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Sources of neurotoxic mercury in forests are dominated by atmospheric gaseous elemental mercury (GEM) deposition, but a dearth of direct GEM exchange measurements causes major uncertainties about processes that determine GEM sinks. Here we present three years of forest-level GEM deposition measurements in a coniferous forest and a deciduous forest in northeastern USA, along with flux partitioning into canopy and forest floor contributions. Annual GEM deposition is 13.4 ± 0.80 μg m⁻²(coniferous forest) and 25.1 ± 2.4 μg m⁻²(deciduous forest) dominating mercury inputs (62 and 76% of total deposition). GEM uptake dominates in daytime during active vegetation periods and correlates with CO₂assimilation, attributable to plant stomatal uptake of mercury. Non-stomatal GEM deposition occurs in the coniferous canopy during nights and to the forest floor in the deciduous forest and accounts for 24 and 39% of GEM deposition, respectively. Our study shows that GEM deposition includes various pathways and is highly ecosystem-specific, which complicates global constraints of terrestrial GEM sinks.

 

This study explores subjective and objective driving style similarity to identify how similarity can be used to develop driver-compatible vehicle automation.

Similarity in the ways that interaction partners perform tasks can be measured subjectively, through questionnaires, or objectively by characterizing each agent’s actions. Although subjective measures have advantages in prediction, objective measures are more useful when operationalizing interventions based on these measures. Showing how objective and subjective similarity are related is therefore prudent for aligning future machine performance with human preferences.

A driving simulator study was conducted with stop-and-go scenarios. Participants experienced conservative, moderate, and aggressive automated driving styles and rated the similarity between their own driving style and that of the automation. Objective similarity between the manual and automated driving speed profiles was calculated using three distance measures: dynamic time warping, Euclidean distance, and time alignment measure. Linear mixed effects models were used to examine how different components of the stopping profile and the three objective similarity measures predicted subjective similarity.

Objective similarity using Euclidean distance best predicted subjective similarity. However, this was only observed for participants’ approach to the intersection and not their departure.

Developing driving styles that drivers perceive to be similar to their own is an important step toward driver-compatible automation. In determining what constitutes similarity, it is important to (a) use measures that reflect the driver’s perception of similarity, and (b) understand what elements of the driving style govern subjective similarity.

 

Data classification is central to human factors research, and manual data classification is tedious and error prone. Supervised learning enables analysts to train an algorithm by manually classifying a few cases and then have that algorithm classify many cases. However, algorithms often fail to leverage human insight. To address this, we augment supervised learning with unsupervised learning and data visualization. Unsupervised learning highlights potential classification errors, explains the underlying classification, and identifies additional cases that merit manual classification. We illustrate this using the Occupational Information Network database to classify occupations as having tasks that might be performed in an automated vehicle. 

ABSTRACT Intestinal mucus is the first line of defense against intestinal pathogens. It acts as a physical barrier between epithelial tissues and the lumen that enteropathogens must overcome to establish a successful infection. We investigated the motile behavior of two Vibrio cholerae strains (El Tor C6706 and Classical O395) in mucus using single-cell tracking in unprocessed porcine intestinal mucus. We determined that V. cholerae can penetrate mucus using flagellar motility and that alkaline pH increases swimming speed and, consequently, improves mucus penetration. Microrheological measurements indicate that changes in pH between 6 and 8 (the physiological range for the human small intestine) had little effect on the viscoelastic properties of mucus. Finally, we determined that acidic pH promotes surface attachment by activating the mannose-sensitive hemagglutinin (MshA) pilus in V. cholerae El Tor C6706 without a measurable change in the total cellular concentration of the secondary messenger cyclic dimeric GMP (c-di-GMP). Overall, our results support the hypothesis that pH is an important factor affecting the motile behavior of V. cholerae and its ability to penetrate mucus. Therefore, changes in pH along the human small intestine may play a role in determining the preferred site for V. cholerae during infection. IMPORTANCE The diarrheal disease cholera is still a burden for populations in developing countries with poor sanitation. To develop effective vaccines and prevention strategies against Vibrio cholerae , we must understand the initial steps of infection leading to the colonization of the small intestine. To infect the host and deliver the cholera toxin, V. cholerae has to penetrate the mucus layer protecting the intestinal tissues. However, the interaction of V. cholerae with intestinal mucus has not been extensively investigated. In this report, we demonstrated using single-cell tracking that V. cholerae can penetrate intestinal mucus using flagellar motility. In addition, we observed that alkaline pH improves the ability of V. cholerae to penetrate mucus. This finding has important implications for understanding the dynamics of infection, because pH varies significantly along the small intestine, between individuals, and between species. Blocking mucus penetration by interfering with flagellar motility in V. cholerae , reinforcing the mucosa, controlling intestinal pH, or manipulating the intestinal microbiome will offer new strategies to fight cholera.