scholarly journals Predicting Damage to Hop Cones by Tetranychus urticae (Acari: Tetranychidae)

2021 ◽  
Author(s):  
Joanna L Woods ◽  
Anne E Iskra ◽  
David H Gent
Keyword(s):  
Risk Factors ◽  
Tetranychus Urticae ◽  
Crop Damage ◽  
Equation Modeling ◽  
Data Sets ◽  
Current Management ◽  
Late Season ◽  
Model Based ◽  
Predatory Arthropods ◽  
Cone Damage

Abstract Twospotted spider mite (Tetranychus urticae Koch) is a cosmopolitan pest of numerous plants, including hop (Humulus lupulus L.). The most costly damage from the pest on hop results from infestation of cones, which are the harvested product, which can render crops unsalable if cones become discolored. We analyzed 14 yr of historical data from 312 individual experimental plots in western Oregon to identify risk factors associated with visual damage to hop cones from T. urticae. Logistic regression models were fit to estimate the probability of cone damage. The most predictive model was based on T. urticae-days during mid-July to harvest, which correctly predicted occurrence and nonoccurrence of cone damage in 91 and 93% of data sets, respectively, based on Youden’s index. A second model based on the ratio of T. urticae to predatory arthropods late in the season correctly predicted cone damage in 92% of data sets and nonoccurrence of damage in 77% of data sets. The model based on T. urticae abundance performed similarly when validated in 23 commercial hop yards, whereas the model based on the predator:prey ratio was relatively conservative and yielded false-positive predictions in 11 of the 23 yards. Antecedents of these risk factors were explored and quantified by structural equation modeling. A simple path diagram was constructed that conceptualizes T. urticae invasion of hop cones as dependent on prior density of the pest on leaves in early spring and summer, which in turn influences the development of predatory arthropods that mediate late-season density of the pest. In summary, the biological insights and models developed here provide guidance to pest managers on the likelihood of visual cone damage from T. urticae that can inform late-season management based on both abundance of the pest and its important predators. This is critically important because a formal economic threshold for T. urticae on hop does not exist and current management efforts may be mistimed to influence the pest when crop damage is most probable. More broadly, this research suggests that current management practices that target T. urticae early in the season may in fact predispose yards to later outbreaks of the pest.

How Community and Individual Risk Factors Mutually Impact Youth’s Perceived Safety: A Syndemic Analysis Using Structural Equation Modeling

2021 ◽  
pp. 088626052110283
Author(s):  
Yingwei Yang ◽  
Karen D. Liller ◽  
Martha Coulter ◽  
Abraham Salinas-Miranda ◽  
Dinorah Martinez Tyson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Community Safety ◽  
Equation Modeling ◽  
Individual Risk ◽  
Latent Factor ◽  
Community Factors ◽  
Individual Risk Factors ◽  
Syndemic Theory

The purpose of this study was to evaluate the mutual impact of community and individual factors on youth’s perceptions of community safety, using structural equation modeling (SEM) conceptualized by syndemic theory. This study used survey data collected from a county wide sample of middle and high school students (N=25,147) in West Central Florida in 2015. The outcome variable was youth’s perceptions of community safety. Predictors were latent individual and community factors constructed from 14 observed variables including gun accessibility, substance use, depressive symptoms, and multiple neighborhood disadvantage questions. Three structural equation models were conceptualized based on syndemic theory and analyzed in Mplus 8 using weighted least squares (WLS) estimation. Each model’s goodness of fit was assessed. Approximately seven percent of youth reported feeling unsafe in their community. After model modifications, the final model showed a good fit of the data and adhered to the theoretical assumption. In the final SEM model, an individual latent factor was implied by individual predictors measuring gun accessibility without adult’s permission (β=0.70), sadness and hopelessness (β=0.52), alcohol use (β=0.79), marijuana use (β=0.94), and illegal drug use (β=0.77). Meanwhile, a community latent factor was indicated by multiple community problems including public drinking (β=0.88), drug addiction (β=0.96), drug selling (β=0.97), lack of money (β=0.83), gang activities (β=0.90), litter and trash (β=0.79), graffiti (β=0.91), deserted houses (β=0.86), and shootings (β=0.93). A second-order syndemic factor that represented the individual and community factors showed a very strong negative association with youth’s safe perception (β=-0.98). This study indicates that individual risk factors and disadvantaged community conditions interacted with each other and mutually affected youth’s perceptions of community safety. To reduce these co-occurring effects and improve safe perceptions among youth, researchers and practitioners should develop and implement comprehensive strategies targeting both individual and community factors.

