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Polarized resonant soft X‐ray scattering (P‐RSoXS) has emerged as a powerful synchrotron‐based tool that combines the principles of X‐ray scattering and X‐ray spectroscopy. P‐RSoXS provides unique sensitivity to molecular orientation and chemical heterogeneity in soft materials such as polymers and biomaterials. Quantitative extraction of orientation information from P‐RSoXS pattern data is challenging, however, because the scattering processes originate from sample properties that must be represented as energy‐dependent three‐dimensional tensors with heterogeneities at nanometre to sub‐nanometre length scales. This challenge is overcome here by developing an open‐source virtual instrument that uses graphical processing units (GPUs) to simulate P‐RSoXS patterns from real‐space material representations with nanoscale resolution. This computational framework – called CyRSoXS (https://github.com/usnistgov/cyrsoxs) – is designed to maximize GPU performance, including algorithms that minimize both communication and memory footprints. The accuracy and robustness of the approach are demonstrated by validating against an extensive set of test cases, which include both analytical solutions and numerical comparisons, demonstrating an acceleration of over three orders of magnitude relative to the current state‐of‐the‐art P‐RSoXS simulation software. Such fast simulations open up a variety of applications that were previously computationally unfeasible, including pattern fitting, co‐simulation with the physical instrument for operando analytics, data exploration and decision support, data creation and integration into machine learning workflows, and utilization in multi‐modal data assimilation approaches. Finally, the complexity of the computational framework is abstracted away from the end user by exposing CyRSoXS to Python using Pybind. This eliminates input/output requirements for large‐scale parameter exploration and inverse design, and democratizes usage by enabling seamless integration with a Python ecosystem (https://github.com/usnistgov/nrss) that can include parametric morphology generation, simulation result reduction, comparison with experiment and data fitting approaches. [ABSTRACT FROM AUTHOR]

Estimating a patient‐specific computational model's parameters relies on data that is often unreliable and ill‐suited for a deterministic approach. We develop an optimization‐based uncertainty quantification framework for probabilistic model tuning that discovers model inputs distributions that generate target output distributions. Probabilistic sampling is performed using a surrogate model for computational efficiency, and a general distribution parameterization is used to describe each input. The approach is tested on seven patient‐specific modeling examples using CircAdapt, a cardiovascular circulatory model. Six examples are synthetic, aiming to match the output distributions generated using known reference input data distributions, while the seventh example uses real‐world patient data for the output distributions. Our results demonstrate the accurate reproduction of the target output distributions, with a correct recreation of the reference inputs for the six synthetic examples. Our proposed approach is suitable for determining the parameter distributions of patient‐specific models with uncertain data and can be used to gain insights into the sensitivity of the model parameters to the measured data. [ABSTRACT FROM AUTHOR]

Premise: Maize yields have significantly increased over the past half‐century owing to advances in breeding and agronomic practices. Plants have been grown in increasingly higher densities due to changes in plant architecture resulting in plants with more upright leaves, which allows more efficient light interception for photosynthesis. Natural variation for leaf angle has been identified in maize and sorghum using multiple mapping populations. However, conventional phenotyping techniques for leaf angle are low throughput and labor intensive, and therefore hinder a mechanistic understanding of how the leaf angle of individual leaves changes over time in response to the environment. Methods: High‐throughput time series image data from water‐deprived maize (Zea mays subsp. mays) and sorghum (Sorghum bicolor) were obtained using battery‐powered time‐lapse cameras. A MATLAB‐based image processing framework, Leaf Angle eXtractor (LAX), was developed to extract and quantify leaf angles from images of maize and sorghum plants under drought conditions. Results: Leaf angle measurements showed differences in leaf responses to drought in maize and sorghum. Tracking leaf angle changes at intervals as short as one minute enabled distinguishing leaves that showed signs of wilting under water deprivation from other leaves on the same plant that did not show wilting during the same time period. Discussion: Automating leaf angle measurements using LAX makes it feasible to perform large‐scale experiments to evaluate, understand, and exploit the spatial and temporal variations in plant response to water limitations. [ABSTRACT FROM AUTHOR]

Premise: Trichomes are hair‐like appendages extending from the plant epidermis. They serve many important biotic roles, including interference with herbivore movement. Characterizing the number, density, and distribution of trichomes can provide valuable insights on plant response to insect infestation and define the extent of plant defense capability. Automated trichome counting would speed up this research but poses several challenges, primarily because of the variability in coloration and the high occlusion of the trichomes. Methods and Results: We developed a simplified method for image processing for automated and semi‐automated trichome counting. We illustrate this process using 30 leaves from 10 genotypes of soybean (Glycine max) differing in trichome abundance. We explored various heuristic image‐processing methods including thresholding and graph‐based algorithms to facilitate trichome counting. Of the two automated and two semi‐automated methods for trichome counting tested and with the help of regression analysis, the semi‐automated manually annotated trichome intersection curve method performed best, with an accuracy of close to 90% compared with the manually counted data. Conclusions: We address trichome counting challenges including occlusion by combining image processing with human intervention to propose a semi‐automated method for trichome quantification. This provides new opportunities for the rapid and automated identification and quantification of trichomes, which has applications in a wide variety of disciplines. [ABSTRACT FROM AUTHOR]

