scholarly journals Risk Assessment Models for Wheat Fusarium Head Blight Epidemics Based on Within-Season Weather Data

2003 ◽  
Vol 93 (4) ◽  
pp. 428-435 ◽  
Author(s):  
E. D. De Wolf ◽  
L. V. Madden ◽  
P. E. Lipps
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Fusarium Head Blight ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Weather Conditions ◽  
Predictor Variables ◽  
Stepwise Logistic Regression ◽  
Head Blight ◽  
Logistic Regression Models

Logistic regression models for wheat Fusarium head blight were developed using information collected at 50 location-years, including four states, representing three different U.S. wheat-production regions. Non-parametric correlation analysis and stepwise logistic regression analysis identified combinations of temperature, relative humidity, and rainfall or durations of specified weather conditions, for 7 days prior to anthesis, and 10 days beginning at crop anthesis, as potential predictor variables. Prediction accuracy of developed logistic regression models ranged from 62 to 85%. Models suitable for application as a disease warning system were identified based on model prediction accuracy, sensitivity, specificity, and availability of weather variables at crop anthesis. Four of the identified models correctly classified 84% of the 50 location-years. A fifth model that used only pre-anthesis weather conditions correctly classified 70% of the location-years. The most useful predictor variables were the duration (h) of precipitation 7 days prior to anthesis, duration (h) that temperature was between 15 and 30°C 7 days prior to anthesis, and the duration (h) that temperature was between 15 and 30°C and relative humidity was greater than or equal to 90%. When model performance was evaluated with an independent validation set (n = 9), prediction accuracy was only 6% lower than the accuracy for the original data sets. These results indicate that narrow time periods around crop anthesis can be used to predict Fusarium head blight epidemics.

scholarly journals Predicting Fusarium Head Blight Epidemics With Weather-Driven Pre- and Post-Anthesis Logistic Regression Models

2013 ◽  
Vol 103 (9) ◽  
pp. 906-919 ◽  
Author(s):  
D. A. Shah ◽  
J. E. Molineros ◽  
P. A. Paul ◽  
K. T. Willyerd ◽  
L. V. Madden ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Fusarium Head Blight ◽  
Regression Models ◽  
The United States ◽  
Misclassification Rate ◽  
Predictor Variables ◽  
Head Blight ◽  
Data Set ◽  
Logistic Regression Models

Our objective was to identify weather-based variables in pre- and post-anthesis time windows for predicting major Fusarium head blight (FHB) epidemics (defined as FHB severity ≥ 10%) in the United States. A binary indicator of major epidemics for 527 unique observations (31% of which were major epidemics) was linked to 380 predictor variables summarizing temperature, relative humidity, and rainfall in 5-, 7-, 10-, 14-, or 15-day-long windows either pre- or post-anthesis. Logistic regression models were built with a training data set (70% of the 527 observations) using the leaps-and-bounds algorithm, coupled with bootstrap variable and model selection methods. Misclassification rates were estimated on the training and remaining (test) data. The predictive performance of models with indicator variables for cultivar resistance, wheat type (spring or winter), and corn residue presence was improved by adding up to four weather-based predictors. Because weather variables were intercorrelated, no single model or subset of predictor variables was best based on accuracy, model fit, and complexity. Weather-based predictors in the 15 final empirical models selected were all derivatives of relative humidity or temperature, except for one rainfall-based predictor, suggesting that relative humidity was better at characterizing moisture effects on FHB than other variables. The average test misclassification rate of the final models was 19% lower than that of models currently used in a national FHB prediction system.

scholarly journals Predicting Fusarium Head Blight Epidemics with Boosted Regression Trees

2014 ◽  
Vol 104 (7) ◽  
pp. 702-714 ◽  
Author(s):  
D. A. Shah ◽  
E. D. De Wolf ◽  
P. A. Paul ◽  
L. V. Madden
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Fusarium Head Blight ◽  
Regression Models ◽  
Boosted Regression Trees ◽  
Misclassification Rate ◽  
Head Blight ◽  
Data Set ◽  
Logistic Regression Models ◽  
Daily Relative Humidity

Predicting major Fusarium head blight (FHB) epidemics allows for the judicious use of fungicides in suppressing disease development. Our objectives were to investigate the utility of boosted regression trees (BRTs) for predictive modeling of FHB epidemics in the United States, and to compare the predictive performances of the BRT models with those of logistic regression models we had developed previously. The data included 527 FHB observations from 15 states over 26 years. BRTs were fit to a training data set of 369 FHB observations, in which FHB epidemics were classified as either major (severity ≥ 10%) or non-major (severity < 10%), linked to a predictor matrix consisting of 350 weather-based variables and categorical variables for wheat type (spring or winter), presence or absence of corn residue, and cultivar resistance. Predictive performance was estimated on a test (holdout) data set consisting of the remaining 158 observations. BRTs had a misclassification rate of 0.23 on the test data, which was 31% lower than the average misclassification rate over 15 logistic regression models we had presented earlier. The strongest predictors were generally one of mean daily relative humidity, mean daily temperature, and the number of hours in which the temperature was between 9 and 30°C and relative humidity ≥ 90% simultaneously. Moreover, the predicted risk of major epidemics increased substantially when mean daily relative humidity rose above 70%, which is a lower threshold than previously modeled for most plant pathosystems. BRTs led to novel insights into the weather–epidemic relationship.

