Association between metabolic syndrome and incidence of ocular motor nerve palsy

To assess the association between metabolic syndrome (MetS) and the development of third, fourth, and sixth cranial nerve palsy (CNP). Health checkup data of 4,067,842 individuals aged between 20 and 90 years provided by the National Health Insurance Service (NHIS) of South Korea between January 1, 2009, and December 31, 2009, were analyzed. Participants were followed up to December 31, 2017. Hazard ratio (HR) and 95% confidence interval (CI) of CNP were estimated using Cox proportional hazards regression analysis after adjusting for potential confounders. Model 1 included only incident CNP as a time-varying covariate. Model 2 included model 1 and individual’s age and sex. Model 3 included model 2, smoking status, alcohol consumption, and physical activity of individuals. We identified 5,835 incident CNP cases during the follow-up period (8.22 ± 0.94 years). Individuals with MetS (n = 851,004) showed an increased risk of CNP compared to individuals without MetS (n = 3,216,838) after adjustment (model 3: HR = 1.35, 95% CI 1.273–1.434). CNP incidence was positively correlated with the number of MetS components (log-rank p < 0.0001). The HR of CNP for males with MetS compared to males without MetS was higher than that of females with MetS compared to females without MetS (HR: 1.407, 95% CI 1.31–1.51 in men and HR: 1.259, 95% CI 1.13–1.40 in women, p for interaction = 0.0017). Our population-based large-scale cohort study suggests that MetS and its components might be risk factors for CNP development.

Statistical assessment of feeding corn with higher oil content to piglets in the starter phase

The aim of this study was to assess the digestibility coefficients (DC) of corn [maize] with an oil content above 3.46% and its effects on the performance of piglets when fed as dry grain (DG) and as rehydrated corn grain silage (RCGS). In Experiment I, 15 piglets (22.51 + 2.39 kg) were allocated to a reference diet (RD) and to two test diets in which corn in the RD was replaced with DG or RCGS. There were five replications of each treatment. Experiment II involved 36 piglets (14.76 ± 2.72 kg), which were assigned to a control diet with common corn grain and to diets in which DG or RCGS replaced the common corn. There were six replications of each treatment. Data were analysed with four statistical models. Model 1 included only the effect of treatment. Model 2 was similar to Model 1 but included initial bodyweight as a covariate. Model 3 was similar to model 1 but included the interaction of diet and period. Model 4 was similar to Model 3 but included the covariate. The more complicated models were generally preferred to Model 1 as they controlled more of the nuisance variation. Feeding a diet that contained RCGS reduced feed intake and improved feed conversion ratio (FCR).

Covariate Model of Pixel Vector Intensities of Invasive H. sosnowskyi Plants

This article describes an agricultural application of remote sensing methods. The idea is to aid in eradicating an invasive plant called Sosnowskyi borscht (H. sosnowskyi). These plants contain strong allergens and can induce burning skin pain, and may displace native plant species by overshadowing them, meaning that even solitary individuals must be controlled or destroyed in order to prevent damage to unused rural land and other neighbouring land of various types (mostly violated forest or housing areas). We describe several methods for detecting H. sosnowskyi plants from Sentinel-2A images, and verify our results. The workflow is based on recently improved technologies, which are used to pinpoint exact locations (small areas) of plants, allowing them to be found more efficiently than by visual inspection on foot or by car. The results are in the form of images that can be classified by several methods, and estimates of the cross-covariance or single-vector auto-covariance functions of the contaminant parameters are calculated from random functions composed of plant pixel vector data arrays. The correlation of the pixel vectors for H. sosnowskyi images depends on the density of the chlorophyll content in the plants. Estimates of the covariance functions were computed by varying the quantisation interval on a certain time scale and using a computer programme based on MATLAB. The correlation between the pixels of the H. sosnowskyi plants and other plants was found, possibly because their structures have sufficiently unique spectral signatures (pixel values) in raster images. H. sosnowskyi can be identified and confirmed using a combination of two classification methods (using supervised and unsupervised approaches). The reliability of this combined method was verified by applying the theory of covariance function, and the results showed that H. sosnowskyi plants had a higher correlation coefficient. This can be used to improve the results in order to get rid of plants in particular areas. Further experiments will be carried out to confirm these results based on in situ fieldwork, and to calculate the efficiency of our method.

Metabolite-Investigator: an integrated user-friendly workflow for metabolomics multi-study analysis

Motivation Many diseases have a metabolic background, which is increasingly investigated due to improved measurement techniques allowing high-throughput assessment of metabolic features in several body fluids. Integrating data from multiple cohorts is of high importance to obtain robust and reproducible results. However, considerable variability across studies due to differences in sampling, measurement techniques and study populations needs to be accounted for. Results We present Metabolite-Investigator, a scalable analysis workflow for quantitative metabolomics data from multiple studies. Our tool supports all aspects of data pre-processing including data integration, cleaning, transformation, batch analysis as well as multiple analysis methods including uni- and multivariable factor-metabolite associations, network analysis and factor prioritization in one or more cohorts. Moreover, it allows identifying critical interactions between cohorts and factors affecting metabolite levels and inferring a common covariate model, all via a graphical user interface. Availability and implementation We constructed Metabolite-Investigator as a free and open web-tool and stand-alone Shiny-app. It is hosted at https://apps.health-atlas.de/metabolite-investigator/, the source code is freely available at https://github.com/cfbeuchel/Metabolite-Investigator. Supplementary information Supplementary data are available at Bioinformatics online.

