The shipping industry is one of the strongest anthropogenic emitters of NOxNOx—a substance harmful both to human health and the environment. The rapid growth of the industry causes societal... Show moreThe shipping industry is one of the strongest anthropogenic emitters of NOxNOx—a substance harmful both to human health and the environment. The rapid growth of the industry causes societal pressure on controlling the emission levels produced by ships. All the methods currently used for ship emission monitoring are costly and require proximity to a ship, which makes global and continuous emission monitoring impossible. A promising approach is the application of remote sensing. Studies showed that some of the NO2NO2 plumes from individual ships can visually be distinguished using the TROPOspheric Monitoring Instrument on board the Copernicus Sentinel 5 Precursor (TROPOMI/S5P). To deploy a remote-sensing-based global emission monitoring system, an automated procedure for the estimation of NO2NO2 emissions from individual ships is needed. The extremely low signal-to-noise ratio of the available data, as well as the absence of the ground truth makes the task very challenging. Here, we present a methodology for the automated segmentation of NO2NO2 plumes produced by seagoing ships using supervised machine learning on TROPOMI/S5P data. We show that the proposed approach leads to more than a 20% increase in the average precision score in comparison to the methods used in previous studies and results in a high correlation of 0.834 with the theoretically derived ship emission proxy. This work is a crucial step towards the development of an automated procedure for global ship emission monitoring using remote sensing data. Show less
Background\Objectives\Methods\Results\Conclusions\Collateral effects of antibiotic resistance occur when resistance to one antibiotic agent leads to increased resistance or increased sensitivity to... Show moreBackground\Objectives\Methods\Results\Conclusions\Collateral effects of antibiotic resistance occur when resistance to one antibiotic agent leads to increased resistance or increased sensitivity to a second agent, known respectively as collateral resistance (CR) and collateral sensitivity (CS). Collateral effects are relevant to limit impact of antibiotic resistance in design of antibiotic treatments. However, methods to detect antibiotic collateral effects in clinical population surveillance data of antibiotic resistance are lacking.\nTo develop a methodology to quantify collateral effect directionality and effect size from large-scale antimicrobial resistance population surveillance data. We propose a methodology to quantify and test collateral effects in clinical surveillance data based on a conditional t-test. Our methodology was evaluated using MIC data for 419 Escherichia coli strains, containing MIC data for 20 antibiotics, which were obtained from the Pathosystems Resource Integration Center (PATRIC) database. We demonstrate that the proposed approach identifies several antibiotic combinations that show symmetrical or non-symmetrical CR and CS. For several of these combinations, collateral effects were previously confirmed in experimental studies. We furthermore provide insight into the power of our method for multiple collateral effect sizes and MIC distributions. Our proposed approach is of relevance as a tool for analysis of large-scale population surveillance studies to provide broad systematic identification of collateral effects related to antibiotic resistance, and is made available to the community as an R package. This method can help mapping CS and CR, which could guide combination therapy and prescribing in the future. Show less
In meta-analysis, heterogeneity often exists between studies. Knowledge about study features (i.e., moderators) that can explain the heterogeneity in effect sizes can be useful for researchers to... Show moreIn meta-analysis, heterogeneity often exists between studies. Knowledge about study features (i.e., moderators) that can explain the heterogeneity in effect sizes can be useful for researchers to assess the effectiveness of existing interventions and design new potentially effective interventions. When there are multiple moderators, they may amplify or attenuate each other's effect on treatment effectiveness. However, in most meta-analysis studies, interaction effects are neglected due to the lack of appropriate methods. The method meta-CART was recently proposed to identify interactions between multiple moderators. The analysis result is a tree model in which the studies are partitioned into more homogeneous subgroups by combinations of moderators. This paper describes the R-packagemetacart, which provides user-friendly functions to conduct meta-CART analyses in R. This package can fit both fixed- and random-effects meta-CART, and can handle dichotomous, categorical, ordinal and continuous moderators. In addition, a new look ahead procedure is presented. The application of the package is illustrated step-by-step using diverse examples. Show less
In meta‐analytic studies, there are often multiple moderators available (eg, study characteristics). In such cases, traditional meta‐analysis methods often lack sufficient power to investigate... Show moreIn meta‐analytic studies, there are often multiple moderators available (eg, study characteristics). In such cases, traditional meta‐analysis methods often lack sufficient power to investigate interaction effects between moderators, especially high‐order interactions. To overcome this problem, meta‐CART was proposed: an approach that applies classification and regression trees (CART) to identify interactions, and then subgroup meta‐analysis to test the significance of moderator effects. The aim of this study is to improve meta‐CART upon two aspects: 1) to integrate the two steps of the approach into one and 2) to consistently take into account the fixed‐effect or random‐effects assumption in both the the interaction identification and testing process. For fixed effect meta‐CART, weights are applied, and subgroup analysis is adapted. For random effects meta‐CART, a new algorithm has been developed. The performance of the improved meta‐CART was investigated via an extensive simulation study on different types of moderator variables (ie, dichotomous, nominal, ordinal, and continuous variables). The simulation results revealed that the new method can achieve satisfactory performance (power greater than 0.80 and Type I error less than 0.05) if appropriate pruning rule is applied and the number of studies is large enough. The required minimum number of studies ranges from 40 to 120 depending on the complexity and strength of the interaction effects, the within‐study sample size, the type of moderators, and the residual heterogeneity. Show less
OBJECTIVE: The aim is to characterize subgroups or phenotypes of rheumatoid arthritis (RA) patients using a systems biology approach. The discovery of subtypes of rheumatoid arthritis patients is... Show moreOBJECTIVE: The aim is to characterize subgroups or phenotypes of rheumatoid arthritis (RA) patients using a systems biology approach. The discovery of subtypes of rheumatoid arthritis patients is an essential research area for the improvement of response to therapy and the development of personalized medicine strategies. METHODS: In this study, 39 RA patients are phenotyped using clinical chemistry measurements, urine and plasma metabolomics analysis and symptom profiles. In addition, a Chinese medicine expert classified each RA patient as a Cold or Heat type according to Chinese medicine theory. Multivariate data analysis techniques are employed to detect and validate biochemical and symptom relationships with the classification. RESULTS: The questionnaire items 'Red joints', 'Swollen joints', 'Warm joints' suggest differences in the level of inflammation between the groups although c-reactive protein (CRP) and rheumatoid factor (RHF) levels were equal. Multivariate analysis of the urine metabolomics data revealed that the levels of 11 acylcarnitines were lower in the Cold RA than in the Heat RA patients, suggesting differences in muscle breakdown. Additionally, higher dehydroepiandrosterone sulfate (DHEAS) levels in Heat patients compared to Cold patients were found suggesting that the Cold RA group has a more suppressed hypothalamic-pituitary-adrenal (HPA) axis function. CONCLUSION: Significant and relevant biochemical differences are found between Cold and Heat RA patients. Differences in immune function, HPA axis involvement and muscle breakdown point towards opportunities to tailor disease management strategies to each of the subgroups RA patient. Show less
Wietmarschen, H.A. van; Reijmers, T.H.; Kooij, A.J. van der; Schroen, J.; Wei, H.; Hankemeier, T.; ... ; Greef, J. van der 2011
