Assessment of contributors to the metabolic syndrome among law enforcement officers

The metabolic syndrome has received increased attention in the past few years. The metabolic syndrome is a cluster of cardiometabolic risk factors including central obesity, dyslipidemia, hypertension, and glucose intolerance. Due to higher prevalence of obesity, lack of physical activity, and high stress levels, law enforcement officers (LEOs) may be at high risk for the metabolic syndrome. However, the risk factors for the metabolic syndrome in this specific occupation have not been fully examined. The purpose of this dissertation was to assess the contributors to the metabolic syndrome among LEOs. This dissertation consists of a series of manuscripts which focus on the association between risk factors of the metabolic syndrome and the metabolic health risk among LEOs. Sworn LEOs of the Iowa Department of Public Safety were invited to participate in this study. The results of current studies suggest that LEOs who are overweight or obese, physically inactive, or gaining more weight are at a greater risk for the metabolic syndrome and its individual components than other LEOs. Weight control and regular physical activity should be encouraged for LEOs to maintain an optimal BMI and to reduce the metabolic syndrome-related morbidity and mortality. Finally, the prevalence of the metabolic syndrome in this LEO cohort does not appear to be increased above that of the general population

Evaluation of NCEP GFS cloud properties using satellite retrievals and ground-based measurements

Cloud properties and their vertical structure are important for meteorological studies due to their impact on both the Earth's radiation budget and adiabatic heating. Examination of bulk cloud properties and vertical distribution simulated by the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS) using various satellite products and ground-based measurements is a main objective of this study. Cloud variables evaluated include the occurrence and fraction of clouds in three layers, cloud optical depth, liquid water path, and ice water path. Cloud vertical structure data are retrieved from both active and passive sensors that are compared with GFS model results. In general, the GFS model captures the spatial patterns of hydrometeors reasonably well and follows the general features seen in satellite measurements, but large discrepancies exist in low-level cloud properties. More boundary layer clouds over the interior continents were generated by the GFS model whereas satellite retrievals showed more low-level clouds over oceans. The GFS model simulations also missed low, shallow stratocumulus clouds along the west coast of North America, South America, and southwestern Africa and overestimated thick, large-scale clouds associated with the Asian summer monsoon. Although the frequencies of global multi-layer clouds from observations are similar to those from the model, latitudinal variations show large discrepancies in terms of structure and pattern. The modeled cloud optical depth for optically thin or intermediate clouds is less than that from passive sensor and is overestimated for optically thick clouds. The distributions of ice water path (IWP) agree better with satellite observations than do liquid water path (LWP) distributions. Mistreatment of such stratocumulus clouds in the GFS model leads to an overestimation of upward longwave flux, and an underestimation of upward shortwave flux at the top-of-atmosphere (TOA). With respect to input data bias in cloud fields, the GFS temperature is comparable with satellite retrievals and ground-based measurements, but the GFS relative humidity shows a wet bias at 150 and 850 hPa both from satellite retrievals and ground-based measurements. Discrepancies in cloud fields between observations and the model are attributed to differences in cloud water mixing ratio and mean relative humidity fields, which are major control variables determining the formation of clouds. To improve the simulation of cloud fields, application of other cloud parameterization scheme to the GFS model is performed. The new scheme generates a large quantity of marine stratocumulus clouds over the eastern tropical oceans as well as low cloud amounts in the other regions. High-level and middle-level clouds generated from the new scheme are more comparable with the satellite retrievals in terms of the spatial distributions and zonally averaged cloud fractions. An application of a simple linear relationship between de-correlation lengths (Lcf) and latitudes to the GFS model is conducted in order to see how successfully the equation explains the characteristics of cloud vertical structure on the changes in cloud fraction at different vertical levels. The method to solve for Lcf is a combination of Brent (1973) approach and a stochastic cloud generator using data collected from space-borne active sensors. Cloud fractions derived from a simple linear fit are compared to those computed from Lcf values based on observations. The pattern of zonal Lcf values from a simple linear fit is quite different from that of Lcf values based on observations. An offset pattern in subtropical regions is notable. The distribution of median Lcf values calculated from observed clouds do not show much dependence on latitude. This suggests that other physics, such as convection and cloud formation mechanism rather than simply latitude, should be considered when explaining how Lcf behaves. Such findings are expected to help improve the inherent problems of the GFS cloud parameterization scheme and to gain insight into the method used in determining cloud fraction

