Evaluation of the efficacy and economics of irrigation management, plant resistance and Brassica(spot)(TM) models for management of white blister on Brassica crops

Options for the integrated management of white blister (caused by Albugo candida) of Brassica crops include the use of well timed overhead irrigation, resistant cultivars, programs of weekly fungicide sprays or strategic fungicide applications based on the disease risk prediction model, Brassica(spot)(TM). Initial systematic surveys of radish producers near Melbourne, Victoria, indicated that crops irrigated overhead in the morning (0800-1200 h) had a lower incidence of white blister than those irrigated overhead in the evening (2000-2400 h). A field trial was conducted from July to November 2008 on a broccoli crop located west of Melbourne to determine the efficacy and economics of different practices used for white blister control, modifying irrigation timing, growing a resistant cultivar and timing spray applications based on Brassica(spot)(TM). Growing the resistant cultivar, 'Tyson', instead of the susceptible cultivar, 'Ironman', reduced disease incidence on broccoli heads by 99 %. Overhead irrigation at 0400 h instead of 2000 h reduced disease incidence by 58 %. A weekly spray program or a spray regime based on either of two versions of the Brassica(spot)(TM) model provided similar disease control and reduced disease incidence by 72 to 83 %. However, use of the Brassica(spot)(TM) models greatly reduced the number of sprays required for control from 14 to one or two. An economic analysis showed that growing the more resistant cultivar increased farm profit per ha by 12 %, choosing morning irrigation by 3 % and using the disease risk predictive models compared with weekly sprays by 15 %. The disease risk predictive models were 4 % more profitable than the unsprayed control.

Prediction of individual genetic risk to prostate cancer using a polygenic score

BACKGROUND Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. METHODS We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. RESULTS The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. CONCLUSIONS Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction. Prostate 75:1467–1474, 2015. © 2015 Wiley Periodicals, Inc.

Multiple Biomarkers for the prediction of ischemic stroke: the PRIME study

Objective: To simultaneously evaluate 14 biomarkers from distinct biological pathways for risk prediction of ischemic stroke, including biomarkers of hemostasis, inflammation, and endothelial activation as well as chemokines and adipocytokines.
Methods and Results: The Prospective Epidemiological Study on Myocardial Infarction (PRIME) is a cohort of 9771 healthy men 50 to 59 years of age who were followed up over 10 years. In a nested case–control study, 95 ischemic stroke cases were matched with 190 controls. After multivariable adjustment for traditional risk factors, fibrinogen (odds ratio [OR], 1.53; 95% confidence interval [CI], 1.03–2.28), E-selectin (OR, 1.76; 95% CI, 1.06–2.93), interferon-γ-inducible-protein-10 (OR, 1.72; 95% CI, 1.06–2.78), resistin (OR, 2.86; 95% CI, 1.30–6.27), and total adiponectin (OR, 1.82; 95% CI, 1.04–3.19) were significantly associated with ischemic stroke. Adding E-selectin and resistin to a traditional risk factor model significantly increased the area under the receiver-operating characteristic curve from 0.679 (95% CI, 0.612–0.745) to 0.785 and 0.788, respectively, and yielded a categorical net reclassification improvement of 29.9% (P=0.001) and 28.4% (P=0.002), respectively. Their simultaneous inclusion in the traditional risk factor model increased the area under the receiver-operating characteristic curve to 0.824 (95% CI, 0.770–0.877) and resulted in an net reclassification improvement of 41.4% (P<0.001). Results were confirmed when using continuous net reclassification improvement.
Conclusion: Among multiple biomarkers from distinct biological pathways, E-selectin and resistin provided incremental and additive value to traditional risk factors in predicting ischemic stroke.

Associations Between Personal Cancer History and Lung Cancer Risk

The study aim was to investigate the relationship between factors related to personal cancer history and lung cancer risk as well as assess their predictive utility. Characteristics of interest included the number, anatomical site(s), and age of onset of previous cancer(s). Data from the Prostate, Lung, Colorectal and Ovarian Screening (PLCO) Cancer Screening Trial (N = 154,901) and National Lung Screening Trial (N = 53,452) were analysed. Logistic regression models were used to assess the relationships between each variable of interest and 6-year lung cancer risk. Predictive utility was assessed through changes in area-under-the-curve (AUC) after substitution into the PLCOall2014 lung cancer risk prediction model. Previous lung, uterine and oral cancers were strongly and significantly associated with elevated 6-year lung cancer risk after controlling for confounders. None of these refined measures of personal cancer history offered more predictive utility than the simple (yes/no) measure already included in the PLCOall2014 model.

