Pathogenetische Faktoren der Reflux-assoziierten chronischen Erkrankung der Lunge : die Magenentleerungszeit

Ein gastroösophagealer Reflux, der keine gastroenterologischen Symptome wie Erbrechen oder saure Regurgitation zeigt, kann im Kindesalter chronische Erkrankungen der Lunge auslösen. Typische Krankheitsbilder sind hierbei zum Beispiel Asthma bronchiale oder rezidivierende Bronchitiden. Die Inzidenz hierfür beträgt 1 : 300 bis 1 : 500. Für die Entstehung eines gastroösophagealen Reflux wird ein multifaktorielles Geschehen diskutiert. So kann zum Beispiel ein verminderter Druck des unteren Ösophagussphinkters, eine verminderte Leistung der Clearancefunktion des Ösophagus, eine pathologische Magensäuresekretion und auch eine verlängerte Entleerung des Magens ursächlich sein. Studien haben einen Zusammenhang zwischen einer pathologischen Magenentleerungszeit und einem symptomatischen gastroösophagealen Reflux beschrieben. Ein primärer Defekt wird hierbei in einer Motilitätsstörung vermutet, da ein signifikanter Zusammenhang zwischen pathologischen Magenentleerungszeiten und Dysrhythmien (abnormen elektrischen Potentialen) des Magens beschrieben ist. Bisher ist kein diagnostisches Verfahren bekannt, dass mit hoher Sensitivität und Spezifität das Vorliegen eines gastroösophagealen Reflux beweist. Vielmehr umfasst die derzeitige Diagnostik lediglich Teilaspekte der Erkrankung und liefert uneinheitliche Bilder. Bei insgesamt 25 Kindern mit Lungenproblemen bedingt durch einen gastroösophagealen Reflux wurde die Magenentleerungszeit, eine 2 Punkt-pH Metrie, eine obere Magendarmpassage sowie eine quantitative Bestimmung von fettbeladenen Alveolarmakrophagen im Rahmen einer Bronchoskopie erhoben. Im Gegensatz zur bisher üblichen Bestimmung der Magenentleerungszeit per Szintigraphie konnten im Rahmen dieser Arbeit die Werte mit einem 13C-Acetat- Atemtest gemessen werden. Eine pathologische Magenentleerungszeit wurde bei ungefähr der Hälfte der Patienten dargestellt. Obwohl ein Zusammenhang zwischen der Magenentleerungszeit und anderen Untersuchungsbefunden vermutet wurde, konnte keine signifikante Korrelation aufgezeigt werde. Alle Testverfahren lieferten unterschiedliche Ergebnisse. Bei keinem Kind mit klinisch gesichertem gastroösophagealen Reflux waren alle erhobenen Parameter pathologisch. Die Verteilung der Ergebnisse erfolgte auch im grenzpathologischen Bereich nicht signifikant. Als Grund hierfür kann vermutet werden, dass der gastroösophageale Reflux bei Kindern unterschiedliche Ursachen hat. So könnte eine pathologische Magenentleerungszeit bei einem Teil der Kinder ursächlich sein oder im Vordergrund stehen, während andere pathologische Korrelate das gleiche Krankheitsbild verursachen. Die Diagnosestellung eines gastroösophagealen Reflux bei Kindern mit pulmonaler Symptomatik kann somit nur mit hinweisenden Untersuchungen erfolgen, bei denen auch Widersprüche geduldet werden müssen.Gastro-oesophageal reflux without gastroenterological symptoms such as vomiting or sour regurgitation can cause chronical illness of the lung in the childhood. Typical examples are asthma bronchiale or recurrent bronchitis, the overall incidence of lung problems related to gastro-oesophageal reflux is 1: 300 to 1: 500. To explain the pathophysiological process of gastro-oesophageal reflux multiple factors are being discussed. The pressure of the lower oesophageal sphincter, a decreased oesophageal clearance, a pathological gastric secretion, and delayed gastric emptying can be responsible. Different studies have described the relation between a pathological gastric emptying time and a gastro-oesophageal reflux with lung diseases, but its pathophysiologic role has not yet been established. Motility disorders are seen as important factors because significant relations between delayed gastric emptying and dysrhythmia (abnormal electric potentials) of the stomach have been described. Until present no diagnostic procedures with a high sensitivity and specificity to prove a gastro-oesophageal reflux are known. Present diagnostic techniques only cover some aspects of the illness and generate non-uniform results. 25 children suffering from gastro-oesophageal reflux related lung diseases were included into the study. In all of these gastric emptying time tests, a 24h oesophageal pH-monitoring, an upper stomach intestine passage and a quantitative testing of lipid laden alveolar macrophages were performed. In contrary to previous studies the gastric emptying time was measured with a 13Cacetate breath test. In half of the patients delayed gastric emptying was documented. Although a relation between the gastric emptying time and other tests was assumed, no statistical significance was found. All test procedures supplied different results. No patient with gastro-oesophageal reflux had a pathological result in all performed tests. The distribution of results was also non-significant in the border pathological range. It can be assumed that there are different reasons for gastro-oesophageal reflux in children. In some children a pathological gastric emptying time causes the problems whereas other pathological disorders may cause similar symptoms. Diagnostics of gastro-oesophageal reflux in children with pulmonal symptoms can only take place with referring examinations. Contradictions have to be accepted