Validity of physical activity monitors for assessing lower intensity activity in adults

Background: Accelerometers can provide accurate estimates of moderate-to-vigorous physical activity (MVPA). However, one of the limitations of these instruments is the inability to capture light activity within an acceptable range of error. The purpose of the present study was to determine the validity of different activity monitors for estimating energy expenditure (EE) of light intensity, semi-structured activities. Methods: Forty healthy participants wore a SenseWear Pro3 Armband (SWA, v.6.1), the SenseWear Mini, the Actiheart, ActiGraph, and ActivPAL monitors, while being monitored with a portable indirect calorimetry (IC). Participants engaged in a variety of low intensity activities but no formalized scripts or protocols were used during these periods. Results: The Mini and SWA overestimated total EE on average by 1.0% and 4.0%, respectively, while the AH, the GT3X, and the AP underestimated total EE on average by 7.8%, 25.5%, and 22.2%, respectively. The pattern-recognition monitors yielded non-significant differences in EE estimates during the semi-structured period (p = 0.66, p = 0.27, and p = 0.21 for the Mini, SWA, and AH, respectively). Conclusions: The SenseWear Mini provided more accurate estimates of EE during light to moderate intensity semi-structured activities compared to other activity monitors. This monitor should be considered when there is interest in tracking low intensity activities in groups of individuals.This research was funded by a grant from Bodymedia Inc. awarded to Dr. Greg Welk

Assessment of contributors to the metabolic syndrome among law enforcement officers

The metabolic syndrome has received increased attention in the past few years. The metabolic syndrome is a cluster of cardiometabolic risk factors including central obesity, dyslipidemia, hypertension, and glucose intolerance. Due to higher prevalence of obesity, lack of physical activity, and high stress levels, law enforcement officers (LEOs) may be at high risk for the metabolic syndrome. However, the risk factors for the metabolic syndrome in this specific occupation have not been fully examined. The purpose of this dissertation was to assess the contributors to the metabolic syndrome among LEOs. This dissertation consists of a series of manuscripts which focus on the association between risk factors of the metabolic syndrome and the metabolic health risk among LEOs. Sworn LEOs of the Iowa Department of Public Safety were invited to participate in this study. The results of current studies suggest that LEOs who are overweight or obese, physically inactive, or gaining more weight are at a greater risk for the metabolic syndrome and its individual components than other LEOs. Weight control and regular physical activity should be encouraged for LEOs to maintain an optimal BMI and to reduce the metabolic syndrome-related morbidity and mortality. Finally, the prevalence of the metabolic syndrome in this LEO cohort does not appear to be increased above that of the general population.</p

Analysis of Deep Convective Clouds (DCC) for Near Infrared Channels

Deep convective clouds (DCCs) are used as vicarious calibration targets because of their stability, brightness, and relatively small angular and spectral variation. DCCs at visible channels usually have relatively sharp histogram distributions with high reflectance. However, as a calibration target, there remains relatively large uncertainty in identifying uniform cold cloud top of DCCs at near infrared (NIR) channels. The main objective of this study is to reduce DCC reflectance uncertainty at NIR channels for GOES-R Advanced Baseline Imager (ABI). The DCC pixels information from Visible Infrared Imaging Radiometer Suite (VIIRS) Level1B and Level2 product are used as proxy data and analyzed to determine where convective cloud core locates and to characterize the internal structure of DCCs. This work uses DCC calibration technique suggested by Doelling et al. (2004) to get DCC pixels from VIIRS Level1B data and the target region is over GOES-R ABI check-out spatial domain (20°N-20°S and 109.5°W-69.5°W). Identification of DCC pixels from VIIRS Level2 product is followed by International Satellite Cloud Climatology Project cloud classification based on cloud optical depth and cloud top pressure (Rossow and Schiffer 1999). The overlapped Level1B and Level2 DCC pixels show different patterns of cloud top properties between the visible channels and NIR channels. The preliminary results show that the patterns of DCC properties are well correlated with DCC reflectance and their relationship with DCC reflectance are differentiated between the visible and NIR channel. Among the DCC microphysical parameters, optical thickness seems to be most important variable to characterize the overshooting part of DCCs. This work provides useful guidance toward finding core and anvil part of DCCs and thus helps to reduce the DCC NIR reflectance uncertainty from Level1B data. We will use these results to validate the algorithm with the collocated DCC area from AHI and VIIRS data

