Variation in Disease Incidence of Phomopsis Cane and Leaf Spot of Grape in Commercial Vineyards in Ohio

A statewide survey for incidence of Phomopsis cane and leaf spot of grape (caused by Phomopsis viticola) was conducted during the 2002 to 2004 growing seasons. Over the 3 years, disease was observed in all surveyed vineyards, and mean disease incidence for leaves and internodes was 42 and 50%, respectively. A hierarchical linear mixed model was used to evaluate effects of region, farm within region, vineyard within farm, sampling site (i.e., vine) within vineyard, and shoot (i.e., cane) within vine on disease incidence. Region of the state did not have a significant effect on incidence but there was significant variation at all other levels of the hierarchy (P < 0.05); the greatest variation was at the lowest scale (shoots within vines). The potential effects of weather and management practices on disease risk at the vineyard scale were determined by using nonparametric correlation and binary logistic analyses after first classifying mean incidence per vineyard as being below or above 20% (D20 = 0,1) and 40% (D40 = 0,1). Overall results indicated that variables for predicted number of moderate infection events (DM; based on ambient temperature and hours when either there was measured rainfall or relative humidity above 90%), the extent of fungicide application (C) during early- and mid-May (M1 and M2, respectively), and the use of a dormant-period application of fungicide (DOR) were the key factors in predicting disease risk (for either D20 or D40). Accuracy (percentage of high and low disease vineyards correctly predicted) and area under the receiver operating characteristic curve (an overall measure of the accuracy of a model) for a generic model combining these predictor variables were 74 and 0.84, respectively, for D40 and 87 and 0.97, respectively, for D20. Models based on management practices were as accurate as those that incorporated weather variables. Although the degree of control of this disease is inadequate in Ohio, based on the survey results for incidence, the results from the risk-model analysis showed that improved management might be obtained by applying fungicide early during the growing season.

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A Bayesian linear mixed model for prediction of complex traits

Bioinformatics ◽

10.1093/bioinformatics/btaa1023 ◽

2020 ◽

Author(s):

Yang Hai ◽

Yalu Wen

Keyword(s):

Complex Traits ◽

Mixed Model ◽

Linear Mixed Model ◽

Rare Variants ◽

Disease Risk ◽

R Package ◽

Underlying Disease ◽

Supplementary Information ◽

True Effect Size ◽

Bayes Algorithm

Abstract Motivation Accurate disease risk prediction is essential for precision medicine. Existing models either assume that diseases are caused by groups of predictors with small-to-moderate effects or a few isolated predictors with large effects. Their performance can be sensitive to the underlying disease mechanisms, which are usually unknown in advance. Results We developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient variational Bayes algorithm. The proposed method can resemble the shape of the true effect size distributions, captures the predictive effects from both common and rare variants, and is robust against various disease models. Through extensive simulations and the application to a whole-genome sequencing dataset obtained from the Alzheimer’s Disease Neuroimaging Initiatives, we have demonstrated that BLMM has better prediction performance than existing methods and can detect variables and/or genetic regions that are predictive. Availability The R-package is available at https://github.com/yhai943/BLMM Supplementary information Supplementary data are available at Bioinformatics online.

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Characterizing Heterogeneity of Disease Incidence in a Spatial Hierarchy: A Case Study from a Decade of Observations of Fusarium Head Blight of Wheat

Phytopathology ◽

10.1094/phyto-11-11-0323 ◽

2012 ◽

Vol 102 (9) ◽

pp. 867-877 ◽

Cited By ~ 11

Author(s):

A. B. Kriss ◽

P. A. Paul ◽

L. V. Madden

Keyword(s):

