The Experts below are selected from a list of 84519 Experts worldwide ranked by ideXlab platform
Reagan L Noland - One of the best experts on this subject based on the ideXlab platform.
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estimating alfalfa yield and nutritive value using remote sensing and air temperature
Field Crops Research, 2018Co-Authors: Reagan L Noland, Scott M Wells, Jeffrey A Coulter, Tyler Tiede, John M Baker, Krishona L Martinson, Craig C SheafferAbstract:Abstract In-field estimation of alfalfa ( Medicago sativa L.) yield and nutritive value can inform management decisions to optimize forage quality and production. However, acquisition of timely information at the field scale is limited using traditional measurements such as destructive sampling and assessment of plant maturity. Remote sensing technologies (e.g., measurement of canopy reflectance) have the potential to enable rapid measurements at the field scale. Canopy reflectance (350–2500 nm) and Light Detection and Ranging (LiDAR)-estimated canopy height were measured in conjunction with destructive sampling of alfalfa across a range of maturities at Rosemount, MN in 2014 and 2015. Sets of specific spectral wavebands were determined via stepwise regression to predict alfalfa yield and nutritive value and Models were reduced by spectral range to improve utility. Cumulative growing degree units (GDUs) and canopy height were tested as Model Covariates. An alternative GDU calculation (GDU ALT ) using a temporally graduating base temperature was also tested against the traditional static base temperature. The inclusion of GDU ALT increased prediction accuracy for all response variables by 9–17%. Models using a common set of seven wavebands, combined with GDU ALT , explained 81–90% of the variability in yield, crude protein (CP), neutral detergent fiber (NDF), and NDF digestibility (NDFd; 48-h in-vitro), respectively. This research establishes potential for remote sensing measurements to be integrated with air temperature information to achieve rapid and accurate predictions of alfalfa yield and nutritive value at the field scale for optimized harvest management.
Wanda K Nicholson - One of the best experts on this subject based on the ideXlab platform.
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antepartum glucose tolerance test results as predictors of type 2 diabetes mellitus in women with a history of gestational diabetes mellitus a systematic review
Gender Medicine, 2009Co-Authors: Sherita Hill Golden, Wendy L Bennett, Kesha Baptistroberts, Lisa M Wilson, Bethany B Barone, Tiffany L Gary, Eric B Bass, Wanda K NicholsonAbstract:Background: Women with a history of gestational diabetes mellitus (GDM) are at high risk for type 2 diabetes mellitus (T2DM). Objective: We reviewed prospective studies of antepartum glucose tolerance test results as risk factors for development of T2DM among women with a history of GDM. Methods: We searched 4 electronic databases and hand-searched 13 journals for literature published through January 2007. The search strategy consisted of medical subject headings and text words for GDM, T2DM, and other relevant terms. Articles were excluded for the following reasons: (1) not written in English; (2) no human data; (3) no original data; (4) <90% of sample was diagnosed with GDM without a separate analysis for women with GDM; (5) case report or series; (6) diagnosis of GDM not based on 3-hour 100-g oral glucose tolerance test (OGTT) or 2-hour 75-g OGTT; (7) T2DM not evaluated as outcome; (8) no relative measure of association or incidence reported; or (9) design did not address antepartum OGTT as a predictor of T2DM. Two investigators independently reviewed citations, performed serial data abstraction on full articles, and assessed the quality of each article. Data were abstracted for study participants and characteristics, T2DM diagnosis, length of follow-up, regression Model Covariates, and measures of association and variability. Results: Of 11,400 unique citations, we identified 11 articles that evaluated antepartum glucose testing and risk of T2DM in women with a history of GDM. Five studies found that the fasting blood glucose (FBG) on the antepartum diagnostic OGTT was a significant predictor of T2DM (odds ratio [OR] range: 11.1–21.0; relative risk [RR] range: 1.37–1.5; relative hazard [RH] = 2.47). Risk of incident T2DM was predicted by the antepartum 2-hour OGTT plasma glucose in 3 studies (OR range: 1.02–1.03; RR = 1.3) and by the antepartum OGTT glucose AUC in 3 other studies (OR range: 3.64–15; RH = 2.13). Overall, study quality was limited by high losses to follow-up (>20% in 6 studies) and short duration. Few studies adjusted for adiposity, an established diabetes risk factor. Conclusion: FBG, OGTT 2-hour blood glucose, and OGTT glucose AUC appeared to be strong and consistent predictors of subsequent T2DM among women who met diagnostic criteria for GDM using the OGTT.
Craig C Sheaffer - One of the best experts on this subject based on the ideXlab platform.