Development of a Food Literacy Assessment Tool for Healthy, Joyful, and Sustainable Diet in South Korea

Background: Food literacy (FL) is important as the ability to consider the unique aspects of food in our lives, society, and environment. The main objectives of this study were as follows: (1) to revisit the definition of FL, considering the cultural, relational, and ecological aspects that were often neglected in previous research, and (2) to develop a measurement tool for adults. Methods: Expert workshops, the Delphi survey, the test–retest survey, and one-on-one interviews were conducted. The content validity ratio was calculated from the Delphi survey. The correlation coefficient of each item was measured twice, and the Cronbach’s alpha was calculated. Results: This study proposed a new definition of FL, including future-oriented values, and suggested three main domains with 33 items: (1) 14 questions in nutrition and safety FL (Cronbach’s α = 0.877, average correlation coefficient = 0.70), (2) 8 questions in cultural and relational FL (Cronbach’s α = 0.705, average correlation coefficient = 0.71), and (3) 11 questions in socio-ecological FL (Cronbach’s α = 0.737, average correlation coefficient = 0.61). Conclusions: This newly developed questionnaire should be tested in different populations; however, this questionnaire can be a basis for measuring and improving FL for healthy, joyful, and sustainable diets for adults

Direct Comparison of ABI Infrared Channels on Different GOES

NOAA has a long history of using a constellation of two operational GOES satellites to provide the imagery of the Western Hemisphere for the accurate weather forecasting and severe environmental monitoring. As the weather instruments onboard the satellites usually have similar spectral responses, the direct comparison between the infrared (IR) radiance from the overlapped area was used to evaluate the radiance difference between the satellites with high temporal resolution. To date, three of the NOAA’s current generation of geostationary (GEO) satellites, GOES-16/17/18, have been launched since November 2016. GOES-16 has been serving as the GOES-East satellite 75.2oW since December 2017, and GOES-17 was the GOES-West satellite near 137oW, which was recently replaced by GOES-18 on 4 January 2023. The GEO-GEO inter-comparison algorithm was improved for the weather instrument of Advanced Baseline Imager (ABI) onboard the GOES-R satellites with carefully selecting collocation targets. It is implemented to routinely monitor the calibration variation between the ABI IR channels since GOES-17 was in-orbit in 2018. Due to the high temporal resolution and high precision for stability monitoring, this inter-comparison is a powerful tool for detecting operational calibration anomaly in near real time, assisting the anomaly root cause investigations, validating the calibration algorithm updates, and examining the ABI IR radiometric calibration performance at varying temporal scales. The analyses of the diurnal and long-term results show that the ABI IR calibration in general is very stable. Although GOES-17 ABI suffered the Loop-Heat-Pipe (LHP) anomaly, the comparison results between GOES-17 and GOES-16/18 indicate that GOES-17 IR radiometric calibration was stable when the IR focal plane module (FPM) temperature was controlled. The predictive calibration (pCal) algorithm, which was deployed over the floating FPM period for G17 IR bands, greatly improved the radiometric calibration accuracy and thus reduced the diurnal variation for the valid images. Some examples of the GEOGEO inter-comparison to support the operational ABI calibration will also be presented and discussed in the meeting

Correction for Dependence of Instrument Elevation/Beta Angles on GOES-16 ABI Solar Calibration

