Personal Meaning Mapping as a Tool to Uncover Learning from an Out-of-doors Free-choice Learning Garden

Garden-based learning (GBL), a form of outdoor education contextualized and framed within unpredictable and real-world learning environments, is ideally suited to the teaching of science. However, the vast majority of GBL educational research has utilized a cognitive and positivist research paradigm, one that artificially restricts the investigative lens. The goal of the larger project from which this paper was drawn was to develop a better understanding of how youth perceived a garden experience. This paper shares the affordances and constraints of the constructivist framework utilized and the primary measurement tool, Person Meaning Mapping (PMM). Despite some inherent limitations, the PMM methodology enabled important insights that enhanced understandings of the effects of GBL

Measurement of teicoplanin by liquid chromatography-tandem mass spectrometry:development of a novel method

Teicoplanin is an antibiotic used for the treatment of endocarditis, osteomyelitis, septic arthritis and methicillin-resistant Staphylococcus aureus. Teicoplanin is emerging as a suitable alternative antibiotic to vancomycin, where their trough serum levels are monitored by immunoassay routinely. This is the first report detailing the development of a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for measuring teicoplanin in patients' serum

The visual binary AG Tri in β\beta Pictoris Association: can a debris disc cause very different rotation periods of its components?

We measure the photometric rotation periods of the components of multiple systems in young stellar associations to investigate the causes of the observed rotation period dispersion. We present the case of the wide binary AG Tri in the 23-Myr young beta Pictoris Association consisting of K4 + M1 dwarfs. Our multi-band, multi-season photometric monitoring allowed us to measure the rotation periods of both components P_A = 12.4d and P_B = 4.66d, to detect a prominent magnetic activity in the photosphere, likely responsible for the measured radial velocity variations, and for the first time, a flare event on the M1 component AG Tri B. We investigate either the possibility that the faster rotating component may have suffered an enhanced primordial disc dispersal, starting its PMS spin-up earlier than the slower rotating component, or the possibility that the formation of a debris disc may have prevented AG Tri A from gaining part of the angular momentum from the accreting disc.Comment: 28 pages, 7 figures, accepted for publication in Astrophysics and Space Science 2015, (ASTR-D-15-00445R2

Evolutionary history of the ADRB2 gene in humans

No abstract available

Predictive Archaeological Modeling using GIS-Based Fuzzy Set Estimation: A Case Study in Woodford County, Kentucky

Analytic predictive archaeological models can have great utility for state Departments of Transportation, but it is difficult to model the likelihood of prehistoric settlement using geographical proxy predictor variables because of the complexity of how settlement choices were actually made, and the complex interaction between these variables using GIS. In many cases classic statistical modeling approaches require too much data to be useful. This research reports on a preliminary predictive model that combines Spatial Analyst and fuzzy logic modeling to capture expert archaeological knowledge and convert this into predictive surface. A test area was defined in Woodford County, KY and five influencing factors were defined and calculated using ArcMap. Locations were sampled and probabilities estimated using both small and large group structured processes from a range of archeologists that fed an iterative fuzzy logic induction process. An output probability function was generated to create a predictive decision support layer

TB88: Descriptive and Comparative Studies of Maine Lakes

This is a descriptive and comparative study of 17 lakes in Maine. The major objectives of this study are (1) to characterize the pelagial zone of the lakes physically, chemically, and biologically, (2) to assess bacterial pollution, (3) to compare the lakes to each other and classify them trophically, and (4) to compare the lakes to others in different geographic regions.https://digitalcommons.library.umaine.edu/aes_techbulletin/1117/thumbnail.jp

The NOESY Jigsaw: Automated Protein Secondary Structure and Main-Chain Assignment from Sparse, Unassigned NMR Data

High-throughput, data-directed computational protocols for Structural Genomics (or Proteomics) are required in order to evaluate the protein products of genes for structure and function at rates comparable to current gene-sequencing technology. This paper presents the Jigsaw algorithm, a novel high-throughput, automated approach to protein structure characterization with nuclear magnetic resonance (NMR). Jigsaw consists of two main components: (1) graph-based secondary structure pattern identification in unassigned heteronuclear NMR data, and (2) assignment of spectral peaks by probabilistic alignment of identified secondary structure elements against the primary sequence. Jigsaw\u27s deferment of assignment until after secondary structure identification differs greatly from traditional approaches, which begin by correlating peaks among dozens of experiments. By deferring assignment, Jigsaw not only eliminates this bottleneck, it also allows the number of experiments to be reduced from dozens to four, none of which requires 13C-labeled protein. This in turn dramatically reduces the amount and expense of wet lab molecular biology for protein expression and purification, as well as the total spectrometer time to collect data. Our results for three test proteins demonstrate that we are able to identify and align approximately 80 percent of alpha-helical and 60 percent of beta-sheet structure. Jigsaw is extremely fast, running in minutes on a Pentium-class Linux workstation. This approach yields quick and reasonably accurate (as opposed to the traditional slow and extremely accurate) structure calculations, utilizing a suite of graph analysis algorithms to compensate for the data sparseness. Jigsaw could be used for quick structural assays to speed data to the biologist early in the process of investigation, and could in principle be applied in an automation-like fashion to a large fraction of the proteome