scholarly journals Development of Weather-Based Predictive Models for Fusarium Head Blight and Deoxynivalenol Accumulation for Spring Malting Barley

Plant Disease ◽  
2012 ◽  
Vol 96 (5) ◽  
pp. 673-680 ◽  
Author(s):  
K. D. Bondalapati ◽  
J. M. Stein ◽  
S. M. Neate ◽  
S. H. Halley ◽  
L. E. Osborne ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Fusarium Head Blight ◽  
Weather Conditions ◽  
Gibberella Zeae ◽  
Predictor Variables ◽  
Northern Great Plains ◽  
Malting Barley ◽  
Head Blight ◽  
Hourly Temperature ◽  
Full Head

The associations between Fusarium head blight (FHB), caused by Gibberella zeae, and deoxynivalenol (DON) accumulation in spring malting barley (Hordeum vulgare) and hourly weather conditions predictive of DON accumulation were examined using data from six growing seasons in the U.S. Northern Great Plains. Three commonly grown cultivars were planted throughout the region, and FHB disease and DON concentration were recorded. Nine predictor variables were calculated using hourly temperature and relative humidity during the 10 days preceding full head spike emergence. Simple logistic regression models were developed using these predictor variables based on a binary threshold for DON of 0.5 mg/kg. Four of the nine models had sensitivity greater than 80%, and specificity of these models ranged from 67 to 84% (n = 150). The most useful predictor was the joint effect of average hourly temperature and a weighted duration of uninterrupted hours (h) with relative humidity greater than or equal to 90%. The results of this study confirm that FHB incidence is significantly associated with DON accumulation in the grain and that weather conditions prior to full head emergence could be used to accurately predict the risk of economically significant DON accumulation for spring malting barley.

scholarly journals Untargeted Fecal Metabolomics to Investigate the Role of the Microbiome and Nutrients in Osteoarthritis

2021 ◽  
Vol 5 (Supplement_2) ◽  
pp. 47-47
Author(s):  
Blake Rushing ◽  
Susan McRitchie ◽  
Liubov Arbeeva ◽  
Amanda Nelson ◽  
M. Andrea Azcarate-Peril ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Intestinal Inflammation ◽  
Stepwise Logistic Regression ◽  
Microbial Metabolites ◽  
Leaky Gut ◽  
Microbial Composition ◽  
Fecal Samples ◽  
Logistic Regression Models

Abstract Objectives The objective of this study was to determine if perturbations in gut microbial composition and the gut metabolome could be linked to individuals with obesity and osteoarthritis (OA). Methods Fecal samples were collected from 92 participants with obesity recruited from the Johnston County Osteoarthritis Project. OA cases (n = 59) had radiographic hand plus knee OA, defined as involvement of at least 3 joints across both hands, and a Kellgren-Lawrence (KL) grade 2–4 in at least one knee. Controls (n = 33) were without hand OA and with KL grade 0–1 knees. Fecal metabolomes were analyzed by a UHPLC/Q Exactive HFx mass spectrometer. Microbiome composition was determined in fecal samples by 16S ribosomal RNA amplicon sequencing (rRNA-seq). Stepwise logistic regression models were built to determine predictors of OA status. Spearman correlations were performed to determine associations between metabolites and microbiota in OA or healthy individuals. Results Untargeted metabolomics analysis indicated that OA cases had significantly higher levels of di- and tri-peptides (P &lt; 0.05), and significant perturbations (P &lt; 0.1) in microbial metabolites. Pathway analysis revealed several significantly perturbed pathways (P &lt; 0.05) associated with OA, including leukotriene metabolism, amino acid metabolism and fatty acid utilization. Logistic regression models selected metabolites associated with the microbiota and leaky gut syndrome as significant predictors of OA status, particularly when combined with the 16S rRNA sequencing data. Omega-3/6 polyunsaturated fatty acids (PUFAs) levels were significantly correlated with the phyla Bacteriodetes and Firmicutes. Conclusions Adults with obesity and OA have distinct fecal metabolomes characterized by perturbations in microbial metabolites, PUFAs, and protein digestion compared with healthy controls. These metabolic perturbations suggest a role of intestinal inflammation and leaky gut in OA. Funding Sources Supported by the Arthritis Foundation, the National Center for Advancing Translational Sciences (NCATS) (UL1TR002489), and the National Institute of Arthritis and Musculoskeletal and Skin Diseases (P30AR072580).

scholarly journals Determining the likelihood of incidents caused by human error during dynamic positioning drilling operations