GOES-16, the first in a series of new generation Geostationary Operational Environmental Satellite (GOES), carries an Advanced Baseline Imager (ABI) with six Visible and Near Infrared (VNIR) channels that are calibrated periodically with an onboard solar diffuser. After its launch in 2016, we found seasonal changes in the time series of some VNIR channel gains that cannot be explained by instrument degradation nor measurement errors. This was thought to be related to the implementation of the different solar calibration algorithm version and the impacts of satellite instrument angles condition. To characterize and quantify this seasonal variation and measurement bias, we firstly reprocessed all GOES-16 solar calibration events since January 15, 2017 with the same calibration algorithm and look-up-table (LUT), which excluded the impact of algorithm difference in implementation. Secondly, to correct for the impact of “imperfect” solar calibration timing (elevation angle not equal to zero) before Dec 12, 2017, we used the extra solar calibration events in 2018 with the same elevation angle as the ones in 2017 to estimate the degradation rate in the first year, and then corrected this for the previous events. After removal of the bias due to non-zero elevation angle, we then derived the relationship between seasonal variation of corrected gains with the instrument beta angle. Our result shows that there is an asymmetrical pattern between beta angle and the gains, especially band 1. This dependence of the gains on beta angle was likely related to Bidirectional Reflectance Distribution Function (BRDF) factor in solar calibration. We finally derive the bias correction coefficient and then applied to correct such a dependence. Our results show clearly that it improves the assessment the GOES-16 VNIR instrument degradation and calibration repeatability. We will present these and other details at the conference

Radiometric Calibration Performance of GOES-16/17 Advanced Baseline Imagers (ABI)

The Advanced Baseline Imager (ABI) is the primary instrument onboard NOAA’s current Geostationary Operational Environmental Satellites (GOES), GOES-16 (GOES East) and GOES-17 (GOES West). These 16-band instruments are collecting imagery critical to the National Weather Service for accurate weather nowcasting and forecasting over the Earth’s Western Hemisphere. While GOES-16 operates as designed, the partial failure of the GOES-17 ABI cooling system leads to a set of different operational configurations that optimizes the instrument performance under the circumstances. Since GOES-17 ABI became operational in February 2019, several major Ground System (GS) updates have been successfully implemented to improve the ABI radiance quality of the solar reflective and infrared (IR) bands. The impacts of the GS updates on radiance and image quality were intensively validated and carefully monitored using different methods. This study is to report the radiometric calibration performance after each major update. The update of the scan mode in April 2019 reduces the calibration difference between the swaths within the timeline for the GOES-17 IR bands. The predictive calibration (pCal) algorithm implemented in July 2019 significantly improves the calibration accuracy for the GOES-17 IR data when they are available during the period of unstable focal plane module (FPM) temperature, and thus greatly reduces the calibration error at night. After several solar calibration updates to both ABIs from April to June 2019, the overall difference is less than 5% for all the solar reflective bands as compared to the corresponding channels of S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS). Details will be reported in the meeting

Protective Effect of Tetrahydroquinolines from the Edible Insect Allomyrina dichotoma on LPS-Induced Vascular Inflammatory Responses

The larva of Allomyrina dichotoma (family Scarabaeidae) is an edible insect that is registered in the Korean Food Standards Codex as a food resource. The chemical study on the larvae of A. dichotoma resulted in the isolation of three new tetrahydroquinolines, allomyrinaines A&ndash;C (1&ndash;3), one new dopamine derivative, allomyrinamide A (4), and four known compounds (5&ndash;8). The structures were elucidated on the basis of 1D and 2D nuclear magnetic resonance (NMR) and MS spectroscopic data analysis. Allomyrinaines A&ndash;C (1&ndash;3) possessed three stereogenic centers at C-2, C-3, and C-4, whose relative configurations were determined by analyses of the coupling constants and the nuclear Overhauser enhancement spectroscopy (NOESY) data, as well as DP4+ calculation. The anti-inflammatory effects of compounds 1&ndash;4 were evaluated in human endothelial cells. Allomyrinaines A&ndash;C (1&ndash;3) could stabilize vascular barrier integrity on lipopolysaccharide (LPS)-induced vascular inflammation via inhibition of the nuclear factor-&kappa;B (NF-&kappa;B) pathway. The physiologically relevant concentration was confirmed by Q-TOF-MS-based quantitative analysis on allomyrinaines A&ndash;C in crude extract. This study suggests that allomyrinaines A&ndash;C (1&ndash;3) are bioactive constituents of A. dichotoma to treat vascular inflammatory disorder