Spatial Heterogeneity ◽

Fusarium Head Blight ◽

Mixed Model ◽

Linear Mixed Model ◽

Disease Incidence ◽

Geographical Location ◽

Disease Status ◽

Link Function ◽

Head Blight ◽

Growing Seasons

A multilevel analysis of heterogeneity of disease incidence was conducted based on observations of Fusarium head blight (caused by Fusarium graminearum) in Ohio during the 2002–11 growing seasons. Sampling consisted of counting the number of diseased and healthy wheat spikes per 0.3 m of row at 10 sites (about 30 m apart) in a total of 67 to 159 sampled fields in 12 to 32 sampled counties per year. Incidence was then determined as the proportion of diseased spikes at each site. Spatial heterogeneity of incidence among counties, fields within counties, and sites within fields and counties was characterized by fitting a generalized linear mixed model to the data, using a complementary log-log link function, with the assumption that the disease status of spikes was binomially distributed conditional on the effects of county, field, and site. Based on the estimated variance terms, there was highly significant spatial heterogeneity among counties and among fields within counties each year; magnitude of the estimated variances was similar for counties and fields. The lowest level of heterogeneity was among sites within fields, and the site variance was either 0 or not significantly greater than 0 in 3 of the 10 years. Based on the variances, the intracluster correlation of disease status of spikes within sites indicated that spikes from the same site were somewhat more likely to share the same disease status relative to spikes from other sites, fields, or counties. The estimated best linear unbiased predictor (EBLUP) for each county was determined, showing large differences across the state in disease incidence (as represented by the link function of the estimated probability that a spike was diseased) but no consistency between years for the different counties. The effects of geographical location, corn and wheat acreage per county, and environmental conditions on the EBLUP for each county were not significant in the majority of years.

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Temperature and incubation period affect Septoria pistaciarum conidium germination: disease forecasting and validation

Phytopathologia Mediterranea ◽

10.36253/phyto-11216 ◽

2021 ◽

Vol 60 (1) ◽

pp. 113-117

Author(s):

Thomas THOMIDIS ◽

Konstantinos MICHOS ◽

Fotis CHATZIPAPADOPOULOS ◽

Amalia TAMPAKI

Keyword(s):

Incubation Period ◽

Disease Severity ◽

Leaf Spot ◽

Disease Risk ◽

Generic Model ◽

High Disease Severity ◽

Conidium Germination ◽

Effects Of Temperature ◽

Important Disease ◽

Septoria Leaf Spot

Septoria leaf spot is an important disease of pistachio trees in Greece. This study aimed to determine effects of temperature and the incubation period on germination of conidia of Septoria pistaciarum, and to evaluate a generic model to forecast pistachio leaf spot under the field conditions of Aegina Island, Greece. The optimum temperature for conidium germination was 23°C, and germination was inhibited at 35 and 4°C. At constant temperature of 23°C, conidia commenced germination after 9 h. The predictive model indexed disease risk close to 100 at 10 May at two locations (Rachi Moschona and Vigla) in 2017, and first leaf spot symptoms were observed on 17 May. Moderate to high disease severity (>25% leaves infected) were observed in unsprayed trees at the end of May. In 2018, the model indexed risk close to 100 on 9 May at Rachi Moschona, and first symptoms were observed on 18 May. Moderate to high disease severity (>25% leaves infected) were observed in unsprayed trees on 25th of May. This study has shown that the forecasting model can be used in Aegina Island, Greece, to predict the severity of Septoria leaf spot of pistachio.

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Developing Biomarkers of Rice Bran and Navy Bean Intake via Integrated Metabolomics from Infants, Children and Adults for Association with Gut Health Properties

Current Developments in Nutrition ◽

10.1093/cdn/nzaa045_096 ◽

2020 ◽

Vol 4 (Supplement_2) ◽

pp. 463-463

Author(s):

Elizabeth Ryan ◽

Bridget Baxter ◽

Katherine Li ◽

Lisa Wolfe ◽

Linxing Yao ◽

...

Keyword(s):