2021 ◽  
pp. 1-13
Author(s):  
Zaloa Sanchez-Varela ◽  
David Boullosa-Falces ◽  
Juan L. Larrabe-Barrena ◽  
Miguel A. Gomez-Solaeche
Keyword(s):  
Logistic Regression ◽  
Water Depth ◽  
Regression Models ◽  
Human Error ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Weather Conditions ◽  
Dynamic Positioning ◽  
Logistic Regression Models ◽  
Drilling Operations

Abstract The probability of a human-caused incident occurring during dynamic positioning (DP) drilling operations is determined in this paper using binary logistic regression models built with data on 42 incidents that took place during the period 2011–2015. For each case, a range of variables characterising the configuration of the DP system, weather conditions and water depth are taken into account. These variables are taken into account to develop a logistic regression model that shows the likelihood of an incident being caused by human error. The results obtained show that human-based incidents are significantly more likely to occur when there is a lower usage of thrusters. These results are useful for focusing our attention on variables that may be associated with incidents attributable to human error, as well as for setting operational limits that could help to prevent these incidents and improve safety during these operations.

Relationships between Lower Extremity Musculoskeletal Disorders Symptoms and Tibial Acceleration Measures in Distribution Center Workers

2016 ◽  
Vol 60 (1) ◽  
pp. 980-981 ◽  
Author(s):  
Mohini Dutt ◽  
Steven A. Lavender ◽  
Carolyn M. Sommerich ◽  
Ajit M.W. Chaudhari
Keyword(s):  
Logistic Regression ◽  
Lower Extremity ◽  
Regression Models ◽  
Distribution Center ◽  
Musculoskeletal Symptoms ◽  
Predictor Variables ◽  
Logistic Regression Models ◽  
Outcome Score ◽  
Symptom Frequency ◽  
Tibial Acceleration

In a survey of 341 workers, we have found lower extremity musculoskeletal symptoms to be prevalent in distribution center employees working in material handling jobs. This study was a cross-sectional field study aimed at developing risk models showing associations between tibial acceleration and lower extremity musculoskeletal disorder symptoms. One hundred thirty two participants volunteered to wear uni-axial accelerometers that quantified their bilateral tibial acceleration exposures during two hours of normal work activities and also completed a questionnaire assessing individual factors and their experience with lower extremity musculoskeletal symptoms. The questionnaire and accelerometer data were used to develop logistic regression models exploring the relationships between the likelihood of self-reported lower extremity symptoms in the hip/thigh, knee, and ankles/feet and relevant biomechanical and individual exposure variables. An outcome score was created by multiplying the symptom frequency score by the symptom severity score by the therapy score for both the knees and the ankles/feet regions. Only the symptom frequency and severity scores were multiplied to create the hip/thigh outcome score. Multiple logistic regression models were used to predict the probability of being symptomatic based on the accelerometer, work exposure, and individual characteristics predictor variables. Table 1 shows the sensitivity of the models predicting symptoms in the hip/thighs, knees, and ankles/feet and the contributing predictor variables.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

scholarly journals Effect of prostatic apex shape (Lee types) and urethral sphincter length in preoperative MRI on very early continence rates after radical prostatectomy

2021 ◽  
Author(s):  
Mike Wenzel ◽  
Felix Preisser ◽  
Matthias Mueller ◽  
Lena H. Theissen ◽  
Maria N. Welte ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Radical Prostatectomy ◽  
Regression Models ◽  
Urethral Sphincter ◽  
Anatomical Characteristics ◽  
Logistic Regression Models ◽  
Pad Test ◽  
Type D ◽  
Multivariable Logistic Regression ◽  
Type C

Abstract Purpose To test the effect of anatomic variants of the prostatic apex overlapping the membranous urethra (Lee type classification), as well as median urethral sphincter length (USL) in preoperative multiparametric magnetic resonance imaging (mpMRI) on the very early continence in open (ORP) and robotic-assisted radical prostatectomy (RARP) patients. Methods In 128 consecutive patients (01/2018–12/2019), USL and the prostatic apex classified according to Lee types A–D in mpMRI prior to ORP or RARP were retrospectively analyzed. Uni- and multivariable logistic regression models were used to identify anatomic characteristics for very early continence rates, defined as urine loss of ≤ 1 g in the PAD-test. Results Of 128 patients with mpMRI prior to surgery, 76 (59.4%) underwent RARP vs. 52 (40.6%) ORP. In total, median USL was 15, 15 and 10 mm in the sagittal, coronal and axial dimensions. After stratification according to very early continence in the PAD-test (≤ 1 g vs. > 1 g), continent patients had significantly more frequently Lee type D (71.4 vs. 54.4%) and C (14.3 vs. 7.6%, p = 0.03). In multivariable logistic regression models, the sagittal median USL (odds ratio [OR] 1.03) and Lee type C (OR: 7.0) and D (OR: 4.9) were independent predictors for achieving very early continence in the PAD-test. Conclusion Patients’ individual anatomical characteristics in mpMRI prior to radical prostatectomy can be used to predict very early continence. Lee type C and D suggest being the most favorable anatomical characteristics. Moreover, longer sagittal median USL in mpMRI seems to improve very early continence rates.

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