Occurrence and Ecosystem Effects of Hiking Off-Trail in Michaux State Forest

American public lands provide significant outdoor recreational opportunities that enhance an individual\u27s physical and mental well-being. Hiking is an example of a highly accessible and affordable recreational activity that is popular and easy for people to engage in no matter how experienced they are. While hiking has improved the well-being of many individuals, its impacts on local ecosystems are often disregarded. For our research, we focused on the impacts that hikers deviating off-trail may have on a local ecosystem in Michaux State Forest in Southern Pennsylvania. Through partnering with the foresters at Michaux State Forest and using AllTrails data, we identified heavily trafficked, unmaintained trail areas and conducted numerous field visits to observe the ecological impacts of this continued off-trail use. At each of the sites, we set up trail cameras in order to measure trail traffic, measured trail dimensions at numerous locations, and used quadrats to examine noticeable impacts on ground cover and plant ecology. We found substantial off-trail use at Michaux State Forest, from legal trail “shortcuts” to fully illegal trails. Surprisingly, we found no evidence that off-trail use impacted overall vegetative cover. In all of the study sites, the official trail was wider than the beginning of the illegal trail area and the beginning of the illegal trail was wider than the trail at the placement of the trail camera. For future analysis, we recommend that soil analyses and longer data collection periods potentially through different seasons should be conducted, as our quadrat photos and physical observations were limited due to the leafy ground cover. Our recommendations for future management include increased signage intended to prevent off-trail travel as well as improved hiker education on the principles of Leave No Trace

Collaboratively Navigating Autonomous Systems

The objective of this project is to focus on technologies for enabling heterogeneous networks of autonomous vehicles to cooperate together on a specific task. The prototyped test bed consists of a retrofitted electric golf cart and a quadrotor designed to perform distributed information gathering to guide decision making across the entire test bed. The system prototype demonstrates several aspects of this technology and lays the groundwork for future projects in this area

Attributes of researchers and their strategies to recruit minority populations: Results of a national survey

Despite NIH mandates for inclusion, recruiting minorities is challenging for biomedical and public health researchers. Little is known about how attributes of researchers affect their choice of recruitment strategies. The purpose of this study was to address this gap by examining how use of recruitment strategies relates to other researcher characteristics. To do this, we conducted an online survey from May to August 2010 with researchers (principal investigators, research staff, and IRB members) in which we measured the number and types of recruitment strategies utilized, along with other characteristics of the researchers and their research. We identified two clusters of researchers: comprehensive researchers who utilized a greater number and more diverse and active recruitment strategies, and traditional researchers, who utilized fewer and more passive strategies. Additional characteristics that distinguished the two groups were that comprehensive researchers were more likely than traditional researchers to 1) report racial and ethnic differences as one of their specific aims or hypotheses, 2) receive federal (CDC and NIH) funding, 3) conduct behavioral or epidemiological research, and 4) have received training in conducting research with and recruiting minorities. Traditional researchers, on the other hand, were more likely to conduct clinical research and a greater (though non-significant) percentage received funding from pharmaceutical sources. This study provides a novel description of how researcher attributes are related to their recruitment strategies and raises a number of future research questions to further examine the implications of this relationship.http://dx.doi.org/10.1016/j.cct.2012.06.01

Nano-assemblies of cationic mPEG brush block copolymers with gadolinium polyoxotungstate [Gd(W5O18)2]9− form stable, high relaxivity MRI contrast agents

