Search for: All records

As species move into new environments through founder events, their phenotypes may diverge from native populations. Identifying the drivers underlying such variation and the constraints on the adaptive potential of this variation is essential for understanding how organisms respond to new or rapidly changing habitats. Such phenotypic divergence may be especially evident in populations introduced to new environments via human-assisted transport or in dramatically altered environments such as cities. Sexually dimorphic species beg the additional questions of how these new environments may influence the sexes differently and how dimorphism may shape the range of potential responses. The repeated translocation, establishment, and spread of wall lizards (Podarcis spp.) from native European populations to new locations in North America provide an excellent natural experiment to explore how phenotypes may differ after establishment in a new environment. Here, we quantify body shape and the multivariate morphological phenotype (incorporating limb dimensions and head length) of common wall lizards (P. muralis) and Italian wall lizards (P. siculus) in replicated North American introductions. In both species, males are larger and have larger head length and limb dimensions than females across all sampled groups. Sexual dimorphism in the multivariate morphological phenotype was of similar magnitude when comparing native and introduced populations for both species, though the trajectory angles in multivariate trait space differed in P. siculus. When comparing introduced lizards from contemporary and historically collected museum specimens, we identified differences of similar magnitude but in different trajectories between sexes in P. siculus, and differences in both magnitude and direction of sexual dimorphism in P. muralis. These idiosyncratic patterns in phenotypic trajectories provide insight to the potential array of processes generating phenotypic variation within species at the intersection of invasion biology and urban evolution. 

Human operators of remote and semi-autonomous systems must have a high level of executive function to safely and efficiently conduct operations. These operators face unique cognitive challenges when monitoring and controlling robotic machines, such as vehicles, drones, and construction equipment. The development of safe and experienced human operators of remote machines requires structured training and credentialing programs. This review critically evaluates the potential for incorporating neurotechnology into remote systems operator training and work to enhance human-machine interactions, performance, and safety. Recent evidence demonstrating that different noninvasive neuromodulation and neurofeedback methods can improve critical executive functions such as attention, learning, memory, and cognitive control is reviewed. We further describe how these approaches can be used to improve training outcomes, as well as teleoperator vigilance and decision-making. We also describe how neuromodulation can help remote operators during complex or high-risk tasks by mitigating impulsive decision-making and cognitive errors. While our review advocates for incorporating neurotechnology into remote operator training programs, continued research is required to evaluate the how these approaches will impact industrial safety and workforce readiness. 

Technology for outdoor recreation, like the hiking app AllTrails, can improve access and safety for hikers. However, these tools can also negatively impact hikers on the trail, for example, by distracting them from experiencing nature. Using the walkthrough method, we critically evaluate the hiking app AllTrails to uncover implicit values underlying the app’s design and features, using a body-inclusive lens inspired by the community group Fat Girls Hiking. We found that AllTrails subtly nudges users towards a more fitness-oriented approach to hiking. This orientation may negatively impact novice hikers and those who are already marginalized in the hiking industry and we suggest alternative designs that could promote greater inclusivity. 

Breathing in fine particulate matter of diameter less than 2.5 µm (PM2.5) greatly increases an individual’s risk of cardiovascular and respiratory diseases. As climate change progresses, extreme weather events, including wildfires, are expected to increase, exacerbating air pollution. However, models often struggle to capture extreme pollution events due to the rarity of high PM2.5 levels in training datasets. To address this, we implemented cluster-based undersampling and trained Transformer models to improve extreme event prediction using various cutoff thresholds (12.1 µg/m3 and 35.5 µg/m3) and partial sampling ratios (10/90, 20/80, 30/70, 40/60, 50/50). Our results demonstrate that the 35.5 µg/m3 threshold, paired with a 20/80 partial sampling ratio, achieved the best performance, with an RMSE of 2.080, MAE of 1.386, and R2 of 0.914, particularly excelling in forecasting high PM2.5 events. Overall, models trained on augmented data significantly outperformed those trained on original data, highlighting the importance of resampling techniques in improving air quality forecasting accuracy, especially for high-pollution scenarios. These findings provide critical insights into optimizing air quality forecasting models, enabling more reliable predictions of extreme pollution events. By advancing the ability to forecast high PM2.5 levels, this study contributes to the development of more informed public health and environmental policies to mitigate the impacts of air pollution, and advanced the technology for building better air quality digital twins. 

Abstract Fungal plasma membrane proteins represent key therapeutic targets for antifungal agents, yet their native structure and spatial distribution remain poorly characterized. Herein, we employ an integrative approach to investigate the organization of plasma membrane protein complexes inCandida glabrata, focusing on two abundant and essential membrane proteins, the β-(1,3)-glucan synthase (GS) and the proton pump Pma1. We show that treatment with caspofungin, an echinocandin antifungal that targets GS, disrupts the native distribution of membrane protein complexes and alters membrane biophysical properties. Perturbation of the sphingolipid biosynthesis further modulates drug susceptibility, revealing that the lipid environment plays an integral role in membrane protein organization and GS-echinocandin interactions. Our work highlights the importance of characterizing membrane proteins in their native context to understand their functions and inform the development of novel antifungal therapies.