Regional Climate Model (RCM) emulators enable rapid and computationally efficient RCM projections given Global Climate Model (GCM) inputs, complementing dynamical downscaling by approximating physical representations with statistical models. However, while existing RCM emulators perform well in deterministic emulations, they do not sample internal RCM variability and remain computationally expensive. Here, we present MESMER-RCM, a probabilistic RCM emulator designed for spatially resolved annual 2 m temperature. MESMER-RCM is a generative model that enables both data-efficient learning and interpretability. It can generate large ensembles of synthetic, yet physically plausible, RCM realizations, capturing the internal RCM variability at a fraction of the computational cost. This work offers a fast and reliable RCM emulation framework, supporting finer-scale what-if analyses of regional climate responses and informing local adaptation and mitigation strategies.
The interaction of drug formulations with cells is a critical factor in the development of effective therapeutics. Conventional in vitro models, such as static horizontal monolayer cultures, often fail to account for key parameters such as sedimentation, flotation or shear stress, which influence the cellular dose and interaction dynamics. In this study, the FlowCube, an in vitro platform designed to simulate dynamic flow conditions was used to investigate the impact of motion and cell layer orientation on the cell interaction of various particle formulations. Polymeric nanoparticles, microparticles, and buoyant microcapsules were prepared and characterized for size, stability, and sedimentation behaviour. Cell binding of these particles, along with a dissolved lectin ligand as a model soluble substance, was evaluated using the FlowCube and compared with horizontal multiwell plate experiments. Microparticles exhibited significantly lower cell association with vertically oriented cell layers in the FlowCube than with horizontal monolayers, indicating sedimentation-driven accumulation under static conditions. In contrast, buoyant microcapsules showed enhanced cell interaction in the FlowCube, highlighting the role of density-dependent particle dynamics. Cell association of the soluble ligand and the nanoparticle formulation were rather affected by the induced shear stress. These findings demonstrate the critical role of sedimentation, flotation, and shear stress in drug formulation-cell interactions and highlight the need to incorporate controlled motion and consider cell layer orientation for more reliable, physiologically relevant outcomes. The FlowCube is a versatile and valuable addition to conventional in vitro models with the potential to improve the accuracy, reproducibility, and translational relevance of in vitro drug formulation studies.
CryoRad is an Earth Explorer candidate mission concept featuring a wideband hyperspectral microwave radiometer operating from 0.4 GHz to 2 GHz to observe cryospheric processes at high latitudes. Although CryoRad uses near-nadir circular polarization to mitigate ionospheric Faraday rotation (FR), the combination of a single-polarization radiometer and non-ideal antenna polarization purity can convert FR into a systematic brightness temperature ($T_{b}$) bias. This paper formulates the end-to-end effect using the Stokes-Mueller formalism, derives a compact first-order expression for the $T_{b}$ error as a function of FR angle and antenna imperfections, and applies it to global simulations driven by IONEX vertical total electron content (VTEC) maps and the IGRF-13 geomagnetic field model. For representative near-nadir viewing (observation zenith angle 8°) and an antenna axial ratio of 3 dB, the predicted bias exhibits strong geographic structure driven by the line-of-sight geomagnetic field component, with peak errors occurring at high latitudes. Over the tested range of ionospheric activity levels, the peak bias is approximately constant with VTEC but increases with frequency due to seawater permittivity sensitivity, reaching 0.19 K at 0.4 GHz and 0.25 K at 2 GHz.
Water potential gradients drive water flow within and between soils and plants, and the internal plant water potential controls a wide range of physiological processes including photosynthesis, growth, and mortality. Notwithstanding this clear relevance for many critical aspects of ecosystem function, water potential data have historically been relatively inaccessible and unnetworked. The absence of a centralized repository for plant water potential time series limits our ability to integrate a wealth of ecophysiological information from other networks and from remote sensing. Closing this gap is necessary to address unresolved questions about plant responses to drought and heat stress, and to make confident predictions about plant and ecosystem function in a warming world. Here, we introduce the PSInet database -- a global collection of plant water potential time series from 285 datasets representing 523 species. We present the workflow that guided database development and evaluate its key features. Through a series of preliminary analyses, we then highlight the potential of the PSInet database for applications including: a) advancing plant water use strategy frameworks; b) disentangling the impacts of soil versus atmospheric drought stress; c) assessing the long-held assumption of pre-dawn equilibration of ecosystem water potential; d) understanding the risk of drought-driven mortality; and e) benchmarking remote-sensing data products and land-surface models.
Cerebral aneurysms are relatively common vascular abnormalities with potentially severe consequences. Although most remain unruptured, a small number do rupture, sometimes leading to subarachnoid hemorrhage. Reliably identifying which aneurysms pose the greatest risk remains an unresolved challenge in clinical practice. In this work, we show that combining computational fluid dynamics (CFD) with statistical testing and basic classification models can help identify features linked to aneurysm rupture and assess rupture risk. Using simulations of 103 aneurysms, we extracted a wide set of hemodynamic metrics, combined with other available geometric, anatomical, and patient-specific information. Using PERMANOVA and simple classification models, we assessed the ability of different datasets to differentiate between ruptured and unruptured cases. Results show that monovariate analyses do not perform especially well, with a prediction accuracy of 65% at best, and that combining features from different categories improves rupture prediction significantly. More specifically, we showed that datasets using non-CFD data reached up to 67% accuracy, while adding flow-based features increased performance to nearly 79%. Some hemodynamic variables also showed stronger statistical relevancy during specific phases of the cardiac cycle, suggesting that transient flow effects may be key in uncovering rupture mechanisms. Overall, our findings demonstrate that combining hemodynamics with statistical techniques enables a robust assessment of rupture risk and offers insights into the underlying mechanisms of rupture. Given rapid advances in image-based hemodynamic analysis using CFD in clinical settings, we anticipate that our approach could be translated into clinical practice to support more informed and individualized rupture risk assessment.