QSAR model based on weighted MCS trees approach for the representation of molecule data sets

2013 ◽  
Vol 27 (2) ◽  
pp. 185-201 ◽  
Author(s):  
Bernardo Palacios-Bejarano ◽  
Gonzalo Cerruela García ◽  
Irene Luque Ruiz ◽  
Miguel Ángel Gómez-Nieto
Keyword(s):  
Qsar Model ◽  
Data Sets ◽  
Model Based

Iranian Coaches Forgiveness and Relationship Quality to Their Athletes

2021 ◽  
pp. 1-8
Author(s):  
Hassan Gharayagh Zandi ◽  
Sahar Zarei ◽  
Mohammad Ali Besharat ◽  
Davoud Houminiyan sharif abadi ◽  
Ahmad Bagher Zadeh
Keyword(s):  
Relationship Quality ◽  
Structural Equation ◽  
Equation Modeling ◽  
Data Sets ◽  
Mediating Role ◽  
Interpersonal Behaviors ◽  
Key Factor ◽  
The Relationship

Coaching has often been viewed as a context within which coaches operate to largely bring about changes in athlete’s performance and flourishing. One key factor to successful outcomes in coaching is the quality of the relationship between coaches and athletes. The coach–athlete relationship is at the heart of coaching; however, limited studies have been conducted on its antecedents. The aim of this study was to investigate the relationship between coaches’ forgiveness and perceived relationship quality toward their athletes through verifying the mediating role of interpersonal behaviors of coaches. A total of 270 Iranian coaches participated in the survey, and the data sets were analyzed using structural equation modeling. Results revealed that forgiveness positively predicted the coaches’ perceived relationship quality with their athletes, and this pathway was mediated by the coaches’ interpersonal behaviors.

Prevalence and risk factors of root resorption of adjacent teeth in maxillary canine impaction, among untreated children and adolescents

2018 ◽  
Vol 41 (5) ◽  
pp. 447-453 ◽  
Author(s):  
Frédéric Rafflenbeul ◽  
Catherine-Isabelle Gros ◽  
François Lefebvre ◽  
Sophie Bahi-Gross ◽  
Raphaëlle Maizeray ◽  
...  
Keyword(s):  
Risk Factors ◽  
Children And Adolescents ◽  
Root Resorption ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Data Sets ◽  
Maxillary Canine ◽  
3D Data ◽  
Potential Risk Factors ◽  
Associated Risk Factors

Summary Objectives The aim of this retrospective study was to assess in maxillary canine impaction cases both the prevalence of root resorption of adjacent teeth among untreated children and adolescents, and its associated risk factors. Subjects and methods Sixty subjects (mean age 12.2 years; SD 1.9; range 8–17 years) with 83 displaced maxillary canines and without any past or ongoing orthodontic treatment were included in this study. The presence of root resorption was evaluated on images from a single cone beam computed tomography (CBCT) unit. Potential risk factors were measured on the CBCT images and on panoramic reconstructions of the 3D data sets. The sample was characterized by descriptive statistics and multiple logistic regressions were performed to predict root resorption. Results Root resorption of at least one adjacent tooth was detected in 67.5 per cent of the affected quadrants. It was found that 55.7 per cent of the lateral incisors, 8.4 per cent of the central incisors, and 19.5 per cent of first premolars were resorbed. Of the detected resorptions, 71.7 per cent were considered slight, 14.9 per cent moderate, and 13.4 per cent severe. Contact between the displaced canine(s) and the adjacent teeth roots was the only identified statistically significant risk factor, all teeth being considered (odds ratio [OR] = 18.7, 95% confidence interval: 2.26–756, P < 0.01). An enlarged canine dental follicle, a peg upper lateral, or an upper lateral agenesis were not significantly associated with root resorption of adjacent teeth, nor were age nor gender. Conclusions Root resorption of adjacent teeth was detected in more than two-thirds of a sample of sixty untreated children and adolescents.