Rice Bran ◽

Dietary Patterns ◽

Mixed Model ◽

Linear Mixed Model ◽

Disease Risk ◽

Placebo Control ◽

Targeted Metabolomics ◽

Navy Bean ◽

Dietary Biomarkers ◽

Navy Beans

Abstract Objectives Self-reporting methods for dietary exposure are error-prone and have had limited impact to identify food components that mitigate disease risk. The purpose of this study was to use non-targeted and targeted metabolomics from feeding trials with rice bran and navy beans for the identification of dietary biomarkers across the lifespan. Methods Prepared meals/snacks, and biological samples from randomized-controlled trials performed in 50 infants, 38 children and 49 adults were utilized in this study. Diet groups were placebo control, rice bran, cooked navy bean powder, or a combination of rice bran/navy beans with increasing daily doses by age group and for duration of 4, 12 or 24 weeks per protocol. Plasma/dried blood spots, urine or stool samples were collected at a baseline, midpoint and endpoint. Non-targeted profiling was performed with UPLC-MS/MS, and metabolite quantification by LC-triple-quadropole (LC-QQQ-MS). A linear mixed model to compare between time points in each group was performed using SAS. Results The plasma/blood metabolomes contained between 771–1001 metabolites and showed variation in ∼20–30% of the profile following intervention. Fold changes over time and fold-differences in metabolite abundance were assessed by age (P < 0.05). There were 10–20 candidate identified from metabolomics across studies and with relevance to rice bran and/or navy bean were applied for targeted assay development. Food metabolomes confirmed metabolite origins and the host and microbial metabolism. Candidate metabolites included pipecolate, S-methlycysteine, S-methylcysteine sulfoxide, trigonelline, N-methyl-pipecolate, pyridoxal, 2-hydroxyhippurate, apigenin, xanthurenate, chiro-inositol, and salicylate. Inter-individual variation was reported across studies, ages and dietary patterns. Conclusions Dietary biomarkers for rice bran and/or navy bean intake merit additional selection criteria from non-targeted metabolomics. Targeted assays will need validation in larger cohort investigations using cross-over study designs and diverse dietary patterns. Funding Sources This work was supported by a grant from the National Institute of Foods and Agriculture-U.S Department of Agriculture (NIFA-USDA).

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Fatalism, Social Support and Self-Management Perceptions among Rural African Americans Living with Diabetes and Pre-Diabetes

Nursing Reports ◽

10.3390/nursrep11020024 ◽

2021 ◽

Vol 11 (2) ◽

pp. 242-252

Author(s):

Laurie Abbott ◽

Elizabeth Slate ◽

Lucinda Graven ◽

Jennifer Lemacks ◽

Joan Grant

Keyword(s):

Social Support ◽

Mixed Model ◽

Linear Mixed Model ◽

Diabetes Management ◽

Disease Risk ◽

Public Health Problem ◽

Clinical Trial Data ◽

Population Data ◽

The United States ◽

Self Management

Diabetes is a public health problem and a major risk factor for cardiovascular disease, the leading cause of death in the United States. Diabetes is prevalent among underserved rural populations. The purposes of this study were to perform secondary analyses of existing clinical trial data to determine whether a diabetes health promotion and disease risk reduction intervention had an effect on diabetes fatalism, social support, and perceived diabetes self-management and to provide precise estimates of the mean levels of these variables in an understudied population. Data were collected during a cluster randomized trial implemented among African American participants (n = 146) in a rural, southern area and analyzed using a linear mixed model. The results indicated that the intervention had no significant effect on perceived diabetes management (p = 0.8), diabetes fatalism (p = 0.3), or social support (p = 0.4). However, the estimates showed that, in the population, diabetes fatalism levels were moderate (95% CI = (27.6, 31.3)), and levels of social support (CI = (4.0, 4.4)) and perceived diabetes self-management (CI = (27.7, 29.3)) were high. These findings suggest that diabetes fatalism, social support, and self-management perceptions influence diabetes self-care and rural health outcomes and should be addressed in diabetes interventions.

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Effects of Temperature, Wetness Duration, and Leaflet Age on Infection of Strawberry Foliage by Phomopsis obscurans

Plant Disease ◽

10.1094/pdis.2003.87.5.579 ◽

2003 ◽

Vol 87 (5) ◽

pp. 579-584 ◽

Cited By ~ 9

Author(s):

M. Nita ◽

M. A. Ellis ◽

L. V. Madden

Keyword(s):

