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Abstract Morphological factors significantly impact maize stalk strength, but no study has fully characterized the impact of maize stalk shape on stalk strength. This study uses a data-driven and machine-learning modeling approach to characterize these relationships through a comprehensive sensitivity analysis of model inputs. Using 3D parameterized maize stalk models gave a higher level of control than previous studies by adding more parameters, but with the increase of the dimensionality. The large dimensionality of the models was greatly reduced via principal component analysis (PCA). Analysis revealed that model flexural stiffness, failure strength, and biomass were primarily determined by the first principal component. Material sensitivity analysis was also conducted on the models, and its results were consistent with past studies. The results of this study improve researchers’ understanding of the parameters that influence 3D parameterized maize stalk models. Statistical analysis indicates a strong relationship between the first principal component and section modulus, which further validates the 3D parameterized maize stalk model. Results also show that maize stalk morphology is primarily controlled by only one factor: the first principal component. This may limit researchers’ ability to increase stalk strength without increasing mass. 

Maize is the most grown feed crop in the United States. Due to wind storms and other factors, 5% of maize falls over annually. The longitudinal shear modulus of maize stalk tissues is currently unreported and may have a signifi- cant influence on stalk failure. To better understand the causes of this phenomenon, maize stalk material properties need to be measured so that they can be used as material constants in computational models that provide detailed analysis of maize stalk failure. This study reports longitudinal shear modulus of maize stalk tissue through repeated tor- sion testing of dry and fully mature maize stalks. Measurements were focused on the two tissues found in maize stalks: the hard outer rind and the soft inner pith. Uncertainty analysis and comparison of multiple methodologies indicated that all measurements are subject to low error and bias. The results of this study will allow researchers to better under- stand maize stalk failure modes through computational modeling. This will allow researchers to prevent annual maize loss through later studies. This study also provides a methodology that could be used or adapted in the measurement of tissues from other plants such as sorghum, sugarcane, etc 

Background Modern computational modeling could provide the key to obtaining new insights into the mechanisms of maize stalk failure as well as suggesting new ways to improve stalk strength. However, a complete set of mechanical properties of maize tissues is required to enable computational modeling of maize stems. This study developed two compression test methods for obtaining the longitudinal modulus of elasticity of both rind and pith tissues, assessed the influence of water content on tissue properties, and investigated the relationship between rind modulus and pith modulus. These methods involved uniform 5–7 cm segments of maize stems which were scanned using a flatbed scanner then tested in compression using a universal testing machine in both intact and dissected (rind-only and pith-only) states. Results The modulus of elasticity of pith tissues was highest for fully turgid specimens and decreased as water was removed from the specimens. Water content was negatively correlated with the modulus of elasticity of the rind. Rind and pith tissues were found to be weakly correlated. The median ratio of rind modulus to pith modulus was found to be 17. Of the two methods investigated, the pith-only specimen preparation was found to be simple reliable while the rind-only method was found to be adversely affected by lateral bowing of the specimen. Conclusions Researchers can use the information in this paper to improve computational models of maize stems in three ways: (1) by incorporating realistic values of the longitudinal modulus of elasticity of pith and rind tissues; (2) by selecting pith and rind properties that match empirically observed ratios; and (3) by incorporating appropriate dependencies between these material properties and water content. From an experimental perspective, the intact/pith-only experimental method outlined in this paper is simpler than previously reported methods and provides reliable estimates of both pith and rind modulus of elasticity values. Further research using this measurement method is recommended to more clearly understand the influence of water content and turgor pressure on tissue properties. 

Maize stalk lodging is the structural failure of the stalk prior to harvest and is a major problem for maize (corn) producers and plant breeders. To address this problem, it is critical to understand precisely how geometric and material parameters of the maize stalk influence stalk strength. Computational models could be a powerful tool in such investigations, but current methods of creating computational models are costly, time-consuming and, most importantly, do not provide parameterized control of the maize stalk parameters. The purpose of this study was to develop and validate a parameterized 3D model of the maize stalk. The parameterized model provides independent control over all aspects of the maize stalk geometry and material properties. The model accurately captures the shape of actual maize stalks and is predictive of maize stalk stiffness and strength. The model was validated using stochastic sampling of material properties to account for uncertainty in the values and influence of mechanical tissue properties. Results indicated that buckling is influenced by material properties to a greater extent that flexural stiffness. Finally, we demonstrate that this model can be used to create an unlimited number of synthetic stalks from within the parameter space. This model will enable the future implementation of parameter sweep studies, sensitivity analysis and optimization studies, and can be used to create computational models of maize stalks with any desired combination of geometric and material properties. 

Synopsis Reduction or complete loss of traits is a common occurrence throughout evolutionary history. In spite of this, numerous questions remain about why and how trait loss has occurred. Cave animals are an excellent system in which these questions can be answered, as multiple traits, including eyes and pigmentation, have been repeatedly reduced or lost across populations of cave species. This review focuses on how the blind Mexican cavefish, Astyanax mexicanus, has been used as a model system for examining the developmental, genetic, and evolutionary mechanisms that underlie eye regression in cave animals. We focus on multiple aspects of how eye regression evolved in A. mexicanus, including the developmental and genetic pathways that contribute to eye regression, the effects of the evolution of eye regression on other traits that have also evolved in A. mexicanus, and the evolutionary forces contributing to eye regression. We also discuss what is known about the repeated evolution of eye regression, both across populations of A. mexicanus cavefish and across cave animals more generally. Finally, we offer perspectives on how cavefish can be used in the future to further elucidate mechanisms underlying trait loss using tools and resources that have recently become available. 

Abstract The semiconductor tracker (SCT) is one of the tracking systems for charged particles in the ATLAS detector. It consists of 4088 silicon strip sensor modules.During Run 2 (2015–2018) the Large Hadron Collider delivered an integrated luminosity of 156 fb -1 to the ATLAS experiment at a centre-of-mass proton-proton collision energy of 13 TeV. The instantaneous luminosity and pile-up conditions were far in excess of those assumed in the original design of the SCT detector.Due to improvements to the data acquisition system, the SCT operated stably throughout Run 2.It was available for 99.9% of the integrated luminosity and achieved a data-quality efficiency of 99.85%.Detailed studies have been made of the leakage current in SCT modules and the evolution of the full depletion voltage, which are used to study the impact of radiation damage to the modules.