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In this work, the degradation of mono‐ and polyfluorinated phenolic compounds was demonstrated by a series of crude plant peroxidases, including horseradish root (HRP) and six members of the Cucurbita genus. Highly active samples were identified using a library screening approach in which more than 50 crude plant samples were initially evaluated for defluorination activity toward 4‐fluorophenol. The highest concentrations were observed in the HRP, pumpkin skin (PKS), and butternut squash skin (BNS), which consistently gave the highest intrinsic rates of decomposition for all the substrates tested. Although HRP exhibited a significant decrease in activity with increased fluorination of the phenolic substrate, PKS showed only minor reductions. Furthermore, in silico studies indicated that the active site of HRP poorly accommodates the steric bulk of additional fluorines, causing the substrate to dock farther from the catalytic heme and thus slowing the catalysis rate. We propose that the PKS active site might be larger, allowing closer access to the perfluorinated substrate, and therefore maintaining higher activity compared to the HRP enzyme. However, detailed kinetic characterization studies of the peroxidases are recommended. Conclusively, the high catalytic activity of PKS and its high yield per gram of tissue make it an excellent candidate for developing environmentally friendly biocatalytic methods for degrading fluorinated aromatics. Finally, the success of the library approach in identifying highly active samples for polyfluorinated aromatic compound (PFAC) degradation suggests the method may find utility in the quest for other advanced catalysts for PFAS degradation. [ABSTRACT FROM AUTHOR]

Global climate change will probably exacerbate crop losses from insect pests, reducing agricultural production, and threatening food security. To predict where crop losses will occur, scientists have mainly used correlative models of species' distributions, but such models are unreliable when extrapolated to future environments. To minimize extrapolation, we developed mechanistic and hybrid models that explicitly capture range‐limiting processes, and we explored how incorporating mechanisms altered the projected impacts of climate change for an agricultural pest, the South American locust (Schistocerca cancellata). Because locusts are generalist herbivores surrounded by food, their population growth may be limited by thermal effects on digestion more than food availability. To incorporate this mechanism into a distribution model, we measured the thermal effects on the consumption and defecation of field‐captured locusts and used these data to model energy gain in current and future climates. We then created hybrid models by using outputs of the mechanistic model as predictor variables in correlative models, estimating the potential distribution of gregarious outbreaking locusts based on multiple predictor sets, modeling algorithms, and climate scenarios. Based on the mechanistic model, locusts can assimilate relatively high amounts of energy throughout temperate and tropical South America; however, correlative and hybrid modeling revealed that most tropical areas are unsuitable for locusts. When estimating current distributions, the top‐ranked model was always the one fit with mechanistic predictors (i.e., the hybrid model). When projected to future climates, top‐ranked hybrid models projected range expansions that were 23%–30% points smaller than those projected by correlative models. Therefore, a combination of the correlative and mechanistic approaches bracketed the potential outcomes of climate change and enhanced confidence where model projections agreed. Because all models projected a poleward range expansion under climate change, agriculturists should consider enhanced monitoring and the management of locusts near the southern margin of the range. [ABSTRACT FROM AUTHOR]

Due to the aging of structures, the issues of plant life management and license extension are receiving increasing emphasis in many countries. Understanding failure of structures due to random roughness on surfaces at early stages of degradation is therefore crucial. It has been shown that even slightly sinusoidal roughness can increase stress concentration by a factor of two or three, which can be critical for a brittle component due to the significant reduction of its load-carrying capacity, even with slight roughness. A more in-depth fracture analysis of surfaces possessing random roughness is needed in order to more profoundly understand, and, hence, develop models that will predict more accurately, failure of structural materials exposed to degrading, in-service conditions. Using a technique previously developed and successfully applied, replicates of random rough surfaces, imprinted with various levels of degradation, and at three distinct auto correlation lengths, were realized and mechanical testing was performed on them. The stress, strain, and energy at fracture are reported. Finite element analysis was carried out to elucidate experimental results. Besides the expected reduction of energy at fracture with degradation, a relaxation region was observed where the energy slightly increases. This phenomenon implies that even after degradation has progressed there is a local maximum of energy at fracture due to the competing effect of tendons and growth of pits. The results find applications on the early stage of maintenance of surfaces of structures in service. [ABSTRACT FROM AUTHOR]