Ertragsstabilität im Ökolandbau: Wo steht die Forschung?

Despite the importance of yield stability in organic agriculture, little quantitative information is currently available on the factors limiting stability or on optimal approaches for improving it. Research so far indicates that organic systems are not always more stable than conventional systems; which system is more stable is likely to depend on the spatial and temporal scale of stability and on the measure of stability used. We show that opportunities for quantifying yield stability in organic farming lie in the targeted coordination of existing data networks within the organic community in order to increase yield stability on farms and beyond

Parametric Bootstrap Methods for Testing Multiplicative Terms in GGE and AMMI Models

The genotype main effects and genotype-by-environment interaction effects (GGE) model and the additive main effects and multiplicative interaction (AMMI) model are two common models for analysis of genotype-by-environment data. These models are frequently used by agronomists, plant breeders, geneticists and statisticians for analysis of multi-environment trials. In such trials, a set of genotypes, e.g. crop cultivars, are compared across a range of environments, e.g. locations. The GGE and AMMI models use singular value decomposition to partition genotype-by-environment interaction into an ordered sum of multiplicative terms. This article deals with the problem of testing the significance of these multiplicative terms in order to decide how many terms to retain in the final model. We propose parametric bootstrap methods for this problem. Models with fixed main effects, fixed multiplicative terms and random normally distributed errors are considered. Two methods are derived: a full and a simple parametric bootstrap method. These are compared with the alternatives of using approximate F-tests and cross-validation. In a simulation study based on four multi-environment trials, both bootstrap methods performed well with regard to Type I error rate and power. The simple parametric bootstrap method is particularly easy to use, since it only involves repeated sampling of standard normally distributed values. This method is recommended for selecting the number of multiplicative terms in GGE and AMMI models. The proposed methods can also be used for testing components in principal component analysis

Statistical analysis of 'White Riesling' (Vitis vinifera ssp. sativa L.) clonal performance at 16 locations in the Rheinland-Pfalz region of Germany between 1971 and 2007

Performance trials have been evaluated of 30 'White Riesling' clones grown at 16 locations in the Rheinland-Pfalz region between 1971 and 2007. A mixed linear model approach was used to handle the highly-unbalanced data structure. Environmental factors accounted for about 95 % of the variation for individual observations. Genotypic clone variation contributed only 0.65 % to the total variation for grape yield, 0.29 % for total soluble solids (TSS) and 0.22 % for acidity. F-tests for clonal differences showed significant F-values for each characteristic. Estimated clone means ranged from 107.4 to 130.8 kg·ar-1 (1 ar = 100 m2) for grape yield, from 72.0 to 75.2 °Oechsle for TSS and from 12.5 to 13.4 g·l-1 for acidity. Significant mean differences were found only for clones located near the lower and upper extremes of the performance range. Long-term time trends of clonal performance are also present. On average over the 36 year period, grape yields increased by 2.00 kg·ar-1 each year and TSS by 0.87 °Oechsle each year, whereas acidity decreased by 0.21 g·l-1 each year. No significant deviations of individual clones from the general long-term trends were verifiable for grape yield but some clones showed significant deviations for TSS and acidity.A closer look at the linear trend for grape yield displayed a discontinuity around 1989. Before 1989 a linear gain of about 3.99 kg·ar-1 was apparent whereas, after this time a very slight decrease of 0.28 kg·ar-1 was observed. For mean daily temperature, the long-term trend was remarkably parallel to that of grape yield and TSS. For the Rheinland-Pfalz region, daily temperature increased significantly by 0.046 °C per year, whereas average daily sunshine showed a no significant change over time.