Bayesian Analysis for Errors in Variables with Changepoint Models

Changepoint regression models have originally been developed in connection with applications in quality control, where a change from the in-control to the out-of-control state has to be detected based on the avaliable random observations. Up to now various changepoint models have been suggested for differents applications like reliability, econometrics or medicine. In many practical situations the covariate cannot be measured precisely and an alternative model are the errors in variable regression models. In this paper we study the regression model with errors in variables with changepoint from a Bayesian approach. From the simulation study we found that the proposed procedure produces estimates suitable for the changepoint and all other model parameters.

Prediction of Psychosis by Mismatch Negativity

BACKGROUND: To develop risk-adapted prevention of psychosis, an accurate estimation of the individual risk of psychosis at a given time is needed. Inclusion of biological parameters into multilevel prediction models is thought to improve predictive accuracy of models on the basis of clinical variables. To this aim, mismatch negativity (MMN) was investigated in a sample clinically at high risk, comparing individuals with and without subsequent conversion to psychosis. METHODS: At baseline, an auditory oddball paradigm was used in 62 subjects meeting criteria of a late risk at-state who remained antipsychotic-naive throughout the study. Median follow-up period was 32 months (minimum of 24 months in nonconverters, n = 37). Repeated-measures analysis of covariance was employed to analyze the MMN recorded at frontocentral electrodes; additional comparisons with healthy controls (HC, n = 67) and first-episode schizophrenia patients (FES, n = 33) were performed. Predictive value was evaluated by a Cox regression model. RESULTS: Compared with nonconverters, duration MMN in converters (n = 25) showed significantly reduced amplitudes across the six frontocentral electrodes; the same applied in comparison with HC, but not FES, whereas the duration MMN in in nonconverters was comparable to HC and larger than in FES. A prognostic score was calculated based on a Cox regression model and stratified into two risk classes, which showed significantly different survival curves. CONCLUSIONS: Our findings demonstrate the duration MMN is significantly reduced in at-risk subjects converting to first-episode psychosis compared with nonconverters and may contribute not only to the prediction of conversion but also to a more individualized risk estimation and thus risk-adapted prevention.

Framingham risk score and alternatives for prediction of coronary heart disease in older adults

Background Guidelines for the prevention of coronary heart disease (CHD) recommend use of Framingham-based risk scores that were developed in white middle-aged populations. It remains unclear whether and how CHD risk prediction might be improved among older adults. We aimed to compare the prognostic performance of the Framingham risk score (FRS), directly and after recalibration, with refit functions derived from the present cohort, as well as to assess the utility of adding other routinely available risk parameters to FRS. Methods Among 2193 black and white older adults (mean age, 73.5 years) without pre-existing cardiovascular disease from the Health ABC cohort, we examined adjudicated CHD events, defined as incident myocardial infarction, CHD death, and hospitalization for angina or coronary revascularization. Results During 8-year follow-up, 351 participants experienced CHD events. The FRS poorly discriminated between persons who experienced CHD events vs. not (C-index: 0.577 in women; 0.583 in men) and underestimated absolute risk prediction by 51% in women and 8% in men. Recalibration of the FRS improved absolute risk prediction, particulary for women. For both genders, refitting these functions substantially improved absolute risk prediction, with similar discrimination to the FRS. Results did not differ between whites and blacks. The addition of lifestyle variables, waist circumference and creatinine did not improve risk prediction beyond risk factors of the FRS. Conclusions The FRS underestimates CHD risk in older adults, particularly in women, although traditional risk factors remain the best predictors of CHD. Re-estimated risk functions using these factors improve accurate estimation of absolute risk.

