Discussion on Competition for Spatial Statistics for Large Datasets

We discuss the experiences and results of the AppStatUZH team's participation in the comprehensive and unbiased comparison of different spatial approximations conducted in the Competition for Spatial Statistics for Large Datasets. In each of the different sub-competitions, we estimated parameters of the covariance model based on a likelihood function and predicted missing observations with simple kriging. We approximated the covariance model either with covariance tapering or a compactly supported Wendland covariance function.Comment: 5 pages, 1 figur

Early β-amyloid accumulation in the brain is associated with peripheral T cell alterations

INTRODUCTION Fast and minimally invasive approaches for early diagnosis of Alzheimer's disease (AD) are highly anticipated. Evidence of adaptive immune cells responding to cerebral β-amyloidosis has raised the question of whether immune markers could be used as proxies for β-amyloid accumulation in the brain. METHODS Here, we apply multidimensional mass-cytometry combined with unbiased machine-learning techniques to immunophenotype peripheral blood mononuclear cells from a total of 251 participants in cross-sectional and longitudinal studies. RESULTS We show that increases in antigen-experienced adaptive immune cells in the blood, particularly CD45RA-reactivated T effector memory (TEMRA) cells, are associated with early accumulation of brain β-amyloid and with changes in plasma AD biomarkers in still cognitively healthy subjects. DISCUSSION Our results suggest that preclinical AD pathology is linked to systemic alterations of the adaptive immune system. These immunophenotype changes may help identify and develop novel diagnostic tools for early AD assessment and better understand clinical outcomes

Mediation of coffee-induced improvements in human vascular function by chlorogenic acids and its metabolites: two randomized, controlled, crossover intervention trials

Background & aims Polyphenol intake has been linked to improvements in human vascular function, although data on hydroxycinnamates, such as chlorogenic acid (CGA) have not yet been studied. We aimed to investigate the impact of coffee intake rich in chlorogenic acid on human vascular function and whether CGAs are involved in potential effects. Methods Two acute randomized, controlled, cross-over human intervention trials were conducted. The impact of coffee intake, matched for caffeine but differing in CGA content (89, and 310 mg) on flow-mediated dilation (FMD) was assessed in 15 healthy male subjects. In a second intervention trial conducted with 24 healthy male subjects, the impact of pure 5-caffeoylquinic acid (5-CQA), the main CGA in coffee (5-CQA; 450 mg and 900 mg) on FMD was also investigated. Results We observed a bi-phasic FMD response after low and high polyphenol, (89 mg and 310 mg CGA) intake, with increases at 1 (1.10 ± 0.43% and 1.34 ± 0.62%, respectively) and 5 (0.79% ± 0.32 and 1.52% ± 0.40, respectively) hours post coffee consumption. FMD responses to coffee intake was closely paralleled by the appearance of CGA metabolites in plasma, notably 3-, 4- and 5-CQA and ferulic-4′-O-sulfate at 1 h and isoferulic-4′-O-glucuronide and ferulic-4′-O-sulfate at 5 h. Intervention with purified 5-CQA (450 mg) also led to an improvement in FMD response relative to control (0.75 ± 1.31% at 1 h post intervention, p = 0.06) and concomitant appearance of plasma metabolites. Conclusions Coffee intake acutely improves human vascular function, an effect, in part, mediated by 5-CQA and its physiological metabolites

Multiresolution decomposition of areal count data

Multiresolution decomposition is commonly understood as a procedure to capture scale-dependent features in random signals. Such methods were first established for image processing andtypically rely on raster or regularly gridded data. In this article, we extend a particular multiresolutiondecomposition procedure to areal count data, i.e. discrete irregularly gridded data. More specifically,we incorporate in a new model concept and distributions from the so-called Besag–York–Mollié modelto include a priori demographical knowledge. These adaptions and subsequent changes in the com-putation schemes are carefully outlined below, whereas the main idea of the original multiresolutiondecomposition remains. Finally, we show the extension’s feasibility by applying it to oral cavity cancercounts in Germany

Identification of dominant features in spatial data

Dominant features of spatial data are connected structures or patterns that emerge from location-based variation and manifest at specific scales or resolutions. To identify dominant features, we propose a sequential application of multiresolution decomposition and variogram function estimation. Multiresolution decomposition separates data into additive components, and in this way enables the recognition of their dominant features. A dedicated multiresolution decomposition method is developed for arbitrary gridded spatial data, where the underlying model includes a precision and spatial-weight matrix to capture spatial correlation. The data are separated into their components by smoothing on different scales, such that larger scales have longer spatial correlation ranges. Moreover, our model can handle missing values, which is often useful in applications. Variogram function estimation can be used to describe properties in spatial data. Such functions are therefore estimated for each component to determine its effective range, which assesses the width-extent of the dominant feature. Finally, Bayesian analysis enables the inference of identified dominant features and to judge whether these are credibly different. The efficient implementation of the method relies mainly on a sparse-matrix data structure and algorithms. By applying the method to simulated data we demonstrate its applicability and theoretical soundness. In disciplines that use spatial data, this method can lead to new insights, as we exemplify by identifying the dominant features in a forest dataset. In that application, the width-extents of the dominant features have an ecological interpretation, namely the species interaction range, and their estimates support the derivation of ecosystem properties such as biodiversity indices

Pipeline to identify dominant features in spatial data

Dominant-feature identification decomposes spatial data into several additive components to make different features apparent on each component. It recognizes their dominant features credibly and assesses feature attributes. This paper describes the pipeline to apply this method to regular and irregular lattice data as well as geostatistical data. These implementations are all openly available and templates for each case are provided in an associated git repository. As geostatistical data is typically large, we propose several efficient approximations suitable for such data. Emphasizing the use of these approximations in the context of dominant-feature identification, we apply them to data from a climate model describing the monthly mean diurnal range for the period between the years 2081 and 2100

Dominant-feature identification in data from Gaussian processes applied to Finnish forest inventory records

In spatial data, location-dependent variation leads to connected structures known as features. Variations occur at different spatial scales and possibly originate from distinct underlying processes. Each of these scales is characterized by its own dominant features. Here we introduce a statistical method for identifying these scales and their dominant features in data from Gaussian processes. This identification involves credibly recognizing the dominant features by scale-space decomposition and assessing feature attributes by estimating covariance function parameters of the underlying processes and their associations to potential drivers. We analyze Finnish forest inventory data from the 1920s using this dominant-feature identification method and identify the scales of variation in basal area estimates of most common Finnish trees, including Scots pine, Norway spruce, birch, and other native deciduous trees. Comparing the resulting scale-dependent features and their attributes in these tree species, we identify the different effects of edaphic and anthropogenic drivers on the spatial distribution of their basal areas. These data are analyzed for the first time in terms of their scale of variation, and the resulting scale-dependent maps and estimates are an essential contribution to the historical forest ecology of Fennoscandia. Until now, this analysis was not possible with conventional methods