Key message
A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials
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Adjustment for ...spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes. Current mixed model methods of spatial analysis are based on a multi-step modelling process where global and local trends are fitted after trying several candidate spatial models. This paper reports the application of a novel spatial method that accounts for all types of continuous field variation in a single modelling step by fitting a smooth surface. The method uses two-dimensional P-splines with anisotropic smoothing formulated in the mixed model framework, referred to as SpATS model. We applied this methodology to a series of large and partially replicated sorghum breeding trials. The new model was assessed in comparison with the more elaborate standard spatial models that use autoregressive correlation of residuals. The improvements in precision and the predictions of genotypic values produced by the SpATS model were equivalent to those obtained using the best fitting standard spatial models for each trial. One advantage of the approach with SpATS is that all patterns of spatial trend and genetic effects were modelled simultaneously by fitting a single model. Furthermore, we used a flexible model to adequately adjust for field trends. This strategy reduces potential parameter identification problems and simplifies the model selection process. Therefore, the new method should be considered as an efficient and easy-to-use alternative for routine analyses of plant breeding trials.
Abstract Attention deficit/hyperactivity disorder (ADHD) is a common and highly heritable psychiatric disorder. In addition, early life environmental factors contribute to the occurrence of ADHD. ...Recently, DNA methylation has emerged as a mechanism potentially mediating genetic and environmental effects. Here, we investigated whether newborn DNA methylation patterns of selected candidate genes involved in psychiatric disorders or fetal growth are associated with ADHD symptoms in childhood. Participants were 426 children from a large population based cohort of Dutch national origin. Behavioral data were obtained at age 6 years with the Child Behavior Checklist. For the current study, 11 regions at 7 different genes were selected. DNA methylation levels of cord blood DNA were measured for the 11 regions combined and for each region separately. We examined the association between DNA methylation levels at different regions and ADHD symptoms with linear mixed models. DNA methylation levels were negatively associated with ADHD symptom score in the overall analysis of all 11 regions. This association was largely explained by associations of DRD4 and 5-HTT regions. Other candidate genes showed no association between DNA methylation levels and ADHD symptom score. Associations between DNA methylation levels and ADHD symptom score were attenuated by co-occurring Oppositional defiant disorder and total symptoms. Lower DNA methylation levels of the 7 genes assessed at birth, were associated with more ADHD symptoms of the child at 6 years of age. Further studies are needed to confirm our results and to investigate the possible underlying mechanism.
A Perfect Smoother Eilers, Paul H. C
Analytical chemistry (Washington),
07/2003, Letnik:
75, Številka:
14
Journal Article
Recenzirano
The well-known and popular Savitzky−Golay filter has several disadvantages. A very attractive alternative is a smoother based on penalized least squares, extending ideas presented by Whittaker 80 ...years ago. This smoother is extremely fast, gives continuous control over smoothness, interpolates automatically, and allows fast leave-one-out cross-validation. It can be programmed in a few lines of Matlab code. Theory, implementation, and applications are presented.
During an infectious disease outbreak, timely information on the number of new symptomatic cases is crucial. However, the reporting of new cases is usually subject to delay due to the incubation ...period, time to seek care, and diagnosis. This results in a downward bias in the numbers of new cases by the times of symptoms onset towards the current day. The real-time assessment of the current situation while correcting for underreporting is called nowcasting. We present a nowcasting method based on bivariate P-spline smoothing of the number of reported cases by time of symptoms onset and delay. Our objective is to predict the number of symptomatic-but-not-yet-reported cases and combine these with the already reported symptomatic cases into a nowcast. We assume the underlying two-dimensional reporting intensity surface to be smooth. We include prior information on the reporting process as additional constraintsthe smooth surface is unimodal in the reporting delay dimension, is (almost) zero at a predefined maximum delay and has a prescribed shape at the beginning of the outbreak. Parameter estimation is done efficiently by penalized iterative weighted least squares. We illustrate our method on a large measles outbreak in the Netherlands. We show that even with very limited information the method is able to accurately predict the number of symptomatic-but-not-yet-reported cases. This results in substantially improved monitoring of new symptomatic cases in real time.
Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical ...parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas is subject to a large interobserver variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histologic subtypes and grades. We identified seven distinct molecular subgroups that correlate with survival. These include two favorable prognostic subgroups (median survival, >4.7 years), two with intermediate prognosis (median survival, 1-4 years), two with poor prognosis (median survival, <1 year), and one control group. The intrinsic molecular subtypes of glioma are different from histologic subgroups and correlate better to patient survival. The prognostic value of molecular subgroups was validated on five independent sample cohorts (The Cancer Genome Atlas, Repository for Molecular Brain Neoplasia Data, GSE12907, GSE4271, and Li and colleagues). The power of intrinsic subtyping is shown by its ability to identify a subset of prognostically favorable tumors within an external data set that contains only histologically confirmed glioblastomas (GBM). Specific genetic changes (epidermal growth factor receptor amplification, IDH1 mutation, and 1p/19q loss of heterozygosity) segregate in distinct molecular subgroups. We identified a subgroup with molecular features associated with secondary GBM, suggesting that different genetic changes drive gene expression profiles. Finally, we assessed response to treatment in molecular subgroups. Our data provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histologic classification. Molecular classification therefore may aid diagnosis and can guide clinical decision making.
Parametric Time Warping Eilers, Paul H. C
Analytical chemistry (Washington),
01/2004, Letnik:
76, Številka:
2
Journal Article
Recenzirano
A parametric model is proposed for the warping function when aligning chromatograms. A very fast and stable algorithm results that consumes little memory and avoids the artifacts of dynamic time ...warping. The parameters of the warping function are useful for quality control. They also are easily interpolated, allowing alignment of batches of chromatograms based on warping functions for a limited number of calibration samples.
We present a model for direct semi-parametric estimation of the state price density (SPD) implied by quoted option prices. We treat the observed prices as expected values of possible pay-offs at ...maturity, weighted by the unknown probability density function. We model the logarithm of the latter as a smooth function, using P-splines, while matching the expected values of the potential pay-offs with the observed prices. This leads to a special case of the penalized composite link model. Our estimates do not rely on any parametric assumption on the underlying asset price dynamics and are consistent with no-arbitrage conditions. The model shows excellent performance in simulations and in applications to real data.
Abstract Objective To construct new Dutch reference curves for birthweight by parity, sex and ethnic background. Design Retrospective nationwide study. Material and methods Reference curves for ...birthweight were constructed using the LMS model and were based on 176,000 singleton births in the Netherlands in the year 2001 (approximately 95% of all births in that year). Results Separate birthweight curves were constructed for male and female babies born from primiparous and multiparous women from 25 to 43 weeks gestational age. The reference curves are similar to the Swedish references. Birthweight at early gestation was lower than in the previous Dutch reference curves and higher from term onwards. Infants of Hindustani women had a significantly lower birthweight, so that a separate reference curve was constructed. Conclusion The new Dutch reference curves show a different pattern than the Dutch reference curves collected more than 50 years ago, reflecting changes in prenatal conditions and care.
Low resolution nuclear magnetic resonance (LR-NMR) is a common technique to identify the constituents of complex materials (such as food and biological samples). The output of LR-NMR experiments is a ...relaxation signal which can be modelled as a type of convolution of an unknown density of relaxation times with decaying exponential functions, plus random Gaussian noise. The challenge is to estimate that density, a severely ill-posed problem. A complication is that non-negativity constraints need to be imposed in order to obtain valid results.
We present a smooth deconvolution model for solution of the inverse estimation problem in LR-NMR relaxometry experiments. We model the logarithm of the relaxation time density as a smooth function using (adaptive) P-splines while matching the expected residual magnetisations with the observed ones. The roughness penalty removes the singularity of the deconvolution problem, and the estimated density is positive by design (since we model its logarithm). The model is non-linear, but it can be linearized easily. The penalty has to be tuned for each given sample. We describe an efficient EM-type algorithm to optimize the smoothing parameter(s).
We analyze a set of food samples (potato tubers). The relaxation spectra extracted using our method are similar to the ones described in the previous experiments but present sharper peaks. Using penalized signal regression we are able to accurately predict dry matter content of the samples using the estimated spectra as covariates.
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•Fast and stable deconvolution of low-field NMR spectra..•Guaranteed positive result and sharper peaks, because the logarithm Is being modeled..•Automatically tuned adaptive smoothness..•Open source software, written in R, is available..
Time series of vegetation indices like NDVI are used in numerous applications ranging from ecology to climatology and agriculture. Often, these time series have to be filtered before application. The ...smoothing removes noise introduced by undetected clouds and poor atmospheric conditions. Ground reference measurements are usually difficult to obtain due to the medium/coarse resolution of the imagery. Hence, new filter algorithms are typically only (visually) assessed against the existing smoother. The present work aims to propose a range of quality indicators that could be useful to qualify filter performance in the absence of ground-based reference measurements. The indicators comprise (i) plausibility checks, (ii) distance metrics and (iii) geostatistical measures derived from variogram analysis. The quality measures can be readily derived from any imagery. For illustration, a large SPOT VGT dataset (1999–2008) covering South America at 1 km spatial resolution was filtered using the Whittaker smoother.
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