Search for: All records

Crop production is among the most extensive human activities on the planet – with critical importance for global food security, land use, environmental burden, and climate. Yet despite the key role that croplands play in global land use and Earth systems, there remains little understanding of how spatial patterns of global crop cultivation have recently evolved and which crops have contributed most to these changes. Here we construct a new data library of subnational crop-specific irrigated and rainfed harvested area statistics and combine it with global gridded land cover products to develop a global gridded (5-arcminute) irrigated and rainfed cropped area (MIRCA-OS) dataset for the years 2000 to 2015 for 23 crop classes. These global data products support critical insights into the spatially detailed patterns of irrigated and rainfed cropland change since the start of the century and provide an improved foundation for a wide array of global assessments spanning agriculture, water resource management, land use change, climate impact, and sustainable development. 

The advent of artificial intelligence and machine learning has led to significant technological and scientific progress, but also to new challenges. Partial differential equations, usually used to model systems in the sciences, have shown to be useful tools in a variety of tasks in the data sciences, be it just as physical models to describe physical data, as more general models to replace or construct artificial neural networks, or as analytical tools to analyse stochastic processes appearing in the training of machine-learning models. This article acts as an introduction of a theme issue covering synergies and intersections of partial differential equations and data science. We briefly review some aspects of these synergies and intersections in this article and end with an editorial foreword to the issue. This article is part of the theme issue ‘Partial differential equations in data science’. 

Increasing evidence strongly links neuroinflammation to Alzheimer’s disease (AD) pathogenesis. Peripheral monocytes are crucial components of the human immune system, but their contribution to AD pathogenesis is still largely understudied partially due to limited human models. Here, we introduce human cortical organoid microphysiological systems (hCO-MPSs) to study AD monocyte-mediated neuroinflammation. By culturing doughnut-shape organoids on 3D-printed devices within standard 96-well plates, we generate hCO-MPSs with reduced necrosis, minimized hypoxia, and improved viability. Using these models, we found that monocytes from AD patients exhibit increased infiltration ability, decreased amyloid-β clearance capacity, and stronger inflammatory response than monocytes from age-matched control donors. Moreover, we observed that AD monocytes induce pro-inflammatory effects such as elevated astrocyte activation and neuronal apoptosis. Furthermore, the marked increase in IL1B and CCL3 expression underscores their pivotal role in AD monocyte-mediated neuroinflammation. Our findings provide insight into understanding monocytes’ role in AD pathogenesis, and our lab-compatible MPS models may offer a promising way for studying various neuroinflammatory diseases. 

Abstract Converting CO₂into industrially useful products is an appealing strategy for utilization of an abundant chemical resource. Electrochemical CO₂reduction (eCO₂R) offers a pathway to convert CO₂into CO and ethylene, using renewable electricity. These products can be efficiently copolymerized by organometallic catalysts to generate polyketones. However, the conditions for these reactions are very different, presenting the challenge of coupling microenvironments typically encountered for the transformation of CO₂into highly complex but desirable multicarbon products. Herein, we present a system to produce polyketone plastics entirely derived from CO₂and water, where both the CO and C₂H₄intermediates are produced by eCO₂R. In this system, a combination of Cu and Ag gas diffusion electrodes is used to generate a gas mixture with nearly equal concentrations of CO and C₂H₄, and a recirculatory CO₂reduction loop is used to reach concentrations of above 11% each, leading to a current‐to‐polymer efficiency of up to 51% and CO₂utilization of 14%. 

Bayesian boundary condition (BC) calibration approaches from clinical measurements have successfully quantified inherent uncertainties in cardiovascular fluid dynamics simulations. However, estimating the posterior distribution for all BC parameters in three-dimensional (3D) simulations has been unattainable due to infeasible computational demand. We propose an efficient method to identify Windkessel parameter posteriors: We only evaluate the 3D model once for an initial choice of BCs and use the result to create a highly accurate zero-dimensional (0D) surrogate. We then perform Sequential Monte Carlo (SMC) using the optimized 0D model to derive the high-dimensional Windkessel BC posterior distribution. Optimizing 0D models to match 3D dataa priorilowered their median approximation error by nearly one order of magnitude in 72 publicly available vascular models. The optimized 0D models generalized well to a wide range of BCs. Using SMC, we evaluated the high-dimensional Windkessel parameter posterior for different measured signal-to-noise ratios in a vascular model, which we validated against a 3D posterior. The minimal computational demand of our method using a single 3D simulation, combined with the open-source nature of all software and data used in this work, will increase access and efficiency of Bayesian Windkessel calibration in cardiovascular fluid dynamics simulations. This article is part of the theme issue ‘Uncertainty quantification for healthcare and biological systems (Part 1)’. 

Abstract We study the problem of stability of the catenoid, which is an asymptotically flat rotationally symmetric minimal surface in Euclidean space, viewed as a stationary solution to the hyperbolic vanishing mean curvature equation in Minkowski space. The latter is a quasilinear wave equation that constitutes the hyperbolic counterpart of the minimal surface equation in Euclidean space. Our main result is the nonlinear asymptotic stability, modulo suitable translation and boost (i.e., modulation), of the$$n$$ $n$-dimensional catenoid with respect to a codimension one set of initial data perturbations without any symmetry assumptions, for$$n \geq 5$$ $n\ge 5$. The modulation and the codimension one restriction on the data are necessary and optimal in view of the kernel and the unique simple eigenvalue, respectively, of the stability operator of the catenoid. In a broader context, this paper fits in the long tradition of studies of soliton stability problems. From this viewpoint, our aim here is to tackle some new issues that arise due to the quasilinear nature of the underlying hyperbolic equation. Ideas introduced in this paper include a new profile construction and modulation analysis to track the evolution of the translation and boost parameters of the stationary solution, a new scheme for proving integrated local energy decay for the perturbation in the quasilinear and modulation-theoretic context, and an adaptation of the vectorfield method in the presence of dynamic translations and boosts of the stationary solution. 

Technology for outdoor recreation, like the hiking app AllTrails, can improve access and safety for hikers. However, these tools can also negatively impact hikers on the trail, for example, by distracting them from experiencing nature. Using the walkthrough method, we critically evaluate the hiking app AllTrails to uncover implicit values underlying the app’s design and features, using a body-inclusive lens inspired by the community group Fat Girls Hiking. We found that AllTrails subtly nudges users towards a more fitness-oriented approach to hiking. This orientation may negatively impact novice hikers and those who are already marginalized in the hiking industry and we suggest alternative designs that could promote greater inclusivity. 

Modeling of atmosphere–snow exchange provides insight into fundamental processes driving pollutant deposition. Gas properties, such as solubility and stickiness to ice, influence the role of the snowpack as a trace gas reservoir and chemical reactor.