Predicting the risk of hospital admission in older persons--validation of a brief self-administered questionnaire in three European countries

OBJECTIVES: To validate the Probability of Repeated Admission (Pra) questionnaire, a widely used self-administered tool for predicting future healthcare use in older persons, in three European healthcare systems. DESIGN: Prospective study with 1-year follow-up. SETTING: Hamburg, Germany; London, United Kingdom; Canton of Solothurn, Switzerland. PARTICIPANTS: Nine thousand seven hundred thirteen independently living community-dwelling people aged 65 and older. MEASUREMENTS: Self-administered eight-item Pra questionnaire at baseline. Self-reported number of hospital admissions and physician visits during 1 year of follow-up. RESULTS: In the combined sample, areas under the receiver operating characteristic curves (AUCs) were 0.64 (95% confidence interval (CI)=0.62-0.66) for the prediction of one or more hospital admissions and 0.68 (95% CI=0.66-0.69) for the prediction of more than six physician visits during the following year. AUCs were similar between sites. In comparison, prediction models based on a person's age and sex alone exhibited poor predictive validity (AUC risk individuals (Pra score >or= 0.5) were 2.3 times as likely (95% CI=2.1-2.6) as low-risk individuals to have a hospital admission, and 2.1 times as likely (95% CI=2.0-2.2) to have more than six physician visits. CONCLUSION: The Pra instrument exhibits good validity for predicting future health service use on a population level in different healthcare settings. Administrative data have shown similar predictive validity, but in practice, such data are often not available. The Pra is likely of high interest to governments and health insurance companies worldwide as a basis for programs aimed at health risk management in older persons.

Prediction of arrhythmic events by Wedensky modulation in patients with coronary artery disease.

PRINCIPLES Prediction of arrhythmic events (AEs) has gained importance with the availability of implantable cardioverter-defibrillators (ICDs), but is still imprecise. This study evaluated the innovative Wedensky modulation index (WMI) as predictor of AEs. METHODS In this prospective cohort, 179 patients with coronary artery disease (CAD) referred for AE risk assessment underwent baseline evaluation including measurement of R-/T-wave WMI (WMI(RT)) and left ventricular ejection fraction (LVEF). Two endpoints were assessed 3 years after the baseline evaluation: sudden cardiac death or appropriate ICD event (EP1) and any cardiac death or appropriate ICD event (EP2). Associations between baseline predictors (WMI(RT) and LVEF) and endpoints were evaluated in regression models. RESULTS Only three patients were lost to follow-up. EP1 and EP2 occurred in 24 and 27 patients, respectively. WMI(RT) (odds ratio [OR] per 1 point increase for EP1 20.1, 95% confidence interval [CI] 1.8-221.4, p = 0.014, and for EP2 73.3, 95% CI 6.6-817.7, p <0.001) and LVEF (OR per 1% increase for EP1 0.94, 95% CI 0.90-0.99, p = 0.013, and for EP2 0.93, 95% CI 0.89-0.97, p = 0.002) were significantly associated with both endpoints. In bivariable regression controlled for LVEF, WMI(RT) was independently associated with EP1 (p = 0.047) and EP2 (p = 0.007). The combination of WMI(RT) ≥0.60 and LVEF ≤30% resulted in a positive predictive value of 36% for EP1 and 50% for EP2. CONCLUSIONS WMI(RT) is a significant predictor of AEs independent of LVEF and has potential to improve AE risk prediction in CAD patients. However, WMI(RT) should be evaluated in larger and independent samples before recommendations for clinical routine can be made.

Normal Thyroid Function and the Risk of Atrial Fibrillation: the Rotterdam Study

CONTEXT Hyperthyroidism is an established risk factor for atrial fibrillation (AF), but information concerning the association with variations within the normal range of thyroid function and subgroups at risk is lacking. OBJECTIVE This study aimed to investigate the association between normal thyroid function and AF prospectively and explore potential differential risk patterns. DESIGN, SETTING, AND PARTICIPANTS From the Rotterdam Study we included 9166 participants ≥ 45 y with TSH and/or free T4 (FT4) measurements and AF assessment (1997-2012 median followup, 6.8 y), with 399 prevalent and 403 incident AF cases. MAIN OUTCOME MEASURES Outcome measures were 3-fold: 1) hazard ratios (HRs) for the risk of incident AF by Cox proportional-hazards models, 2) 10-year absolute risks taking competing risk of death into account, and 3) discrimination ability of adding FT4 to the CHARGE-AF simple model, an established prediction model for AF. RESULTS Higher FT4 levels were associated with higher risks of AF (HR 1.63, 95% confidence interval, 1.19-2.22), when comparing those in the highest quartile to those in lowest quartile. Absolute 10-year risks increased with higher FT4 in participants ≤ 65 y from 1-9% and from 6-12% in subjects ≥ 65 y. Discrimination of the prediction model improved when adding FT4 to the simple model (c-statistic, 0.722 vs 0.729; P = .039). TSH levels were not associated with AF. CONCLUSIONS There is an increased risk of AF with higher FT4 levels within the normal range, especially in younger subjects. Adding FT4 to the simple model slightly improved discrimination of risk prediction.

