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Currently, there is a lack of knowledge of how complex metal oxide nanomaterials, like LiCoO2 (LCO) nanosheets, interact with eukaryotic green algae. Previously, LCO was reported to cause a number of physiological impacts to Raphidocelis subcapitata including endpoints related to growth, reproduction, pigment & lipid biosynthesis, and carbon biomass assimilation. Furthermore, LCO was proven to physically enter the cells, thus indicating the possibility for it to directly interact with key subcellular components. However, the mechanisms through which LCO interacts with these key subcellular components is still unknown. This study assesses the interactions of LCO at the biointerface of R. subcapitata using a novel multiplexed algal cytological imaging (MACI) assay and machine learning in order to predict its phytotoxic mechanism of action (MoA). Algal cells were exposed to varying concentrations of LCO, and their phenotypic profiles were compared to that of cells treated with reference chemicals which had already established MoAs. Hierarchical clustering and machine learning analyses indicated photosynthetic electron transport to be the most probable phytotoxic MoA of LCO. Additionally, single-cell chlorophyll fluorescence results demonstrated an increase in irreversibly oxidized photosystem II proteins. Lastly, LCO-treated cells were observed to have less nuclei/cell and less DNA content/nucleus when compared to non-treated cell controls. 

Developing a materials perspective of how to control the degradation and negative impact of complex metal oxides requires an integrated understanding of how these nanomaterials transform in the environment and interact with biological systems. Doping with aluminum is known to stabilize oxide materials, but has not been assessed cohesively from synthesis to environmental fate and biological impact. In the present study, the influence of aluminum doping on metal ion release from transition metal oxides was investigated by comparing aqueous transformations of lithium nickel cobalt aluminum oxide (LiNi0.82Co0.15Al0.03O2; NCA) and lithium nickel cobalt oxide (LiNi0.80Co0.20O2; NC) nanoparticles and by calculating the energetics of metal release using a density functional theory (DFT) and thermodynamics method. Two model environmental organisms were used to assess biological impact, and metal ion release was compared for NCA and NC nanoparticles incubated in their respective growth media: moderately hard reconstituted water (MHRW) for the freshwater invertebrate Daphnia magna (D. magna) and minimal growth medium for the Gram-negative bacterium Shewanella oneidensis MR-1 (S. oneidensis). The amount of metal ions released was reduced for NCA compared to NC in MHRW, which correlated to changes in the modeled energetics of release upon Al substitution in the lattice. In minimal medium, metal ion release was approximately an order of magnitude higher compared to MHRW and was similar to the stoichiometry of the bulk nanoparticles for both NCA and NC. Interpretation of the release profiles and modeling indicated that the increase in total metal ion release and the reduced influence of Al doping arises from lactate complexation of metal ions in solution. The relative biological impacts of NC and NCA exposure for both S. oneidensis and D. magna were consistent with the metal release trends observed for minimal medium and MHRW, respectively. Together, these results demonstrate how a combined experimental and computational approach provides valuable insight into the aqueous transformations and biological impacts of complex metal oxide nanoparticles. 

This study investigated electric-scooter (e-scooter) rider behaviors and preferences to inform ways to increase safety for e-scooter riders. Data was collected from 329 e-scooter riders via two online and one in-person survey. Survey questions considered rider roadway infrastructure preferences, safety perceptions, and helmet-wearing behavior. Protected bike lanes were more commonly indicated as the safest infrastructure (62.4%) but were less likely to be the most preferred infrastructure (49.7%). Sidewalks were better matched between riders, indicating them as their preferred riding infrastructure (22.7%) and the perceived safest riding infrastructure (24.5%). Riders had low feelings of safety and preference for riding on major/neighborhood streets or on unprotected bike lanes. Riders reported significant concern about being hit by a moving vehicle, running into a pothole/rough roadway, and running into a moving vehicle. In line with the Theory of Planned Behavior, a significant relationship was found between the frequency of riding and helmet-wearing behavior, with more frequent riders being more likely to wear helmets. Findings suggest that existing roadway infrastructure may pose safety challenges and encourage rider-selected workarounds. Public policy may consider emphasizing protected bicycle lane development, rather than helmet mandates, to support e-scooter riding safety for all vulnerable road users.

 

Use of complex metal oxide nanoparticles has drastically risen in recent years, especially due to their utility in electric vehicle batteries. However, use of these materials has outpaced our understanding of how they might affect environmental organisms, which they could encounter through release during manufacture, use, and disposal. In particular, little is known about the effects of chronic exposure to complex metal oxide nanoparticles. Here, we have focused on an environmentally-relevant bacterial species, Shewanella oneidensis, which is ubiquitous in nature and responsible for bioremediation of heavy metals, and assessed the toxic effects of nanoscale lithiated nickel manganese cobalt oxide (NMC), which is an emerging battery cathode material for electronic devices. We previously reported that chronic exposure of S. oneidensis to NMC results in the emergence of an adaptive phenotype where the bacteria are able to tolerate otherwise lethal concentrations of NMC. In the present study, we aim to investigate the role of reactive oxygen species (ROS) and changes in phenotype of the NMC-adapted bacterial population. We found that NMC-exposed bacteria possess ROS-containing membrane vesicles, as well as an increased propensity to generate random DNA mutations and harbor other DNA damage. Thus, our data indicate substantial genetic-level variation in bacteria that results from chronic exposure to toxic complex metal oxide nanomaterials. 

Electric scooters (or e-scooters) are among the most popular micromobility options that have experienced an enormous expansion in urban transportation systems across the world in recent years. Along with the increased usage of e-scooters, the increasing number of e-scooter-related injuries has also become an emerging global public health concern. However, little is known regarding the risk factors for e-scooter-related crashes and injury crashes. This study consisted of a two-phase survey questionnaire administered to a cohort of e-scooter riders (n = 210), which obtained exposure information on riders’ demographics, riding behaviors (including infrastructure selection), helmet use, and other crash-related factors. The risk ratios of riders’ self-reported involvement in an e-scooter-related crash (i.e., any crash versus no crash) and injury crash (i.e., injury crash versus non-injury crash) were estimated across exposure subcategories using the Negative Binomial regression approach. Males and frequent users of e-scooters were associated with an increased risk of e-scooter-related crashes of any type. For the e-scooter-related injury crashes, more frequently riding on bike lanes (i.e., greater than 25% of the time), either protected or unprotected, was identified as a protective factor. E-scooter-related injury crashes were more likely to occur among females, who reported riding on sidewalks and non-paved surfaces more frequently. The study may help inform public policy regarding e-scooter legislation and prioritize efforts to establish suitable road infrastructure for improved e-scooter riding safety. 

George Price showed how the effects of natural selection and environmental change could be mathematically partitioned. This partitioning may be especially useful for understanding host–parasite coevolution, where each species represents the environment for the other species. Here, we use coupled Price equations to study this kind of antagonistic coevolution. We made the common assumption that parasites must genetically match their host's genotype to avoid detection by the host's self/nonself recognition system, but we allowed for the possibility that non‐matching parasites have some fitness. Our results show how natural selection on one species results in environmental change for the other species. Numerical iterations of the model show that these environmental changes can periodically exceed the changes in mean fitness due to natural selection, as suggested by R.A. Fisher. Taken together, the results give an algebraic dissection of the eco‐evolutionary feedbacks created during host–parasite coevolution.