Polyoxometalates (POMs) incorporating paramagnetic ions, such as gadolinium, show promise as contrast agents for application in magnetic resonance imaging (MRI). Specifically, [Gd(W5O18)2]9− (denoted as GdWO) has been reported to have a higher relaxivity than commercially available contrast agents, but it's clinical utility has been limited by the intrinsic instability of POMs at physiological pH (7.4). In the current report we present a stability study on neat GdWO and nano-assemblies of block copolymers with GdWO in the pH range 5.0–7.4 to assess their suitability as MRI contrast agents. Neat GdWO only maintained structural stability between pH 5.4 and 6.4, and demonstrated poor MRI contrast at pH 7.4. To address this pH instability, GdWO was self-assembled with cationic mPEG brush block copolymers containing 20 or 40 units derived from the cationic monomer, 2-dimethylaminoethyl methacrylate (DMAEMA). Nano-assemblies with different charge ratios were synthesised and characterised according to their size, stability, contrasting properties and toxicity. The longitudinal relaxivity (r1) of the nano-assemblies was found to be dependent on the charge ratio, but not on the length of the cationic polymer block. Further investigation of PDMAEMA20 nano-assemblies demonstrated that they were stable over the pH range 5.0–7.4, exhibiting a higher r1 than either neat GdWO (2.77 s−1 mM−1) or clinical MRI contrast agent Gd-DTPA (4.1 s−1 mM−1) at pH 7.4. Importantly, the nano-assembly with the lowest charge ratio (0.2), showed the highest r1 (12.1 s−1 mM−1) whilst, stabilising GdWO over the pH range studied, eliciting low toxicity with MDA-MB231 cells

Next generation tools for genomic data generation, distribution, and visualization

BACKGROUND: With the rapidly falling cost and availability of high throughput sequencing and microarray technologies, the bottleneck for effectively using genomic analysis in the laboratory and clinic is shifting to one of effectively managing, analyzing, and sharing genomic data. RESULTS: Here we present three open-source, platform independent, software tools for generating, analyzing, distributing, and visualizing genomic data. These include a next generation sequencing/microarray LIMS and analysis project center (GNomEx); an application for annotating and programmatically distributing genomic data using the community vetted DAS/2 data exchange protocol (GenoPub); and a standalone Java Swing application (GWrap) that makes cutting edge command line analysis tools available to those who prefer graphical user interfaces. Both GNomEx and GenoPub use the rich client Flex/Flash web browser interface to interact with Java classes and a relational database on a remote server. Both employ a public-private user-group security model enabling controlled distribution of patient and unpublished data alongside public resources. As such, they function as genomic data repositories that can be accessed manually or programmatically through DAS/2-enabled client applications such as the Integrated Genome Browser. CONCLUSIONS: These tools have gained wide use in our core facilities, research laboratories and clinics and are freely available for non-profit use. See http://sourceforge.net/projects/gnomex/, http://sourceforge.net/projects/genoviz/, and http://sourceforge.net/projects/useq

Poke: An open-source ray-based physical optics platform

Integrated optical models allow for accurate prediction of the as-built performance of an optical instrument. Optical models are typically composed of a separate ray trace and diffraction model to capture both the geometrical and physical regimes of light. These models are typically separated across both open-source and commercial software that don't interface with each other directly. To bridge the gap between ray trace models and diffraction models, we have built an open-source optical analysis platform in Python called Poke that uses commercial ray tracing APIs and open-source physical optics engines to simultaneously model scalar wavefront error, diffraction, and polarization. Poke operates by storing ray data from a commercial ray tracing engine into a Python object, from which physical optics calculations can be made. We present an introduction to using Poke, and highlight the capabilities of two new propagation physics modules that add to the utility of existing scalar diffraction models. Gaussian Beamlet Decomposition is a ray-based approach to diffraction modeling that allows us to integrate physical optics models with ray trace models to directly capture the influence of ray aberrations in diffraction simulations. Polarization Ray Tracing is a ray-based method of vector field propagation that can diagnose the polarization aberrations in optical systems. Poke has been recently used to study the next generation of astronomical observatories, including the ground-based Extremely Large Telescopes and a 6 meter space telescope early concept for NASA's Habitable Worlds Observatory.Comment: 11 Pages, 9 Figures, Published in Proceedings of SPIE Optical Modeling and Performance Predictions XIII Paper 12664-

Carbon Dioxide Fluxes Reflect Plant Zonation and Belowground Biomass in a Coastal Marsh