Gender associated risk factors of aortic aneurysms and dissections

Objectives. Predict who will develop a dissection. To create male and female prediction models using the risk factors: age, ethnicity, hypertension, high cholesterol, smoking, alcohol use, diabetes, heart attack, congestive heart failure, congenital and non-congenital heart disease, Marfan syndrome, and bicuspid aortic valve. ^ Methods. Using 572 patients diagnosed with aortic aneurysms, a model was developed for each of males and females using 80% of the data and then verified using the remaining 20% of the data. ^ Results. The male model predicted the probability of a male in having a dissection (p=0.076) and the female model predicted the probability of a female in having a dissection (p=0.054). The validation models did not support the choice of the developmental models. ^ Conclusions. The best models obtained suggested that those who are at a greater risk of having a dissection are males with non-congenital heart disease and who drink alcohol, and females with non-congenital heart disease and bicuspid aortic valve.^

Space-Time Smoothing Models for Surveillance and Complex Survey Data

Thesis (Ph.D.)--University of Washington, 2016-06

Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Prediction Models yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.

Bayesian inference for wind field retrieval

In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model for the relationships between local variables and to use this as the prior in a Bayesian inference procedure. We show how a Gaussian process with hyper-parameters estimated from Numerical Weather Prediction Models yields meteorologically convincing wind fields. We use neural networks to make local estimates of wind vector probabilities. The resulting inference problem cannot be solved analytically, but Markov Chain Monte Carlo methods allow us to retrieve accurate wind fields.

A pragmatic Bayesian approach to wind field retrieval

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about 800 km, carrying a C-band scatterometer. A scatterometer measures the amount of backscatter microwave radiation reflected by small ripples on the ocean surface induced by sea-surface winds, and so provides instantaneous snap-shots of wind flow over large areas of the ocean surface, known as wind fields. Inherent in the physics of the observation process is an ambiguity in wind direction; the scatterometer cannot distinguish if the wind is blowing toward or away from the sensor device. This ambiguity implies that there is a one-to-many mapping between scatterometer data and wind direction. Current operational methods for wind field retrieval are based on the retrieval of wind vectors from satellite scatterometer data, followed by a disambiguation and filtering process that is reliant on numerical weather prediction models. The wind vectors are retrieved by the local inversion of a forward model, mapping scatterometer observations to wind vectors, and minimising a cost function in scatterometer measurement space. This thesis applies a pragmatic Bayesian solution to the problem. The likelihood is a combination of conditional probability distributions for the local wind vectors given the scatterometer data. The prior distribution is a vector Gaussian process that provides the geophysical consistency for the wind field. The wind vectors are retrieved directly from the scatterometer data by using mixture density networks, a principled method to model multi-modal conditional probability density functions. The complexity of the mapping and the structure of the conditional probability density function are investigated. A hybrid mixture density network, that incorporates the knowledge that the conditional probability distribution of the observation process is predominantly bi-modal, is developed. The optimal model, which generalises across a swathe of scatterometer readings, is better on key performance measures than the current operational model. Wind field retrieval is approached from three perspectives. The first is a non-autonomous method that confirms the validity of the model by retrieving the correct wind field 99% of the time from a test set of 575 wind fields. The second technique takes the maximum a posteriori probability wind field retrieved from the posterior distribution as the prediction. For the third technique, Markov Chain Monte Carlo (MCMC) techniques were employed to estimate the mass associated with significant modes of the posterior distribution, and make predictions based on the mode with the greatest mass associated with it. General methods for sampling from multi-modal distributions were benchmarked against a specific MCMC transition kernel designed for this problem. It was shown that the general methods were unsuitable for this application due to computational expense. On a test set of 100 wind fields the MAP estimate correctly retrieved 72 wind fields, whilst the sampling method correctly retrieved 73 wind fields.