The Added Value of Large-Eddy and Storm-Resolving Models for Simulating Clouds and Precipitation

More than one hundred days were simulated over very large domains with fine (0.156 km to 2.5 km) grid spacing for realistic conditions to test the hypothesis that storm (kilometer) and large-eddy (hectometer) resolving simulations would provide an improved representation of clouds and precipitation in atmospheric simulations. At scales that resolve convective storms (storm-resolving for short), the vertical velocity variance becomes resolved and a better physical basis is achieved for representing clouds and precipitation. Similarly to past studies we found an improved representation of precipitation at kilometer scales, as compared to models with parameterized convection. The main precipitation features (location, diurnal cycle and spatial propagation) are well captured already at kilometer scales, and refining resolution to hectometer scales does not substantially change the simulations in these respects. It does, however, lead to a reduction in the precipitation on the time-scales considered – most notably over the ocean in the tropics. Changes in the distribution of precipitation, with less frequent extremes are also found in simulations incorporating hectometer scales. Hectometer scales appear to be more important for the representation of clouds, and make it possible to capture many important aspects of the cloud field, from the vertical distribution of cloud cover, to the distribution of cloud sizes, and to the diel (daily) cycle. Qualitative improvements, particularly in the ability to differentiate cumulus from stratiform clouds, are seen when one reduces the grid spacing from kilometer to hectometer scales. At the hectometer scale new challenges arise, but the similarity of observed and simulated scales, and the more direct connection between the circulation and the unconstrained degrees of freedom make these challenges less daunting. This quality, combined with already improved simulation as compared to more parameterized models, underpins our conviction that the use and further development of storm-resolving models offers exciting opportunities for advancing understanding of climate and climate change

Histograms and brain surface plots for experimental ADC [10⁻⁶ mm/s²] values (left) and modelled maturation scores (right, four key RWSs) in ROI groups.

<p>Top: ROIs; bottom: incident connections. Highest Pearson’s correlations are indicated with a star for the corresponding RWS (and colour).</p

Connection properties.

<p>A: Weighted groupwise connectivity matrix including 450 connections present for > = 50% of the subjects. Color coding indicates the number of streamlines. ROIs are ordered per hemisphere (right: top, left: bottom) according to the atlas by Shi et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177466#pone.0177466.ref043" target="_blank">43</a>]. Fro: frontal; Li: limbic; Oc: occipital; Pa: parietal; Te: temporal cortex and G: basal ganglia. B: Length distribution of the 450 connections. C: Connection repartition between the different groups. <i>SUB</i> in black, <i>PRIM</i> in dark gray, <i>SEC</i> in gray, <i>TER</i> in light gray.</p

Modelled maturation scores projected on a standard brain surface for four representative simulated time points (RWSs): ROI in blue (top), connections in red (bottom).

<p>Box: Experimental data (ADC [10⁻⁶ mm/s²]) on brain surface and Pearson’s correlations with modelled maturation scores for RWS 1–10. The RWS with the best correlation is represented by a star.</p

ROIs and incident connections.

<p>A: Distribution of ADC [10⁻⁶ mm/s²] (top) and T1 [ms] (bottom) values for each ROI group and the incident connections. B: Scatterplot for ADC (top) and T1 (bottom) in ROIs and their incident connections, for all ROIs. Pearson’s correlation: ADC <i>r</i> = 0.67, p < 10⁻¹⁰; T1 <i>r</i> = 0.84, p < 10⁻¹⁰. Groups: <i>SUB</i> (n = 12) in black (diamonds), <i>PRIM</i> (n = 12) in dark gray (circles), <i>SEC</i> (n = 34) in gray (stars), <i>TER</i> (n = 32) in light gray (squares). C: Correlation between mean ADC [10⁻⁶ mm/s²] (top) and T1 [ms] (bottom) of GM ROI pairs and average ADC value along the connecting WM tracts (ADC: Pearson <i>r</i> = 0.54, p < 10⁻¹⁰, T1: Pearson <i>r</i> = 0.77, p < 10⁻¹⁰; n = 450).</p

Macroporous biohybrid cryogels for co-housing pancreatic islets with mesenchymal stromal cells

Intrahepatic transplantation of allogeneic pancreatic islets offers a promising therapy for type 1 diabetes. However, long-term insulin independency is often not achieved due to severe islet loss shortly after transplantation. To improve islet survival and function, extrahepatic biomaterial-assisted transplantation of pancreatic islets to alternative sites has been suggested. Herein, we present macroporous, star-shaped poly(ethylene glycol) (starPEG)-heparin cryogel scaffolds, covalently modified with adhesion peptides, for the housing of pancreatic islets in three-dimensional (3D) co-culture with adherent mesenchymal stromal cells (MSC) as accessory cells. The implantable biohybrid scaffolds provide efficient transport properties, mechanical protection, and a supportive extracellular environment as a desirable niche for the islets. MSC colonized the cryogel scaffolds and produced extracellular matrix proteins that are important components of the natural islet microenvironment known to facilitate matrix-cell interactions and to prevent cellular stress. Islets survived the seeding procedure into the cryogel scaffolds and secreted insulin after glucose stimulation in vitro. In a rodent model, intact islets and MSC could be visualized within the scaffolds seven days after subcutaneous transplantation. Overall, this demonstrates the potential of customized macroporous starPEG-heparin cryogel scaffolds in combination with MSC to serve as a multifunctional islet supportive carrier for transplantation applications. Statement of Significance Diabetes results in the insufficient production of insulin by the pancreatic β-cells in the islets of Langerhans. Transplantation of pancreatic islets offers valuable options for treating the disease; however, many transplanted islets often do not survive the transplantation or die shortly thereafter. Co-transplanted, supporting cells and biomaterials can be instrumental for improving islet survival, function and protection from the immune system. In the present study, islet supportive hydrogel sponges were explored for the co-transplantation of islets and mesenchymal stromal cells. Survival and continued function of the supported islets were demonstrated in vitro. The in vivo feasibility of the approach was shown by transplantation in a mouse model

The role of ni in increasing the reversibility of the hydrogen release from nanoconfined libh(4)

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