Treatment comparisons in agricultural field trials accounting for spatial correlation

This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The classical analysis model for agricultural field trials is based on the principles of experimental design – randomization, replication and blocking – and it assumes independent residual effects. Accounting for any existent spatial correlation as an add-on component may be beneficial, but it requires selection of a suitable spatial model and modification of classical tests of treatment contrasts. Using a sugar beet trial laid out in complete blocks for illustration, it is shown that tests obtained with different modifications yield diverging results. Simulations were performed to decide whether different test modifications lead to valid statistical inferences. For the spherical, power and Gaussian models, each with six different values of the range parameter and without a nugget effect, the suitability of the following modifications was studied: a generalization of the Satterthwaite method (1941), the method of Kenward and Roger (1997), and the first-order corrected method described by Kenward and Roger (2009). A second-order method described by Kenward and Roger (2009) is also discussed and detailed results are provided as Supplemental Material (available at: http://journals.cambridge.org/AGS). Simulations were done for experiments with 10 or 30 treatments in complete and incomplete block designs. Model selection was performed using the corrected Akaike information criterion and likelihood-ratio tests. When simulation and analysis models were identical, at least one of the modifications for the t-test guaranteed control of the nominal Type I error rate in most cases. When the first-order method of Kenward and Roger was used, control of the t-test Type I error rate was poor for 10 treatments but on average very good for 30 treatments, when considering the best-fitting models for a given simulation setting. Results were not satisfactory for the F-test. The more pronounced the spatial correlation, the more substantial was the gain in power compared to classical analysis. For experiments with 20 treatments or more, the recommendation is to select the best-fitting model and then use the first-order method for t-tests. For F-tests, a randomization-based model with independent error effects should be used.Peer Reviewe

Robust estimation of heritability and predictive accuracy in plant breeding: evaluation using simulation and empirical data

UID/MAT/00297/2019 UIDB/00297/2020 SFRH/BSAB/105935/2014 SFRH/BSAB/142919/2018 Project PT/A13/17-DE/57339863 Grant PI 377/18-1 Grant OG 83/1-1 & OG 83/1-2BACKGROUND: Genomic prediction (GP) is used in animal and plant breeding to help identify the best genotypes for selection. One of the most important measures of the effectiveness and reliability of GP in plant breeding is predictive accuracy. An accurate estimate of this measure is thus central to GP. Moreover, regression models are the models of choice for analyzing field trial data in plant breeding. However, models that use the classical likelihood typically perform poorly, often resulting in biased parameter estimates, when their underlying assumptions are violated. This typically happens when data are contaminated with outliers. These biases often translate into inaccurate estimates of heritability and predictive accuracy, compromising the performance of GP. Since phenotypic data are susceptible to contamination, improving the methods for estimating heritability and predictive accuracy can enhance the performance of GP. Robust statistical methods provide an intuitively appealing and a theoretically well justified framework for overcoming some of the drawbacks of classical regression, most notably the departure from the normality assumption. We compare the performance of robust and classical approaches to two recently published methods for estimating heritability and predictive accuracy of GP using simulation of several plausible scenarios of random and block data contamination with outliers and commercial maize and rye breeding datasets. RESULTS: The robust approach generally performed as good as or better than the classical approach in phenotypic data analysis and in estimating the predictive accuracy of heritability and genomic prediction under both the random and block contamination scenarios. Notably, it consistently outperformed the classical approach under the random contamination scenario. Analyses of the empirical maize and rye datasets further reinforce the stability and reliability of the robust approach in the presence of outliers or missing data. CONCLUSIONS: The proposed robust approach enhances the predictive accuracy of heritability and genomic prediction by minimizing the deleterious effects of outliers for a broad range of simulation scenarios and empirical breeding datasets. Accordingly, plant breeders should seriously consider regularly using the robust alongside the classical approach and increasing the number of replicates to three or more, to further enhance the accuracy of the robust approach.publishersversionpublishe