time and terminal regime. We propose numerical modifications that significantly improve the model for all of the systems studied without introducing additional parameters. Our approach allows accurate predictions for long macromolecules based on data from detailed simulations with moderately entangled polyisoprene systems.

Volume electron microscopy (vEM) datasets such as those generated for connectome studies allow nanoscale quantifications and comparisons of the cell biological features underpinning circuit architectures. Quantifying cell biological relationships in the connectome yields rich, multidimensional datasets that benefit from data science approaches, including dimensionality reduction and integrated graphical representations of neuronal relationships. We developed NeuroSC (also known as NeuroSCAN,https://neurosc.net/) an open source online platform that bridges sophisticated graph analytics from data science approaches with the underlying cell biological features in the connectome. We analyze a series of published C. elegans brain neuropils and demonstrate how these integrated representations of neuronal relationships facilitate comparisons across connectomes, catalyzing new insights into the structure-function relationships of the circuits and their changes during development. NeuroSC is designed for intuitive examination and comparisons across connectomes, enabling synthesis of knowledge from high-level abstractions of neuronal relationships derived from data science techniques to the detailed identification of the cell biological features underpinning these abstractions.

Abstract This paper concerns the topological classification of continuous Hamiltonians that find applications in biased cold plasmas and photonics. Besides a magnetic bias, the Hamiltonians are parameterized by a plasma frequency and a fixed vertical wavenumber. Eight distinct phases of matter are identified as these parameters vary. When insulating gaps are shared by two such phases, asymmetric edge modes propagate along interfaces separating the two phases. Here we apply the notion of a bulk difference invariant (BDI) to this Hamiltonian, and show by numerical diagonalizations of interface Hamiltonians that after an appropriate regularization our BDI correctly predicts edge transport as described by a bulk edge correspondence (BEC). We also derive theoretical tools to compute the BDI and show the limitations of the BEC when the phase transition is too singular.

Abstract. The proportionality between global mean temperature and cumulative emissions of CO2 predicted in Earth system models (ESMs) is the foundation of carbon budgeting frameworks. Deviations from this behavior could impact estimates of required net-zero timings and negative emissions requirements to meet the Paris Agreement climate targets. However, existing ESM diagnostic experiments do not allow for direct estimation of these deviations as a function of defined emissions pathways. Here, we perform a set of climate model diagnostic experiments for the assessment of transient climate response to cumulative CO2 emissions (TCRE), the Zero Emissions Commitment (ZEC), and climate reversibility metrics in an emissions-driven framework. The emissions-driven experiments provide consistent independent variables simplifying simulation, analysis and interpretation, with emissions rates more comparable to recent levels than existing protocols using model-specific compatible emissions from the CMIP DECK 1pctCO2 experiment, where emissions rates tend to increase during the experiment, such that at the time of CO2 doubling in year 70, emissions are much greater than present-day values. A base experiment, “esm-flat10”, has constant emissions of CO2 of 10 GtC per year (near-present-day values), and initial results show that the TCRE estimated in this experiment is about 0.1 K less than that obtained using 1pctCO2. A subset of ESMs exhibit land carbon sinks that saturate during this experiment. A branch experiment, esm-flat10-zec, illustrates that both positive and negative ZEC effects are less pronounced under esm-flat10 than under 1pctCO2 – the magnitude of ZEC50 in ESMs is, on average, reduced by 30 % compared with 1pctCO2 branch experiments. A final experiment, esm-flat10-cdr, assesses climate reversibility under negative emissions, where we find that peak warming may occur before or after net zero and that the asymmetry in temperature at a given level of cumulative emissions between the positive and negative emissions phases is well described by ZEC in most models. Further, we find that existing probabilistic simple climate model (SCM) ensembles tend to overestimate temperature reversibility compared with ESMs, highlighting the need for additional constraints. We propose a set of climate diagnostic indicators to quantify various aspects of climate reversibility. These experiments were suggested as potential candidates in CMIP7 and have since been adopted as “fast track” simulations.