Comparison of covariate selection methods with correlated covariates: prior information versus data information, or a mixture of both?

The inclusion of covariates in population models during drug development is a key step to understanding drug variability and support dosage regimen proposal, but high correlation among covariates often complicates the identification of the true covariate. We compared three covariate selection methods balancing data information and prior knowledge: (1) full fixed effect modelling (FFEM), with covariate selection prior to data analysis, (2) simplified stepwise covariate modelling (sSCM), data driven selection only, and (3) Prior-Adjusted Covariate Selection (PACS) mixing both. PACS penalizes the a priori less likely covariate model by adding to its objective function value (OFV) a prior probability-derived constant: $$2*{\kern 1pt} \,{\ln}\left( {{\Pr}\left( X \right)/\left( {1 - {\Pr}\left( X \right)} \right)} \right)$$ 2 ∗ ln Pr X / 1 - Pr X , Pr(X) being the probability of the more likely covariate. Simulations were performed to compare their external performance (average OFV in a validation dataset of 10,000 subjects) in selecting the true covariate between two highly correlated covariates: 0.5, 0.7, or 0.9, after a training step on datasets of 12, 25 or 100 subjects (increasing power). With low power data no method was superior, except FFEM when associated with highly correlated covariates ($$r=0.9$$ r = 0.9 ), sSCM and PACS suffering both from selection bias. For high power data, PACS and sSCM performed similarly, both superior to FFEM. PACS is an alternative for covariate selection considering both the expected power to identify an anticipated covariate relation and the probability of prior information being correct. A proposed strategy is to use FFEM whenever the expected power to distinguish between contending models is < 80%, PACS when > 80% but < 100%, and SCM when the expected power is 100%.

P658CHA2DS2-VASc score calibration in anticoagulated vs non-anticoagulated patients in a healthcare area

Introduction Atrial fibrillation (AF) is a highly prevalent heart disease, affecting a significant proportion of patients over 65 years old. The CHA2DS2-VASc score predicts 1 year risk of a thromboembolic (TE) event and is well validated against several populations. However, calibration may vary if there is subgroup heterogeneity. Purpose To compare the CHA2DS2-VASc score calibration, in patients with or without anticoagulation (AC) in a real population of AF patients in our healthcare area. Methods Patients with an episode with atrial fibrillation/flutter were selected from a general population in a healthcare area (383,000 subjects), with 21/12/2013 as a cut-off date. Patients with valve disease, anticoagulation or antiplatelet therapy were identified. The CHA2DS2-VASc score was calculated as stipulated in the European Society of Cardiology guidelines. A CHA2DS2-VASc score of 0 is considered to be low risk for TE events (0% at 1 year), score of 1 intermediate risk (0.6% rate at 1 year), and greater than 1 high risk (3% rate at 1 year). Quantitative variables are presented as mean and standard deviation (SD). Categorical variables were presented as frequencies and percentages. A logistic regression was fitted to predict 1-year risk TE outcomes with CHA2DS2-VASc as the only covariate. Model calibration was assessed using the predicted versus actual probabilities of TE events. All analyses were performed using R v.3.4 (R Core Team, Vienna, Austria) with the packages rms and ggplot2. Results CHA2DS2-VASc was calculated in 7990 patients with AF. A total of 1824 patients were excluded either due to valvular disease (846) or due to previous antiplatelet treatment (1047). From them, 143 patients were excluded for an incomplete follow-up time (<1 year). As of December 31, 2015, 67 stroke cases had been notified from 6023 patients (1.1%) (Table 1). Mortality rate was 181 (3%) at 1 year. Patients presented overall low risks of stroke with a poor score calibration. Higher scores presented risks that were lower than predicted by CHA2DS2-VASc. Event rate at 1 year was similar regardless of the AC regime at the initial date, and also similar to a previous cohort of anticoagulated patients (Lip et al.). This similarity may indicate confounding by later AC therapy initiation, before the final assessment date. Table 1. Comparison of thromboembolic event rates in several studies % (No-AC) % (AC) % Lip 2010 % Poli 2011 % Friberg 2012 % Okumura 2014 0.01 0.01 0.02 4.5 4.5 1 AC: anticoagulation. CHA2DS2VASc score calibration Conclusion Higher CHA2DS2-VASc scores are not associated to higher risks of stroke in our healthcare area, in patients with non-valvular AF and without antiplatelet therapy.

Estimating spatial–temporal differences in Chinook salmon outmigration survival with habitat- and predation-related covariates

Low survival rates of Chinook salmon (Oncorhynchus tshawytscha) smolts in California’s Central Valley have been attributed to multiple biological and physical factors, but it is not clear which factors have the largest impact. We used 5 years of acoustic telemetry data for 1709 late-fall Chinook salmon smolts to evaluate the effect of habitat- and predation-related covariates on outmigration survival through the Sacramento River. Using a Cormack–Jolly–Seber mark–recapture model, we estimated survival rates both as a function of covariates (covariate model) and as a function of river location and release year (spatial–temporal model). Our covariate model was overwhelmingly supported as the preferred model based on model selection criteria, suggesting the covariates adequately replicated spatial and temporal patterns in smolt survival. The covariates in the selected model included individual fish covariates, habitat-specific covariates, and temporally variable physical conditions. The most important covariate affecting salmon survival was flow. We describe the importance of these parameters in the context of juvenile salmon predation risk and suggest that additional research on predator distribution and density could improve model estimates.