Coastal wetlands are major global carbon sinks; however, they are heterogeneous and dynamic ecosystems. To characterize spatial and temporal variability in a New England salt marsh, greenhouse gas (GHG) fluxes were compared among major plant-defined zones during growing seasons. Carbon dioxide (CO2) and methane (CH4) fluxes were compared in two mensurative experiments during summer months (2012–2014) that included low marsh (Spartina alterniflora), high marsh (Distichlis spicata and Juncus gerardiidominated), invasive Phragmites australis zones, and unvegetated ponds. Day- and nighttime fluxes were also contrasted in the native marsh zones. N2O fluxes were measured in parallel with CO2 and CH4 fluxes, but were not found to be significant. To test the relationships of CO2 and CH4 fluxes with several native plant metrics, a multivariate nonlinear model was used. Invasive P. australis zones (−7 to −15 μmol CO2·m−2·s−1) and S. alterniflora low marsh zones (up to −14 μmol CO2·m−2·s−1) displayed highest average CO2 uptake rates, while those in the native high marsh zone (less than −2 μmol CO2·m−2·s−1) were much lower. Unvegetated ponds were typically small sources of CO2 to the atmosphere (\u3c0.5 μmol CO2·m−2·s−1). Nighttime emissions of CO2 averaged only 35% of daytime uptake in the low marsh zone, but they exceeded daytime CO2 uptake by up to threefold in the native high marsh zone. Based on modeling, belowground biomass was the plant metric most strongly correlated with CO2 fluxes in native marsh zones, while none of the plant variables correlated significantly with CH4 fluxes. Methane fluxes did not vary between day and night and did not significantly offset CO2 uptake in any vegetated marsh zones based on sustained global warming potential calculations. These findings suggest that attention to spatial zonation as well as expanded measurements and modeling of GHG emissions across greater temporal scales will help to improve accuracy of carbon accounting in coastal marshe

Carbon Dioxide Fluxes Reflect Plant Zonation and Belowground Biomass in a Coastal Marsh

Coastal wetlands are major global carbon sinks; however, they are heterogeneous and dynamic ecosystems. To characterize spatial and temporal variability in a New England salt marsh, greenhouse gas (GHG) fluxes were compared among major plant-defined zones during growing seasons. Carbon dioxide (CO2) and methane (CH4) fluxes were compared in two mensurative experiments during summer months (2012–2014) that included low marsh (Spartina alterniflora), high marsh (Distichlis spicata and Juncus gerardiidominated), invasive Phragmites australis zones, and unvegetated ponds. Day- and nighttime fluxes were also contrasted in the native marsh zones. N2O fluxes were measured in parallel with CO2 and CH4 fluxes, but were not found to be significant. To test the relationships of CO2 and CH4 fluxes with several native plant metrics, a multivariate nonlinear model was used. Invasive P. australis zones (−7 to −15 μmol CO2·m−2·s−1) and S. alterniflora low marsh zones (up to −14 μmol CO2·m−2·s−1) displayed highest average CO2 uptake rates, while those in the native high marsh zone (less than −2 μmol CO2·m−2·s−1) were much lower. Unvegetated ponds were typically small sources of CO2 to the atmosphere (\u3c0.5 μmol CO2·m−2·s−1). Nighttime emissions of CO2 averaged only 35% of daytime uptake in the low marsh zone, but they exceeded daytime CO2 uptake by up to threefold in the native high marsh zone. Based on modeling, belowground biomass was the plant metric most strongly correlated with CO2 fluxes in native marsh zones, while none of the plant variables correlated significantly with CH4 fluxes. Methane fluxes did not vary between day and night and did not significantly offset CO2 uptake in any vegetated marsh zones based on sustained global warming potential calculations. These findings suggest that attention to spatial zonation as well as expanded measurements and modeling of GHG emissions across greater temporal scales will help to improve accuracy of carbon accounting in coastal marshe