Prediction Model ◽

Environmental Conditions ◽

Mixed Model ◽

Linear Mixed Model ◽

Disease Incidence ◽

Leaf Wetness ◽

Controlled Environment ◽

Mixed Model Analysis ◽

Time Of Infection ◽

Effects Of Temperature

Temperature, leaf wetness, and leaflet age effects on infection of strawberry foliage by Phomopsis obscurans were examined in controlled-environment experiments. A mid-season (‘Honeoye’) and early-season (‘Earliglow’) cultivar were used. Tested temperatures were 10, 15, 20, 25, 30, and 35°C, and tested wetness periods were 5, 10, 15, 20, 25, and 35 h. Leaflets were labeled based on age: 0 to 1, 2 to 6, and 7 to 14 days old. Effects of temperature, wetness duration, and leaflet age on the logit of disease incidence and severity were quantified using a linear mixed model analysis of variance (ANOVA). Age, wetness duration, and their interaction significantly affected these measures of disease. Disease intensity decreased dramatically with increasing leaflet age at the time of infection, indicating a decrease in susceptibility with maturation of foliage, and increased with increasing wetness duration. Temperature only affected disease incidence with ‘Honeoye’. A prediction model was developed for leaflet infection based on ANOVA results. Coefficients of determination were approximately 0.8 for both cultivars and measures of disease (incidence and severity), indicating that disease could be described accurately based on environmental conditions and leaflet age.

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Analysis of the Spatial Pattern of Strawberry Angular Leaf Spot in California Nursery Production

Phytopathology ◽

10.1094/phyto-07-16-0275-r ◽

2017 ◽

Vol 107 (10) ◽

pp. 1243-1255 ◽

Cited By ~ 6

Author(s):

Christophe Gigot ◽

William Turechek ◽

Neil McRoberts

Keyword(s):

Spatial Pattern ◽

Leaf Spot ◽

Management Practices ◽

Disease Incidence ◽

Spatial Association ◽

Angular Leaf Spot ◽

Disease Spread ◽

Point Pattern ◽

Common Disease ◽

Nursery Production

In California, angular leaf spot (ALS) is a common disease in strawberry nursery production, and a major concern for nurseries wishing to export plants. As the spatial pattern of a disease can offer insight into pathogen source, mode of dissemination, and how current crop management practices affect epidemic development, an understanding of the spatial pattern of ALS would allow nursery growers to make informed decisions regarding disease management. Ninety-seven field assessments of disease incidence were performed at different nursery locations in 2014 and 2015 to quantify ALS spatial pattern under commercial conditions. Both point-pattern and geostatistical statistical procedures were used to analyze the data. The spatial pattern of ALS was characterized by a high degree of heterogeneity, as indicated by high median values of the beta-binomial distribution’s theta parameter (0.643), and the index of dispersion, D (4.218). The binary power law provided a robust description of the data with estimated slope and intercept parameters significantly greater than 1 and 0, respectively (P < 0.001). Spatial analysis by distance indices (SADIE) detected significant nonrandom spatial arrangements for 64% of the data sets. Analysis of directional disease spread showed a strong spatial association between sampling units along the same planting row. This suggests that recurrent crop operations during the growing season play a significant role in ALS spread and should be taken into account to improve disease control.

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Drivers for Annual Cork Growth under Two Understory Management Alternatives on a Podzolic Cork Oak Stand

Forests ◽

10.3390/f10020133 ◽

2019 ◽

Vol 10 (2) ◽

pp. 133 ◽

Cited By ~ 1

Author(s):

Sónia P. Faias ◽

Joana A. Paulo ◽

Paulo N. Firmino ◽

Margarida Tomé

Keyword(s):

Intraspecific Competition ◽

Management Practices ◽

Mixed Model ◽

Linear Mixed Model ◽

Two Dimensions ◽

Cork Oak ◽

Tree Level ◽

Cork Growth ◽

Management Alternatives ◽

Positive Effect

Understory management practices and stand density characteristics allow one to distinguish a cork oak traditional silvopastoral system (known as a montado) from a cork oak forest system. Although understanding the manner in which different management practices affect cork growth is imperative, there are still only a few outputs from experimental research that contribute to this knowledge. The effect of potential drivers on annual cork growth was analyzed using a linear mixed model approach. Two dimensions of drivers were considered: intraspecific competition, assessed by tree level distance-dependent indices; and interspecific competition, assessed by variables characterizing understory management. The present dataset was collected from an experimental trial established on a cork oak stand in Podzolic soil on the Tagus river basin, covering two different cork growth cycles over the period from 2003 to 2015. The adjusted models considered two understory management alternatives: spontaneous shrubs maintenance and forage application. In both models, annual precipitation displayed a positive effect on annual cork growth, as expected. However, no significant effect of intraspecific competition was found. Additionally, there was a positive effect on annual cork growth associated with the spontaneous shrubs growth and a negative effect associated with lupine presence; both effects linked to different cork ring ages’ thresholds. The study main contributions are the following: (i) the introduction of the interaction between cork growth cycle stage and understory management practices, only possible with cork sample collections from different cork rotation cycles; (ii) the finding that there was no significant effect of intraspecific competition on cork growth.