Elastic model-based segmentation of 3-D neuroradiological data sets

1999 ◽  
Vol 18 (10) ◽  
pp. 828-839 ◽  
Author(s):  
A. Kelemen ◽  
G. Szekely ◽  
G. Gerig
Keyword(s):  
Elastic Model ◽  
Data Sets ◽  
Model Based

scholarly journals Towards a Parsimonious Pathway Model of Modifiable and Mediating Risk Factors Leading to Diabetes Risk

2021 ◽  
Author(s):  
Yi-Ching Lynn Ho ◽  
Vivian Shu Yi Lee ◽  
Moon-Ho Ringo Ho ◽  
Gladis Jing Lin ◽  
Julian Thumboo
Keyword(s):  
Risk Factors ◽  
Disease Prevention ◽  
Structural Equation ◽  
Diabetes Risk ◽  
Risk Indicators ◽  
Equation Modeling ◽  
Pathway Model ◽  
Physiological Load ◽  
Modifiable Factors ◽  
Lifestyle Risk

Abstract Background: The development of diabetes mellitus has been closely linked to multiple risk factors, of which modifiable factors are of particular interest for disease prevention. Yet few studies have assessed the system of pathways though which risk factors lead to diabetes, and how the different groups of risk factors may interact,both as independent or mediating factors. Methods: We aimed to develop a broad pathway model for diabetes risk with modifiable lifestyle risk factors as the start point, hypothesising that Lifestyle Risk (physical inactivity, smoking, poor diet and insufficient sleep) would impact Diabetes Risk (HbA1c) through the mediating factor of Physiological Load (BMI, resting pulse rate, CRP, systolic and diastolic blood pressure). The lifestyle and physiological factors were grouped via principal components analysis and a summary index respectively. Non modifiable risk factors, such as sociodemographics were specified as covariates. We used structural equation modeling to test this model, first using Wave 5 data from the Indonesian Family Life Survey (IFLS), as this was the only wave that collected all indicators of interest. To fit in longitudinal data from an earlier wave (IFLS4), we further tested a smaller model with the two Lifestyle Risk indicators available. Results: Both models showed indirect effects of Lifestyle Risk on Diabetes Risk via Physiological Load, with the cross-sectional model also showing a direct effect. The effect sizes were within the range of other studies that assessed the variables separately. Conclusion: Taken together, the results support the model of an indirect effect of Lifestyle Risk on Diabetes through Physiological Load. Specifying Lifestyle Risk as an observable, composite variable incorporates the cumulative effect of risk behaviour and differentiates this study from previous studies assessing it as a latent construct. We were able to assess causality with retrospective cohort data. Finally, the parsimonious model groups and summarises the multifarious risk factors and illustrates parsimonious and modifiable pathways that could be applied in chronic disease prevention efforts.

scholarly journals COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches

2021 ◽  
pp. 395-405
Author(s):  
David Opeoluwa Oyewola ◽  
Emmanuel Gbenga Dada ◽  
Juliana Ngozi Ndunagu ◽  
Terrang Abubakar Umar ◽  
Akinwunmi S.A
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Structural Equation Modeling ◽  
Latent Variables ◽  
Structural Equation ◽  
Equation Modeling ◽  
Support Vector ◽  
Economic Factors ◽  
Negative Effects ◽  
The Impact

Since the declaration of COVID-19 as a global pandemic, it has been transmitted to more than 200 nations of the world. The harmful impact of the pandemic on the economy of nations is far greater than anything suffered in almost a century. The main objective of this paper is to apply Structural Equation Modeling (SEM) and Machine Learning (ML) to determine the relationships among COVID-19 risk factors, epidemiology factors and economic factors. Structural equation modeling is a statistical technique for calculating and evaluating the relationships of manifest and latent variables. It explores the causal relationship between variables and at the same time taking measurement error into account. Bagging (BAG), Boosting (BST), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) Machine Learning techniques was applied to predict the impact of COVID-19 risk factors. Data from patients who came into contact with coronavirus disease were collected from Kaggle database between 23 January 2020 and 24 June 2020. Results indicate that COVID-19 risk factors have negative effects on epidemiology factors. It also has negative effects on economic factors.

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