Global Surveillance of Emerging Influenza Virus Genotypes by Mass Spectrometry

Effective influenza surveillance requires new methods capable of rapid and inexpensive genomic analysis of evolving viral species for pandemic preparedness, to understand the evolution of circulating viral species, and for vaccine strain selection. We have developed one such approach based on previously described broad-range reverse transcription PCR/electrospray ionization mass spectrometry (RT-PCR/ESI-MS) technology.Analysis of base compositions of RT-PCR amplicons from influenza core gene segments (PB1, PB2, PA, M, NS, NP) are used to provide sub-species identification and infer influenza virus H and N subtypes. Using this approach, we detected and correctly identified 92 mammalian and avian influenza isolates, representing 30 different H and N types, including 29 avian H5N1 isolates. Further, direct analysis of 656 human clinical respiratory specimens collected over a seven-year period (1999-2006) showed correct identification of the viral species and subtypes with >97% sensitivity and specificity. Base composition derived clusters inferred from this analysis showed 100% concordance to previously established clades. Ongoing surveillance of samples from the recent influenza virus seasons (2005-2006) showed evidence for emergence and establishment of new genotypes of circulating H3N2 strains worldwide. Mixed viral quasispecies were found in approximately 1% of these recent samples providing a view into viral evolution.Thus, rapid RT-PCR/ESI-MS analysis can be used to simultaneously identify all species of influenza viruses with clade-level resolution, identify mixed viral populations and monitor global spread and emergence of novel viral genotypes. This high-throughput method promises to become an integral component of influenza surveillance

Triggers of contingency in mathematics teaching

Our research in the last decade has been into classroom situations that we perceive to make demands on mathematics teachers' disciplinary knowledge of content and pedagogy. Amongst the most visible of such situations are those that we describe as ‘contingent’, in which a teacher is faced with some unexpected event, and challenged to deviate from their planned agenda for the lesson. Our findings and the associated grounded theories have been open to enhancement and revision as new classroom data has been gathered. In this article, we propose a classification of the origins of contingent classroom episodes: namely the students; the teacher him/herself; and pedagogical tools and resources. This classification extends and elaborates our original conception of ‘contingent’, in response to more recent data

Gravitational Effects on Cerebrospinal Fluid Pressure and Flow in an Anatomical Model

The Boise State University Microgravity Research Team, “Gravitational Effects on Cerebrospinal Fluid Pressure and Flow in an Anatomical Model,” seeks to address Section C.6.7 “Microgravity Biomedical Counter-Measures for Long Duration Spaceflight” in NASA’s Critical Technology Determination (CTD) for Future Human Space Flight document. This section states that intracranial hypertension has the potential to have temporary and permanent health risks. Based on this, an experiment was designed to provide a foundation of information on cerebrospinal fluid (CSF) movement inside the cranium. Using an anatomically representative model, this team seeks to better understand CSF movement and changes in intracranial pressure (ICP) in response to hyper- and microgravity in real time during parabolic flight. We propose to monitor these changes using pressure and flow sensors positioned throughout our “CSF Flow Apparatus,” allowing us to collect data at multiple locations. Results of our study could provide a preliminary explanation for some of the symptoms seen in extended spaceflight, as well as providing a foundation for future research in monitoring and treatment of increased ICP. Besides designing an experiment, for NASA\u27s Microgravity University Program, The Boise State University Microgravity Research Team is hosting numerous community outreach events. Of these events, one will be Mini-Microgravity Challenge offered to a Fifth grade class at Mountain View Elementary School in Boise, ID. Students will be given the opportunity to develop their own Mar\u27s Rover using a blown out egg. They will create an appropriate hypothesis, drawing or model, and be given an opportunity to test their project

Precipitation Data

Ten precipitation stations were set up to collect precipitation data (including rainfall/snowfall and snow depth measurements) across 6 USGS HUC10 watersheds. The heated tipping bucket rain gauge was utilized to obtain precipitation measurements. The heating mechanism engaged when temperatures were below freezing and thawed frozen precipitation to acquire measurements year-round (Heated Tipping Bucket Rain Gauge, Hydrological Services America). The data accuracy is within +/- 3% even with high rainfall intensities due to the integrated syphon mechanism which controls the release of precipitation into the tipping bucket (TB3 Tipping Bucket Rain Gauge, Hydrological Services America). The data were originally collected at a 1-minute time interval, which was changed to a 5-minute time interval in June 2016; however, not all data are continuous at this point. See the ReadMe file for complete details located in the zip file of precipitation data

Snow Depth Data

Ten precipitation stations were set up to collect precipitation data (including rainfall/snowfall and snow depth measurements) across 6 USGS HUC10 watersheds. The ultrasonic snow depth sensor was utilized to obtain continuous and non-contact snow depth measurements (Ultra Sonic Snow Depth Sensor (USH-8), Hydrological Services America). Data accuracy is within 0.1% due to the integrated temperature compensation and filtering of snow and rainfall using intelligent spectrum analysis. However, the sensor does pick up any vegetation beneath the sensor. Thus, the snow depth data were processed. See the ReadMe file for complete details located in the zip file of snow depth data

Spatially Distributed Pothole Depth Data

The locations of our surveyed potholes are located in the Cottonwood Lake Study Area (CLSA) of the USGS Northern Prairie Wildlife Research Center (NPWRC). RiverSurveyor M9 by SonTec (RiverSurveyor M9) was utilized to obtain spatially distributed pothole depth data. This way a modified DEM was created which reflected the pothole bathymetry rather than the water surface elevations. The M9 records water depths using multi-band multiple acoustic frequencies from the vertical beam which “pings” the pothole bed while simultaneously collecting the GPS coordinates of each “ping”. High ping rates ensure robust data collection and high resolution. The vertical beam can measure a depth range from 0.2 to 80m, depth accuracy of 1%, and depth resolution of 0.001 m. Two types of data were collected: pothole shapefile (boundary) data and inside pothole data. To create the pothole boundary, the M9 was walked around the water line of the entire pothole in a backpack so all depth measurements read 0 m. The inside pothole data were obtained by attaching the RiverSurveyor M9 to a floatable platform and the floatable platform was attached to a small row boat. The RiverSurveyor was then paddled around the pothole and through the pothole along many vertical and horizontal transects in order to get a good coverage of the pothole bathymetry. For more information, see the ReadMe file within the zip file of spatially distributed pothole depths

Temporal Pothole Depth Data

The locations of our surveyed potholes are located in the Cottonwood Lake Study Area (CLSA) of the USGS Northern Prairie Wildlife Research Center (NPWRC).The HOBO pressure transducers (HOBO Pressure Transducer) used in this hydrologic monitoring study are wireless water level loggers with Bluetooth capabilities for data download. These sensors have a 3-point NIST-traceable calibration certificate from ONSET. They were self-calibrated/lab tested by our NDSU hydrologic modeling group. The measured water level accuracy is ± 0.05 – 0.1%. The sensors were installed in the summers of 2016 and 2017 and removed in late September or early October of each year, respectively. The water level data are unprocessed data, so any missing data have not been corrected or interpolated at this time. The recorded data have a 15-minute time interval. For more information, see the ReadMe file within the zip file of temporal pothole depths