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Combination of longitudinal biomarkers in predicting binary events

Biostatistics ◽

10.1093/biostatistics/kxu020 ◽

2014 ◽

Vol 15 (4) ◽

pp. 706-718 ◽

Cited By ~ 15

Author(s):

Danping Liu ◽

Paul S. Albert

Keyword(s):

Random Effects ◽

Mixed Model ◽

Linear Mixed Model ◽

Disease Risk ◽

Likelihood Ratio Statistic ◽

Disease Status ◽

Analytical Form ◽

Mixed Effects ◽

Mixed Effects Model ◽

Shared Random Effects

Abstract In disease screening, the combination of multiple biomarkers often substantially improves the diagnostic accuracy over a single marker. This is particularly true for longitudinal biomarkers where individual trajectory may improve the diagnosis. We propose a pattern mixture model (PMM) framework to predict a binary disease status from a longitudinal sequence of biomarkers. The marker distribution given the disease status is estimated from a linear mixed effects model. A likelihood ratio statistic is computed as the combination rule, which is optimal in the sense of the maximum receiver operating characteristic (ROC) curve under the correctly specified mixed effects model. The individual disease risk score is then estimated by Bayes’ theorem, and we derive the analytical form of the 95% confidence interval. We show that this PMM is an approximation to the shared random effects (SRE) model proposed by Albert (2012. A linear mixed model for predicting a binary event from longitudinal data under random effects mis-specification. Statistics in Medicine31(2), 143–154). Further, with extensive simulation studies, we found that the PMM is more robust than the SRE model under wide classes of models. This new PPM approach for combining biomarkers is motivated by and applied to a fetal growth study, where the interest is in predicting macrosomia using longitudinal ultrasound measurements.

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Management of Septoria leaf spot disease in tomato at Bhaktapur.

10.31220/agrirxiv.2021.00059 ◽

2021 ◽

Author(s):

S. Khyaju ◽

G. K. C. ◽

R. Timila ◽

S. Shrestha

Keyword(s):

Leaf Spot ◽

Management Practices ◽

Disease Incidence ◽

Agricultural Practices ◽

Viral Disease ◽

Leaf Spot Disease ◽

Spot Disease ◽

Randomized Design ◽

Cow Urine ◽

Septoria Leaf Spot

Abstract A farmers' field survey was conducted during 2014 in Bhaktapur district to study socioeconomic status, agricultural practices and occurrence of Septoria Leaf Spot (Septoria lycopersici Mill.) of tomato and its management practices using random sampling of 25 respondents. Field experiment on management of Septoria Leaf Spot was conducted in a completely randomized design (CRD) with six treatments and four replications. The six treatments were (i) Astha Killer 15 (Azadirachta indica) 1500 ppm, (ii) Cow Urine (@ 5% concentration of cow urine; solar activation for 48 hours), (iii) Neem (2 ml/l water) + cow urine (5%) (1:1 ratio; final solution of 2 ml/l water), (iv) Chlorothalonil @ 2.5 gm/lt water, (v) Mancozeb (@ 2.5 gm/lt water), and (vi) control (water). Majority of farmers (70%) raised seedlings by themselves. Septoria Leaf Spot disease was the third important disease after Late Blight and viral disease. Septoria Leaf Spot disease was found in farms of 84% respondents. Septoria Leaf Spot symptom was found in leaf and both in leaf and stem in 47.62% and 52.38% respondents respectively. The disease incidence in field of 85.71% respondents was since 1-2 years. Higher relative humidity, higher precipitation and higher temperature were congenial for disease development. 80% of the respondents used synthetic fungicides for controlling the disease, where Mancozeb and Chlorothalonil were used by 44% and 20% respectively. Mancozeb was found most effective in controlling disease, followed by Chlorothalonil. Among the botanical treatments, Neem (2 ml/l water) + Cow urine (5%) was found effective in disease control than other two botanical treatments.

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