Technical Note: Intra-pane refractive index variability and adequate sample sizes in forensic glass analysis.

A vital aspect for the forensic analysis of glass using refractive index (RI) is the determination of adequate sample size, or how many glass fragments are needed to sufficiently characterize the RI from a source of glass, such as a side window of an automobile. The number of fragments required is inversely related to the variance of the RI across the source of glass. Previous literature indicates decreased variability across tempered glass panes over time of manufacture; however, the most recent work is over two decades old and may not reflect potential increased homogeneity in more modern glass. A set of 218 tempered vehicle windows was constructed and 30 different edge RI measurements were gathered from ten bulk fragments of each window. Variability analysis was conducted using a linear mixed effects model. Within-source (between-fragments) and within-fragment variances were found to be similar (approximately 4.3e-5 and 4.7e-5) and relatively lower than previous studies have reported. Simulation studies were also conducted, estimating the error rates based on the comparison conclusions of the sample set. 21 RI measurements were taken from seven randomly selected glass fragments of one window to characterize the "known" source and either nine RI measurements (three fragments, each taken from separate tempered dice) or three RI measurements (one fragment) were used to characterize the "recovered" source. The conclusion of the comparisons where nine and 21 RI measurements were used yielded a lower false exclusion rate (approximately 1.97%) as compared to three and 21 RI measurements (6.73%), while the false inclusion rate remained mostly stable regardless of recovered glass sample size (approximately 4.05%).

Using retro-reflective cloth to enhance drivers' judgment of pedestrian walking direction at night-time.

PURPOSE: Fatal pedestrian collisions are over-represented at night and poor conspicuity is believed to be a leading causative factor. Retro-reflective clothing enhances pedestrian conspicuity, particularly when placed in a biological motion or "biomotion" configuration. In this study, we explored how various retro-reflective clothing configurations affected the ability to judge the direction of a pedestrian walking across the road, which has important implications for collision avoidance. METHODS: Participants included 21 young drivers (mean age 21.6 ±â€¯2.0 years) with normal vision. A closed-road circuit was used to assess the accuracy of drivers' judgement of the direction of walking of a pedestrian at night-time wearing one of five different clothing configurations: four with retro-reflective materials placed in different locations (Biomotion, Legs + Torso, Torso Only, Legs Only), and a control wearing only black clothing (Street). Participants were seated in a stationary vehicle with low beam headlamps, 135 m from a pedestrian, who walked across the road from both sides, in different directions (towards the car, straight across the road, or away from the car). Outcome measures included drivers' response accuracy and confidence ratings for judging pedestrian walking direction. RESULTS: Accuracy in judging pedestrian walking direction differed significantly across the clothing configurations (p < 0.001). Response accuracy was significantly higher for the Biomotion configuration (80% correct), compared to the other retro-reflective (Legs + Torso 64%; Torso Only 53%; Legs Only 50%) and Street configurations (33%). Similar trends were noted for confidence ratings across the clothing conditions, yet the relationship between confidence ratings and response accuracy within each clothing configurations was poor. CONCLUSIONS: The use of retro-reflective clothing in a biomotion configuration facilitated the highest accuracy and confidence in drivers' judgment of pedestrian walking direction, compared to other configurations. These findings highlight the importance of using biomotion clothing for pedestrians at night, to not only facilitate drivers' earlier recognition of pedestrians, but also increase their accuracy in determining the walking direction of pedestrians as they cross the road. Practical applications: The use of clothing incorporating retro-reflective material in a biomotion configuration for pedestrians crossing roads at night provides enhanced cues for drivers regarding the presence and walking direction of pedestrians.

Thermodynamic Surface Analyses to Inform Biofilm Resistance.

Biofilms are the habitat of 95% of bacteria successfully protecting bacteria from many antibiotics. However, inhibiting biofilm formation is difficult in that it is a complex system involving the physical and chemical interaction of both substrate and bacteria. Focusing on the substrate surface and potential interactions with bacteria, we examined both physical and chemical properties of substrates coated with a series of phenyl acrylate monomer derivatives. Atomic force microscopy (AFM) showed smooth surfaces often approximating surgical grade steel. Induced biofilm growth of five separate bacteria on copolymer samples comprising varying concentrations of phenyl acrylate monomer derivatives evidenced differing degrees of biofilm resistance via optical microscopy. Using goniometric surface analyses, the van Oss-Chaudhury-Good equation was solved linear algebraically to determine the surface energy profile of each polymerized phenyl acrylate monomer derivative, two bacteria, and collagen. Based on the microscopy and surface energy profiles, a thermodynamic explanation for biofilm resistance is posited.

LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes.

Understanding the factors that affect water quality and the ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how water quality will respond to global changes not only requires water quality data, but also information about the ecological context of individual water bodies across broad spatial extents. Because lake water quality is usually sampled in limited geographic regions, often for limited time periods, assessing the environmental controls of water quality requires compilation of many data sets across broad regions and across time into an integrated database. LAGOS-NE accomplishes this goal for lakes in the northeastern-most 17 US states.LAGOS-NE contains data for 51 101 lakes and reservoirs larger than 4 ha in 17 lake-rich US states. The database includes 3 data modules for: lake location and physical characteristics for all lakes; ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes; and in situ measurements of lake water quality for a subset of the lakes from the past 3 decades for approximately 2600-12 000 lakes depending on the variable. The database contains approximately 150 000 measures of total phosphorus, 200 000 measures of chlorophyll, and 900 000 measures of Secchi depth. The water quality data were compiled from 87 lake water quality data sets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This database is one of the largest and most comprehensive databases of its type because it includes both in situ measurements and ecological context data. Because ecological context can be used to study a variety of other questions about lakes, streams, and wetlands, this database can also be used as the foundation for other studies of freshwaters at broad spatial and ecological scales.

Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method.

Understanding broad-scale ecological patterns and processes often involves accounting for regional-scale heterogeneity. A common way to do so is to include ecological regions in sampling schemes and empirical models. However, most existing ecological regions were developed for specific purposes, using a limited set of geospatial features and irreproducible methods. Our study purpose was to: (1) describe a method that takes advantage of recent computational advances and increased availability of regional and global data sets to create customizable and reproducible ecological regions, (2) make this algorithm available for use and modification by others studying different ecosystems, variables of interest, study extents, and macroscale ecology research questions, and (3) demonstrate the power of this approach for the research question-How well do these regions capture regional-scale variation in lake water quality? To achieve our purpose we: (1) used a spatially constrained spectral clustering algorithm that balances geospatial homogeneity and region contiguity to create ecological regions using multiple terrestrial, climatic, and freshwater geospatial data for 17 northeastern U.S. states (~1,800,000 km2); (2) identified which of the 52 geospatial features were most influential in creating the resulting 100 regions; and (3) tested the ability of these ecological regions to capture regional variation in water nutrients and clarity for ~6,000 lakes. We found that: (1) a combination of terrestrial, climatic, and freshwater geospatial features influenced region creation, suggesting that the oft-ignored freshwater landscape provides novel information on landscape variability not captured by traditionally used climate and terrestrial metrics; and (2) the delineated regions captured macroscale heterogeneity in ecosystem properties not included in region delineation-approximately 40% of the variation in total phosphorus and water clarity among lakes was at the regional scale. Our results demonstrate the usefulness of this method for creating customizable and reproducible regions for research and management applications.

Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse.

Although there are considerable site-based data for individual or groups of ecosystems, these datasets are widely scattered, have different data formats and conventions, and often have limited accessibility. At the broader scale, national datasets exist for a large number of geospatial features of land, water, and air that are needed to fully understand variation among these ecosystems. However, such datasets originate from different sources and have different spatial and temporal resolutions. By taking an open-science perspective and by combining site-based ecosystem datasets and national geospatial datasets, science gains the ability to ask important research questions related to grand environmental challenges that operate at broad scales. Documentation of such complicated database integration efforts, through peer-reviewed papers, is recommended to foster reproducibility and future use of the integrated database. Here, we describe the major steps, challenges, and considerations in building an integrated database of lake ecosystems, called LAGOS (LAke multi-scaled GeOSpatial and temporal database), that was developed at the sub-continental study extent of 17 US states (1,800,000 km(2)). LAGOS includes two modules: LAGOSGEO, with geospatial data on every lake with surface area larger than 4 ha in the study extent (~50,000 lakes), including climate, atmospheric deposition, land use/cover, hydrology, geology, and topography measured across a range of spatial and temporal extents; and LAGOSLIMNO, with lake water quality data compiled from ~100 individual datasets for a subset of lakes in the study extent (~10,000 lakes). Procedures for the integration of datasets included: creating a flexible database design; authoring and integrating metadata; documenting data provenance; quantifying spatial measures of geographic data; quality-controlling integrated and derived data; and extensively documenting the database. Our procedures make a large, complex, and integrated database reproducible and extensible, allowing users to ask new research questions with the existing database or through the addition of new data. The largest challenge of this task was the heterogeneity of the data, formats, and metadata. Many steps of data integration need manual input from experts in diverse fields, requiring close collaboration.

Long-term citizen-collected data reveal geographical patterns and temporal trends in lake water clarity.

We compiled a lake-water clarity database using publically available, citizen volunteer observations made between 1938 and 2012 across eight states in the Upper Midwest, USA. Our objectives were to determine (1) whether temporal trends in lake-water clarity existed across this large geographic area and (2) whether trends were related to the lake-specific characteristics of latitude, lake size, or time period the lake was monitored. Our database consisted of >140,000 individual Secchi observations from 3,251 lakes that we summarized per lake-year, resulting in 21,020 summer averages. Using Bayesian hierarchical modeling, we found approximately a 1% per year increase in water clarity (quantified as Secchi depth) for the entire population of lakes. On an individual lake basis, 7% of lakes showed increased water clarity and 4% showed decreased clarity. Trend direction and strength were related to latitude and median sample date. Lakes in the southern part of our study-region had lower average annual summer water clarity, more negative long-term trends, and greater inter-annual variability in water clarity compared to northern lakes. Increasing trends were strongest for lakes with median sample dates earlier in the period of record (1938-2012). Our ability to identify specific mechanisms for these trends is currently hampered by the lack of a large, multi-thematic database of variables that drive water clarity (e.g., climate, land use/cover). Our results demonstrate, however, that citizen science can provide the critical monitoring data needed to address environmental questions at large spatial and long temporal scales. Collaborations among citizens, research scientists, and government agencies may be important for developing the data sources and analytical tools necessary to move toward an understanding of the factors influencing macro-scale patterns such as those shown here for lake water clarity.