Mammals of Southwestern Arkansas Part II. Rodents

This study investigated the composition and habitat affinities of the mammalian fauna of southwestern Arkansas. The study area was comprised of the 21 counties located south and/or west of and including Pulaski County. The previously existing data set pertaining to the mammals of Arkansas was notably incomplete and this study area in particular, was poorly known mammalogically. Specimens were collected by standard trapping and salvage methods throughout the study area. The mammals considered during this study were limited to those species meeting a set of criteria designed to eliminate species that had been introduced or artificially maintained. This study has accumulated records of 25 species of rodents; over 1500 specimens have been recorded; and a total of 95 new county records have been documented

Genetic algorithms for Hyperspectral Range and Operator Selection

A novel genetic algorithm was developed using mathematical operations on spectral ranges to explore spectral operator space and to discover useful mathematical range operations for relating spectral data to reference parameters. For each range, the starting wavelength and length of the range, and a mathematical range operation were selected with a genetic algorithm. Partial least squares (PLS) regression was used to develop models predicting reference variables from the range operations. Reflectance spectra from corn plant canopies were investigated, with proportion of plants (1) with visible tassels and (2) starting to shed pollen as reference data. PLS models developed using the spectral range operator framework had similar fitness than PLS models developed using the full spectrum. This range/operator framework enabled identification of those spectral ranges with most predictive capability and which mathematical operators were most effective in using that predictive capability. Detection of operator locality may have utility in sensor and algorithm design and in developing breeding stock for other algorithms

Novel Analysis of Hyperspectral Reflectance Data for Detecting Onset of Pollen Shed in Maize

Knowledge of pollen shed dynamics in and around seed production fields is critical for ensuring a high yield of genetically pure corn seed. Recently, changes in canopy reflectance using hyperspectral reflectance have been associated with tassel emergence, which is known to precede pollen shed in a predictable manner. Practical application of this remote sensing technology, however, requires a simple and reliable method to evaluate changes in spectral images associated with the onset of tassel emergence and pollen shed. In this study, several numerical methods were investigated for estimating percentage of plants with visible tassels (VT) and percentage of plants that initiated pollen shed (IPS) from remotely sensed hyperspectral reflectance data (397 to 902 nm). Correlation analysis identified regions of the spectra that were associated with tassel emergence and anthesis (i.e., 50% of plants shedding pollen). No single band, however, generated correlations greater than 0.40 for either VT or IPS. Classification using an artificial neural network (ANN) was predictive, correctly classifying 83.5% and 88.3% of the VT and IPS data, respectively. The extensive preprocessing necessary and the black box nature of ANNs, however, rendered analysis of spectral regions difficult using this method. Partial least squares (PLS) analysis yielded models with high predictive capability (R2 of 0.80 for VT and 0.79 for IPS). The PLS coefficients, however, did not exhibit a spectrally consistent pattern. A novel range operator-enabled genetic algorithm (ROE-GA), designed to consider the shape of the spectra, had similar predictive capabilities to the ANN and PLS, but provided the added advantage of allowing information transfer for increased domain knowledge. The ROE-GA analysis is the preferred method to evaluate hyperspectral reflectance data and associate spectral changes to tassel emergence and the onset of pollen shed in corn on a field scale

Distribution and Status of the Brazilian Free-tailed Bat (Tadarida brasiliensis cynocephala) in Arkansas

Investigations of building infestations, mist netting activities, and specimens submitted to the Arkansas Department of Health document the Brazilian free-tailed bat to be found in 14 Arkansas counties. Both overwintering and maternity colonies were examined. Numbers of individuals ranged from one to several thousand

Mammals of Southwestern Arkansas Part III. Carnivores

This study investigates the composition of the carnivore fauna of southwestern Arkansas and presents data on habitat affinities. The study area is comprised of the 21 counties located south and/or west of and including Pulaski County. The previously existing data set pertaining to the mammals of Arkansas was notably incomplete and the study area, in particular, was poorly known mammalogically. Specimens were collected by standard trapping and salvage methods throughout the study area. Species considered during this study were limited to those meeting a set of criteria designed to eliminate species that had been introduced or artificially maintained. This study has accumulated records of11 species of carnivores present in the study area and proposes the presence of one other; over 2100 specimens have been recorded; and a total of 81 new county records has been documented

Bat Fauna of Southeast Arkansas

A systematic survey of the mammalian fauna of Southwest Arkansas has resulted in the accumulation of more than 200 records of bats from the 21 counties comprising the study area. The records reveal distributional patterns for 12 species of bats and represent a total of 68 new county records for this area of Arkansas

A distributed data component for the Open Modeling Interface

As the volume of collected data continues to increase in the environmental sciences, so too does the need for effective means for accessing those data. We have developed an Open Modeling Interface (OpenMI) data component that retrieves input data for model components from environmental information systems and delivers output data to those systems. The adoption of standards for both model component input–output interfaces and web services make it possible for the component to be reconfigured for use with different linked models and various online systems. The data component employs three techniques tailored to the unique design of the OpenMI that enable efficient operation: caching, prefetching, and buffering, making it capable of scaling to large numbers of simultaneous simulations executing on a computational grid. We present the design of the component, an evaluation of its performance, and a case study demonstrating how it can be incorporated into modeling studies

The impact of obstructive sleep apnea variability measured in-lab versus in-home on sample size calculations

<p>Abstract</p> <p>Background</p> <p>When conducting a treatment intervention, it is assumed that variability associated with measurement of the disease can be controlled sufficiently to reasonably assess the outcome. In this study we investigate the variability of Apnea-Hypopnea Index obtained by polysomnography and by in-home portable recording in untreated mild to moderate obstructive sleep apnea (OSA) patients at a four- to six-month interval.</p> <p>Methods</p> <p>Thirty-seven adult patients serving as placebo controls underwent a baseline polysomnography and in-home sleep study followed by a second set of studies under the same conditions. The polysomnography studies were acquired and scored at three independent American Academy of Sleep Medicine accredited sleep laboratories. The in-home studies were acquired by the patient and scored using validated auto-scoring algorithms. The initial in-home study was conducted on average two months prior to the first polysomnography, the follow-up polysomnography and in-home studies were conducted approximately five to six months after the initial polysomnography.</p> <p>Results</p> <p>When comparing the test-retest Apnea-hypopnea Index (AHI) and apnea index (AI), the in-home results were more highly correlated (r = 0.65 and 0.68) than the comparable PSG results (r = 0.56 and 0.58). The in-home results provided approximately 50% less test-retest variability than the comparable polysomnography AHI and AI values. Both the overall polysomnography AHI and AI showed a substantial bias toward increased severity upon retest (8 and 6 events/hr respectively) while the in-home bias was essentially zero. The in-home percentage of time supine showed a better correlation compared to polysomnography (r = 0.72 vs. 0.43). Patients biased toward more time supine during the initial polysomnography; no trends in time supine for in-home studies were noted.</p> <p>Conclusion</p> <p>Night-to-night variability in sleep-disordered breathing can be a confounding factor in assessing treatment outcomes. The sample size of this study was small given the night-to-night variability in OSA and limited understanding of polysomnography reliability. We found that in-home studies provided a repeated measure of sleep disordered breathing less variable then polysomnography. Investigators using polysomnography to assess treatment outcomes should factor in the increased variability and bias toward increased AHI values upon retest to ensure the study is adequately powered.</p