A flow cell simulating a subsurface rock fracture for investigations of groundwater-derived biofilms

Laboratory scale continuous-flow-through chambers (flow cells) facilitate the observation of microbes in a controlled, fully hydrated environment, although these systems often do not simulate the environmental conditions under which microorganisms are found. We developed a flow cell that mimics a subsurface groundwater-saturated rock fracture and isamenable to confocal laser scanning microscopy while allowing for the simple removal of the attached biomass. This flow cell was used to investigate the effect of toluene, a representative contaminant for non-aqueous phase liquids, on groundwater-derived biofilms. Reduced average biofilm biomass and thickness, and diminished diversity of amplifiable 16S rRNA sequences were observed for biofilms that developed in the presence of toluene, compared to the biofilms grown in the absence of toluene. The flow cell also allowed the detection of fluorescent protein-labelled cells

Fermi Gamma-ray Imaging of a Radio Galaxy

The Fermi Gamma-ray Space Telescope has detected the gamma-ray glow emanating from the giant radio lobes of the radio galaxy Centaurus A. The resolved gamma-ray image shows the lobes clearly separated from the central active source. In contrast to all other active galaxies detected so far in high-energy gamma-rays, the lobe flux constitutes a considerable portion (>1/2) of the total source emission. The gamma-ray emission from the lobes is interpreted as inverse Compton scattered relic radiation from the cosmic microwave background (CMB), with additional contribution at higher energies from the infrared-to-optical extragalactic background light (EBL). These measurements provide gamma-ray constraints on the magnetic field and particle energy content in radio galaxy lobes, and a promising method to probe the cosmic relic photon fields.Comment: 27 pages, includes Supplementary Online Material; corresponding authors: C.C. Cheung, Y. Fukazawa, J. Knodlseder, L. Stawar

Level Design in a Music Education Video Game

In both video games and teaching practices, the way that information is presented and experienced by the user is an important factor in information retention, immersion, and other components. This project examined the role of level design in an educational video game that introduces musical concepts to kids in an informal manner. I developed a prototype game demo that embodied the level design theory outlined In my background research, and administered a survey to a select group of past WPI music students to gather information about specific aspects of the level design in the game prototype. This validated that the world I created achieved its goals, but still has much room for improvement

Unsupervised Clustering of Multi-Perspective 3D Point Cloud Data in Marshes: A Case Study

Dense three-dimensional (3D) point cloud data sets generated by Terrestrial Laser Scanning (TLS) and Unmanned Aircraft System based Structure-from-Motion (UAS-SfM) photogrammetry have different characteristics and provide different representations of the underlying land cover. While there are differences, a common challenge associated with these technologies is how to best take advantage of these large data sets, often several hundred million points, to efficiently extract relevant information. Given their size and complexity, the data sets cannot be efficiently and consistently separated into homogeneous features without the use of automated segmentation algorithms. This research aims to evaluate the performance and generalizability of an unsupervised clustering method, originally developed for segmentation of TLS point cloud data in marshes, by extending it to UAS-SfM point clouds. The combination of two sets of features are extracted from both datasets: “core” features that can be extracted from any 3D point cloud and “sensor specific” features unique to the imaging modality. Comparisons of segmented results based on producer’s and user’s accuracies allow for identifying the advantages and limitations of each dataset and determining the generalization of the clustering method. The producer’s accuracies suggest that UAS-SfM (94.7%) better represents tidal flats, while TLS (99.5%) is slightly more suitable for vegetated areas. The users’ accuracies suggest that UAS-SfM outperforms TLS in vegetated areas with 98.6% of those points identified as vegetation actually falling in vegetated areas whereas TLS outperforms UAS-SfM in tidal flat areas with 99.2% user accuracy. Results demonstrate that the clustering method initially developed for TLS point cloud data transfers well to UAS-SfM point cloud data to enable consistent and accurate segmentation of marsh land cover via an unsupervised method

Prediction of indoor climbing performance in women rock climbers.

0ntbrkt