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estimating alfalfa yield and nutritive value using remote sensing and air temperature
Field Crops Research, 2018Co-Authors: Reagan L Noland, Scott M Wells, Jeffrey A Coulter, Tyler Tiede, John M Baker, Krishona L Martinson, Craig C SheafferAbstract:Abstract In-field estimation of alfalfa ( Medicago sativa L.) yield and nutritive value can inform management decisions to optimize forage quality and production. However, acquisition of timely information at the field scale is limited using traditional measurements such as destructive sampling and assessment of plant maturity. Remote sensing technologies (e.g., measurement of canopy reflectance) have the potential to enable rapid measurements at the field scale. Canopy reflectance (350–2500 nm) and Light Detection and Ranging (LiDAR)-estimated canopy height were measured in conjunction with destructive sampling of alfalfa across a range of maturities at Rosemount, MN in 2014 and 2015. Sets of specific spectral wavebands were determined via stepwise regression to predict alfalfa yield and nutritive value and Models were reduced by spectral range to improve utility. Cumulative growing degree units (GDUs) and canopy height were tested as Model Covariates. An alternative GDU calculation (GDU ALT ) using a temporally graduating base temperature was also tested against the traditional static base temperature. The inclusion of GDU ALT increased prediction accuracy for all response variables by 9–17%. Models using a common set of seven wavebands, combined with GDU ALT , explained 81–90% of the variability in yield, crude protein (CP), neutral detergent fiber (NDF), and NDF digestibility (NDFd; 48-h in-vitro), respectively. This research establishes potential for remote sensing measurements to be integrated with air temperature information to achieve rapid and accurate predictions of alfalfa yield and nutritive value at the field scale for optimized harvest management.
John G Park - One of the best experts on this subject based on the ideXlab platform.
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pharmacologic treatment of intensive care unit delirium and the impact on duration of delirium length of intensive care unit stay length of hospitalization and 28 day mortality
Mayo Clinic proceedings, 2018Co-Authors: Lisa M Daniels, Sarah Nelson, Ryan D Frank, John G ParkAbstract:Abstract Objective To determine whether treatment of delirium affects outcomes. Patients and Methods A retrospective cohort study of patients admitted to the medical intensive care unit (ICU) from July 1, 2015, through June 30, 2016, was conducted. Patients with ICU delirium, defined by a positive Confusion Assessment Method for the ICU score, were included. Patients were stratified into 4 treatment groups based on exposure to melatonin and antipsychotic agents during ICU stay: no pharmacologic treatment, melatonin only, antipsychotics only, and both melatonin and antipsychotics. A time-dependent cause-specific hazards Model with death as a competing risk was used to evaluate the effect of melatonin or antipsychotic drug use for delirium on duration of ICU delirium, length of ICU stay, and length of hospitalization. A logistic regression was used to evaluate 28-day mortality. Covariates significantly associated with exposure to melatonin and antipsychotics were included in the minimally adjusted Model. Covariates significantly associated in the minimally adjusted Model were included in a final adjusted Model. Results A total of 449 admissions to the medical ICU were included in the analysis. Exposure to melatonin or antipsychotic agents did not reduce the duration of ICU delirium, ICU/hospital length of stay, or 28-day mortality. However, antipsychotic use only was associated with longer hospitalization. Conclusion Antipsychotic drugs for the treatment ICU delirium may not provide the benefit documented in earlier literature. Further investigation on patient selection, type of antipsychotic, and dosing is needed.
John M Baker - One of the best experts on this subject based on the ideXlab platform.
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estimating alfalfa yield and nutritive value using remote sensing and air temperature
Field Crops Research, 2018Co-Authors: Reagan L Noland, Scott M Wells, Jeffrey A Coulter, Tyler Tiede, John M Baker, Krishona L Martinson, Craig C SheafferAbstract:Abstract In-field estimation of alfalfa ( Medicago sativa L.) yield and nutritive value can inform management decisions to optimize forage quality and production. However, acquisition of timely information at the field scale is limited using traditional measurements such as destructive sampling and assessment of plant maturity. Remote sensing technologies (e.g., measurement of canopy reflectance) have the potential to enable rapid measurements at the field scale. Canopy reflectance (350–2500 nm) and Light Detection and Ranging (LiDAR)-estimated canopy height were measured in conjunction with destructive sampling of alfalfa across a range of maturities at Rosemount, MN in 2014 and 2015. Sets of specific spectral wavebands were determined via stepwise regression to predict alfalfa yield and nutritive value and Models were reduced by spectral range to improve utility. Cumulative growing degree units (GDUs) and canopy height were tested as Model Covariates. An alternative GDU calculation (GDU ALT ) using a temporally graduating base temperature was also tested against the traditional static base temperature. The inclusion of GDU ALT increased prediction accuracy for all response variables by 9–17%. Models using a common set of seven wavebands, combined with GDU ALT , explained 81–90% of the variability in yield, crude protein (CP), neutral detergent fiber (NDF), and NDF digestibility (NDFd; 48-h in-vitro), respectively. This research establishes potential for remote sensing measurements to be integrated with air temperature information to achieve rapid and accurate predictions of alfalfa yield and nutritive value at the field scale for optimized harvest management.