Recent advances in efficiency of organic photovoltaics are driven by judicious selection of processing conditions that result in a 'desired' morphology. An important theme of morphology research is quantifying the effect of processing conditions on morphology and relating it to device efficiency. State-of-the-art morphology quantification methods provide film-averaged or 2D-projected features that only indirectly correlate with performance, making causal reasoning nontrivial. Accessing the 3D distribution of material, however, provides a means of directly mapping processing to performance. In this paper, two recently developed techniques are integrated-reconstruction of 3D morphology and subsequent conversion into intuitive morphology descriptors -to comprehensively image and quantify morphology. These techniques are applied on films generated by doctor blading and spin coating, additionally investigating the effect of thermal annealing. It is found that morphology of all samples exhibits very high connectivity to electrodes. Not surprisingly, thermal annealing consistently increases the average domain size in the samples, aiding exciton generation. Furthermore, annealing also improves the balance of interfaces, enhancing exciton dissociation. A comparison of morphology descriptors impacting each stage of photophysics (exciton generation, dissociation, and charge transport) reveals that spin-annealed sample exhibits superior morphology-based performance indicators. This suggests substantial room for improvement of blade-based methods (process optimization) for morphology tuning to enhance performance of large area devices. [ABSTRACT FROM AUTHOR]

Blending of small-molecule organic semiconductors (OSCs) with amorphous polymers is known to yield high performance organic thin film transistors (OTFTs). Vertical stratification of the OSC and polymer binder into welldefined layers is crucial in such systems and their vertical order determines whether the coating is compatible with a top and/or a bottom gate OTFT configuration. Here, we investigate the formation of blends prepared via spin-coating in conditions which yield bilayer and trilayer stratifications. We use a combination of in situ experimental and computational tools to study the competing effects of formulation thermodynamics and process kinetics in mediating the final vertical stratification. It is shown that trilayer stratification (OSC/polymer/OSC) is the thermodynamically favored configuration and that formation of the buried OSC layer can be kinetically inhibited in certain conditions of spin-coating, resulting in a bilayer stack instead. The analysis reveals here that preferential loss of the OSC, combined with early aggregation of the polymer phase due to rapid drying, inhibit the formation of the buried OSC layer. The fluid dynamics and drying kinetics are then moderated during spin-coating to promote trilayer stratification with a high quality buried OSC layer which yields unusually high mobility >2 cm² V-1 s-1 in the bottom-gate top-contact configuration. [ABSTRACT FROM AUTHOR]

Spatial adjustments are used to improve the estimate of plot seed yield across crops and geographies. Moving means (MM) and P‐Spline are examples of spatial adjustment methods used in plant breeding trials to deal with field heterogeneity. Within the trial, spatial variability primarily comes from soil feature gradients, such as nutrients, but a study of the importance of various soil factors including nutrients is lacking. We analyzed plant breeding progeny row (PR) and preliminary yield trial (PYT) data of a public soybean breeding program across 3 years consisting of 43,545 plots. We compared several spatial adjustment methods: unadjusted (as a control), MM adjustment, P‐spline adjustment, and a machine learning‐based method called XGBoost. XGBoost modeled soil features at: (a) the local field scale for each generation and per year, and (b) all inclusive field scale spanning all generations and years. We report the usefulness of spatial adjustments at both PR and PYT stages of field testing and additionally provide ways to utilize interpretability insights of soil features in spatial adjustments. Our work shows that using soil features for spatial adjustments increased the relative efficiency by 81%, reduced the similarity of selection by 30%, and reduced the Moran's I from 0.13 to 0.01 on average across all experiments. These results empower breeders to further refine selection criteria to make more accurate selections and select for macro‐ and micro‐nutrients stress tolerance. [ABSTRACT FROM AUTHOR]

Given the changing climate and increasing impact of agriculture on global resources, it is important to identify phenotypes which are global and sustainable optima. Here, an in silico framework is constructed by coupling evolutionary optimization with thermodynamically sound crop physiology, and its ability to rationally design phenotypes with maximum productivity is demonstrated, within well‐defined limits on water availability.  Results reveal that in mesic environments, such as the North American Midwest, and semi‐arid environments, such as Colorado, phenotypes optimized for maximum productivity and survival under drought are similar to those with maximum productivity under irrigated conditions. In hot and dry environments like California, phenotypes adapted to drought produce 40% lower yields when irrigated compared to those optimized for irrigation. In all three representative environments, the trade‐off between productivity under drought versus that under irrigation was shallow, justifying a successful strategy of breeding crops combining best productivity under irrigation and close to best productivity under drought. [ABSTRACT FROM AUTHOR]