North Dakota Economic-Demographic Assessment Model (NEDAM): Technical Description

This report describes the logic, structure, data bases, and operational procedures of the North Dakota model.Research Methods/ Statistical Methods,

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

IL-7 immunotherapy in a nonimmunocompromised patient with intractable fungal wound sepsis

A nonimmunocompromised patient developed life-threatening soft tissue infection wit

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 \u3c 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 \u3c 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions

A Public–Industry Partnership for Enhancing Corn Nitrogen Research and Datasets: Project Description, Methodology, and Outcomes

Due to economic and environmental consequences of N lost from fertilizer applications in corn (Zea mays L.), considerable public and industry attention has been devoted to the development of N decision tools. Needed are research and databases and associated metadata, at numerous locations and years to represent a wide geographic range of soil and weather scenarios, for evaluating tool performance. The goals of this research were to conduct standardized corn N rate response field studies to evaluate the performance of multiple public-domain N decision tools across diverse soils and environmental conditions, develop and publish new agronomic science for improved crop N management, and train new scientists. The geographic scope, scale, and unique collaborative arrangement warrant documenting details of this research. The objectives of this paper are to describe how the research was undertaken, reasons for the methods, and the project’s anticipated value. The project was initiated in a partnership between eight U.S. Midwest land-grant universities, USDA-ARS, and DuPont Pioneer. Research using a standardized protocol was conducted over the 2014 through 2016 growing seasons, yielding a total of 49 sites. Preliminary observations of soil and crop variables measured from each site revealed a magnitude of differences in soil properties (e.g., texture and organic matter) as well as differences in agronomic and economic responses to applied N. The project has generated a valuable dataset across a wide array of weather and soils that allows investigators to perform robust evaluation of N use in corn and N decision tools

Sub-second periodicity in a fast radio burst

Fast radio bursts (FRBs) are millisecond-duration flashes of radio waves that are visible at distances of billions of light-years. The nature of their progenitors and their emission mechanism remain open astrophysical questions. Here we report the detection of the multi-component FRB 20191221A and the identification of a periodic separation of 216.8(1) ms between its components with a significance of 6.5 sigmas. The long (~3 s) duration and nine or more components forming the pulse profile make this source an outlier in the FRB population. Such short periodicity provides strong evidence for a neutron-star origin of the event. Moreover, our detection favours emission arising from the neutron-star magnetosphere, as opposed to emission regions located further away from the star, as predicted by some models.Comment: Updated to conform to the accepted versio

Gamma-ray and radio properties of six pulsars detected by the fermi large area telescope

We report the detection of pulsed γ-rays for PSRs J0631+1036, J0659+1414, J0742-2822, J1420-6048, J1509-5850, and J1718-3825 using the Large Area Telescope on board the Fermi Gamma-ray Space Telescope (formerly known as GLAST). Although these six pulsars are diverse in terms of their spin parameters, they share an important feature: their γ-ray light curves are (at least given the current count statistics) single peaked. For two pulsars, there are hints for a double-peaked structure in the light curves. The shapes of the observed light curves of this group of pulsars are discussed in the light of models for which the emission originates from high up in the magnetosphere. The observed phases of the γ-ray light curves are, in general, consistent with those predicted by high-altitude models, although we speculate that the γ-ray emission of PSR J0659+1414, possibly featuring the softest spectrum of all Fermi pulsars coupled with a very low efficiency, arises from relatively low down in the magnetosphere. High-quality radio polarization data are available showing that all but one have a high degree of linear polarization. This allows us to place some constraints on the viewing geometry and aids the comparison of the γ-ray light curves with high-energy beam models

Continuous glucose monitoring in pregnant women with type 1 diabetes (CONCEPTT): a multicentre international randomised controlled trial.

BACKGROUND: Pregnant women with type 1 diabetes are a high-risk population who are recommended to strive for optimal glucose control, but neonatal outcomes attributed to maternal hyperglycaemia remain suboptimal. Our aim was to examine the effectiveness of continuous glucose monitoring (CGM) on maternal glucose control and obstetric and neonatal health outcomes. METHODS: In this multicentre, open-label, randomised controlled trial, we recruited women aged 18-40 years with type 1 diabetes for a minimum of 12 months who were receiving intensive insulin therapy. Participants were pregnant (≤13 weeks and 6 days' gestation) or planning pregnancy from 31 hospitals in Canada, England, Scotland, Spain, Italy, Ireland, and the USA. We ran two trials in parallel for pregnant participants and for participants planning pregnancy. In both trials, participants were randomly assigned to either CGM in addition to capillary glucose monitoring or capillary glucose monitoring alone. Randomisation was stratified by insulin delivery (pump or injections) and baseline glycated haemoglobin (HbA1c). The primary outcome was change in HbA1c from randomisation to 34 weeks' gestation in pregnant women and to 24 weeks or conception in women planning pregnancy, and was assessed in all randomised participants with baseline assessments. Secondary outcomes included obstetric and neonatal health outcomes, assessed with all available data without imputation. This trial is registered with ClinicalTrials.gov, number NCT01788527. FINDINGS: Between March 25, 2013, and March 22, 2016, we randomly assigned 325 women (215 pregnant, 110 planning pregnancy) to capillary glucose monitoring with CGM (108 pregnant and 53 planning pregnancy) or without (107 pregnant and 57 planning pregnancy). We found a small difference in HbA1c in pregnant women using CGM (mean difference -0·19%; 95% CI -0·34 to -0·03; p=0·0207). Pregnant CGM users spent more time in target (68% vs 61%; p=0·0034) and less time hyperglycaemic (27% vs 32%; p=0·0279) than did pregnant control participants, with comparable severe hypoglycaemia episodes (18 CGM and 21 control) and time spent hypoglycaemic (3% vs 4%; p=0·10). Neonatal health outcomes were significantly improved, with lower incidence of large for gestational age (odds ratio 0·51, 95% CI 0·28 to 0·90; p=0·0210), fewer neonatal intensive care admissions lasting more than 24 h (0·48; 0·26 to 0·86; p=0·0157), fewer incidences of neonatal hypoglycaemia (0·45; 0·22 to 0·89; p=0·0250), and 1-day shorter length of hospital stay (p=0·0091). We found no apparent benefit of CGM in women planning pregnancy. Adverse events occurred in 51 (48%) of CGM participants and 43 (40%) of control participants in the pregnancy trial, and in 12 (27%) of CGM participants and 21 (37%) of control participants in the planning pregnancy trial. Serious adverse events occurred in 13 (6%) participants in the pregnancy trial (eight [7%] CGM, five [5%] control) and in three (3%) participants in the planning pregnancy trial (two [4%] CGM and one [2%] control). The most common adverse events were skin reactions occurring in 49 (48%) of 103 CGM participants and eight (8%) of 104 control participants during pregnancy and in 23 (44%) of 52 CGM participants and five (9%) of 57 control participants in the planning pregnancy trial. The most common serious adverse events were gastrointestinal (nausea and vomiting in four participants during pregnancy and three participants planning pregnancy). INTERPRETATION: Use of CGM during pregnancy in patients with type 1 diabetes is associated with improved neonatal outcomes, which are likely to be attributed to reduced exposure to maternal hyperglycaemia. CGM should be offered to all pregnant women with type 1 diabetes using intensive insulin therapy. This study is the first to indicate potential for improvements in non-glycaemic health outcomes from CGM use. FUNDING: Juvenile Diabetes Research Foundation, Canadian Clinical Trials Network, and National Institute for Health Research